Correlation between aggregated molecular cancer subtypes and selected clinical features
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 45 clinical features across 304 patients, 68 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'NEOPLASM_HISTOLOGIC_GRADE',  'MENOPAUSE_STATUS', and 'AGE_AT_DIAGNOSIS'.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'PREGNANCIES_COUNT_STILLBIRTH',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'KERATINIZATION_SQUAMOUS_CELL', and 'AGE_AT_DIAGNOSIS'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'LYMPH_NODES_EXAMINED',  'KERATINIZATION_SQUAMOUS_CELL', and 'CERVIX_SUV_RESULTS'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'HISTOLOGICAL_TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES',  'RACE',  'NEOPLASM_HISTOLOGIC_GRADE',  'MENOPAUSE_STATUS',  'LYMPH_NODES_EXAMINED_HE_COUNT',  'KERATINIZATION_SQUAMOUS_CELL', and 'AGE_AT_DIAGNOSIS'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'LYMPH_NODES_EXAMINED_HE_COUNT',  'KERATINIZATION_SQUAMOUS_CELL',  'INITIAL_PATHOLOGIC_DX_YEAR',  'HISTORY_HORMONAL_CONTRACEPTIVES_USE', and 'AGE_AT_DIAGNOSIS'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE',  'TUMOR_SAMPLE_PROCUREMENT_COUNTRY',  'NEOPLASM_HISTOLOGIC_GRADE',  'RADIATION_THERAPY_SITE',  'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT',  'INITIAL_PATHOLOGIC_DX_YEAR', and 'STAGE_EVENT.CLINICAL_STAGE'.

  • 5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION',  'NEOPLASM_HISTOLOGIC_GRADE',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'MENOPAUSE_STATUS',  'AJCC_TUMOR_PATHOLOGIC_PT',  'AGE_AT_DIAGNOSIS', and 'STAGE_EVENT.CLINICAL_STAGE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION',  'TUMOR_SAMPLE_PROCUREMENT_COUNTRY',  'NEOPLASM_HISTOLOGIC_GRADE',  'RADIATION_TOTAL_DOSE',  'RADIATION_THERAPY_TYPE',  'KERATINIZATION_SQUAMOUS_CELL',  'INITIAL_PATHOLOGIC_DX_YEAR', and 'STAGE_EVENT.CLINICAL_STAGE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION',  'TUMOR_SAMPLE_PROCUREMENT_COUNTRY',  'RADIATION_THERAPY_TYPE',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'KERATINIZATION_SQUAMOUS_CELL', and 'INITIAL_PATHOLOGIC_DX_YEAR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 45 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 68 significant findings detected.

Clinical
Features 		Statistical
Tests 		Copy
Number
Ratio
CNMF
subtypes 		METHLYATION
CNMF 		RPPA
CNMF
subtypes 		RPPA
cHierClus
subtypes 		RNAseq
CNMF
subtypes 		RNAseq
cHierClus
subtypes 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRseq
Mature
CNMF
subtypes 		MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.271
(0.607) 		0.117
(0.458) 		0.0125
(0.137) 		0.233
(0.567) 		0.52
(0.764) 		0.013
(0.139) 		0.377
(0.688) 		0.475
(0.759) 		0.957
(0.984) 		0.793
(0.918)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0246
(0.191) 		0.0108
(0.127) 		0.136
(0.465) 		0.0822
(0.385) 		1.68e-05
(0.000539) 		0.00736
(0.0946) 		0.539
(0.767) 		8.6e-05
(0.00228) 		0.48
(0.761) 		0.857
(0.938)
PATHOLOGY T STAGE Fisher's exact test 0.218
(0.553) 		0.493
(0.762) 		0.488
(0.762) 		0.565
(0.791) 		0.593
(0.806) 		0.716
(0.879) 		0.516
(0.762) 		0.00436
(0.0677) 		0.463
(0.751) 		0.512
(0.762)
PATHOLOGY N STAGE Fisher's exact test 0.18
(0.514) 		0.427
(0.733) 		0.655
(0.843) 		0.233
(0.567) 		0.349
(0.665) 		0.28
(0.618) 		0.819
(0.927) 		0.183
(0.514) 		0.177
(0.514) 		0.149
(0.47)
PATHOLOGY M STAGE Fisher's exact test 0.526
(0.767) 		0.597
(0.809) 		1
(1.00) 		0.949
(0.982) 		0.227
(0.567) 		0.0507
(0.284) 		0.197
(0.533) 		0.34
(0.659) 		0.065
(0.33) 		0.336
(0.659)
HISTOLOGICAL TYPE Fisher's exact test 0.294
(0.63) 		1e-05
(0.000346) 		0.721
(0.879) 		0.00253
(0.0464) 		1e-05
(0.000346) 		1e-05
(0.000346) 		1e-05
(0.000346) 		1e-05
(0.000346) 		1e-05
(0.000346) 		1e-05
(0.000346)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.909
(0.96) 		0.0875
(0.402) 		0.926
(0.973) 		0.797
(0.918) 		0.284
(0.619) 		0.0395
(0.253) 		0.199
(0.533) 		0.0268
(0.197) 		1e-05
(0.000346) 		1e-05
(0.000346)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.475
(0.759) 		0.402
(0.71) 		0.0998
(0.428) 		0.54
(0.767) 		0.145
(0.466) 		0.437
(0.733) 		0.0518
(0.284) 		0.157
(0.48) 		0.193
(0.526) 		0.142
(0.465)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.271
(0.607) 		0.13
(0.465) 		0.824
(0.927) 		0.185
(0.514) 		0.0113
(0.127) 		0.0281
(0.197) 		0.332
(0.657) 		0.125
(0.464) 		0.209
(0.539) 		0.0694
(0.34)
RACE Fisher's exact test 0.104
(0.436) 		0.671
(0.845) 		0.669
(0.845) 		0.203
(0.533) 		0.0106
(0.127) 		0.351
(0.665) 		0.322
(0.652) 		0.376
(0.687) 		0.139
(0.465) 		0.563
(0.791)
ETHNICITY Fisher's exact test 0.508
(0.762) 		0.967
(0.991) 		0.0642
(0.33) 		0.496
(0.762) 		0.269
(0.607) 		0.85
(0.938) 		0.601
(0.812) 		0.401
(0.71) 		0.131
(0.465) 		0.455
(0.749)
WEIGHT KG AT DIAGNOSIS Kruskal-Wallis (anova) 0.503
(0.762) 		0.0994
(0.428) 		0.0897
(0.402) 		0.686
(0.858) 		0.798
(0.918) 		0.528
(0.767) 		0.736
(0.888) 		0.905
(0.96) 		0.744
(0.89) 		0.808
(0.918)
TUMOR STATUS Fisher's exact test 0.764
(0.905) 		0.928
(0.973) 		0.26
(0.6) 		0.419
(0.728) 		0.278
(0.618) 		0.856
(0.938) 		0.622
(0.824) 		0.569
(0.793) 		0.782
(0.914) 		0.254
(0.595)
TUMOR SAMPLE PROCUREMENT COUNTRY Fisher's exact test 0.566
(0.791) 		0.663
(0.843) 		0.517
(0.762) 		0.617
(0.824) 		0.0415
(0.253) 		0.372
(0.684) 		0.00038
(0.0095) 		0.118
(0.458) 		0.00149
(0.0292) 		0.00051
(0.0121)
NEOPLASM HISTOLOGIC GRADE Fisher's exact test 0.034
(0.225) 		0.82
(0.927) 		0.113
(0.457) 		0.515
(0.762) 		0.00258
(0.0464) 		0.152
(0.475) 		0.00701
(0.0946) 		0.0181
(0.173) 		0.0169
(0.166) 		0.0444
(0.259)
TOBACCO SMOKING YEAR STOPPED Kruskal-Wallis (anova) 0.211
(0.542) 		0.806
(0.918) 		0.738
(0.888) 		0.72
(0.879) 		0.871
(0.94) 		0.536
(0.767) 		0.313
(0.649) 		0.147
(0.466) 		0.288
(0.619) 		0.266
(0.607)
TOBACCO SMOKING PACK YEARS SMOKED Kruskal-Wallis (anova) 0.475
(0.759) 		0.402
(0.71) 		0.0998
(0.428) 		0.54
(0.767) 		0.145
(0.466) 		0.437
(0.733) 		0.0518
(0.284) 		0.157
(0.48) 		0.193
(0.526) 		0.142
(0.465)
TOBACCO SMOKING HISTORY Fisher's exact test 0.439
(0.733) 		0.231
(0.567) 		0.339
(0.659) 		0.577
(0.801) 		0.355
(0.665) 		0.325
(0.652) 		0.0886
(0.402) 		0.386
(0.699) 		0.0416
(0.253) 		0.288
(0.619)
PATIENT AGEBEGANSMOKINGINYEARS Kruskal-Wallis (anova) 0.332
(0.657) 		0.443
(0.736) 		0.521
(0.764) 		0.808
(0.918) 		0.431
(0.733) 		0.655
(0.843) 		0.846
(0.938) 		0.764
(0.905) 		0.685
(0.858) 		0.899
(0.959)
RADIATION TOTAL DOSE Kruskal-Wallis (anova) 0.826
(0.927) 		0.512
(0.762) 		0.797
(0.918) 		0.662
(0.843) 		0.636
(0.833) 		0.992
(1.00) 		0.941
(0.98) 		0.395
(0.708) 		0.00376
(0.0626) 		0.048
(0.273)
RADIATION THERAPY TYPE Fisher's exact test 0.948
(0.982) 		0.22
(0.556) 		0.838
(0.936) 		0.776
(0.911) 		0.436
(0.733) 		0.298
(0.633) 		0.117
(0.458) 		0.0459
(0.265) 		1e-05
(0.000346) 		1e-05
(0.000346)
RADIATION THERAPY STATUS Fisher's exact test 0.261
(0.6) 		0.903
(0.96) 		0.501
(0.762) 		1
(1.00) 		0.736
(0.888) 		1
(1.00) 		0.397
(0.708) 		0.859
(0.938) 		1
(1.00) 		0.457
(0.749)
RADIATION THERAPY SITE Fisher's exact test 0.279
(0.618) 		0.66
(0.843) 		0.315
(0.649) 		0.474
(0.759) 		0.764
(0.905) 		0.116
(0.458) 		0.0273
(0.197) 		0.12
(0.458) 		0.54
(0.767) 		0.589
(0.806)
RADIATION ADJUVANT UNITS Fisher's exact test 1
(1.00) 		0.718
(0.879) 		0.232
(0.567) 		0.0995
(0.428) 		0.492
(0.762) 		0.369
(0.683) 		0.354
(0.665) 		0.353
(0.665) 		1
(1.00) 		0.458
(0.749)
PREGNANCIES COUNT TOTAL Kruskal-Wallis (anova) 0.44
(0.733) 		0.316
(0.649) 		0.12
(0.458) 		0.621
(0.824) 		0.0416
(0.253) 		0.0652
(0.33) 		0.744
(0.89) 		0.088
(0.402) 		0.775
(0.911) 		0.172
(0.51)
PREGNANCIES COUNT STILLBIRTH Kruskal-Wallis (anova) 0.245
(0.579) 		0.00107
(0.0219) 		0.318
(0.651) 		0.185
(0.514) 		0.171
(0.509) 		0.583
(0.806) 		0.86
(0.938) 		0.487
(0.762) 		0.202
(0.533) 		0.201
(0.533)
PATIENT PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT Kruskal-Wallis (anova) 0.865
(0.938) 		0.882
(0.947) 		0.732
(0.888) 		0.83
(0.929) 		0.858
(0.938) 		0.111
(0.456) 		0.0036
(0.0623) 		0.138
(0.465) 		0.236
(0.568) 		0.112
(0.457)
PREGNANCIES COUNT LIVE BIRTH Kruskal-Wallis (anova) 0.538
(0.767) 		0.0224
(0.189) 		0.0229
(0.189) 		0.133
(0.465) 		0.0533
(0.288) 		0.0221
(0.189) 		0.847
(0.938) 		0.0185
(0.173) 		0.687
(0.858) 		0.0231
(0.189)
PATIENT PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT Kruskal-Wallis (anova) 0.894
(0.955) 		0.306
(0.646) 		0.338
(0.659) 		0.592
(0.806) 		0.805
(0.918) 		0.535
(0.767) 		0.464
(0.751) 		0.933
(0.974) 		0.695
(0.861) 		0.781
(0.914)
PREGNANCIES COUNT ECTOPIC Kruskal-Wallis (anova) 0.213
(0.545) 		0.661
(0.843) 		0.974
(0.996) 		0.663
(0.843) 		0.586
(0.806) 		0.873
(0.94) 		0.32
(0.651) 		0.957
(0.984) 		0.981
(1.00) 		0.653
(0.843)
POS LYMPH NODE LOCATION Fisher's exact test 0.704
(0.87) 		0.802
(0.918) 		0.415
(0.723) 		0.639
(0.833) 		0.499
(0.762) 		0.913
(0.963) 		0.864
(0.938) 		0.758
(0.905) 		0.865
(0.938) 		0.721
(0.879)
MENOPAUSE STATUS Fisher's exact test 0.00956
(0.119) 		0.135
(0.465) 		0.509
(0.762) 		0.802
(0.918) 		0.0246
(0.191) 		0.0424
(0.255) 		0.507
(0.762) 		0.0142
(0.145) 		0.261
(0.6) 		0.288
(0.619)
LYMPHOVASCULAR INVOLVEMENT Fisher's exact test 0.121
(0.458) 		0.369
(0.683) 		0.0439
(0.259) 		0.333
(0.657) 		0.616
(0.824) 		0.635
(0.833) 		0.631
(0.832) 		0.37
(0.683) 		0.85
(0.938) 		0.0782
(0.378)
LYMPH NODES EXAMINED HE COUNT Kruskal-Wallis (anova) 0.271
(0.607) 		0.13
(0.465) 		0.824
(0.927) 		0.185
(0.514) 		0.0113
(0.127) 		0.0281
(0.197) 		0.332
(0.657) 		0.125
(0.464) 		0.209
(0.539) 		0.0694
(0.34)
LYMPH NODES EXAMINED Kruskal-Wallis (anova) 0.411
(0.719) 		0.688
(0.858) 		0.00736
(0.0946) 		0.237
(0.568) 		0.146
(0.466) 		0.347
(0.665) 		0.605
(0.813) 		0.311
(0.649) 		0.246
(0.579) 		0.769
(0.908)
KERATINIZATION SQUAMOUS CELL Fisher's exact test 0.952
(0.982) 		0.00073
(0.0156) 		0.0331
(0.222) 		0.0411
(0.253) 		0.0206
(0.189) 		0.00558
(0.081) 		0.131
(0.465) 		0.154
(0.477) 		0.0211
(0.189) 		0.0321
(0.219)
INITIAL PATHOLOGIC DX YEAR Kruskal-Wallis (anova) 0.392
(0.706) 		0.185
(0.514) 		0.485
(0.762) 		0.873
(0.94) 		0.354
(0.665) 		0.0251
(0.191) 		0.00401
(0.0645) 		0.0391
(0.253) 		4.31e-06
(0.000346) 		5.03e-08
(2.26e-05)
HISTORY HORMONAL CONTRACEPTIVES USE Fisher's exact test 0.429
(0.733) 		0.619
(0.824) 		0.18
(0.514) 		0.507
(0.762) 		0.128
(0.465) 		0.0311
(0.215) 		0.593
(0.806) 		0.298
(0.633) 		0.564
(0.791) 		0.314
(0.649)
HEIGHT CM AT DIAGNOSIS Kruskal-Wallis (anova) 0.0643
(0.33) 		0.184
(0.514) 		0.342
(0.66) 		0.165
(0.496) 		0.424
(0.73) 		0.143
(0.466) 		0.907
(0.96) 		0.693
(0.861) 		0.511
(0.762) 		0.949
(0.982)
CORPUS INVOLVEMENT Fisher's exact test 0.325
(0.652) 		0.387
(0.699) 		0.182
(0.514) 		0.193
(0.526) 		0.0661
(0.33) 		0.283
(0.619) 		0.734
(0.888) 		0.259
(0.6) 		0.652
(0.843) 		0.46
(0.75)
CHEMO CONCURRENT TYPE Fisher's exact test 0.537
(0.767) 		0.2
(0.533) 		0.632
(0.832) 		0.423
(0.73) 		0.406
(0.714) 		0.367
(0.683) 		1
(1.00) 		0.446
(0.738) 		0.206
(0.539) 		0.138
(0.465)
CERVIX SUV RESULTS Kruskal-Wallis (anova) 0.121
(0.458) 		0.0803
(0.38) 		0.0239
(0.191) 		0.231
(0.567) 		0.226
(0.567) 		0.141
(0.465) 		0.242
(0.577) 		0.479
(0.761) 		0.0797
(0.38)
AJCC TUMOR PATHOLOGIC PT Fisher's exact test 0.133
(0.465) 		0.557
(0.788) 		0.592
(0.806) 		0.797
(0.918) 		0.514
(0.762) 		0.134
(0.465) 		0.665
(0.843) 		0.022
(0.189) 		0.315
(0.649) 		0.932
(0.974)
AGE AT DIAGNOSIS Kruskal-Wallis (anova) 0.0165
(0.165) 		0.0138
(0.144) 		0.102
(0.434) 		0.106
(0.443) 		2.72e-05
(0.000817) 		0.00591
(0.0831) 		0.603
(0.812) 		6.89e-05
(0.00194) 		0.439
(0.733) 		0.885
(0.948)
STAGE EVENT CLINICAL STAGE Fisher's exact test 0.237
(0.568) 		0.0605
(0.321) 		0.489
(0.762) 		0.16
(0.486) 		0.0537
(0.288) 		0.0902
(0.402) 		0.0259
(0.194) 		0.00073
(0.0156) 		0.00554
(0.081) 		0.163
(0.492)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 128 71 93
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.271 (logrank test), Q value = 0.61

Table S2.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 278 65 0.0 - 210.7 (19.8)
subtype1 125 25 0.1 - 210.7 (17.9)
subtype2 64 20 0.1 - 147.3 (19.2)
subtype3 89 20 0.0 - 173.3 (23.0)

Figure S1.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0246 (Kruskal-Wallis (anova)), Q value = 0.19

Table S3.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 288 48.0 (13.8)
subtype1 128 47.7 (14.5)
subtype2 70 45.5 (14.4)
subtype3 90 50.5 (12.0)

Figure S2.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.218 (Fisher's exact test), Q value = 0.55

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 134 68 21 8
subtype1 61 36 5 4
subtype2 29 17 6 1
subtype3 44 15 10 3

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.18 (Fisher's exact test), Q value = 0.51

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 127 55
subtype1 66 21
subtype2 24 16
subtype3 37 18

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.526 (Fisher's exact test), Q value = 0.77

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 107 10
subtype1 43 5
subtype2 23 3
subtype3 41 2

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.294 (Fisher's exact test), Q value = 0.63

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 241 6 22 3 15
subtype1 3 97 3 14 2 9
subtype2 2 60 2 4 1 2
subtype3 0 84 1 4 0 4

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.909 (Fisher's exact test), Q value = 0.96

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 30 262
subtype1 12 116
subtype2 8 63
subtype3 10 83

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.76

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 90 17.5 (14.3)
subtype1 39 14.9 (11.8)
subtype2 23 18.2 (12.8)
subtype3 28 20.5 (18.0)

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 0.61

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 148 0.9 (2.1)
subtype1 75 1.0 (2.5)
subtype2 27 1.0 (1.8)
subtype3 46 0.8 (1.3)

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.104 (Fisher's exact test), Q value = 0.44

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 8 18 28 2 201
subtype1 6 7 11 1 90
subtype2 0 1 8 1 54
subtype3 2 10 9 0 57

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.508 (Fisher's exact test), Q value = 0.76

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 23 161
subtype1 13 70
subtype2 5 40
subtype3 5 51

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'Copy Number Ratio CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.503 (Kruskal-Wallis (anova)), Q value = 0.76

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 73.5 (21.5)
subtype1 111 74.1 (18.5)
subtype2 67 70.7 (16.5)
subtype3 86 74.8 (27.6)

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.764 (Fisher's exact test), Q value = 0.91

Table S14.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 134 41
subtype1 61 17
subtype2 29 11
subtype3 44 13

Figure S13.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.566 (Fisher's exact test), Q value = 0.79

Table S15.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 53 5 1 11 7 201 14
subtype1 21 3 0 3 4 92 5
subtype2 14 1 1 3 1 50 1
subtype3 18 1 0 5 2 59 8

Figure S14.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.034 (Fisher's exact test), Q value = 0.22

Table S16.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 129 113 1 24
subtype1 10 58 48 0 10
subtype2 2 20 36 1 7
subtype3 6 51 29 0 7

Figure S15.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.211 (Kruskal-Wallis (anova)), Q value = 0.54

Table S17.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 16 1996.2 (17.3)
subtype2 15 2004.3 (10.6)
subtype3 11 1998.5 (11.1)

Figure S16.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.76

Table S18.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 90 17.5 (14.3)
subtype1 39 14.9 (11.8)
subtype2 23 18.2 (12.8)
subtype3 28 20.5 (18.0)

Figure S17.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.439 (Fisher's exact test), Q value = 0.73

Table S19.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 37 9 3 62 142
subtype1 15 3 1 27 71
subtype2 13 1 1 14 27
subtype3 9 5 1 21 44

Figure S18.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'Copy Number Ratio CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.66

Table S20.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 84 21.2 (7.7)
subtype1 35 20.2 (5.9)
subtype2 23 20.4 (8.8)
subtype3 26 23.3 (8.7)

Figure S19.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.826 (Kruskal-Wallis (anova)), Q value = 0.93

Table S21.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 142 3601.6 (1718.4)
subtype1 60 3649.4 (1709.0)
subtype2 37 3415.9 (1837.3)
subtype3 45 3690.7 (1656.4)

Figure S20.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.948 (Fisher's exact test), Q value = 0.98

Table S22.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM INTERNAL
ALL 19 102 12 25
subtype1 7 45 5 12
subtype2 4 26 4 6
subtype3 8 31 3 7

Figure S21.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.261 (Fisher's exact test), Q value = 0.6

Table S23.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 25 3
subtype1 9 1
subtype2 6 2
subtype3 10 0

Figure S22.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.279 (Fisher's exact test), Q value = 0.62

Table S24.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 36 33
subtype1 2 0 15 12
subtype2 0 2 11 8
subtype3 0 0 10 13

Figure S23.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 1 (Fisher's exact test), Q value = 1

Table S25.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 56 5
subtype1 22 2
subtype2 17 2
subtype3 17 1

Figure S24.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.44 (Kruskal-Wallis (anova)), Q value = 0.73

Table S26.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 253 3.6 (2.5)
subtype1 113 3.4 (2.2)
subtype2 60 3.5 (2.5)
subtype3 80 4.0 (2.9)

Figure S25.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.245 (Kruskal-Wallis (anova)), Q value = 0.58

Table S27.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 105 0.1 (0.4)
subtype1 53 0.1 (0.3)
subtype2 19 0.0 (0.0)
subtype3 33 0.1 (0.5)

Figure S26.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.865 (Kruskal-Wallis (anova)), Q value = 0.94

Table S28.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 139 0.6 (1.0)
subtype1 68 0.5 (0.8)
subtype2 25 0.5 (0.7)
subtype3 46 0.7 (1.3)

Figure S27.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.538 (Kruskal-Wallis (anova)), Q value = 0.77

Table S29.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 249 2.8 (2.0)
subtype1 112 2.7 (1.8)
subtype2 60 3.0 (2.3)
subtype3 77 3.0 (2.1)

Figure S28.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.894 (Kruskal-Wallis (anova)), Q value = 0.96

Table S30.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 115 0.8 (1.7)
subtype1 58 0.7 (1.1)
subtype2 23 0.7 (1.0)
subtype3 34 1.2 (2.6)

Figure S29.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.213 (Kruskal-Wallis (anova)), Q value = 0.55

Table S31.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 109 0.1 (0.4)
subtype1 53 0.1 (0.2)
subtype2 20 0.1 (0.4)
subtype3 36 0.2 (0.5)

Figure S30.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'Copy Number Ratio CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.704 (Fisher's exact test), Q value = 0.87

Table S32.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 35 1 8
subtype1 1 1 17 1 4
subtype2 0 3 7 0 1
subtype3 1 3 11 0 3

Figure S31.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'Copy Number Ratio CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.00956 (Fisher's exact test), Q value = 0.12

Table S33.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 81 120
subtype1 0 7 33 62
subtype2 0 4 16 30
subtype3 2 14 32 28

Figure S32.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'Copy Number Ratio CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.121 (Fisher's exact test), Q value = 0.46

Table S34.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 70 73
subtype1 39 32
subtype2 9 19
subtype3 22 22

Figure S33.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 0.61

Table S35.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 148 0.9 (2.1)
subtype1 75 1.0 (2.5)
subtype2 27 1.0 (1.8)
subtype3 46 0.8 (1.3)

Figure S34.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.411 (Kruskal-Wallis (anova)), Q value = 0.72

Table S36.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 168 22.4 (12.8)
subtype1 82 21.6 (12.5)
subtype2 34 24.8 (12.6)
subtype3 52 22.1 (13.6)

Figure S35.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'Copy Number Ratio CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.952 (Fisher's exact test), Q value = 0.98

Table S37.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 48 116
subtype1 21 50
subtype2 10 27
subtype3 17 39

Figure S36.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'Copy Number Ratio CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.392 (Kruskal-Wallis (anova)), Q value = 0.71

Table S38.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 290 2008.5 (4.6)
subtype1 126 2008.7 (4.8)
subtype2 71 2008.3 (4.4)
subtype3 93 2008.3 (4.7)

Figure S37.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'Copy Number Ratio CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.429 (Fisher's exact test), Q value = 0.73

Table S39.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 84
subtype1 9 27 33
subtype2 3 12 19
subtype3 3 14 32

Figure S38.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'Copy Number Ratio CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.0643 (Kruskal-Wallis (anova)), Q value = 0.33

Table S40.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 251 161.2 (6.9)
subtype1 108 161.2 (8.0)
subtype2 60 162.6 (6.6)
subtype3 83 160.1 (5.2)

Figure S39.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'Copy Number Ratio CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.325 (Fisher's exact test), Q value = 0.65

Table S41.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 92 17
subtype1 46 6
subtype2 15 5
subtype3 31 6

Figure S40.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'Copy Number Ratio CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.537 (Fisher's exact test), Q value = 0.77

Table S42.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 1 15 1
subtype1 1 4 1
subtype2 0 7 0
subtype3 0 4 0

Figure S41.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.121 (Kruskal-Wallis (anova)), Q value = 0.46

Table S43.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 10 12.7 (7.0)
subtype2 3 20.1 (5.2)
subtype3 4 9.4 (6.4)

Figure S42.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'Copy Number Ratio CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.133 (Fisher's exact test), Q value = 0.47

Table S44.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 34 69 30 6 10 7 9 36 2 2 17 8 1 16
subtype1 1 15 31 14 4 6 3 7 16 2 0 3 4 0 7
subtype2 0 8 13 8 0 0 4 2 11 0 0 6 1 1 3
subtype3 0 11 25 8 2 4 0 0 9 0 2 8 3 0 6

Figure S43.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'Copy Number Ratio CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0165 (Kruskal-Wallis (anova)), Q value = 0.17

Table S45.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 292 48.1 (13.8)
subtype1 128 47.7 (14.5)
subtype2 71 45.5 (14.3)
subtype3 93 50.7 (11.9)

Figure S44.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'Copy Number Ratio CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.237 (Fisher's exact test), Q value = 0.57

Table S46.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 33 77 36 4 8 5 7 43 1 3 42 7 12
subtype1 1 0 1 0 12 40 16 1 5 2 5 17 1 1 16 2 7
subtype2 2 0 0 1 8 13 10 0 2 3 2 16 0 0 11 1 1
subtype3 2 1 0 0 13 24 10 3 1 0 0 10 0 2 15 4 4

Figure S45.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S47.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 70 58 59 61 11 45
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.117 (logrank test), Q value = 0.46

Table S48.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 69 0.0 - 210.7 (20.0)
subtype1 68 16 0.1 - 137.2 (17.9)
subtype2 56 12 0.1 - 195.8 (19.3)
subtype3 56 11 0.0 - 210.7 (22.1)
subtype4 56 21 0.0 - 154.3 (20.4)
subtype5 10 1 0.6 - 155.8 (23.8)
subtype6 44 8 0.1 - 146.9 (19.4)

Figure S46.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0108 (Kruskal-Wallis (anova)), Q value = 0.13

Table S49.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 300 48.1 (13.8)
subtype1 68 46.6 (11.5)
subtype2 57 51.2 (15.5)
subtype3 59 47.6 (13.0)
subtype4 60 43.2 (13.8)
subtype5 11 53.0 (12.0)
subtype6 45 52.5 (14.5)

Figure S47.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.493 (Fisher's exact test), Q value = 0.76

Table S50.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 70 21 10
subtype1 38 19 4 1
subtype2 26 10 6 4
subtype3 25 18 3 3
subtype4 23 13 5 1
subtype5 4 1 2 0
subtype6 24 9 1 1

Figure S48.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.427 (Fisher's exact test), Q value = 0.73

Table S51.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 59
subtype1 37 12
subtype2 19 14
subtype3 25 15
subtype4 25 8
subtype5 4 2
subtype6 23 8

Figure S49.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.597 (Fisher's exact test), Q value = 0.81

Table S52.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 10
subtype1 25 5
subtype2 20 1
subtype3 27 1
subtype4 22 2
subtype5 4 0
subtype6 17 1

Figure S50.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S53.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 253 6 22 3 15
subtype1 5 21 5 22 3 14
subtype2 0 58 0 0 0 0
subtype3 0 58 0 0 0 1
subtype4 0 61 0 0 0 0
subtype5 0 11 0 0 0 0
subtype6 0 44 1 0 0 0

Figure S51.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0875 (Fisher's exact test), Q value = 0.4

Table S54.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 35 269
subtype1 4 66
subtype2 3 55
subtype3 8 51
subtype4 11 50
subtype5 2 9
subtype6 7 38

Figure S52.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.402 (Kruskal-Wallis (anova)), Q value = 0.71

Table S55.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 12.6 (11.3)
subtype2 19 20.4 (16.8)
subtype3 18 17.2 (12.5)
subtype4 20 15.1 (13.6)
subtype5 4 19.1 (13.0)
subtype6 14 22.5 (16.2)

Figure S53.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.13 (Kruskal-Wallis (anova)), Q value = 0.47

Table S56.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 42 0.9 (2.8)
subtype2 28 1.5 (2.2)
subtype3 28 1.2 (2.2)
subtype4 25 0.6 (1.4)
subtype5 5 3.6 (6.9)
subtype6 29 0.6 (1.3)

Figure S54.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.671 (Fisher's exact test), Q value = 0.85

Table S57.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 8 19 30 2 210
subtype1 1 5 3 0 52
subtype2 1 3 7 0 38
subtype3 1 6 7 0 37
subtype4 4 3 8 2 41
subtype5 0 0 0 0 10
subtype6 1 2 5 0 32

Figure S55.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.967 (Fisher's exact test), Q value = 0.99

Table S58.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 169
subtype1 7 38
subtype2 5 29
subtype3 4 34
subtype4 5 35
subtype5 0 5
subtype6 3 28

Figure S56.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

'METHLYATION CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.0994 (Kruskal-Wallis (anova)), Q value = 0.43

Table S59.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 276 73.3 (21.5)
subtype1 64 74.2 (15.9)
subtype2 56 71.6 (22.2)
subtype3 50 67.9 (18.5)
subtype4 54 74.5 (18.9)
subtype5 10 66.8 (16.8)
subtype6 42 80.4 (31.7)

Figure S57.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'TUMOR_STATUS'

P value = 0.928 (Fisher's exact test), Q value = 0.97

Table S60.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 139 44
subtype1 34 11
subtype2 27 8
subtype3 31 7
subtype4 23 9
subtype5 5 2
subtype6 19 7

Figure S58.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'METHLYATION CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.663 (Fisher's exact test), Q value = 0.84

Table S61.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 54 5 1 12 7 211 14
subtype1 14 3 1 3 1 45 3
subtype2 14 0 0 3 2 37 2
subtype3 11 1 0 3 0 40 4
subtype4 11 0 0 0 3 44 3
subtype5 1 0 0 0 0 10 0
subtype6 3 1 0 3 1 35 2

Figure S59.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'METHLYATION CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.82 (Fisher's exact test), Q value = 0.93

Table S62.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 118 1 24
subtype1 7 30 27 0 6
subtype2 2 27 25 0 3
subtype3 3 29 22 0 4
subtype4 1 26 24 1 5
subtype5 0 4 6 0 1
subtype6 5 20 14 0 5

Figure S60.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.806 (Kruskal-Wallis (anova)), Q value = 0.92

Table S63.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 8 2002.4 (13.5)
subtype2 9 1997.7 (14.0)
subtype3 7 2001.7 (7.3)
subtype4 7 2000.7 (9.6)
subtype5 1 1978.0 (NA)
subtype6 10 1999.4 (19.7)

Figure S61.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.402 (Kruskal-Wallis (anova)), Q value = 0.71

Table S64.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 12.6 (11.3)
subtype2 19 20.4 (16.8)
subtype3 18 17.2 (12.5)
subtype4 20 15.1 (13.6)
subtype5 4 19.1 (13.0)
subtype6 14 22.5 (16.2)

Figure S62.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.231 (Fisher's exact test), Q value = 0.57

Table S65.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 39 9 4 64 145
subtype1 8 1 0 11 43
subtype2 7 3 1 12 28
subtype3 7 2 1 12 29
subtype4 7 0 1 17 26
subtype5 0 1 0 3 1
subtype6 10 2 1 9 18

Figure S63.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'METHLYATION CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.443 (Kruskal-Wallis (anova)), Q value = 0.74

Table S66.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 17 20.9 (5.7)
subtype2 19 20.6 (9.4)
subtype3 16 19.2 (5.2)
subtype4 18 22.3 (9.4)
subtype5 3 17.0 (4.0)
subtype6 12 24.2 (7.8)

Figure S64.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'METHLYATION CNMF' versus 'RADIATION_TOTAL_DOSE'

P value = 0.512 (Kruskal-Wallis (anova)), Q value = 0.76

Table S67.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 149 3635.4 (1710.6)
subtype1 34 3772.7 (1748.3)
subtype2 29 3228.9 (1889.5)
subtype3 25 3886.6 (1453.2)
subtype4 36 3520.2 (1823.1)
subtype5 6 4680.0 (278.9)
subtype6 19 3567.9 (1678.5)

Figure S65.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY_TYPE'

P value = 0.22 (Fisher's exact test), Q value = 0.56

Table S68.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 20 104 15 1 25
subtype1 3 29 1 0 3
subtype2 3 20 0 1 8
subtype3 5 17 3 0 4
subtype4 6 22 5 0 6
subtype5 0 4 2 0 1
subtype6 3 12 4 0 3

Figure S66.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.903 (Fisher's exact test), Q value = 0.96

Table S69.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 4 0
subtype2 2 0
subtype3 7 1
subtype4 8 2
subtype5 2 0
subtype6 6 0

Figure S67.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'METHLYATION CNMF' versus 'RADIATION_THERAPY_SITE'

P value = 0.66 (Fisher's exact test), Q value = 0.84

Table S70.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 38 33
subtype1 1 0 6 4
subtype2 0 0 6 12
subtype3 0 1 6 6
subtype4 1 1 12 6
subtype5 0 0 3 2
subtype6 0 0 5 3

Figure S68.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'METHLYATION CNMF' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.718 (Fisher's exact test), Q value = 0.88

Table S71.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 58 5
subtype1 11 0
subtype2 14 1
subtype3 11 1
subtype4 13 3
subtype5 3 0
subtype6 6 0

Figure S69.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.316 (Kruskal-Wallis (anova)), Q value = 0.65

Table S72.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 265 3.6 (2.6)
subtype1 64 3.0 (2.1)
subtype2 50 4.0 (2.5)
subtype3 51 3.6 (2.5)
subtype4 52 4.0 (3.3)
subtype5 10 3.3 (1.7)
subtype6 38 3.6 (2.5)

Figure S70.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.00107 (Kruskal-Wallis (anova)), Q value = 0.022

Table S73.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.3)
subtype1 26 0.0 (0.2)
subtype2 23 0.0 (0.0)
subtype3 20 0.3 (0.7)
subtype4 22 0.0 (0.0)
subtype5 2 0.0 (0.0)
subtype6 18 0.0 (0.0)

Figure S71.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'METHLYATION CNMF' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.882 (Kruskal-Wallis (anova)), Q value = 0.95

Table S74.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 146 0.5 (0.9)
subtype1 30 0.6 (0.9)
subtype2 30 0.5 (1.2)
subtype3 29 0.5 (0.7)
subtype4 30 0.7 (1.2)
subtype5 4 0.5 (0.6)
subtype6 23 0.3 (0.5)

Figure S72.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0224 (Kruskal-Wallis (anova)), Q value = 0.19

Table S75.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 260 2.9 (2.1)
subtype1 60 2.3 (1.9)
subtype2 50 3.6 (2.3)
subtype3 51 2.7 (1.9)
subtype4 50 3.2 (2.4)
subtype5 11 2.3 (1.2)
subtype6 38 2.6 (1.5)

Figure S73.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'METHLYATION CNMF' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.306 (Kruskal-Wallis (anova)), Q value = 0.65

Table S76.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 121 0.9 (1.8)
subtype1 28 0.8 (1.1)
subtype2 24 0.4 (0.9)
subtype3 22 1.2 (2.4)
subtype4 24 0.8 (1.6)
subtype5 2 0.5 (0.7)
subtype6 21 1.2 (2.9)

Figure S74.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.661 (Kruskal-Wallis (anova)), Q value = 0.84

Table S77.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 115 0.1 (0.3)
subtype1 25 0.1 (0.3)
subtype2 24 0.0 (0.2)
subtype3 22 0.1 (0.4)
subtype4 22 0.1 (0.4)
subtype5 3 0.3 (0.6)
subtype6 19 0.2 (0.5)

Figure S75.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'METHLYATION CNMF' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.802 (Fisher's exact test), Q value = 0.92

Table S78.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 8 39 1 10
subtype1 1 1 8 0 2
subtype2 0 2 5 0 2
subtype3 0 0 11 0 2
subtype4 0 2 7 1 1
subtype5 0 1 2 0 0
subtype6 1 2 6 0 3

Figure S76.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'METHLYATION CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.135 (Fisher's exact test), Q value = 0.47

Table S79.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 83 124
subtype1 0 9 17 34
subtype2 1 6 20 17
subtype3 0 4 17 27
subtype4 0 3 8 27
subtype5 0 1 3 4
subtype6 1 2 18 15

Figure S77.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'METHLYATION CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.369 (Fisher's exact test), Q value = 0.68

Table S80.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 22 20
subtype2 7 17
subtype3 16 14
subtype4 12 9
subtype5 2 4
subtype6 12 15

Figure S78.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.13 (Kruskal-Wallis (anova)), Q value = 0.47

Table S81.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 42 0.9 (2.8)
subtype2 28 1.5 (2.2)
subtype3 28 1.2 (2.2)
subtype4 25 0.6 (1.4)
subtype5 5 3.6 (6.9)
subtype6 29 0.6 (1.3)

Figure S79.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.688 (Kruskal-Wallis (anova)), Q value = 0.86

Table S82.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.3 (12.6)
subtype1 47 21.2 (10.2)
subtype2 30 23.6 (14.6)
subtype3 33 25.9 (13.8)
subtype4 30 21.5 (12.8)
subtype5 7 18.6 (10.0)
subtype6 31 20.8 (12.9)

Figure S80.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'METHLYATION CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.00073 (Fisher's exact test), Q value = 0.016

Table S83.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 54 119
subtype1 0 16
subtype2 6 31
subtype3 13 26
subtype4 18 22
subtype5 5 3
subtype6 12 21

Figure S81.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'METHLYATION CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.185 (Kruskal-Wallis (anova)), Q value = 0.51

Table S84.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 302 2008.3 (4.8)
subtype1 69 2009.3 (3.9)
subtype2 58 2008.1 (5.3)
subtype3 59 2008.1 (4.8)
subtype4 61 2007.2 (5.2)
subtype5 11 2007.5 (4.8)
subtype6 44 2008.9 (4.5)

Figure S82.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'METHLYATION CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.619 (Fisher's exact test), Q value = 0.82

Table S85.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 89
subtype1 7 18 20
subtype2 2 11 18
subtype3 3 7 17
subtype4 2 10 17
subtype5 0 2 1
subtype6 1 5 16

Figure S83.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'METHLYATION CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.184 (Kruskal-Wallis (anova)), Q value = 0.51

Table S86.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 262 161.0 (7.3)
subtype1 62 161.6 (7.1)
subtype2 51 161.5 (7.0)
subtype3 49 158.8 (7.9)
subtype4 51 161.4 (7.8)
subtype5 10 159.9 (4.8)
subtype6 39 162.2 (6.6)

Figure S84.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.387 (Fisher's exact test), Q value = 0.7

Table S87.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 18
subtype1 31 5
subtype2 15 2
subtype3 18 5
subtype4 15 1
subtype5 2 2
subtype6 18 3

Figure S85.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'METHLYATION CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.2 (Fisher's exact test), Q value = 0.53

Table S88.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 18 1
subtype1 0 2 0
subtype2 1 1 0
subtype3 1 5 1
subtype4 0 8 0
subtype5 1 1 0
subtype6 0 1 0

Figure S86.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'METHLYATION CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.0803 (Kruskal-Wallis (anova)), Q value = 0.38

Table S89.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 1 7.1 (NA)
subtype2 1 18.5 (NA)
subtype3 5 8.2 (5.7)
subtype4 4 18.0 (6.0)
subtype6 6 14.4 (7.7)

Figure S87.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'METHLYATION CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.557 (Fisher's exact test), Q value = 0.79

Table S90.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 36 72 31 6 10 7 10 37 2 2 17 10 1 17
subtype1 0 6 25 7 1 4 2 3 9 0 0 4 1 0 2
subtype2 0 6 13 7 2 1 0 2 5 1 1 4 4 0 4
subtype3 0 7 15 3 2 1 3 1 11 0 0 3 3 0 3
subtype4 0 11 7 5 0 1 1 2 9 1 1 3 1 1 5
subtype5 0 1 2 1 0 0 1 0 0 0 0 2 0 0 1
subtype6 1 5 10 8 1 3 0 2 3 0 0 1 1 0 2

Figure S88.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'METHLYATION CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0138 (Kruskal-Wallis (anova)), Q value = 0.14

Table S91.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 70 46.7 (11.4)
subtype2 58 51.3 (15.3)
subtype3 59 47.6 (13.0)
subtype4 61 43.6 (13.9)
subtype5 11 53.0 (12.0)
subtype6 45 52.5 (14.5)

Figure S89.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0605 (Fisher's exact test), Q value = 0.32

Table S92.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 37 78 38 5 9 5 7 43 1 3 42 9 12
subtype1 1 0 0 0 5 26 10 1 3 1 2 9 0 0 7 0 5
subtype2 0 0 0 0 6 13 6 1 0 0 1 6 1 1 10 4 4
subtype3 0 1 0 0 7 18 4 1 1 2 0 10 0 0 11 3 1
subtype4 2 0 0 0 13 9 6 0 4 1 1 12 0 2 8 1 1
subtype5 1 0 0 1 1 2 1 0 0 1 0 2 0 0 2 0 0
subtype6 1 0 1 0 5 10 11 2 1 0 3 4 0 0 4 1 1

Figure S90.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S93.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 52 46 74
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0125 (logrank test), Q value = 0.14

Table S94.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 161 30 0.0 - 210.7 (18.0)
subtype1 50 14 0.0 - 144.2 (17.7)
subtype2 40 5 0.4 - 173.3 (19.6)
subtype3 71 11 0.1 - 210.7 (17.8)

Figure S91.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.136 (Kruskal-Wallis (anova)), Q value = 0.47

Table S95.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 169 47.5 (13.6)
subtype1 52 44.4 (12.9)
subtype2 45 49.1 (14.2)
subtype3 72 48.7 (13.5)

Figure S92.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.488 (Fisher's exact test), Q value = 0.76

Table S96.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 99 31 7 3
subtype1 30 8 0 1
subtype2 24 8 4 1
subtype3 45 15 3 1

Figure S93.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.655 (Fisher's exact test), Q value = 0.84

Table S97.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 92 38
subtype1 27 9
subtype2 23 8
subtype3 42 21

Figure S94.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 1 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 83 4
subtype1 22 1
subtype2 24 1
subtype3 37 2

Figure S95.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.721 (Fisher's exact test), Q value = 0.88

Table S99.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 3 144 3 17 2 3
subtype1 2 44 1 4 0 1
subtype2 0 42 0 3 0 1
subtype3 1 58 2 10 2 1

Figure S96.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.926 (Fisher's exact test), Q value = 0.97

Table S100.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 25 147
subtype1 8 44
subtype2 7 39
subtype3 10 64

Figure S97.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0998 (Kruskal-Wallis (anova)), Q value = 0.43

Table S101.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 45 17.7 (13.7)
subtype1 11 12.2 (8.5)
subtype2 11 13.2 (10.9)
subtype3 23 22.5 (15.4)

Figure S98.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.824 (Kruskal-Wallis (anova)), Q value = 0.93

Table S102.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 104 1.2 (2.7)
subtype1 27 1.1 (2.5)
subtype2 23 1.1 (2.1)
subtype3 54 1.2 (3.0)

Figure S99.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.669 (Fisher's exact test), Q value = 0.85

Table S103.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 15 12 1 124
subtype1 1 4 6 1 37
subtype2 1 6 2 0 32
subtype3 1 5 4 0 55

Figure S100.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0642 (Fisher's exact test), Q value = 0.33

Table S104.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 112
subtype1 1 40
subtype2 6 26
subtype3 6 46

Figure S101.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RPPA CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.0897 (Kruskal-Wallis (anova)), Q value = 0.4

Table S105.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 157 74.9 (23.0)
subtype1 45 69.2 (17.9)
subtype2 42 75.9 (17.1)
subtype3 70 77.9 (28.1)

Figure S102.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RPPA CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.26 (Fisher's exact test), Q value = 0.6

Table S106.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 87 24
subtype1 21 10
subtype2 26 5
subtype3 40 9

Figure S103.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RPPA CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.517 (Fisher's exact test), Q value = 0.76

Table S107.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 8 4 12 7 128 13
subtype1 2 1 5 3 38 3
subtype2 4 1 5 1 30 5
subtype3 2 2 2 3 60 5

Figure S104.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RPPA CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.113 (Fisher's exact test), Q value = 0.46

Table S108.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 10 75 73 1 8
subtype1 3 24 19 1 4
subtype2 2 24 14 0 3
subtype3 5 27 40 0 1

Figure S105.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.738 (Kruskal-Wallis (anova)), Q value = 0.89

Table S109.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 30 2001.6 (10.9)
subtype1 8 2004.4 (7.9)
subtype2 8 2000.2 (10.8)
subtype3 14 2000.8 (12.7)

Figure S106.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0998 (Kruskal-Wallis (anova)), Q value = 0.43

Table S110.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 45 17.7 (13.7)
subtype1 11 12.2 (8.5)
subtype2 11 13.2 (10.9)
subtype3 23 22.5 (15.4)

Figure S107.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.339 (Fisher's exact test), Q value = 0.66

Table S111.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 27 6 2 23 86
subtype1 7 2 0 6 27
subtype2 7 2 2 3 26
subtype3 13 2 0 14 33

Figure S108.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RPPA CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.521 (Kruskal-Wallis (anova)), Q value = 0.76

Table S112.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 44 21.5 (7.9)
subtype1 10 22.1 (8.6)
subtype2 10 20.2 (8.3)
subtype3 24 21.8 (7.7)

Figure S109.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RPPA CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.797 (Kruskal-Wallis (anova)), Q value = 0.92

Table S113.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 75 3611.8 (1897.9)
subtype1 22 3467.4 (1894.3)
subtype2 20 3702.4 (1884.9)
subtype3 33 3653.1 (1960.6)

Figure S110.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.838 (Fisher's exact test), Q value = 0.94

Table S114.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 12 50 13 1 8
subtype1 5 15 4 0 3
subtype2 2 12 3 1 3
subtype3 5 23 6 0 2

Figure S111.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.501 (Fisher's exact test), Q value = 0.76

Table S115.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 21 2
subtype1 7 1
subtype2 5 1
subtype3 9 0

Figure S112.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.315 (Fisher's exact test), Q value = 0.65

Table S116.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 20 5
subtype1 2 5 3
subtype2 0 6 1
subtype3 0 9 1

Figure S113.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RPPA CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.232 (Fisher's exact test), Q value = 0.57

Table S117.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 18 4
subtype1 6 3
subtype2 4 1
subtype3 8 0

Figure S114.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 0.46

Table S118.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 150 3.6 (2.6)
subtype1 41 3.2 (2.0)
subtype2 44 4.6 (3.5)
subtype3 65 3.3 (2.0)

Figure S115.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.318 (Kruskal-Wallis (anova)), Q value = 0.65

Table S119.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 71 0.0 (0.2)
subtype1 21 0.1 (0.3)
subtype2 17 0.0 (0.0)
subtype3 33 0.0 (0.2)

Figure S116.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RPPA CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.732 (Kruskal-Wallis (anova)), Q value = 0.89

Table S120.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 88 0.4 (0.8)
subtype1 23 0.3 (0.6)
subtype2 25 0.6 (1.1)
subtype3 40 0.5 (0.8)

Figure S117.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0229 (Kruskal-Wallis (anova)), Q value = 0.19

Table S121.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 150 2.6 (1.9)
subtype1 43 2.2 (1.7)
subtype2 44 3.4 (2.5)
subtype3 63 2.4 (1.3)

Figure S118.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RPPA CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.338 (Kruskal-Wallis (anova)), Q value = 0.66

Table S122.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 80 1.1 (2.2)
subtype1 24 1.1 (1.4)
subtype2 20 1.8 (3.5)
subtype3 36 0.8 (1.5)

Figure S119.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.974 (Kruskal-Wallis (anova)), Q value = 1

Table S123.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 73 0.1 (0.4)
subtype1 21 0.1 (0.3)
subtype2 18 0.1 (0.3)
subtype3 34 0.1 (0.4)

Figure S120.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RPPA CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.415 (Fisher's exact test), Q value = 0.72

Table S124.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 24 1 5
subtype1 1 1 3 1 2
subtype2 0 1 6 0 0
subtype3 1 5 15 0 3

Figure S121.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RPPA CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.509 (Fisher's exact test), Q value = 0.76

Table S125.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 13 46 78
subtype1 0 2 12 29
subtype2 1 5 12 18
subtype3 1 6 22 31

Figure S122.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RPPA CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.0439 (Fisher's exact test), Q value = 0.26

Table S126.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 52 59
subtype1 20 13
subtype2 14 12
subtype3 18 34

Figure S123.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.824 (Kruskal-Wallis (anova)), Q value = 0.93

Table S127.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 104 1.2 (2.7)
subtype1 27 1.1 (2.5)
subtype2 23 1.1 (2.1)
subtype3 54 1.2 (3.0)

Figure S124.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.00736 (Kruskal-Wallis (anova)), Q value = 0.095

Table S128.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 121 20.8 (11.9)
subtype1 35 19.1 (9.6)
subtype2 25 15.7 (10.6)
subtype3 61 23.9 (12.8)

Figure S125.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RPPA CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0331 (Fisher's exact test), Q value = 0.22

Table S129.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 75
subtype1 12 27
subtype2 6 25
subtype3 21 23

Figure S126.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RPPA CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 0.76

Table S130.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 170 2008.2 (5.0)
subtype1 51 2008.5 (5.2)
subtype2 46 2008.6 (4.6)
subtype3 73 2007.8 (5.0)

Figure S127.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RPPA CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.18 (Fisher's exact test), Q value = 0.51

Table S131.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 5 31 51
subtype1 0 9 14
subtype2 0 7 17
subtype3 5 15 20

Figure S128.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RPPA CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.342 (Kruskal-Wallis (anova)), Q value = 0.66

Table S132.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 152 161.7 (7.1)
subtype1 44 162.8 (6.2)
subtype2 43 160.7 (7.9)
subtype3 65 161.6 (7.2)

Figure S129.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RPPA CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.182 (Fisher's exact test), Q value = 0.51

Table S133.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 84 11
subtype1 23 2
subtype2 24 1
subtype3 37 8

Figure S130.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RPPA CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.632 (Fisher's exact test), Q value = 0.83

Table S134.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 2 13 1
subtype1 0 3 1
subtype2 1 3 0
subtype3 1 7 0

Figure S131.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RPPA CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0239 (Kruskal-Wallis (anova)), Q value = 0.19

Table S135.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 11 13.2 (8.7)
subtype1 3 12.8 (3.6)
subtype2 3 24.5 (5.3)
subtype3 5 6.6 (4.4)

Figure S132.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'RPPA CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.592 (Fisher's exact test), Q value = 0.81

Table S136.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 23 52 23 3 4 6 7 11 1 1 5 3 1 8
subtype1 1 7 14 8 0 0 3 2 3 0 0 0 1 0 5
subtype2 0 6 13 5 0 2 0 2 4 0 1 3 1 1 2
subtype3 0 10 25 10 3 2 3 3 4 1 0 2 1 0 1

Figure S133.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RPPA CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.102 (Kruskal-Wallis (anova)), Q value = 0.43

Table S137.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 172 47.7 (13.5)
subtype1 52 44.4 (12.9)
subtype2 46 49.4 (14.2)
subtype3 74 48.9 (13.3)

Figure S134.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RPPA CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.489 (Fisher's exact test), Q value = 0.76

Table S138.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 4 1 1 1 27 48 27 4 5 4 4 16 1 1 19 4 2
subtype1 1 1 1 0 6 13 12 0 1 1 1 7 0 0 5 1 1
subtype2 2 0 0 1 8 11 4 3 2 0 0 5 0 1 7 2 0
subtype3 1 0 0 0 13 24 11 1 2 3 3 4 1 0 7 1 1

Figure S135.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S139.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 31 49 51 23 18
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.233 (logrank test), Q value = 0.57

Table S140.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 161 30 0.0 - 210.7 (18.0)
subtype1 30 6 0.4 - 144.2 (17.9)
subtype2 47 7 0.0 - 210.7 (19.4)
subtype3 47 8 0.1 - 195.8 (19.7)
subtype4 21 6 0.0 - 78.7 (21.1)
subtype5 16 3 0.1 - 147.4 (14.7)

Figure S136.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0822 (Kruskal-Wallis (anova)), Q value = 0.39

Table S141.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 169 47.5 (13.6)
subtype1 30 44.0 (14.5)
subtype2 48 47.5 (13.5)
subtype3 50 48.9 (13.5)
subtype4 23 44.2 (12.3)
subtype5 18 53.8 (12.1)

Figure S137.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.565 (Fisher's exact test), Q value = 0.79

Table S142.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 99 31 7 3
subtype1 19 3 0 0
subtype2 31 9 2 0
subtype3 24 11 4 2
subtype4 13 3 1 1
subtype5 12 5 0 0

Figure S138.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.233 (Fisher's exact test), Q value = 0.57

Table S143.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 92 38
subtype1 16 4
subtype2 30 12
subtype3 19 14
subtype4 13 6
subtype5 14 2

Figure S139.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.949 (Fisher's exact test), Q value = 0.98

Table S144.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 83 4
subtype1 11 1
subtype2 24 1
subtype3 26 1
subtype4 12 1
subtype5 10 0

Figure S140.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00253 (Fisher's exact test), Q value = 0.046

Table S145.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 3 144 3 17 2 3
subtype1 1 26 1 2 0 1
subtype2 2 35 1 8 2 1
subtype3 0 49 0 1 0 1
subtype4 0 23 0 0 0 0
subtype5 0 11 1 6 0 0

Figure S141.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.797 (Fisher's exact test), Q value = 0.92

Table S146.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 25 147
subtype1 6 25
subtype2 7 42
subtype3 8 43
subtype4 3 20
subtype5 1 17

Figure S142.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.54 (Kruskal-Wallis (anova)), Q value = 0.77

Table S147.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 45 17.7 (13.7)
subtype1 8 10.9 (7.4)
subtype2 15 17.3 (10.7)
subtype3 14 20.8 (16.6)
subtype4 6 20.6 (20.0)
subtype5 2 18.0 (11.3)

Figure S143.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.185 (Kruskal-Wallis (anova)), Q value = 0.51

Table S148.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 104 1.2 (2.7)
subtype1 15 1.4 (3.0)
subtype2 39 1.3 (3.5)
subtype3 25 1.2 (1.6)
subtype4 14 0.6 (1.6)
subtype5 11 0.7 (2.4)

Figure S144.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.203 (Fisher's exact test), Q value = 0.53

Table S149.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 15 12 1 124
subtype1 0 4 3 1 22
subtype2 2 1 2 0 37
subtype3 1 4 3 0 37
subtype4 0 2 4 0 15
subtype5 0 4 0 0 13

Figure S145.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.496 (Fisher's exact test), Q value = 0.76

Table S150.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 112
subtype1 1 27
subtype2 3 29
subtype3 6 29
subtype4 1 14
subtype5 2 13

Figure S146.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RPPA cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.686 (Kruskal-Wallis (anova)), Q value = 0.86

Table S151.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 157 74.9 (23.0)
subtype1 26 69.5 (18.6)
subtype2 45 76.0 (20.0)
subtype3 48 76.6 (27.3)
subtype4 20 72.6 (12.8)
subtype5 18 77.8 (31.8)

Figure S147.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RPPA cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.419 (Fisher's exact test), Q value = 0.73

Table S152.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 87 24
subtype1 12 5
subtype2 28 5
subtype3 30 6
subtype4 9 4
subtype5 8 4

Figure S148.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RPPA cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.617 (Fisher's exact test), Q value = 0.82

Table S153.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 8 4 12 7 128 13
subtype1 0 0 2 2 24 3
subtype2 1 2 3 1 41 1
subtype3 6 2 4 2 33 4
subtype4 1 0 2 1 17 2
subtype5 0 0 1 1 13 3

Figure S149.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RPPA cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.515 (Fisher's exact test), Q value = 0.76

Table S154.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 10 75 73 1 8
subtype1 2 15 11 0 3
subtype2 5 16 26 0 2
subtype3 2 26 17 0 2
subtype4 1 9 10 1 1
subtype5 0 9 9 0 0

Figure S150.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.72 (Kruskal-Wallis (anova)), Q value = 0.88

Table S155.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 30 2001.6 (10.9)
subtype1 5 2002.6 (8.9)
subtype2 11 2000.3 (14.3)
subtype3 10 2004.3 (8.2)
subtype4 1 2000.0 (NA)
subtype5 3 1996.3 (11.8)

Figure S151.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.54 (Kruskal-Wallis (anova)), Q value = 0.77

Table S156.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 45 17.7 (13.7)
subtype1 8 10.9 (7.4)
subtype2 15 17.3 (10.7)
subtype3 14 20.8 (16.6)
subtype4 6 20.6 (20.0)
subtype5 2 18.0 (11.3)

Figure S152.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.577 (Fisher's exact test), Q value = 0.8

Table S157.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 27 6 2 23 86
subtype1 2 2 0 4 18
subtype2 10 2 0 6 21
subtype3 11 1 1 7 24
subtype4 2 0 1 5 11
subtype5 2 1 0 1 12

Figure S153.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RPPA cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.808 (Kruskal-Wallis (anova)), Q value = 0.92

Table S158.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 44 21.5 (7.9)
subtype1 7 23.4 (9.6)
subtype2 14 20.9 (7.1)
subtype3 15 20.5 (7.9)
subtype4 5 21.4 (5.9)
subtype5 3 25.3 (13.6)

Figure S154.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RPPA cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.662 (Kruskal-Wallis (anova)), Q value = 0.84

Table S159.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 75 3611.8 (1897.9)
subtype1 13 3483.1 (1881.6)
subtype2 23 3739.5 (1901.8)
subtype3 23 4038.9 (1604.2)
subtype4 10 3103.3 (2194.6)
subtype5 6 2611.3 (2495.8)

Figure S155.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.776 (Fisher's exact test), Q value = 0.91

Table S160.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 12 50 13 1 8
subtype1 3 9 3 0 1
subtype2 2 14 5 0 3
subtype3 4 16 4 0 2
subtype4 3 6 1 0 2
subtype5 0 5 0 1 0

Figure S156.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 1 (Fisher's exact test), Q value = 1

Table S161.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 21 2
subtype1 5 1
subtype2 6 0
subtype3 7 1
subtype4 3 0
subtype5 0 0

Figure S157.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.474 (Fisher's exact test), Q value = 0.76

Table S162.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 20 5
subtype1 2 3 1
subtype2 0 4 1
subtype3 0 9 2
subtype4 0 3 1
subtype5 0 1 0

Figure S158.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RPPA cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.0995 (Fisher's exact test), Q value = 0.43

Table S163.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 18 4
subtype1 3 2
subtype2 4 0
subtype3 8 0
subtype4 2 2
subtype5 1 0

Figure S159.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.621 (Kruskal-Wallis (anova)), Q value = 0.82

Table S164.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 150 3.6 (2.6)
subtype1 24 3.4 (2.1)
subtype2 41 3.3 (1.7)
subtype3 49 3.9 (2.9)
subtype4 21 3.3 (2.9)
subtype5 15 4.8 (3.6)

Figure S160.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.185 (Kruskal-Wallis (anova)), Q value = 0.51

Table S165.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 71 0.0 (0.2)
subtype1 12 0.2 (0.4)
subtype2 21 0.0 (0.2)
subtype3 22 0.0 (0.0)
subtype4 11 0.0 (0.0)
subtype5 5 0.0 (0.0)

Figure S161.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RPPA cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.83 (Kruskal-Wallis (anova)), Q value = 0.93

Table S166.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 88 0.4 (0.8)
subtype1 14 0.4 (0.6)
subtype2 25 0.4 (0.9)
subtype3 28 0.6 (1.1)
subtype4 14 0.4 (0.5)
subtype5 7 0.4 (0.5)

Figure S162.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.47

Table S167.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 150 2.6 (1.9)
subtype1 25 2.3 (1.7)
subtype2 40 2.4 (1.4)
subtype3 48 2.9 (1.8)
subtype4 21 2.4 (2.6)
subtype5 16 3.3 (2.3)

Figure S163.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RPPA cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.592 (Kruskal-Wallis (anova)), Q value = 0.81

Table S168.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 80 1.1 (2.2)
subtype1 15 1.2 (1.5)
subtype2 23 0.7 (1.2)
subtype3 23 1.2 (2.5)
subtype4 12 0.9 (1.2)
subtype5 7 2.6 (4.7)

Figure S164.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.663 (Kruskal-Wallis (anova)), Q value = 0.84

Table S169.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 73 0.1 (0.4)
subtype1 11 0.0 (0.0)
subtype2 23 0.1 (0.3)
subtype3 22 0.1 (0.5)
subtype4 12 0.2 (0.4)
subtype5 5 0.2 (0.4)

Figure S165.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RPPA cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.639 (Fisher's exact test), Q value = 0.83

Table S170.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 24 1 5
subtype1 1 1 3 0 0
subtype2 1 4 10 0 2
subtype3 0 2 6 0 1
subtype4 0 0 3 1 2
subtype5 0 0 2 0 0

Figure S166.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RPPA cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.802 (Fisher's exact test), Q value = 0.92

Table S171.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 13 46 78
subtype1 0 2 7 18
subtype2 0 3 15 23
subtype3 2 6 13 18
subtype4 0 1 5 12
subtype5 0 1 6 7

Figure S167.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RPPA cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.333 (Fisher's exact test), Q value = 0.66

Table S172.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 52 59
subtype1 13 7
subtype2 13 22
subtype3 12 17
subtype4 6 6
subtype5 8 7

Figure S168.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.185 (Kruskal-Wallis (anova)), Q value = 0.51

Table S173.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 104 1.2 (2.7)
subtype1 15 1.4 (3.0)
subtype2 39 1.3 (3.5)
subtype3 25 1.2 (1.6)
subtype4 14 0.6 (1.6)
subtype5 11 0.7 (2.4)

Figure S169.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.237 (Kruskal-Wallis (anova)), Q value = 0.57

Table S174.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 121 20.8 (11.9)
subtype1 21 18.4 (9.7)
subtype2 45 24.2 (12.6)
subtype3 26 19.3 (10.9)
subtype4 16 19.6 (14.9)
subtype5 13 17.3 (8.9)

Figure S170.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RPPA cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0411 (Fisher's exact test), Q value = 0.25

Table S175.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 75
subtype1 7 17
subtype2 16 14
subtype3 10 25
subtype4 6 11
subtype5 0 8

Figure S171.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RPPA cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.94

Table S176.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 170 2008.2 (5.0)
subtype1 31 2008.2 (5.5)
subtype2 47 2008.6 (4.4)
subtype3 51 2008.1 (5.2)
subtype4 23 2007.1 (5.6)
subtype5 18 2009.2 (3.8)

Figure S172.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RPPA cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.507 (Fisher's exact test), Q value = 0.76

Table S177.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 5 31 51
subtype1 0 3 8
subtype2 3 12 13
subtype3 1 6 18
subtype4 0 6 8
subtype5 1 4 4

Figure S173.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RPPA cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.165 (Kruskal-Wallis (anova)), Q value = 0.5

Table S178.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 152 161.7 (7.1)
subtype1 25 162.5 (6.6)
subtype2 43 162.3 (5.7)
subtype3 46 160.1 (8.7)
subtype4 20 164.4 (6.1)
subtype5 18 160.3 (7.2)

Figure S174.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RPPA cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.193 (Fisher's exact test), Q value = 0.53

Table S179.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 84 11
subtype1 16 0
subtype2 24 3
subtype3 25 3
subtype4 9 1
subtype5 10 4

Figure S175.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RPPA cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.423 (Fisher's exact test), Q value = 0.73

Table S180.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 2 13 1
subtype1 0 2 1
subtype2 1 4 0
subtype3 0 5 0
subtype4 0 2 0
subtype5 1 0 0

Figure S176.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RPPA cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.797 (Fisher's exact test), Q value = 0.92

Table S181.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 23 52 23 3 4 6 7 11 1 1 5 3 1 8
subtype1 1 7 7 4 0 0 1 1 1 0 0 0 0 0 3
subtype2 0 7 16 8 1 2 2 2 2 0 0 2 0 0 1
subtype3 0 5 15 4 1 1 0 2 7 1 1 2 2 0 2
subtype4 0 3 7 3 0 0 1 1 1 0 0 1 1 1 2
subtype5 0 1 7 4 1 1 2 1 0 0 0 0 0 0 0

Figure S177.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RPPA cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.106 (Kruskal-Wallis (anova)), Q value = 0.44

Table S182.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 172 47.7 (13.5)
subtype1 31 44.6 (14.7)
subtype2 49 47.7 (13.5)
subtype3 51 48.9 (13.4)
subtype4 23 44.2 (12.3)
subtype5 18 53.8 (12.1)

Figure S178.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RPPA cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.16 (Fisher's exact test), Q value = 0.49

Table S183.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 4 1 1 1 27 48 27 4 5 4 4 16 1 1 19 4 2
subtype1 1 1 1 0 4 7 8 0 1 0 0 3 0 0 3 0 1
subtype2 1 0 0 0 8 20 8 0 1 1 2 3 0 0 3 0 1
subtype3 1 0 0 1 8 10 4 2 1 0 0 8 1 1 10 3 0
subtype4 1 0 0 0 5 5 3 0 1 1 1 2 0 0 3 1 0
subtype5 0 0 0 0 2 6 4 2 1 2 1 0 0 0 0 0 0

Figure S179.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S184.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 56 104 69 72
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.52 (logrank test), Q value = 0.76

Table S185.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 287 69 0.0 - 210.7 (20.1)
subtype1 53 11 0.1 - 146.9 (20.0)
subtype2 99 22 0.1 - 210.7 (20.8)
subtype3 67 15 0.1 - 147.4 (23.7)
subtype4 68 21 0.0 - 154.3 (17.9)

Figure S180.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.68e-05 (Kruskal-Wallis (anova)), Q value = 0.00054

Table S186.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 297 48.1 (13.9)
subtype1 56 53.1 (12.6)
subtype2 103 50.8 (14.9)
subtype3 67 46.1 (12.3)
subtype4 71 42.0 (12.3)

Figure S181.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.593 (Fisher's exact test), Q value = 0.81

Table S187.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 139 69 20 10
subtype1 22 13 6 3
subtype2 48 26 7 4
subtype3 40 14 2 1
subtype4 29 16 5 2

Figure S182.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.349 (Fisher's exact test), Q value = 0.67

Table S188.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 131 59
subtype1 22 11
subtype2 45 27
subtype3 35 10
subtype4 29 11

Figure S183.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.227 (Fisher's exact test), Q value = 0.57

Table S189.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 10
subtype1 23 4
subtype2 43 1
subtype3 23 2
subtype4 26 3

Figure S184.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S190.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 4 251 6 22 3 15
subtype1 2 47 2 3 1 1
subtype2 0 104 0 0 0 0
subtype3 2 28 4 19 2 14
subtype4 0 72 0 0 0 0

Figure S185.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.284 (Fisher's exact test), Q value = 0.62

Table S191.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 35 266
subtype1 8 48
subtype2 12 92
subtype3 4 65
subtype4 11 61

Figure S186.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.145 (Kruskal-Wallis (anova)), Q value = 0.47

Table S192.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 17 20.5 (15.7)
subtype2 38 20.7 (16.0)
subtype3 17 12.3 (10.6)
subtype4 21 13.0 (9.3)

Figure S187.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0113 (Kruskal-Wallis (anova)), Q value = 0.13

Table S193.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 26 1.4 (3.2)
subtype2 60 1.4 (2.3)
subtype3 44 0.8 (2.7)
subtype4 27 0.3 (0.6)

Figure S188.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0106 (Fisher's exact test), Q value = 0.13

Table S194.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 7 19 30 2 208
subtype1 2 6 7 0 35
subtype2 1 7 11 0 71
subtype3 0 2 1 0 55
subtype4 4 4 11 2 47

Figure S189.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.269 (Fisher's exact test), Q value = 0.61

Table S195.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 167
subtype1 2 38
subtype2 8 58
subtype3 5 32
subtype4 9 39

Figure S190.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RNAseq CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.798 (Kruskal-Wallis (anova)), Q value = 0.92

Table S196.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 273 73.1 (21.4)
subtype1 51 73.3 (25.0)
subtype2 94 73.3 (24.9)
subtype3 63 73.3 (15.8)
subtype4 65 72.4 (17.9)

Figure S191.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.278 (Fisher's exact test), Q value = 0.62

Table S197.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 137 44
subtype1 22 13
subtype2 52 13
subtype3 34 9
subtype4 29 9

Figure S192.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.0415 (Fisher's exact test), Q value = 0.25

Table S198.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 53 5 1 12 7 209 14
subtype1 7 0 1 3 1 38 6
subtype2 18 2 0 8 3 68 5
subtype3 16 3 0 1 1 48 0
subtype4 12 0 0 0 2 55 3

Figure S193.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RNAseq CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.00258 (Fisher's exact test), Q value = 0.046

Table S199.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 135 116 1 24
subtype1 2 22 28 0 2
subtype2 7 62 28 0 6
subtype3 7 30 25 0 7
subtype4 2 21 35 1 9

Figure S194.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.871 (Kruskal-Wallis (anova)), Q value = 0.94

Table S200.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 6 1999.0 (13.9)
subtype2 17 1997.5 (16.6)
subtype3 11 2001.5 (12.6)
subtype4 8 2002.2 (9.9)

Figure S195.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.145 (Kruskal-Wallis (anova)), Q value = 0.47

Table S201.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 17 20.5 (15.7)
subtype2 38 20.7 (16.0)
subtype3 17 12.3 (10.6)
subtype4 21 13.0 (9.3)

Figure S196.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.355 (Fisher's exact test), Q value = 0.67

Table S202.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 39 9 4 64 143
subtype1 6 3 1 14 21
subtype2 14 5 2 25 48
subtype3 11 1 0 9 39
subtype4 8 0 1 16 35

Figure S197.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.431 (Kruskal-Wallis (anova)), Q value = 0.73

Table S203.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 15 22.9 (7.9)
subtype2 37 21.6 (8.3)
subtype3 15 19.9 (4.4)
subtype4 18 19.7 (8.5)

Figure S198.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.636 (Kruskal-Wallis (anova)), Q value = 0.83

Table S204.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 148 3643.7 (1713.4)
subtype1 28 3862.9 (1628.2)
subtype2 46 3506.0 (1746.5)
subtype3 34 3686.8 (1737.8)
subtype4 40 3612.1 (1759.3)

Figure S199.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.436 (Fisher's exact test), Q value = 0.73

Table S205.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 20 104 15 1 24
subtype1 3 20 5 0 2
subtype2 8 31 4 1 10
subtype3 3 29 1 0 4
subtype4 6 24 5 0 8

Figure S200.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.736 (Fisher's exact test), Q value = 0.89

Table S206.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 7 0
subtype2 10 1
subtype3 4 0
subtype4 8 2

Figure S201.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.764 (Fisher's exact test), Q value = 0.91

Table S207.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 38 32
subtype1 0 0 7 6
subtype2 0 1 12 13
subtype3 1 0 5 6
subtype4 1 1 14 7

Figure S202.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RNAseq CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.492 (Fisher's exact test), Q value = 0.76

Table S208.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 57 5
subtype1 10 0
subtype2 21 1
subtype3 11 1
subtype4 15 3

Figure S203.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.25

Table S209.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 262 3.6 (2.6)
subtype1 47 3.6 (2.6)
subtype2 92 4.1 (2.7)
subtype3 61 3.0 (2.1)
subtype4 62 3.7 (2.8)

Figure S204.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.171 (Kruskal-Wallis (anova)), Q value = 0.51

Table S210.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.3)
subtype1 17 0.0 (0.0)
subtype2 46 0.2 (0.5)
subtype3 26 0.0 (0.2)
subtype4 22 0.0 (0.0)

Figure S205.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.858 (Kruskal-Wallis (anova)), Q value = 0.94

Table S211.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 146 0.5 (0.9)
subtype1 22 0.5 (1.1)
subtype2 63 0.5 (1.0)
subtype3 28 0.5 (0.8)
subtype4 33 0.6 (0.9)

Figure S206.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0533 (Kruskal-Wallis (anova)), Q value = 0.29

Table S212.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 2.9 (2.1)
subtype1 47 2.7 (2.1)
subtype2 91 3.0 (1.8)
subtype3 59 2.4 (2.1)
subtype4 61 3.1 (2.4)

Figure S207.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.805 (Kruskal-Wallis (anova)), Q value = 0.92

Table S213.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 121 0.9 (1.8)
subtype1 19 0.8 (1.4)
subtype2 51 1.2 (2.5)
subtype3 27 0.6 (0.8)
subtype4 24 0.5 (0.9)

Figure S208.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.586 (Kruskal-Wallis (anova)), Q value = 0.81

Table S214.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 115 0.1 (0.3)
subtype1 18 0.2 (0.4)
subtype2 49 0.1 (0.4)
subtype3 26 0.1 (0.3)
subtype4 22 0.0 (0.2)

Figure S209.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.499 (Fisher's exact test), Q value = 0.76

Table S215.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 8 39 1 10
subtype1 1 1 3 0 3
subtype2 0 2 17 1 4
subtype3 1 2 10 0 2
subtype4 0 3 9 0 1

Figure S210.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RNAseq CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0246 (Fisher's exact test), Q value = 0.19

Table S216.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 81 123
subtype1 0 6 20 20
subtype2 2 8 35 38
subtype3 0 7 19 30
subtype4 0 4 7 35

Figure S211.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RNAseq CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.616 (Fisher's exact test), Q value = 0.82

Table S217.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 70 78
subtype1 12 18
subtype2 26 29
subtype3 18 21
subtype4 14 10

Figure S212.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0113 (Kruskal-Wallis (anova)), Q value = 0.13

Table S218.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 26 1.4 (3.2)
subtype2 60 1.4 (2.3)
subtype3 44 0.8 (2.7)
subtype4 27 0.3 (0.6)

Figure S213.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.146 (Kruskal-Wallis (anova)), Q value = 0.47

Table S219.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 176 22.4 (12.6)
subtype1 31 18.7 (12.0)
subtype2 63 23.0 (14.6)
subtype3 49 24.0 (11.1)
subtype4 33 22.6 (11.2)

Figure S214.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RNAseq CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0206 (Fisher's exact test), Q value = 0.19

Table S220.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 54 118
subtype1 15 20
subtype2 20 54
subtype3 1 16
subtype4 18 28

Figure S215.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.354 (Kruskal-Wallis (anova)), Q value = 0.67

Table S221.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 299 2008.3 (4.8)
subtype1 56 2009.0 (4.0)
subtype2 103 2008.0 (5.4)
subtype3 68 2008.7 (4.1)
subtype4 72 2007.6 (4.9)

Figure S216.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.128 (Fisher's exact test), Q value = 0.47

Table S222.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 52 88
subtype1 0 11 19
subtype2 3 15 31
subtype3 8 15 18
subtype4 4 11 20

Figure S217.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.424 (Kruskal-Wallis (anova)), Q value = 0.73

Table S223.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 259 161.1 (7.1)
subtype1 48 160.1 (6.5)
subtype2 89 160.4 (7.3)
subtype3 61 162.2 (7.3)
subtype4 61 161.6 (6.9)

Figure S218.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.0661 (Fisher's exact test), Q value = 0.33

Table S224.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 18
subtype1 18 4
subtype2 32 10
subtype3 28 4
subtype4 21 0

Figure S219.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.406 (Fisher's exact test), Q value = 0.71

Table S225.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 18 1
subtype1 1 4 0
subtype2 2 5 0
subtype3 0 1 0
subtype4 0 8 1

Figure S220.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RNAseq CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.231 (Kruskal-Wallis (anova)), Q value = 0.57

Table S226.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 3 13.4 (1.3)
subtype2 9 11.7 (8.4)
subtype3 1 7.1 (NA)
subtype4 4 18.0 (6.0)

Figure S221.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'RNAseq CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.514 (Fisher's exact test), Q value = 0.76

Table S227.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 36 71 31 6 10 7 10 36 1 2 17 10 1 17
subtype1 1 2 13 6 2 3 1 3 4 0 1 5 3 0 1
subtype2 0 16 21 11 2 4 2 4 14 1 0 6 4 0 7
subtype3 0 7 26 7 0 2 2 1 9 0 0 2 1 0 3
subtype4 0 11 11 7 2 1 2 2 9 0 1 4 2 1 6

Figure S222.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 2.72e-05 (Kruskal-Wallis (anova)), Q value = 0.00082

Table S228.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 301 48.2 (13.8)
subtype1 56 53.1 (12.6)
subtype2 104 50.8 (14.9)
subtype3 69 46.2 (12.1)
subtype4 72 42.3 (12.5)

Figure S223.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0537 (Fisher's exact test), Q value = 0.29

Table S229.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 37 77 38 5 9 5 7 42 1 2 42 9 12
subtype1 1 1 1 0 4 13 7 1 1 1 4 6 0 1 9 2 3
subtype2 1 0 0 1 15 22 10 4 2 2 2 12 1 0 19 4 5
subtype3 1 0 0 0 5 31 10 0 2 0 0 10 0 0 5 1 3
subtype4 2 0 0 0 13 11 11 0 4 2 1 14 0 1 9 2 1

Figure S224.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S230.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 70 194 37
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.013 (logrank test), Q value = 0.14

Table S231.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 287 69 0.0 - 210.7 (20.1)
subtype1 68 17 0.1 - 137.2 (18.4)
subtype2 184 39 0.1 - 210.7 (20.9)
subtype3 35 13 0.0 - 99.9 (15.4)

Figure S225.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00736 (Kruskal-Wallis (anova)), Q value = 0.095

Table S232.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 297 48.1 (13.9)
subtype1 68 47.1 (11.5)
subtype2 192 49.7 (14.5)
subtype3 37 41.4 (12.7)

Figure S226.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.716 (Fisher's exact test), Q value = 0.88

Table S233.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 139 69 20 10
subtype1 38 19 3 2
subtype2 86 41 16 8
subtype3 15 9 1 0

Figure S227.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.28 (Fisher's exact test), Q value = 0.62

Table S234.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 131 59
subtype1 37 12
subtype2 77 42
subtype3 17 5

Figure S228.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0507 (Fisher's exact test), Q value = 0.28

Table S235.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 10
subtype1 26 5
subtype2 77 3
subtype3 12 2

Figure S229.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S236.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 4 251 6 22 3 15
subtype1 4 22 5 22 3 14
subtype2 0 192 1 0 0 1
subtype3 0 37 0 0 0 0

Figure S230.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0395 (Fisher's exact test), Q value = 0.25

Table S237.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 35 266
subtype1 3 67
subtype2 25 169
subtype3 7 30

Figure S231.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.437 (Kruskal-Wallis (anova)), Q value = 0.73

Table S238.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 14.2 (12.0)
subtype2 63 19.0 (15.2)
subtype3 12 13.8 (9.9)

Figure S232.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0281 (Kruskal-Wallis (anova)), Q value = 0.2

Table S239.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 42 0.7 (2.2)
subtype2 97 1.4 (2.6)
subtype3 18 0.2 (0.5)

Figure S233.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.351 (Fisher's exact test), Q value = 0.67

Table S240.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 7 19 30 2 208
subtype1 1 5 3 0 52
subtype2 4 13 20 2 130
subtype3 2 1 7 0 26

Figure S234.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.85 (Fisher's exact test), Q value = 0.94

Table S241.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 167
subtype1 6 37
subtype2 16 108
subtype3 2 22

Figure S235.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RNAseq cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.528 (Kruskal-Wallis (anova)), Q value = 0.77

Table S242.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 273 73.1 (21.4)
subtype1 64 73.4 (16.3)
subtype2 177 72.9 (23.6)
subtype3 32 73.6 (17.6)

Figure S236.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.856 (Fisher's exact test), Q value = 0.94

Table S243.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 137 44
subtype1 32 12
subtype2 93 28
subtype3 12 4

Figure S237.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.372 (Fisher's exact test), Q value = 0.68

Table S244.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 53 5 1 12 7 209 14
subtype1 15 3 1 2 2 44 3
subtype2 32 2 0 10 3 137 10
subtype3 6 0 0 0 2 28 1

Figure S238.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.152 (Fisher's exact test), Q value = 0.47

Table S245.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 135 116 1 24
subtype1 7 30 27 0 6
subtype2 10 93 69 0 16
subtype3 1 12 20 1 2

Figure S239.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.536 (Kruskal-Wallis (anova)), Q value = 0.77

Table S246.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 8 2002.4 (13.5)
subtype2 30 1999.1 (14.8)
subtype3 4 1999.0 (5.2)

Figure S240.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.437 (Kruskal-Wallis (anova)), Q value = 0.73

Table S247.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 14.2 (12.0)
subtype2 63 19.0 (15.2)
subtype3 12 13.8 (9.9)

Figure S241.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.325 (Fisher's exact test), Q value = 0.65

Table S248.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 39 9 4 64 143
subtype1 8 1 0 11 43
subtype2 27 8 3 43 85
subtype3 4 0 1 10 15

Figure S242.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.655 (Kruskal-Wallis (anova)), Q value = 0.84

Table S249.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 17 20.8 (5.7)
subtype2 56 21.5 (8.1)
subtype3 12 19.8 (8.3)

Figure S243.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.992 (Kruskal-Wallis (anova)), Q value = 1

Table S250.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 148 3643.7 (1713.4)
subtype1 36 3649.1 (1798.7)
subtype2 88 3687.6 (1633.7)
subtype3 24 3475.0 (1925.1)

Figure S244.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.298 (Fisher's exact test), Q value = 0.63

Table S251.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 20 104 15 1 24
subtype1 2 31 1 0 5
subtype2 13 59 12 1 14
subtype3 5 14 2 0 5

Figure S245.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 1 (Fisher's exact test), Q value = 1

Table S252.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 3 0
subtype2 20 2
subtype3 6 1

Figure S246.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.116 (Fisher's exact test), Q value = 0.46

Table S253.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 38 32
subtype1 1 0 6 6
subtype2 0 1 22 23
subtype3 1 1 10 3

Figure S247.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RNAseq cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.369 (Fisher's exact test), Q value = 0.68

Table S254.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 57 5
subtype1 12 1
subtype2 35 2
subtype3 10 2

Figure S248.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0652 (Kruskal-Wallis (anova)), Q value = 0.33

Table S255.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 262 3.6 (2.6)
subtype1 63 3.0 (2.0)
subtype2 167 3.8 (2.7)
subtype3 32 4.0 (2.8)

Figure S249.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.583 (Kruskal-Wallis (anova)), Q value = 0.81

Table S256.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.3)
subtype1 24 0.0 (0.2)
subtype2 74 0.1 (0.4)
subtype3 13 0.0 (0.0)

Figure S250.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.46

Table S257.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 146 0.5 (0.9)
subtype1 28 0.6 (0.9)
subtype2 98 0.5 (0.9)
subtype3 20 0.8 (1.1)

Figure S251.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0221 (Kruskal-Wallis (anova)), Q value = 0.19

Table S258.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 258 2.9 (2.1)
subtype1 61 2.3 (1.9)
subtype2 165 3.0 (2.0)
subtype3 32 3.2 (2.4)

Figure S252.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.535 (Kruskal-Wallis (anova)), Q value = 0.77

Table S259.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 121 0.9 (1.8)
subtype1 26 0.8 (1.0)
subtype2 80 0.9 (2.1)
subtype3 15 0.6 (1.0)

Figure S253.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.94

Table S260.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 115 0.1 (0.3)
subtype1 23 0.1 (0.3)
subtype2 79 0.1 (0.4)
subtype3 13 0.1 (0.3)

Figure S254.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.913 (Fisher's exact test), Q value = 0.96

Table S261.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 8 39 1 10
subtype1 1 1 8 0 2
subtype2 1 5 26 1 7
subtype3 0 2 5 0 1

Figure S255.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RNAseq cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0424 (Fisher's exact test), Q value = 0.25

Table S262.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 81 123
subtype1 0 8 18 33
subtype2 2 15 61 72
subtype3 0 2 2 18

Figure S256.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RNAseq cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.635 (Fisher's exact test), Q value = 0.83

Table S263.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 70 78
subtype1 22 20
subtype2 39 50
subtype3 9 8

Figure S257.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0281 (Kruskal-Wallis (anova)), Q value = 0.2

Table S264.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 42 0.7 (2.2)
subtype2 97 1.4 (2.6)
subtype3 18 0.2 (0.5)

Figure S258.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.347 (Kruskal-Wallis (anova)), Q value = 0.67

Table S265.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 176 22.4 (12.6)
subtype1 46 21.5 (10.4)
subtype2 109 22.2 (13.4)
subtype3 21 25.7 (13.0)

Figure S259.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RNAseq cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.00558 (Fisher's exact test), Q value = 0.081

Table S266.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 54 118
subtype1 0 16
subtype2 46 89
subtype3 8 13

Figure S260.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0251 (Kruskal-Wallis (anova)), Q value = 0.19

Table S267.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 299 2008.3 (4.8)
subtype1 69 2009.4 (3.5)
subtype2 193 2008.2 (4.9)
subtype3 37 2006.4 (5.8)

Figure S261.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.0311 (Fisher's exact test), Q value = 0.22

Table S268.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 52 88
subtype1 7 16 20
subtype2 4 32 59
subtype3 4 4 9

Figure S262.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.143 (Kruskal-Wallis (anova)), Q value = 0.47

Table S269.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 259 161.1 (7.1)
subtype1 62 161.4 (6.6)
subtype2 167 160.5 (7.2)
subtype3 30 163.5 (6.8)

Figure S263.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.283 (Fisher's exact test), Q value = 0.62

Table S270.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 18
subtype1 31 5
subtype2 56 13
subtype3 12 0

Figure S264.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.367 (Fisher's exact test), Q value = 0.68

Table S271.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 18 1
subtype1 0 2 0
subtype2 3 12 0
subtype3 0 4 1

Figure S265.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RNAseq cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.226 (Kruskal-Wallis (anova)), Q value = 0.57

Table S272.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 1 7.1 (NA)
subtype2 13 12.5 (7.0)
subtype3 3 18.4 (7.3)

Figure S266.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'RNAseq cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.134 (Fisher's exact test), Q value = 0.47

Table S273.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 36 71 31 6 10 7 10 36 1 2 17 10 1 17
subtype1 0 6 25 7 1 4 3 3 8 0 0 3 2 0 2
subtype2 1 21 44 20 5 5 2 6 23 1 2 13 8 0 12
subtype3 0 9 2 4 0 1 2 1 5 0 0 1 0 1 3

Figure S267.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.00591 (Kruskal-Wallis (anova)), Q value = 0.083

Table S274.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 301 48.2 (13.8)
subtype1 70 47.2 (11.4)
subtype2 194 49.8 (14.5)
subtype3 37 41.4 (12.7)

Figure S268.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0902 (Fisher's exact test), Q value = 0.4

Table S275.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 37 77 38 5 9 5 7 42 1 2 42 9 12
subtype1 1 0 0 0 5 28 9 1 3 1 2 8 0 0 6 1 5
subtype2 4 1 1 1 24 44 20 4 3 2 4 29 1 2 33 8 7
subtype3 0 0 0 0 8 5 9 0 3 2 1 5 0 0 3 0 0

Figure S269.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S276.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 102 79 123
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.377 (logrank test), Q value = 0.69

Table S277.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 69 0.0 - 210.7 (20.0)
subtype1 99 25 0.1 - 160.4 (19.7)
subtype2 75 14 0.1 - 210.7 (20.4)
subtype3 116 30 0.0 - 195.8 (20.8)

Figure S270.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.539 (Kruskal-Wallis (anova)), Q value = 0.77

Table S278.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 300 48.1 (13.8)
subtype1 100 47.8 (12.6)
subtype2 79 49.4 (13.3)
subtype3 121 47.6 (15.2)

Figure S271.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.516 (Fisher's exact test), Q value = 0.76

Table S279.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 70 21 10
subtype1 50 28 10 5
subtype2 44 16 3 1
subtype3 46 26 8 4

Figure S272.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.819 (Fisher's exact test), Q value = 0.93

Table S280.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 59
subtype1 45 19
subtype2 41 21
subtype3 47 19

Figure S273.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.197 (Fisher's exact test), Q value = 0.53

Table S281.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 10
subtype1 37 6
subtype2 35 1
subtype3 43 3

Figure S274.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S282.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 253 6 22 3 15
subtype1 4 61 5 17 3 12
subtype2 1 70 1 5 0 2
subtype3 0 122 0 0 0 1

Figure S275.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.199 (Fisher's exact test), Q value = 0.53

Table S283.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 35 269
subtype1 8 94
subtype2 8 71
subtype3 19 104

Figure S276.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S284.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 25 13.2 (10.5)
subtype2 27 22.2 (14.5)
subtype3 41 16.8 (15.1)

Figure S277.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.66

Table S285.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 54 0.8 (2.3)
subtype2 55 1.4 (2.5)
subtype3 48 0.9 (2.4)

Figure S278.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.322 (Fisher's exact test), Q value = 0.65

Table S286.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 8 19 30 2 210
subtype1 1 8 9 0 67
subtype2 1 7 7 0 55
subtype3 6 4 14 2 88

Figure S279.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.601 (Fisher's exact test), Q value = 0.81

Table S287.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 169
subtype1 7 52
subtype2 5 50
subtype3 12 67

Figure S280.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.736 (Kruskal-Wallis (anova)), Q value = 0.89

Table S288.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 276 73.3 (21.5)
subtype1 96 73.9 (20.3)
subtype2 69 73.9 (27.6)
subtype3 111 72.3 (18.0)

Figure S281.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'TUMOR_STATUS'

P value = 0.622 (Fisher's exact test), Q value = 0.82

Table S289.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 139 44
subtype1 52 16
subtype2 37 9
subtype3 50 19

Figure S282.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00038 (Fisher's exact test), Q value = 0.0095

Table S290.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 54 5 1 12 7 211 14
subtype1 30 3 1 2 3 58 5
subtype2 5 2 0 6 2 58 6
subtype3 19 0 0 4 2 95 3

Figure S283.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.00701 (Fisher's exact test), Q value = 0.095

Table S291.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 118 1 24
subtype1 8 40 42 0 11
subtype2 8 41 27 1 1
subtype3 2 55 49 0 12

Figure S284.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.313 (Kruskal-Wallis (anova)), Q value = 0.65

Table S292.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 11 2002.7 (11.8)
subtype2 11 1996.2 (11.8)
subtype3 20 2000.0 (15.9)

Figure S285.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S293.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 25 13.2 (10.5)
subtype2 27 22.2 (14.5)
subtype3 41 16.8 (15.1)

Figure S286.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.0886 (Fisher's exact test), Q value = 0.4

Table S294.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 39 9 4 64 145
subtype1 12 1 0 17 61
subtype2 9 4 3 19 34
subtype3 18 4 1 28 50

Figure S287.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.846 (Kruskal-Wallis (anova)), Q value = 0.94

Table S295.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 23 20.7 (7.2)
subtype2 24 21.0 (6.2)
subtype3 38 21.5 (8.9)

Figure S288.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CNMF' versus 'RADIATION_TOTAL_DOSE'

P value = 0.941 (Kruskal-Wallis (anova)), Q value = 0.98

Table S296.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 149 3635.4 (1710.6)
subtype1 58 3649.5 (1721.1)
subtype2 30 3667.4 (1787.3)
subtype3 61 3606.2 (1690.6)

Figure S289.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_TYPE'

P value = 0.117 (Fisher's exact test), Q value = 0.46

Table S297.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 20 104 15 1 25
subtype1 6 44 2 0 10
subtype2 2 22 6 1 5
subtype3 12 38 7 0 10

Figure S290.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.397 (Fisher's exact test), Q value = 0.71

Table S298.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 8 0
subtype2 6 0
subtype3 15 3

Figure S291.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_SITE'

P value = 0.0273 (Fisher's exact test), Q value = 0.2

Table S299.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 38 33
subtype1 1 0 7 17
subtype2 0 1 8 4
subtype3 1 1 23 12

Figure S292.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRSEQ CNMF' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.354 (Fisher's exact test), Q value = 0.67

Table S300.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 58 5
subtype1 24 1
subtype2 9 0
subtype3 25 4

Figure S293.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.89

Table S301.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 265 3.6 (2.6)
subtype1 90 3.7 (2.9)
subtype2 71 3.6 (2.3)
subtype3 104 3.6 (2.5)

Figure S294.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.86 (Kruskal-Wallis (anova)), Q value = 0.94

Table S302.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.3)
subtype1 29 0.0 (0.2)
subtype2 34 0.1 (0.5)
subtype3 48 0.1 (0.2)

Figure S295.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CNMF' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.0036 (Kruskal-Wallis (anova)), Q value = 0.062

Table S303.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 146 0.5 (0.9)
subtype1 40 0.9 (1.2)
subtype2 44 0.4 (0.8)
subtype3 62 0.4 (0.8)

Figure S296.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.847 (Kruskal-Wallis (anova)), Q value = 0.94

Table S304.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 260 2.9 (2.1)
subtype1 86 3.0 (2.5)
subtype2 70 2.7 (1.7)
subtype3 104 2.8 (1.9)

Figure S297.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CNMF' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.464 (Kruskal-Wallis (anova)), Q value = 0.75

Table S305.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 121 0.9 (1.8)
subtype1 32 0.8 (1.5)
subtype2 38 1.1 (2.3)
subtype3 51 0.7 (1.6)

Figure S298.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.32 (Kruskal-Wallis (anova)), Q value = 0.65

Table S306.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 115 0.1 (0.3)
subtype1 29 0.1 (0.3)
subtype2 36 0.2 (0.5)
subtype3 50 0.1 (0.3)

Figure S299.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CNMF' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.864 (Fisher's exact test), Q value = 0.94

Table S307.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 8 39 1 10
subtype1 1 2 8 0 2
subtype2 1 2 15 1 5
subtype3 0 4 16 0 3

Figure S300.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.507 (Fisher's exact test), Q value = 0.76

Table S308.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 83 124
subtype1 1 12 26 43
subtype2 1 6 25 29
subtype3 0 7 32 52

Figure S301.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRSEQ CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.631 (Fisher's exact test), Q value = 0.83

Table S309.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 24 29
subtype2 22 28
subtype3 25 22

Figure S302.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.66

Table S310.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 54 0.8 (2.3)
subtype2 55 1.4 (2.5)
subtype3 48 0.9 (2.4)

Figure S303.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.605 (Kruskal-Wallis (anova)), Q value = 0.81

Table S311.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.3 (12.6)
subtype1 59 20.7 (11.0)
subtype2 61 24.5 (14.3)
subtype3 58 21.7 (12.0)

Figure S304.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.131 (Fisher's exact test), Q value = 0.47

Table S312.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 54 119
subtype1 6 28
subtype2 19 32
subtype3 29 59

Figure S305.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.00401 (Kruskal-Wallis (anova)), Q value = 0.065

Table S313.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 302 2008.3 (4.8)
subtype1 101 2009.4 (4.3)
subtype2 78 2008.1 (4.7)
subtype3 123 2007.5 (5.0)

Figure S306.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.593 (Fisher's exact test), Q value = 0.81

Table S314.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 89
subtype1 8 19 34
subtype2 3 16 19
subtype3 4 18 36

Figure S307.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.907 (Kruskal-Wallis (anova)), Q value = 0.96

Table S315.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 262 161.0 (7.3)
subtype1 91 161.2 (7.3)
subtype2 64 161.6 (7.6)
subtype3 107 160.6 (7.0)

Figure S308.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.734 (Fisher's exact test), Q value = 0.89

Table S316.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 18
subtype1 34 7
subtype2 33 7
subtype3 32 4

Figure S309.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRSEQ CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 1 (Fisher's exact test), Q value = 1

Table S317.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 18 1
subtype1 0 3 0
subtype2 1 3 0
subtype3 2 12 1

Figure S310.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.47

Table S318.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 2 10.8 (5.2)
subtype2 6 9.5 (5.7)
subtype3 9 16.3 (7.5)

Figure S311.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRSEQ CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.665 (Fisher's exact test), Q value = 0.84

Table S319.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 36 72 31 6 10 7 10 37 2 2 17 10 1 17
subtype1 0 11 29 10 2 5 3 6 12 1 1 8 5 0 3
subtype2 1 9 25 9 1 3 2 2 8 0 0 3 1 0 4
subtype3 0 16 18 12 3 2 2 2 17 1 1 6 4 1 10

Figure S312.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.603 (Kruskal-Wallis (anova)), Q value = 0.81

Table S320.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 102 47.8 (12.5)
subtype2 79 49.4 (13.3)
subtype3 123 47.8 (15.1)

Figure S313.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0259 (Fisher's exact test), Q value = 0.19

Table S321.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 37 78 38 5 9 5 7 43 1 3 42 9 12
subtype1 2 0 0 0 8 30 14 1 3 1 5 11 0 2 13 4 8
subtype2 0 1 1 1 8 28 10 1 3 2 2 7 0 0 10 2 1
subtype3 3 0 0 0 21 20 14 3 3 2 0 25 1 1 19 3 3

Figure S314.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S322.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 58 101 74 26 45
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.475 (logrank test), Q value = 0.76

Table S323.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 69 0.0 - 210.7 (20.0)
subtype1 57 15 0.1 - 137.2 (17.5)
subtype2 97 21 0.0 - 160.4 (19.8)
subtype3 69 15 0.1 - 210.7 (29.9)
subtype4 24 6 0.1 - 195.8 (21.0)
subtype5 43 12 0.1 - 144.2 (17.8)

Figure S315.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 8.6e-05 (Kruskal-Wallis (anova)), Q value = 0.0023

Table S324.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 300 48.1 (13.8)
subtype1 56 45.9 (12.4)
subtype2 100 50.4 (14.4)
subtype3 74 51.2 (14.1)
subtype4 25 49.9 (11.3)
subtype5 45 39.7 (11.6)

Figure S316.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00436 (Fisher's exact test), Q value = 0.068

Table S325.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 70 21 10
subtype1 34 12 2 1
subtype2 32 28 12 6
subtype3 47 11 2 1
subtype4 8 9 2 1
subtype5 19 10 3 1

Figure S317.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.183 (Fisher's exact test), Q value = 0.51

Table S326.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 59
subtype1 31 9
subtype2 32 22
subtype3 38 20
subtype4 11 2
subtype5 21 6

Figure S318.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.34 (Fisher's exact test), Q value = 0.66

Table S327.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 115 10
subtype1 17 2
subtype2 29 2
subtype3 38 1
subtype4 12 2
subtype5 19 3

Figure S319.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S328.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 253 6 22 3 15
subtype1 4 13 4 20 3 14
subtype2 1 98 1 1 0 0
subtype3 0 74 0 0 0 0
subtype4 0 24 1 0 0 1
subtype5 0 44 0 1 0 0

Figure S320.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0268 (Fisher's exact test), Q value = 0.2

Table S329.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 35 269
subtype1 3 55
subtype2 7 94
subtype3 12 62
subtype4 3 23
subtype5 10 35

Figure S321.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S330.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 14.7 (11.6)
subtype2 36 19.9 (17.8)
subtype3 21 20.2 (10.9)
subtype4 5 7.7 (8.3)
subtype5 13 13.4 (9.5)

Figure S322.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.46

Table S331.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 38 0.6 (1.9)
subtype2 40 1.7 (3.1)
subtype3 50 1.0 (1.9)
subtype4 8 0.4 (0.7)
subtype5 21 0.9 (3.1)

Figure S323.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.376 (Fisher's exact test), Q value = 0.69

Table S332.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 8 19 30 2 210
subtype1 0 4 2 0 44
subtype2 3 5 13 0 66
subtype3 1 6 7 0 51
subtype4 1 2 3 1 17
subtype5 3 2 5 1 32

Figure S324.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.401 (Fisher's exact test), Q value = 0.71

Table S333.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 169
subtype1 4 30
subtype2 10 52
subtype3 3 50
subtype4 3 16
subtype5 4 21

Figure S325.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CHIERARCHICAL' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.905 (Kruskal-Wallis (anova)), Q value = 0.96

Table S334.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 276 73.3 (21.5)
subtype1 52 72.8 (16.0)
subtype2 96 72.8 (20.3)
subtype3 65 75.2 (28.7)
subtype4 24 68.6 (17.0)
subtype5 39 74.6 (19.2)

Figure S326.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_STATUS'

P value = 0.569 (Fisher's exact test), Q value = 0.79

Table S335.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 139 44
subtype1 28 8
subtype2 45 13
subtype3 36 11
subtype4 10 7
subtype5 20 5

Figure S327.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.118 (Fisher's exact test), Q value = 0.46

Table S336.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 54 5 1 12 7 211 14
subtype1 13 3 0 1 2 37 2
subtype2 23 0 0 5 2 67 4
subtype3 5 2 0 5 1 57 4
subtype4 5 0 1 1 0 17 2
subtype5 8 0 0 0 2 33 2

Figure S328.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0181 (Fisher's exact test), Q value = 0.17

Table S337.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 118 1 24
subtype1 6 27 19 0 6
subtype2 4 46 38 0 8
subtype3 6 39 28 0 1
subtype4 0 14 9 0 3
subtype5 2 10 24 1 6

Figure S329.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 0.47

Table S338.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 42 1999.7 (13.8)
subtype1 8 2002.4 (13.5)
subtype2 16 2001.1 (17.6)
subtype3 8 1992.9 (10.7)
subtype4 3 1998.0 (10.5)
subtype5 7 2002.0 (7.7)

Figure S330.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S339.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 18 14.7 (11.6)
subtype2 36 19.9 (17.8)
subtype3 21 20.2 (10.9)
subtype4 5 7.7 (8.3)
subtype5 13 13.4 (9.5)

Figure S331.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.386 (Fisher's exact test), Q value = 0.7

Table S340.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 39 9 4 64 145
subtype1 8 1 0 11 33
subtype2 16 2 1 21 45
subtype3 4 6 2 17 34
subtype4 3 0 0 5 14
subtype5 8 0 1 10 19

Figure S332.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.764 (Kruskal-Wallis (anova)), Q value = 0.91

Table S341.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 17 19.8 (4.7)
subtype2 32 21.9 (8.9)
subtype3 16 21.6 (6.9)
subtype4 5 24.8 (10.1)
subtype5 15 19.5 (8.0)

Figure S333.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_TOTAL_DOSE'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 0.71

Table S342.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 149 3635.4 (1710.6)
subtype1 29 3839.8 (1694.7)
subtype2 51 3307.3 (1775.1)
subtype3 27 4180.7 (1304.9)
subtype4 13 3702.6 (1454.8)
subtype5 29 3470.0 (1980.7)

Figure S334.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_TYPE'

P value = 0.0459 (Fisher's exact test), Q value = 0.26

Table S343.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 20 104 15 1 25
subtype1 3 23 0 0 5
subtype2 4 34 3 0 14
subtype3 7 19 5 1 1
subtype4 1 10 2 0 1
subtype5 5 18 5 0 4

Figure S335.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_STATUS'

P value = 0.859 (Fisher's exact test), Q value = 0.94

Table S344.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 3 0
subtype2 7 0
subtype3 9 1
subtype4 2 0
subtype5 8 2

Figure S336.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_SITE'

P value = 0.12 (Fisher's exact test), Q value = 0.46

Table S345.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 38 33
subtype1 1 0 4 6
subtype2 0 0 16 19
subtype3 0 1 6 1
subtype4 0 0 5 3
subtype5 1 1 7 4

Figure S337.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.353 (Fisher's exact test), Q value = 0.67

Table S346.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 58 5
subtype1 10 1
subtype2 26 1
subtype3 5 1
subtype4 7 0
subtype5 10 2

Figure S338.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.088 (Kruskal-Wallis (anova)), Q value = 0.4

Table S347.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 265 3.6 (2.6)
subtype1 52 2.9 (1.9)
subtype2 88 4.2 (3.2)
subtype3 63 3.6 (2.0)
subtype4 20 3.7 (2.2)
subtype5 42 3.2 (2.6)

Figure S339.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.487 (Kruskal-Wallis (anova)), Q value = 0.76

Table S348.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.3)
subtype1 19 0.1 (0.2)
subtype2 27 0.0 (0.2)
subtype3 39 0.2 (0.5)
subtype4 9 0.0 (0.0)
subtype5 17 0.0 (0.0)

Figure S340.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.138 (Kruskal-Wallis (anova)), Q value = 0.47

Table S349.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 146 0.5 (0.9)
subtype1 22 0.8 (0.9)
subtype2 41 0.7 (1.3)
subtype3 47 0.4 (0.7)
subtype4 12 0.3 (0.7)
subtype5 24 0.5 (0.7)

Figure S341.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0185 (Kruskal-Wallis (anova)), Q value = 0.17

Table S350.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 260 2.9 (2.1)
subtype1 50 2.3 (1.8)
subtype2 83 3.3 (2.4)
subtype3 66 2.7 (1.6)
subtype4 21 3.2 (1.9)
subtype5 40 2.6 (2.2)

Figure S342.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.933 (Kruskal-Wallis (anova)), Q value = 0.97

Table S351.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 121 0.9 (1.8)
subtype1 21 0.6 (0.7)
subtype2 31 1.5 (3.1)
subtype3 41 0.7 (1.1)
subtype4 9 0.7 (1.4)
subtype5 19 0.6 (0.9)

Figure S343.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.957 (Kruskal-Wallis (anova)), Q value = 0.98

Table S352.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 115 0.1 (0.3)
subtype1 18 0.1 (0.3)
subtype2 30 0.1 (0.3)
subtype3 41 0.1 (0.4)
subtype4 9 0.1 (0.3)
subtype5 17 0.1 (0.2)

Figure S344.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CHIERARCHICAL' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.758 (Fisher's exact test), Q value = 0.9

Table S353.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 8 39 1 10
subtype1 1 0 6 0 2
subtype2 0 3 8 1 4
subtype3 1 3 16 0 3
subtype4 0 0 2 0 0
subtype5 0 2 7 0 1

Figure S345.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CHIERARCHICAL' versus 'MENOPAUSE_STATUS'

P value = 0.0142 (Fisher's exact test), Q value = 0.15

Table S354.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 83 124
subtype1 0 5 15 29
subtype2 1 9 29 37
subtype3 1 5 30 23
subtype4 0 4 5 9
subtype5 0 2 4 26

Figure S346.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.37 (Fisher's exact test), Q value = 0.68

Table S355.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 20 15
subtype2 17 24
subtype3 19 29
subtype4 5 3
subtype5 10 8

Figure S347.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.46

Table S356.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 157 1.1 (2.4)
subtype1 38 0.6 (1.9)
subtype2 40 1.7 (3.1)
subtype3 50 1.0 (1.9)
subtype4 8 0.4 (0.7)
subtype5 21 0.9 (3.1)

Figure S348.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED'

P value = 0.311 (Kruskal-Wallis (anova)), Q value = 0.65

Table S357.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.3 (12.6)
subtype1 41 22.1 (10.8)
subtype2 48 20.6 (12.0)
subtype3 55 24.6 (14.6)
subtype4 10 15.9 (8.3)
subtype5 24 23.8 (12.7)

Figure S349.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CHIERARCHICAL' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.154 (Fisher's exact test), Q value = 0.48

Table S358.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 54 119
subtype1 0 10
subtype2 20 46
subtype3 20 34
subtype4 4 12
subtype5 10 17

Figure S350.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CHIERARCHICAL' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0391 (Kruskal-Wallis (anova)), Q value = 0.25

Table S359.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 302 2008.3 (4.8)
subtype1 57 2009.3 (4.0)
subtype2 101 2008.9 (4.3)
subtype3 73 2007.3 (5.0)
subtype4 26 2008.0 (5.9)
subtype5 45 2007.5 (5.1)

Figure S351.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CHIERARCHICAL' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.298 (Fisher's exact test), Q value = 0.63

Table S360.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 89
subtype1 6 13 17
subtype2 4 17 33
subtype3 1 14 19
subtype4 0 5 7
subtype5 4 4 13

Figure S352.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CHIERARCHICAL' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.693 (Kruskal-Wallis (anova)), Q value = 0.86

Table S361.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 262 161.0 (7.3)
subtype1 50 161.4 (6.9)
subtype2 92 160.7 (7.6)
subtype3 62 160.5 (7.5)
subtype4 21 160.1 (7.4)
subtype5 37 162.7 (6.3)

Figure S353.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CORPUS_INVOLVEMENT'

P value = 0.259 (Fisher's exact test), Q value = 0.6

Table S362.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 18
subtype1 27 3
subtype2 22 5
subtype3 32 8
subtype4 5 2
subtype5 13 0

Figure S354.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.446 (Fisher's exact test), Q value = 0.74

Table S363.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 18 1
subtype1 0 1 0
subtype2 1 2 0
subtype3 2 7 0
subtype4 0 1 0
subtype5 0 7 1

Figure S355.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'CERVIX_SUV_RESULTS'

P value = 0.242 (Kruskal-Wallis (anova)), Q value = 0.58

Table S364.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 1 7.1 (NA)
subtype2 5 15.8 (8.6)
subtype3 8 10.5 (5.5)
subtype5 3 18.4 (7.3)

Figure S356.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRSEQ CHIERARCHICAL' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.022 (Fisher's exact test), Q value = 0.19

Table S365.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 1 36 72 31 6 10 7 10 37 2 2 17 10 1 17
subtype1 0 6 23 5 1 3 3 1 4 0 0 2 1 0 2
subtype2 0 7 14 11 3 2 0 6 17 1 2 9 6 0 7
subtype3 1 13 26 7 0 3 1 0 7 0 0 2 1 0 4
subtype4 0 2 3 3 1 1 1 2 4 1 0 1 1 0 2
subtype5 0 8 6 5 1 1 2 1 5 0 0 3 1 1 2

Figure S357.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CHIERARCHICAL' versus 'AGE_AT_DIAGNOSIS'

P value = 6.89e-05 (Kruskal-Wallis (anova)), Q value = 0.0019

Table S366.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 58 46.0 (12.2)
subtype2 101 50.5 (14.3)
subtype3 74 51.2 (14.1)
subtype4 26 50.4 (11.4)
subtype5 45 39.7 (11.6)

Figure S358.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.00073 (Fisher's exact test), Q value = 0.016

Table S367.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA1 STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 1 37 78 38 5 9 5 7 43 1 3 42 9 12
subtype1 1 0 0 0 4 26 8 1 2 1 0 5 0 0 6 1 3
subtype2 2 0 0 0 7 17 12 3 1 0 3 17 0 3 19 5 6
subtype3 0 1 1 1 13 25 8 1 2 1 1 5 0 0 11 2 1
subtype4 2 0 0 0 6 1 2 0 1 1 2 5 1 0 3 0 2
subtype5 0 0 0 0 7 9 8 0 3 2 1 11 0 0 3 1 0

Figure S359.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S368.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 127 77 87
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.957 (logrank test), Q value = 0.98

Table S369.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 279 66 0.0 - 210.7 (20.4)
subtype1 123 25 0.0 - 210.7 (19.6)
subtype2 72 16 0.1 - 147.4 (17.3)
subtype3 84 25 0.1 - 177.0 (30.0)

Figure S360.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.48 (Kruskal-Wallis (anova)), Q value = 0.76

Table S370.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 288 48.1 (13.9)
subtype1 125 49.0 (13.4)
subtype2 76 48.6 (14.0)
subtype3 87 46.4 (14.4)

Figure S361.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.463 (Fisher's exact test), Q value = 0.75

Table S371.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 135 66 19 9
subtype1 53 31 12 6
subtype2 38 15 3 1
subtype3 44 20 4 2

Figure S362.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.177 (Fisher's exact test), Q value = 0.51

Table S372.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 128 56
subtype1 50 16
subtype2 41 16
subtype3 37 24

Figure S363.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.065 (Fisher's exact test), Q value = 0.33

Table S373.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 111 9
subtype1 39 7
subtype2 32 1
subtype3 40 1

Figure S364.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S374.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 241 6 21 3 15
subtype1 4 89 4 14 3 13
subtype2 1 65 2 7 0 2
subtype3 0 87 0 0 0 0

Figure S365.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S375.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 33 258
subtype1 3 124
subtype2 6 71
subtype3 24 63

Figure S366.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.53

Table S376.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 17.2 (14.0)
subtype1 31 14.5 (14.3)
subtype2 23 19.6 (12.2)
subtype3 34 18.0 (14.9)

Figure S367.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.54

Table S377.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 153 1.0 (2.4)
subtype1 51 0.7 (2.1)
subtype2 48 1.2 (2.3)
subtype3 54 1.2 (2.7)

Figure S368.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.139 (Fisher's exact test), Q value = 0.47

Table S378.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 7 18 27 2 204
subtype1 5 9 15 0 81
subtype2 1 6 3 0 58
subtype3 1 3 9 2 65

Figure S369.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.131 (Fisher's exact test), Q value = 0.47

Table S379.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 160
subtype1 14 63
subtype2 3 48
subtype3 7 49

Figure S370.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.89

Table S380.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 73.2 (21.4)
subtype1 117 72.3 (19.3)
subtype2 69 74.3 (27.8)
subtype3 78 73.5 (17.6)

Figure S371.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.782 (Fisher's exact test), Q value = 0.91

Table S381.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 135 42
subtype1 60 20
subtype2 30 7
subtype3 45 15

Figure S372.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00149 (Fisher's exact test), Q value = 0.029

Table S382.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 53 5 1 12 6 201 13
subtype1 35 3 1 2 4 76 6
subtype2 7 1 0 6 2 55 6
subtype3 11 1 0 4 0 70 1

Figure S373.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0169 (Fisher's exact test), Q value = 0.17

Table S383.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 16 128 116 1 23
subtype1 6 47 59 0 13
subtype2 8 42 24 0 2
subtype3 2 39 33 1 8

Figure S374.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.288 (Kruskal-Wallis (anova)), Q value = 0.62

Table S384.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 40 2000.0 (14.1)
subtype1 16 2002.6 (16.0)
subtype2 9 1996.2 (16.2)
subtype3 15 1999.5 (10.5)

Figure S375.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.53

Table S385.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 17.2 (14.0)
subtype1 31 14.5 (14.3)
subtype2 23 19.6 (12.2)
subtype3 34 18.0 (14.9)

Figure S376.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.0416 (Fisher's exact test), Q value = 0.25

Table S386.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 38 8 4 61 142
subtype1 16 2 1 18 71
subtype2 7 3 3 18 34
subtype3 15 3 0 25 37

Figure S377.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.86

Table S387.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 80 20.9 (7.6)
subtype1 31 21.5 (7.4)
subtype2 19 20.7 (7.2)
subtype3 30 20.4 (8.2)

Figure S378.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.00376 (Kruskal-Wallis (anova)), Q value = 0.063

Table S388.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 145 3646.2 (1702.8)
subtype1 70 3255.7 (1854.7)
subtype2 29 3579.7 (1809.6)
subtype3 46 4282.3 (1145.8)

Figure S379.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S389.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 19 99 15 1 25
subtype1 2 57 1 0 14
subtype2 2 20 5 0 6
subtype3 15 22 9 1 5

Figure S380.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 1 (Fisher's exact test), Q value = 1

Table S390.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 27 3
subtype1 3 0
subtype2 5 0
subtype3 19 3

Figure S381.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.54 (Fisher's exact test), Q value = 0.77

Table S391.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 36 33
subtype1 1 0 17 19
subtype2 1 1 9 6
subtype3 0 1 10 8

Figure S382.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 1 (Fisher's exact test), Q value = 1

Table S392.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 56 5
subtype1 31 3
subtype2 10 1
subtype3 15 1

Figure S383.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.775 (Kruskal-Wallis (anova)), Q value = 0.91

Table S393.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 255 3.6 (2.5)
subtype1 109 3.6 (2.7)
subtype2 68 3.6 (2.2)
subtype3 78 3.6 (2.6)

Figure S384.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.202 (Kruskal-Wallis (anova)), Q value = 0.53

Table S394.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 106 0.1 (0.3)
subtype1 31 0.0 (0.2)
subtype2 29 0.0 (0.0)
subtype3 46 0.1 (0.5)

Figure S385.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.236 (Kruskal-Wallis (anova)), Q value = 0.57

Table S395.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 142 0.6 (1.0)
subtype1 48 0.8 (1.2)
subtype2 40 0.4 (0.7)
subtype3 54 0.5 (0.8)

Figure S386.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.687 (Kruskal-Wallis (anova)), Q value = 0.86

Table S396.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 250 2.8 (2.0)
subtype1 107 3.0 (2.3)
subtype2 67 2.7 (1.7)
subtype3 76 2.6 (1.9)

Figure S387.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.695 (Kruskal-Wallis (anova)), Q value = 0.86

Table S397.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 115 0.8 (1.8)
subtype1 34 0.5 (0.9)
subtype2 34 1.1 (2.4)
subtype3 47 0.8 (1.7)

Figure S388.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.981 (Kruskal-Wallis (anova)), Q value = 1

Table S398.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.4)
subtype1 34 0.1 (0.3)
subtype2 30 0.1 (0.4)
subtype3 47 0.1 (0.3)

Figure S389.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.865 (Fisher's exact test), Q value = 0.94

Table S399.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 1 7 38 1 10
subtype1 0 1 8 0 2
subtype2 1 2 11 1 4
subtype3 0 4 19 0 4

Figure S390.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.261 (Fisher's exact test), Q value = 0.6

Table S400.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 77 119
subtype1 0 15 30 51
subtype2 0 6 22 30
subtype3 2 4 25 38

Figure S391.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRseq Mature CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.85 (Fisher's exact test), Q value = 0.94

Table S401.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 68 73
subtype1 23 23
subtype2 23 23
subtype3 22 27

Figure S392.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.54

Table S402.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 153 1.0 (2.4)
subtype1 51 0.7 (2.1)
subtype2 48 1.2 (2.3)
subtype3 54 1.2 (2.7)

Figure S393.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.58

Table S403.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 170 22.8 (12.7)
subtype1 59 22.0 (12.8)
subtype2 54 25.1 (12.8)
subtype3 57 21.4 (12.4)

Figure S394.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0211 (Fisher's exact test), Q value = 0.19

Table S404.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 51 113
subtype1 9 44
subtype2 18 30
subtype3 24 39

Figure S395.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 4.31e-06 (Kruskal-Wallis (anova)), Q value = 0.00035

Table S405.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 289 2008.3 (4.8)
subtype1 126 2009.5 (4.2)
subtype2 76 2008.3 (4.7)
subtype3 87 2006.5 (5.2)

Figure S396.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.564 (Fisher's exact test), Q value = 0.79

Table S406.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 84
subtype1 6 21 44
subtype2 4 16 17
subtype3 5 16 23

Figure S397.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.511 (Kruskal-Wallis (anova)), Q value = 0.76

Table S407.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 251 160.9 (7.2)
subtype1 114 160.5 (6.8)
subtype2 64 161.5 (7.1)
subtype3 73 160.9 (8.1)

Figure S398.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.652 (Fisher's exact test), Q value = 0.84

Table S408.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 18
subtype1 29 4
subtype2 30 5
subtype3 36 9

Figure S399.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.206 (Fisher's exact test), Q value = 0.54

Table S409.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 17 1
subtype1 0 2 0
subtype2 0 2 1
subtype3 3 13 0

Figure S400.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.479 (Kruskal-Wallis (anova)), Q value = 0.76

Table S410.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 16 13.0 (7.3)
subtype1 4 18.3 (10.8)
subtype2 6 10.5 (4.0)
subtype3 6 11.9 (6.6)

Figure S401.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRseq Mature CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.315 (Fisher's exact test), Q value = 0.65

Table S411.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 35 70 30 6 10 7 9 34 1 2 16 9 1 17
subtype1 12 27 14 3 6 2 5 15 1 2 9 6 1 8
subtype2 6 22 10 0 4 3 2 6 0 0 3 1 0 6
subtype3 17 21 6 3 0 2 2 13 0 0 4 2 0 3

Figure S402.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.439 (Kruskal-Wallis (anova)), Q value = 0.73

Table S412.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 291 48.2 (13.8)
subtype1 127 49.0 (13.2)
subtype2 77 48.8 (14.0)
subtype3 87 46.4 (14.4)

Figure S403.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.00554 (Fisher's exact test), Q value = 0.081

Table S413.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 36 76 36 5 8 5 7 41 3 41 8 11
subtype1 3 0 0 9 35 17 2 4 0 4 18 3 16 3 9
subtype2 0 1 1 5 24 12 2 3 3 2 8 0 9 3 1
subtype3 2 0 0 22 17 7 1 1 2 1 15 0 16 2 1

Figure S404.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S414.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 62 50 31 148
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.793 (logrank test), Q value = 0.92

Table S415.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 279 66 0.0 - 210.7 (20.4)
subtype1 60 15 0.1 - 137.2 (17.7)
subtype2 46 9 0.1 - 147.4 (18.7)
subtype3 31 12 1.2 - 177.0 (35.6)
subtype4 142 30 0.0 - 210.7 (19.6)

Figure S405.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.857 (Kruskal-Wallis (anova)), Q value = 0.94

Table S416.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 288 48.1 (13.9)
subtype1 60 47.1 (12.1)
subtype2 50 49.4 (14.2)
subtype3 31 48.9 (14.4)
subtype4 147 47.9 (14.4)

Figure S406.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.512 (Fisher's exact test), Q value = 0.76

Table S417.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 135 66 19 9
subtype1 32 18 2 2
subtype2 26 8 3 1
subtype3 18 5 1 0
subtype4 59 35 13 6

Figure S407.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.149 (Fisher's exact test), Q value = 0.47

Table S418.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 128 56
subtype1 34 8
subtype2 25 11
subtype3 13 11
subtype4 56 26

Figure S408.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.336 (Fisher's exact test), Q value = 0.66

Table S419.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 111 9
subtype1 21 4
subtype2 24 1
subtype3 15 0
subtype4 51 4

Figure S409.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S420.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 5 241 6 21 3 15
subtype1 3 16 5 20 3 15
subtype2 1 49 0 0 0 0
subtype3 0 30 0 1 0 0
subtype4 1 146 1 0 0 0

Figure S410.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S421.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 33 258
subtype1 1 61
subtype2 3 47
subtype3 19 12
subtype4 10 138

Figure S411.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.47

Table S422.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 17.2 (14.0)
subtype1 17 13.1 (11.1)
subtype2 13 21.9 (12.2)
subtype3 10 19.5 (9.9)
subtype4 48 16.9 (15.8)

Figure S412.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0694 (Kruskal-Wallis (anova)), Q value = 0.34

Table S423.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 153 1.0 (2.4)
subtype1 37 0.6 (1.9)
subtype2 28 1.1 (1.9)
subtype3 25 2.4 (4.5)
subtype4 63 0.6 (1.1)

Figure S413.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.563 (Fisher's exact test), Q value = 0.79

Table S424.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 7 18 27 2 204
subtype1 1 5 3 0 44
subtype2 1 5 2 0 35
subtype3 0 2 3 0 26
subtype4 5 6 19 2 99

Figure S414.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.455 (Fisher's exact test), Q value = 0.75

Table S425.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 160
subtype1 6 32
subtype2 3 31
subtype3 1 22
subtype4 14 75

Figure S415.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.808 (Kruskal-Wallis (anova)), Q value = 0.92

Table S426.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 73.2 (21.4)
subtype1 56 73.5 (16.1)
subtype2 45 75.3 (32.9)
subtype3 28 71.8 (17.7)
subtype4 135 72.6 (19.2)

Figure S416.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.254 (Fisher's exact test), Q value = 0.6

Table S427.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 135 42
subtype1 29 9
subtype2 22 4
subtype3 16 10
subtype4 68 19

Figure S417.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00051 (Fisher's exact test), Q value = 0.012

Table S428.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients BRAZIL CANADA NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 53 5 1 12 6 201 13
subtype1 15 3 1 2 1 37 3
subtype2 5 1 0 6 2 31 5
subtype3 0 0 0 1 0 30 0
subtype4 33 1 0 3 3 103 5

Figure S418.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0444 (Fisher's exact test), Q value = 0.26

Table S429.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 16 128 116 1 23
subtype1 6 28 22 0 6
subtype2 5 28 14 0 2
subtype3 0 17 14 0 0
subtype4 5 55 66 1 15

Figure S419.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 0.61

Table S430.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 40 2000.0 (14.1)
subtype1 7 2001.0 (13.9)
subtype2 5 1995.4 (15.9)
subtype3 5 1995.6 (5.4)
subtype4 23 2001.6 (15.3)

Figure S420.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.47

Table S431.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 88 17.2 (14.0)
subtype1 17 13.1 (11.1)
subtype2 13 21.9 (12.2)
subtype3 10 19.5 (9.9)
subtype4 48 16.9 (15.8)

Figure S421.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.288 (Fisher's exact test), Q value = 0.62

Table S432.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 38 8 4 61 142
subtype1 7 1 0 11 37
subtype2 3 2 2 11 22
subtype3 8 2 0 5 14
subtype4 20 3 2 34 69

Figure S422.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.899 (Kruskal-Wallis (anova)), Q value = 0.96

Table S433.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 80 20.9 (7.6)
subtype1 16 21.0 (5.8)
subtype2 10 19.9 (5.0)
subtype3 8 19.9 (5.2)
subtype4 46 21.2 (8.9)

Figure S423.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.048 (Kruskal-Wallis (anova)), Q value = 0.27

Table S434.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

nPatients Mean (Std.Dev)
ALL 145 3646.2 (1702.8)
subtype1 31 3621.0 (1777.9)
subtype2 17 3381.6 (2017.6)
subtype3 19 4549.5 (914.6)
subtype4 78 3493.8 (1705.5)

Figure S424.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S435.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 19 99 15 1 25
subtype1 1 27 0 0 4
subtype2 0 14 3 0 4
subtype3 13 1 7 1 0
subtype4 5 57 5 0 17

Figure S425.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.457 (Fisher's exact test), Q value = 0.75

Table S436.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 27 3
subtype1 1 0
subtype2 2 0
subtype3 17 1
subtype4 7 2

Figure S426.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.589 (Fisher's exact test), Q value = 0.81

Table S437.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients DISTANT RECURRENCE LOCAL RECURRENCE PRIMARY TUMOR FIELD REGIONAL SITE
ALL 2 2 36 33
subtype1 1 0 6 5
subtype2 0 1 5 4
subtype3 0 0 2 0
subtype4 1 1 23 24

Figure S427.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.458 (Fisher's exact test), Q value = 0.75

Table S438.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 56 5
subtype1 12 0
subtype2 6 1
subtype3 2 0
subtype4 36 4

Figure S428.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.172 (Kruskal-Wallis (anova)), Q value = 0.51

Table S439.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 255 3.6 (2.5)
subtype1 56 3.0 (2.0)
subtype2 42 3.5 (2.5)
subtype3 30 3.3 (1.9)
subtype4 127 4.0 (2.8)

Figure S429.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.201 (Kruskal-Wallis (anova)), Q value = 0.53

Table S440.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 106 0.1 (0.3)
subtype1 20 0.0 (0.0)
subtype2 19 0.0 (0.0)
subtype3 26 0.2 (0.6)
subtype4 41 0.0 (0.2)

Figure S430.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 0.46

Table S441.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 142 0.6 (1.0)
subtype1 25 0.6 (0.9)
subtype2 26 0.3 (0.5)
subtype3 27 0.4 (0.7)
subtype4 64 0.7 (1.2)

Figure S431.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0231 (Kruskal-Wallis (anova)), Q value = 0.19

Table S442.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 250 2.8 (2.0)
subtype1 53 2.5 (1.9)
subtype2 42 2.6 (1.3)
subtype3 31 2.2 (1.6)
subtype4 124 3.2 (2.3)

Figure S432.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.781 (Kruskal-Wallis (anova)), Q value = 0.91

Table S443.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 115 0.8 (1.8)
subtype1 22 0.7 (1.0)
subtype2 23 1.3 (2.8)
subtype3 25 0.6 (1.0)
subtype4 45 0.7 (1.7)

Figure S433.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.653 (Kruskal-Wallis (anova)), Q value = 0.84

Table S444.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 111 0.1 (0.4)
subtype1 20 0.1 (0.2)
subtype2 21 0.2 (0.5)
subtype3 26 0.1 (0.3)
subtype4 44 0.1 (0.3)

Figure S434.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.721 (Fisher's exact test), Q value = 0.88

Table S445.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 1 7 38 1 10
subtype1 0 1 6 0 2
subtype2 1 1 6 1 1
subtype3 0 2 14 0 2
subtype4 0 3 12 0 5

Figure S435.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.288 (Fisher's exact test), Q value = 0.62

Table S446.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 25 77 119
subtype1 0 7 16 29
subtype2 0 4 15 18
subtype3 2 1 12 14
subtype4 0 13 34 58

Figure S436.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.0782 (Fisher's exact test), Q value = 0.38

Table S447.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 68 73
subtype1 20 15
subtype2 14 15
subtype3 6 18
subtype4 28 25

Figure S437.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0694 (Kruskal-Wallis (anova)), Q value = 0.34

Table S448.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 153 1.0 (2.4)
subtype1 37 0.6 (1.9)
subtype2 28 1.1 (1.9)
subtype3 25 2.4 (4.5)
subtype4 63 0.6 (1.1)

Figure S438.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.769 (Kruskal-Wallis (anova)), Q value = 0.91

Table S449.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 170 22.8 (12.7)
subtype1 41 21.6 (10.8)
subtype2 33 21.7 (12.2)
subtype3 25 26.1 (15.2)
subtype4 71 22.8 (13.0)

Figure S439.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0321 (Fisher's exact test), Q value = 0.22

Table S450.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 51 113
subtype1 0 11
subtype2 14 21
subtype3 9 12
subtype4 28 69

Figure S440.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 5.03e-08 (Kruskal-Wallis (anova)), Q value = 2.3e-05

Table S451.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 289 2008.3 (4.8)
subtype1 61 2009.5 (3.6)
subtype2 49 2009.3 (3.9)
subtype3 31 2003.0 (5.1)
subtype4 148 2008.5 (4.8)

Figure S441.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.314 (Fisher's exact test), Q value = 0.65

Table S452.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 84
subtype1 7 13 20
subtype2 0 10 11
subtype3 0 5 10
subtype4 8 25 43

Figure S442.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.949 (Kruskal-Wallis (anova)), Q value = 0.98

Table S453.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 251 160.9 (7.2)
subtype1 55 161.2 (6.8)
subtype2 42 161.7 (7.9)
subtype3 25 160.8 (8.0)
subtype4 129 160.5 (7.1)

Figure S443.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.46 (Fisher's exact test), Q value = 0.75

Table S454.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 18
subtype1 27 4
subtype2 18 4
subtype3 17 6
subtype4 33 4

Figure S444.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.138 (Fisher's exact test), Q value = 0.47

Table S455.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 17 1
subtype1 0 0 0
subtype2 0 1 0
subtype3 3 9 0
subtype4 0 7 1

Figure S445.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0797 (Kruskal-Wallis (anova)), Q value = 0.38

Table S456.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 16 13.0 (7.3)
subtype1 1 7.1 (NA)
subtype2 6 9.0 (5.9)
subtype3 3 11.0 (3.0)
subtype4 6 18.8 (7.3)

Figure S446.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRseq Mature cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.932 (Fisher's exact test), Q value = 0.97

Table S457.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3 T3A T3B T4 TIS TX
ALL 35 70 30 6 10 7 9 34 1 2 16 9 1 17
subtype1 6 20 6 1 4 2 3 8 0 0 2 2 0 2
subtype2 7 14 5 0 3 1 1 3 0 0 3 1 0 3
subtype3 6 9 3 1 0 1 0 3 0 0 1 0 0 0
subtype4 16 27 16 4 3 3 5 20 1 2 10 6 1 12

Figure S447.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.885 (Kruskal-Wallis (anova)), Q value = 0.95

Table S458.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 291 48.2 (13.8)
subtype1 62 47.2 (11.9)
subtype2 50 49.4 (14.2)
subtype3 31 48.9 (14.4)
subtype4 148 48.0 (14.3)

Figure S448.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.163 (Fisher's exact test), Q value = 0.49

Table S459.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients STAGE I STAGE IA STAGE IA2 STAGE IB STAGE IB1 STAGE IB2 STAGE II STAGE IIA STAGE IIA1 STAGE IIA2 STAGE IIB STAGE IIIA STAGE IIIB STAGE IVA STAGE IVB
ALL 5 1 1 36 76 36 5 8 5 7 41 3 41 8 11
subtype1 1 0 0 5 23 9 1 3 1 2 7 0 5 1 4
subtype2 0 1 1 4 17 5 1 2 1 1 6 0 7 2 1
subtype3 0 0 0 12 5 4 0 1 1 0 2 0 6 0 0
subtype4 4 0 0 15 31 18 3 2 2 4 26 3 23 5 6

Figure S449.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/CESC-TP/15095897/CESC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CESC-TP/15076797/CESC-TP.merged_data.txt

  • Number of patients = 304

  • Number of clustering approaches = 10

  • Number of selected clinical features = 45

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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