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 39 clinical features across 307 patients, 63 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 8 subtypes that correlate to 'Time to Death',  'HISTOLOGICAL_TYPE',  'NUMBER_PACK_YEARS_SMOKED',  'TOBACCO_SMOKING_PACK_YEARS_SMOKED',  'PREGNANCIES_COUNT_TOTAL', and 'PREGNANCIES_COUNT_LIVE_BIRTH'.

  • Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'HISTOLOGICAL_TYPE',  'KERATINIZATION_SQUAMOUS_CELL', and 'CHEMO_CONCURRENT_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',  'PREGNANCIES_COUNT_TOTAL',  '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',  'NEOPLASM_HISTOLOGIC_GRADE',  'PREGNANCY_SPONTANEOUS_ABORTION_COUNT',  'INITIAL_PATHOLOGIC_DX_YEAR', and 'CLINICAL_STAGE'.

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 39 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 63 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 0.194
(0.517) 		0.146
(0.471) 		0.0162
(0.161) 		0.35
(0.674) 		0.429
(0.725) 		0.0258
(0.198) 		0.499
(0.749) 		0.607
(0.819) 		0.908
(0.957) 		0.938
(0.973)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.0312
(0.21) 		0.0132
(0.147) 		0.0555
(0.293) 		0.0879
(0.389) 		1.03e-05
(0.000444) 		0.00802
(0.112) 		0.456
(0.737) 		0.000125
(0.00376) 		0.43
(0.725) 		0.894
(0.957)
PATHOLOGY T STAGE Fisher's exact test 0.194
(0.517) 		0.516
(0.759) 		0.389
(0.711) 		0.649
(0.826) 		0.613
(0.824) 		0.705
(0.854) 		0.446
(0.737) 		0.00365
(0.0678) 		0.402
(0.711) 		0.465
(0.737)
PATHOLOGY N STAGE Fisher's exact test 0.147
(0.471) 		0.336
(0.67) 		0.19
(0.517) 		0.21
(0.526) 		0.361
(0.687) 		0.351
(0.674) 		0.688
(0.846) 		0.229
(0.552) 		0.153
(0.479) 		0.189
(0.517)
PATHOLOGY M STAGE Fisher's exact test 0.48
(0.737) 		0.632
(0.826) 		0.641
(0.826) 		1
(1.00) 		0.248
(0.576) 		0.0512
(0.283) 		0.219
(0.54) 		0.35
(0.674) 		0.0637
(0.322) 		0.369
(0.689)
RADIATION THERAPY Fisher's exact test 0.172
(0.508) 		0.731
(0.869) 		0.741
(0.873) 		0.422
(0.725) 		0.955
(0.988) 		0.917
(0.964) 		0.979
(1.00) 		0.417
(0.725) 		0.882
(0.955) 		0.25
(0.576)
HISTOLOGICAL TYPE Fisher's exact test 0.273
(0.593) 		1e-05
(0.000444) 		0.0331
(0.219) 		0.00417
(0.0739) 		1e-05
(0.000444) 		1e-05
(0.000444) 		1e-05
(0.000444) 		1e-05
(0.000444) 		2e-05
(0.000709) 		1e-05
(0.000444)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.475
(0.737) 		0.402
(0.711) 		0.0127
(0.146) 		0.54
(0.769) 		0.145
(0.471) 		0.437
(0.728) 		0.0518
(0.283) 		0.157
(0.482) 		0.193
(0.517) 		0.142
(0.471)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.209
(0.526) 		0.111
(0.453) 		0.52
(0.759) 		0.177
(0.51) 		0.0109
(0.138) 		0.0308
(0.21) 		0.269
(0.589) 		0.134
(0.47) 		0.178
(0.51) 		0.0805
(0.369)
RACE Fisher's exact test 0.198
(0.518) 		0.568
(0.782) 		0.589
(0.8) 		0.208
(0.526) 		0.008
(0.112) 		0.307
(0.64) 		0.257
(0.58) 		0.331
(0.667) 		0.119
(0.459) 		0.484
(0.737)
ETHNICITY Fisher's exact test 0.528
(0.766) 		0.97
(0.996) 		0.0445
(0.267) 		0.474
(0.737) 		0.251
(0.576) 		0.895
(0.957) 		0.601
(0.814) 		0.397
(0.711) 		0.143
(0.471) 		0.468
(0.737)
WEIGHT KG AT DIAGNOSIS Kruskal-Wallis (anova) 0.493
(0.745) 		0.0887
(0.389) 		0.534
(0.769) 		0.542
(0.769) 		0.651
(0.826) 		0.677
(0.843) 		0.729
(0.869) 		0.933
(0.973) 		0.669
(0.839) 		0.925
(0.97)
TUMOR STATUS Fisher's exact test 0.122
(0.459) 		0.45
(0.737) 		0.552
(0.772) 		0.455
(0.737) 		0.288
(0.611) 		0.274
(0.593) 		0.213
(0.529) 		0.508
(0.753) 		0.464
(0.737) 		0.493
(0.745)
NEOPLASM HISTOLOGIC GRADE Fisher's exact test 0.0272
(0.198) 		0.829
(0.934) 		0.206
(0.526) 		0.554
(0.772) 		0.00156
(0.0358) 		0.153
(0.479) 		0.00758
(0.112) 		0.0223
(0.181) 		0.0181
(0.168) 		0.0523
(0.283)
TOBACCO SMOKING YEAR STOPPED Kruskal-Wallis (anova) 0.193
(0.517) 		0.833
(0.935) 		0.196
(0.517) 		0.706
(0.854) 		0.855
(0.943) 		0.569
(0.782) 		0.313
(0.64) 		0.147
(0.471) 		0.312
(0.64) 		0.254
(0.576)
TOBACCO SMOKING PACK YEARS SMOKED Kruskal-Wallis (anova) 0.475
(0.737) 		0.402
(0.711) 		0.0127
(0.146) 		0.54
(0.769) 		0.145
(0.471) 		0.437
(0.728) 		0.0518
(0.283) 		0.157
(0.482) 		0.193
(0.517) 		0.142
(0.471)
TOBACCO SMOKING HISTORY Fisher's exact test 0.502
(0.75) 		0.238
(0.562) 		0.679
(0.843) 		0.701
(0.854) 		0.397
(0.711) 		0.313
(0.64) 		0.103
(0.442) 		0.367
(0.689) 		0.0428
(0.261) 		0.287
(0.611)
AGEBEGANSMOKINGINYEARS Kruskal-Wallis (anova) 0.332
(0.667) 		0.443
(0.736) 		0.887
(0.957) 		0.808
(0.923) 		0.431
(0.725) 		0.655
(0.826) 		0.846
(0.94) 		0.764
(0.89) 		0.685
(0.846) 		0.899
(0.957)
RADIATION THERAPY STATUS Fisher's exact test 0.262
(0.583) 		0.904
(0.957) 		0.857
(0.943) 		1
(1.00) 		0.735
(0.871) 		1
(1.00) 		0.393
(0.711) 		0.858
(0.943) 		1
(1.00) 		0.459
(0.737)
PREGNANCIES COUNT TOTAL Kruskal-Wallis (anova) 0.403
(0.711) 		0.261
(0.583) 		0.0312
(0.21) 		0.622
(0.826) 		0.0401
(0.248) 		0.0475
(0.276) 		0.698
(0.853) 		0.0673
(0.332) 		0.728
(0.869) 		0.135
(0.47)
PREGNANCIES COUNT STILLBIRTH Kruskal-Wallis (anova) 0.254
(0.576) 		0.000982
(0.0239) 		0.58
(0.791) 		0.224
(0.547) 		0.164
(0.491) 		0.574
(0.785) 		0.844
(0.94) 		0.481
(0.737) 		0.196
(0.517) 		0.193
(0.517)
PREGNANCY SPONTANEOUS ABORTION COUNT Kruskal-Wallis (anova) 0.834
(0.935) 		0.908
(0.957) 		0.0872
(0.389) 		0.809
(0.923) 		0.815
(0.924) 		0.13
(0.469) 		0.00548
(0.0928) 		0.179
(0.511) 		0.277
(0.598) 		0.128
(0.469)
PREGNANCIES COUNT LIVE BIRTH Kruskal-Wallis (anova) 0.477
(0.737) 		0.0172
(0.164) 		0.0127
(0.146) 		0.114
(0.455) 		0.0499
(0.283) 		0.0188
(0.17) 		0.787
(0.914) 		0.016
(0.161) 		0.72
(0.864) 		0.0205
(0.179)
PREGNANCY THERAPEUTIC ABORTION COUNT Kruskal-Wallis (anova) 0.865
(0.948) 		0.34
(0.67) 		0.152
(0.479) 		0.655
(0.826) 		0.792
(0.917) 		0.616
(0.825) 		0.497
(0.748) 		0.936
(0.973) 		0.668
(0.839) 		0.804
(0.922)
PREGNANCIES COUNT ECTOPIC Kruskal-Wallis (anova) 0.201
(0.52) 		0.65
(0.826) 		0.568
(0.782) 		0.638
(0.826) 		0.617
(0.825) 		0.855
(0.943) 		0.306
(0.64) 		0.958
(0.988) 		0.989
(1.00) 		0.629
(0.826)
POS LYMPH NODE LOCATION Fisher's exact test 0.717
(0.864) 		0.527
(0.766) 		0.42
(0.725) 		0.368
(0.689) 		0.43
(0.725) 		0.76
(0.89) 		0.707
(0.854) 		0.568
(0.782) 		0.802
(0.922) 		0.693
(0.85)
MENOPAUSE STATUS Fisher's exact test 0.00948
(0.127) 		0.129
(0.469) 		0.541
(0.769) 		0.763
(0.89) 		0.0243
(0.189) 		0.0463
(0.274) 		0.465
(0.737) 		0.0141
(0.149) 		0.241
(0.566) 		0.301
(0.635)
LYMPHOVASCULAR INVOLVEMENT Fisher's exact test 0.192
(0.517) 		0.358
(0.684) 		0.0813
(0.369) 		0.423
(0.725) 		0.639
(0.826) 		0.639
(0.826) 		0.635
(0.826) 		0.378
(0.701) 		0.871
(0.952) 		0.0807
(0.369)
LYMPH NODES EXAMINED HE COUNT Kruskal-Wallis (anova) 0.209
(0.526) 		0.111
(0.453) 		0.52
(0.759) 		0.177
(0.51) 		0.0109
(0.138) 		0.0308
(0.21) 		0.269
(0.589) 		0.134
(0.47) 		0.178
(0.51) 		0.0805
(0.369)
LYMPH NODES EXAMINED Kruskal-Wallis (anova) 0.471
(0.737) 		0.739
(0.873) 		0.0598
(0.307) 		0.236
(0.562) 		0.114
(0.455) 		0.342
(0.67) 		0.645
(0.826) 		0.319
(0.648) 		0.28
(0.6) 		0.746
(0.877)
KERATINIZATION SQUAMOUS CELL Fisher's exact test 1
(1.00) 		0.00056
(0.0156) 		0.896
(0.957) 		0.0395
(0.248) 		0.0275
(0.198) 		0.00364
(0.0678) 		0.107
(0.447) 		0.122
(0.459) 		0.0337
(0.219) 		0.0268
(0.198)
INITIAL PATHOLOGIC DX YEAR Kruskal-Wallis (anova) 0.39
(0.711) 		0.163
(0.491) 		0.879
(0.955) 		0.841
(0.94) 		0.266
(0.589) 		0.0135
(0.147) 		0.0032
(0.0678) 		0.0235
(0.187) 		3.4e-06
(0.000444) 		3.14e-08
(1.22e-05)
HISTORY HORMONAL CONTRACEPTIVES USE Fisher's exact test 0.391
(0.711) 		0.554
(0.772) 		0.804
(0.922) 		0.553
(0.772) 		0.11
(0.453) 		0.0343
(0.219) 		0.653
(0.826) 		0.313
(0.64) 		0.676
(0.843) 		0.347
(0.674)
HEIGHT CM AT DIAGNOSIS Kruskal-Wallis (anova) 0.0962
(0.417) 		0.132
(0.47) 		0.879
(0.955) 		0.167
(0.496) 		0.369
(0.689) 		0.146
(0.471) 		0.963
(0.991) 		0.686
(0.846) 		0.554
(0.772) 		0.929
(0.972)
CORPUS INVOLVEMENT Fisher's exact test 0.426
(0.725) 		0.405
(0.711) 		0.985
(1.00) 		0.456
(0.737) 		0.055
(0.293) 		0.337
(0.67) 		0.633
(0.826) 		0.34
(0.67) 		0.815
(0.924) 		0.484
(0.737)
CHEMO CONCURRENT TYPE Fisher's exact test 0.226
(0.547) 		0.623
(0.826) 		0.82
(0.927) 		0.00356
(0.0678) 		0.397
(0.711) 		0.117
(0.458) 		0.891
(0.957) 		0.46
(0.737) 		0.535
(0.769) 		0.0215
(0.179)
CERVIX SUV RESULTS Kruskal-Wallis (anova) 0.121
(0.459) 		0.0803
(0.369) 		0.231
(0.554) 		0.226
(0.547) 		0.141
(0.471) 		0.242
(0.566) 		0.479
(0.737) 		0.0797
(0.369)
AGE AT DIAGNOSIS Kruskal-Wallis (anova) 0.0214
(0.179) 		0.0165
(0.161) 		0.0592
(0.307) 		0.117
(0.458) 		1.58e-05
(0.000616) 		0.00655
(0.106) 		0.514
(0.759) 		9.35e-05
(0.00304) 		0.406
(0.711) 		0.906
(0.957)
CLINICAL STAGE Fisher's exact test 0.201
(0.52) 		0.0656
(0.328) 		0.507
(0.753) 		0.127
(0.469) 		0.0688
(0.336) 		0.105
(0.445) 		0.0215
(0.179) 		0.00066
(0.0172) 		0.00682
(0.106) 		0.163
(0.491)
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 129 73 93
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.194 (logrank test), Q value = 0.52

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 67 0.0 - 210.7 (23.2)
subtype1 124 25 0.1 - 210.7 (20.8)
subtype2 66 21 0.4 - 147.3 (20.7)
subtype3 88 21 0.0 - 173.3 (27.3)

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 = 0.0312 (Kruskal-Wallis (anova)), Q value = 0.21

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

nPatients Mean (Std.Dev)
ALL 293 48.1 (13.8)
subtype1 129 47.7 (14.5)
subtype2 73 45.8 (14.4)
subtype3 91 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 = 0.194 (Fisher's exact test), Q value = 0.52

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

nPatients T1 T2 T3 T4
ALL 135 70 21 8
subtype1 61 37 5 4
subtype2 30 18 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 = 0.147 (Fisher's exact test), Q value = 0.47

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

nPatients 0 1
ALL 129 56
subtype1 67 21
subtype2 25 17
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 = 0.48 (Fisher's exact test), Q value = 0.74

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

nPatients 0 1
ALL 108 10
subtype1 44 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 'RADIATION_THERAPY'

P value = 0.172 (Fisher's exact test), Q value = 0.51

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

nPatients NO YES
ALL 55 123
subtype1 28 49
subtype2 8 33
subtype3 19 41

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.273 (Fisher's exact test), Q value = 0.59

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: '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 6 242 6 21 3 17
subtype1 4 97 3 14 2 9
subtype2 2 61 2 3 1 4
subtype3 0 84 1 4 0 4

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.74

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 = 0.209 (Kruskal-Wallis (anova)), Q value = 0.53

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

nPatients Mean (Std.Dev)
ALL 150 0.9 (2.1)
subtype1 76 1.0 (2.5)
subtype2 28 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 = 0.198 (Fisher's exact test), Q value = 0.52

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 19 28 2 202
subtype1 6 7 11 1 91
subtype2 0 2 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 = 0.528 (Fisher's exact test), Q value = 0.77

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 23 162
subtype1 13 71
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 = 0.493 (Kruskal-Wallis (anova)), Q value = 0.75

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

nPatients Mean (Std.Dev)
ALL 266 73.2 (21.6)
subtype1 112 73.8 (18.7)
subtype2 68 70.3 (16.7)
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 = 0.122 (Fisher's exact test), Q value = 0.46

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

nPatients TUMOR FREE WITH TUMOR
ALL 183 74
subtype1 88 27
subtype2 37 23
subtype3 58 24

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 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0272 (Fisher's exact test), Q value = 0.2

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

nPatients G1 G2 G3 G4 GX
ALL 18 129 115 1 24
subtype1 10 58 49 0 10
subtype2 2 20 37 1 7
subtype3 6 51 29 0 7

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.52

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

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 17 1996.4 (16.8)
subtype2 15 2004.3 (10.6)
subtype3 11 1998.5 (11.1)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.74

Table S17.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: '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 S16.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.502 (Fisher's exact test), Q value = 0.75

Table S18.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #17: '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 9 3 62 143
subtype1 16 3 1 27 71
subtype2 13 1 1 14 28
subtype3 9 5 1 21 44

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

'Copy Number Ratio CNMF subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.67

Table S19.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #18: '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 S18.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.262 (Fisher's exact test), Q value = 0.58

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.403 (Kruskal-Wallis (anova)), Q value = 0.71

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

nPatients Mean (Std.Dev)
ALL 255 3.6 (2.5)
subtype1 114 3.4 (2.2)
subtype2 61 3.5 (2.5)
subtype3 80 4.0 (2.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.254 (Kruskal-Wallis (anova)), Q value = 0.58

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

nPatients Mean (Std.Dev)
ALL 106 0.1 (0.4)
subtype1 54 0.1 (0.3)
subtype2 19 0.0 (0.0)
subtype3 33 0.1 (0.5)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.834 (Kruskal-Wallis (anova)), Q value = 0.93

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

nPatients Mean (Std.Dev)
ALL 140 0.6 (1.0)
subtype1 69 0.5 (0.8)
subtype2 25 0.5 (0.7)
subtype3 46 0.7 (1.3)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.477 (Kruskal-Wallis (anova)), Q value = 0.74

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

nPatients Mean (Std.Dev)
ALL 251 2.8 (2.0)
subtype1 113 2.6 (1.8)
subtype2 61 3.0 (2.3)
subtype3 77 3.0 (2.1)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.865 (Kruskal-Wallis (anova)), Q value = 0.95

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

nPatients Mean (Std.Dev)
ALL 116 0.8 (1.6)
subtype1 59 0.6 (1.1)
subtype2 23 0.7 (1.0)
subtype3 34 1.2 (2.6)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.201 (Kruskal-Wallis (anova)), Q value = 0.52

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

nPatients Mean (Std.Dev)
ALL 110 0.1 (0.4)
subtype1 54 0.1 (0.2)
subtype2 20 0.1 (0.4)
subtype3 36 0.2 (0.5)

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

'Copy Number Ratio CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.717 (Fisher's exact test), Q value = 0.86

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

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 7 36 1 8
subtype1 2 1 17 1 4
subtype2 0 3 8 0 1
subtype3 1 3 11 0 3

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

'Copy Number Ratio CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.00948 (Fisher's exact test), Q value = 0.13

Table S28.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #27: '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 82 121
subtype1 0 7 33 63
subtype2 0 4 17 30
subtype3 2 14 32 28

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.192 (Fisher's exact test), Q value = 0.52

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

nPatients ABSENT PRESENT
ALL 71 74
subtype1 39 33
subtype2 10 19
subtype3 22 22

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.53

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

nPatients Mean (Std.Dev)
ALL 150 0.9 (2.1)
subtype1 76 1.0 (2.5)
subtype2 28 1.0 (1.8)
subtype3 46 0.8 (1.3)

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.471 (Kruskal-Wallis (anova)), Q value = 0.74

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

nPatients Mean (Std.Dev)
ALL 170 22.3 (12.8)
subtype1 83 21.6 (12.4)
subtype2 35 24.5 (12.6)
subtype3 52 22.1 (13.6)

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

'Copy Number Ratio CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

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

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

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 49 117
subtype1 21 51
subtype2 11 27
subtype3 17 39

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

'Copy Number Ratio CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.39 (Kruskal-Wallis (anova)), Q value = 0.71

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

nPatients Mean (Std.Dev)
ALL 293 2008.5 (4.6)
subtype1 127 2008.7 (4.8)
subtype2 73 2008.4 (4.4)
subtype3 93 2008.3 (4.7)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.391 (Fisher's exact test), Q value = 0.71

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

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 85
subtype1 9 28 33
subtype2 3 12 20
subtype3 3 14 32

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

'Copy Number Ratio CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.0962 (Kruskal-Wallis (anova)), Q value = 0.42

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

nPatients Mean (Std.Dev)
ALL 253 161.1 (6.9)
subtype1 109 161.2 (7.9)
subtype2 61 162.3 (7.0)
subtype3 83 160.1 (5.2)

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

'Copy Number Ratio CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.426 (Fisher's exact test), Q value = 0.73

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

nPatients ABSENT PRESENT
ALL 92 18
subtype1 46 7
subtype2 15 5
subtype3 31 6

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

'Copy Number Ratio CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.226 (Fisher's exact test), Q value = 0.55

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

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 5 100 2
subtype1 4 41 2
subtype2 0 32 0
subtype3 1 27 0

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

'Copy Number Ratio CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

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

Table S38.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #37: '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 S37.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'Copy Number Ratio CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0214 (Kruskal-Wallis (anova)), Q value = 0.18

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

nPatients Mean (Std.Dev)
ALL 295 48.2 (13.8)
subtype1 129 47.7 (14.5)
subtype2 73 45.8 (14.4)
subtype3 93 50.7 (11.9)

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

'Copy Number Ratio CNMF subtypes' versus 'CLINICAL_STAGE'

P value = 0.201 (Fisher's exact test), Q value = 0.52

Table S40.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #39: '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 34 77 37 4 8 5 7 44 1 3 42 7 12
subtype1 1 0 1 0 12 40 17 1 5 2 5 17 1 1 16 2 7
subtype2 2 0 0 1 9 13 10 0 2 3 2 17 0 0 11 1 1
subtype3 2 1 0 0 13 24 10 3 1 0 0 10 0 2 15 4 4

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S41.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 71 58 60 61 11 46
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.146 (logrank test), Q value = 0.47

Table S42.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 71 0.0 - 210.7 (23.8)
subtype1 69 16 0.4 - 137.2 (20.4)
subtype2 55 13 0.3 - 209.6 (26.1)
subtype3 57 11 0.1 - 210.7 (26.3)
subtype4 55 21 0.0 - 154.3 (24.3)
subtype5 11 1 0.4 - 155.8 (23.7)
subtype6 43 9 0.1 - 146.9 (20.8)

Figure S40.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.0132 (Kruskal-Wallis (anova)), Q value = 0.15

Table S43.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 305 48.2 (13.8)
subtype1 71 46.8 (11.3)
subtype2 57 51.2 (15.5)
subtype3 60 48.0 (13.2)
subtype4 60 43.2 (13.8)
subtype5 11 53.0 (12.0)
subtype6 46 52.3 (14.4)

Figure S41.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.516 (Fisher's exact test), Q value = 0.76

Table S44.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 141 72 21 10
subtype1 38 20 4 1
subtype2 26 10 6 4
subtype3 26 18 3 3
subtype4 23 13 5 1
subtype5 4 1 2 0
subtype6 24 10 1 1

Figure S42.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.336 (Fisher's exact test), Q value = 0.67

Table S45.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 135 60
subtype1 38 12
subtype2 19 14
subtype3 25 16
subtype4 25 8
subtype5 4 2
subtype6 24 8

Figure S43.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

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

Table S46.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 26 5
subtype2 20 1
subtype3 27 1
subtype4 22 2
subtype5 4 0
subtype6 17 1

Figure S44.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.731 (Fisher's exact test), Q value = 0.87

Table S47.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 127
subtype1 14 31
subtype2 12 26
subtype3 8 23
subtype4 11 28
subtype5 1 5
subtype6 11 14

Figure S45.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S48.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: '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 6 254 6 21 3 17
subtype1 6 21 5 21 3 15
subtype2 0 58 0 0 0 0
subtype3 0 58 0 0 0 2
subtype4 0 61 0 0 0 0
subtype5 0 11 0 0 0 0
subtype6 0 45 1 0 0 0

Figure S46.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

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

Table S49.  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 S47.  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 = 0.111 (Kruskal-Wallis (anova)), Q value = 0.45

Table S50.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 43 0.9 (2.7)
subtype2 28 1.5 (2.2)
subtype3 29 1.2 (2.1)
subtype4 25 0.6 (1.4)
subtype5 5 3.6 (6.9)
subtype6 29 0.6 (1.3)

Figure S48.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.568 (Fisher's exact test), Q value = 0.78

Table S51.  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 20 30 2 211
subtype1 1 5 3 0 53
subtype2 1 3 7 0 38
subtype3 1 7 7 0 37
subtype4 4 3 8 2 41
subtype5 0 0 0 0 10
subtype6 1 2 5 0 32

Figure S49.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.97 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 170
subtype1 7 39
subtype2 5 29
subtype3 4 34
subtype4 5 35
subtype5 0 5
subtype6 3 28

Figure S50.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

'METHLYATION CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.0887 (Kruskal-Wallis (anova)), Q value = 0.39

Table S53.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 278 73.0 (21.5)
subtype1 65 73.7 (16.2)
subtype2 56 71.6 (22.2)
subtype3 51 67.4 (18.6)
subtype4 54 74.5 (18.9)
subtype5 10 66.8 (16.8)
subtype6 42 80.4 (31.7)

Figure S51.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'TUMOR_STATUS'

P value = 0.45 (Fisher's exact test), Q value = 0.74

Table S54.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 191 78
subtype1 43 20
subtype2 37 14
subtype3 43 11
subtype4 32 20
subtype5 7 3
subtype6 29 10

Figure S52.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'METHLYATION CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.829 (Fisher's exact test), Q value = 0.93

Table S55.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 120 1 24
subtype1 7 30 28 0 6
subtype2 2 27 25 0 3
subtype3 3 29 23 0 4
subtype4 1 26 24 1 5
subtype5 0 4 6 0 1
subtype6 5 20 14 0 5

Figure S53.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.833 (Kruskal-Wallis (anova)), Q value = 0.93

Table S56.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 9 2002.1 (12.6)
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 S54.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

Table S57.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: '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 S55.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.238 (Fisher's exact test), Q value = 0.56

Table S58.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: '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 40 9 4 64 146
subtype1 9 1 0 11 43
subtype2 7 3 1 12 28
subtype3 7 2 1 12 30
subtype4 7 0 1 17 26
subtype5 0 1 0 3 1
subtype6 10 2 1 9 18

Figure S56.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'METHLYATION CNMF' versus 'AGEBEGANSMOKINGINYEARS'

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

Table S59.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: '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 S57.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'METHLYATION CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.904 (Fisher's exact test), Q value = 0.96

Table S60.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: '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 S58.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.261 (Kruskal-Wallis (anova)), Q value = 0.58

Table S61.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 267 3.6 (2.6)
subtype1 65 3.0 (2.1)
subtype2 50 4.0 (2.5)
subtype3 52 3.6 (2.5)
subtype4 52 4.0 (3.3)
subtype5 10 3.3 (1.7)
subtype6 38 3.6 (2.5)

Figure S59.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.000982 (Kruskal-Wallis (anova)), Q value = 0.024

Table S62.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 27 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 S60.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'METHLYATION CNMF' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.908 (Kruskal-Wallis (anova)), Q value = 0.96

Table S63.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 31 0.6 (0.8)
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 S61.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0172 (Kruskal-Wallis (anova)), Q value = 0.16

Table S64.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 262 2.8 (2.0)
subtype1 61 2.3 (1.9)
subtype2 50 3.6 (2.3)
subtype3 52 2.7 (1.9)
subtype4 50 3.2 (2.4)
subtype5 11 2.3 (1.2)
subtype6 38 2.6 (1.5)

Figure S62.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'METHLYATION CNMF' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.34 (Kruskal-Wallis (anova)), Q value = 0.67

Table S65.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 29 0.8 (1.0)
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 S63.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.65 (Kruskal-Wallis (anova)), Q value = 0.83

Table S66.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 26 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 S64.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'METHLYATION CNMF' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.527 (Fisher's exact test), Q value = 0.77

Table S67.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 8 40 1 10
subtype1 2 1 8 0 2
subtype2 0 2 4 0 2
subtype3 0 0 13 0 2
subtype4 0 2 7 1 1
subtype5 0 1 2 0 0
subtype6 1 2 6 0 3

Figure S65.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'METHLYATION CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.129 (Fisher's exact test), Q value = 0.47

Table S68.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: '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 84 125
subtype1 0 9 17 35
subtype2 1 6 20 17
subtype3 0 4 18 27
subtype4 0 3 8 27
subtype5 0 1 3 4
subtype6 1 2 18 15

Figure S66.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'METHLYATION CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.358 (Fisher's exact test), Q value = 0.68

Table S69.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 72 80
subtype1 22 21
subtype2 7 17
subtype3 17 14
subtype4 12 9
subtype5 2 4
subtype6 12 15

Figure S67.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.45

Table S70.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 43 0.9 (2.7)
subtype2 28 1.5 (2.2)
subtype3 29 1.2 (2.1)
subtype4 25 0.6 (1.4)
subtype5 5 3.6 (6.9)
subtype6 29 0.6 (1.3)

Figure S68.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.739 (Kruskal-Wallis (anova)), Q value = 0.87

Table S71.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 180 22.3 (12.6)
subtype1 48 21.1 (10.1)
subtype2 30 23.6 (14.6)
subtype3 34 25.6 (13.8)
subtype4 30 21.5 (12.8)
subtype5 7 18.6 (10.0)
subtype6 31 20.8 (12.9)

Figure S69.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'METHLYATION CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.00056 (Fisher's exact test), Q value = 0.016

Table S72.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 55 120
subtype1 0 17
subtype2 6 31
subtype3 13 26
subtype4 18 22
subtype5 5 3
subtype6 13 21

Figure S70.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'METHLYATION CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.163 (Kruskal-Wallis (anova)), Q value = 0.49

Table S73.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 305 2008.3 (4.8)
subtype1 70 2009.3 (3.9)
subtype2 58 2008.1 (5.3)
subtype3 60 2008.2 (4.8)
subtype4 61 2007.2 (5.2)
subtype5 11 2007.5 (4.8)
subtype6 45 2008.9 (4.4)

Figure S71.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'METHLYATION CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.554 (Fisher's exact test), Q value = 0.77

Table S74.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 90
subtype1 7 19 20
subtype2 2 11 18
subtype3 3 7 18
subtype4 2 10 17
subtype5 0 2 1
subtype6 1 5 16

Figure S72.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'METHLYATION CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.132 (Kruskal-Wallis (anova)), Q value = 0.47

Table S75.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 161.0 (7.3)
subtype1 63 161.6 (7.0)
subtype2 51 161.5 (7.0)
subtype3 50 158.5 (8.1)
subtype4 51 161.4 (7.8)
subtype5 10 159.9 (4.8)
subtype6 39 162.2 (6.6)

Figure S73.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.405 (Fisher's exact test), Q value = 0.71

Table S76.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 31 6
subtype2 15 2
subtype3 18 5
subtype4 15 1
subtype5 2 2
subtype6 18 3

Figure S74.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'METHLYATION CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.623 (Fisher's exact test), Q value = 0.83

Table S77.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 104 2
subtype1 3 23 1
subtype2 1 20 0
subtype3 1 22 1
subtype4 1 26 0
subtype5 1 3 0
subtype6 0 10 0

Figure S75.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'METHLYATION CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.0803 (Kruskal-Wallis (anova)), Q value = 0.37

Table S78.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: '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 S76.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'METHLYATION CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0165 (Kruskal-Wallis (anova)), Q value = 0.16

Table S79.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 307 48.3 (13.8)
subtype1 71 46.8 (11.3)
subtype2 58 51.3 (15.3)
subtype3 60 48.0 (13.2)
subtype4 61 43.6 (13.9)
subtype5 11 53.0 (12.0)
subtype6 46 52.3 (14.4)

Figure S77.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'CLINICAL_STAGE'

P value = 0.0656 (Fisher's exact test), Q value = 0.33

Table S80.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: '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 38 78 39 5 9 5 7 44 1 3 42 9 12
subtype1 1 0 0 0 5 26 11 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 8 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 5 0 0 4 1 1

Figure S78.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S81.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7 8
Number of samples 26 26 20 21 33 27 7 13
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0162 (logrank test), Q value = 0.16

Table S82.  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 (23.8)
subtype1 26 6 0.6 - 39.5 (17.8)
subtype2 24 9 6.6 - 113.2 (21.0)
subtype3 17 3 0.0 - 144.2 (24.6)
subtype4 21 1 0.4 - 210.7 (33.3)
subtype5 31 6 0.1 - 137.2 (26.4)
subtype6 25 3 0.3 - 209.6 (25.9)
subtype7 7 1 12.5 - 146.9 (27.2)
subtype8 10 1 0.1 - 147.4 (21.6)

Figure S79.  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 = 0.0555 (Kruskal-Wallis (anova)), Q value = 0.29

Table S83.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 171 47.5 (13.5)
subtype1 26 49.3 (15.5)
subtype2 25 44.4 (12.3)
subtype3 20 39.0 (9.5)
subtype4 21 47.4 (9.4)
subtype5 33 50.2 (12.1)
subtype6 27 50.5 (15.2)
subtype7 7 51.0 (20.3)
subtype8 12 49.0 (14.9)

Figure S80.  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 = 0.389 (Fisher's exact test), Q value = 0.71

Table S84.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 99 32 7 3
subtype1 16 3 0 0
subtype2 14 4 2 1
subtype3 12 1 1 0
subtype4 13 6 0 0
subtype5 21 7 1 1
subtype6 13 8 1 0
subtype7 3 0 1 1
subtype8 7 3 1 0

Figure S81.  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 = 0.19 (Fisher's exact test), Q value = 0.52

Table S85.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 93 38
subtype1 13 4
subtype2 13 6
subtype3 9 5
subtype4 10 8
subtype5 18 10
subtype6 15 5
subtype7 4 0
subtype8 11 0

Figure S82.  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 = 0.641 (Fisher's exact test), Q value = 0.83

Table S86.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 84 4
subtype1 10 1
subtype2 9 0
subtype3 8 0
subtype4 14 1
subtype5 18 1
subtype6 13 0
subtype7 4 1
subtype8 8 0

Figure S83.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.741 (Fisher's exact test), Q value = 0.87

Table S87.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 28 58
subtype1 4 9
subtype2 4 9
subtype3 2 4
subtype4 3 10
subtype5 4 12
subtype6 4 8
subtype7 3 4
subtype8 4 2

Figure S84.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

Table S88.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: '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 4 144 3 16 2 4
subtype1 2 21 0 2 0 1
subtype2 0 25 0 1 0 0
subtype3 1 15 0 3 0 1
subtype4 0 20 0 1 0 0
subtype5 1 18 2 8 2 2
subtype6 0 27 0 0 0 0
subtype7 0 7 0 0 0 0
subtype8 0 11 1 1 0 0

Figure S85.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 0.15

Table S89.  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 4 7.2 (6.6)
subtype2 10 21.8 (14.0)
subtype3 5 11.4 (8.8)
subtype4 4 11.9 (9.3)
subtype5 10 15.7 (11.9)
subtype6 9 29.9 (14.8)
subtype8 3 6.3 (3.8)

Figure S86.  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 = 0.52 (Kruskal-Wallis (anova)), Q value = 0.76

Table S90.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 105 1.1 (2.7)
subtype1 13 0.8 (1.5)
subtype2 16 2.6 (5.1)
subtype3 11 1.6 (3.6)
subtype4 16 1.0 (1.5)
subtype5 25 0.7 (1.1)
subtype6 14 1.1 (2.3)
subtype7 3 0.0 (0.0)
subtype8 7 0.0 (0.0)

Figure S87.  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 = 0.589 (Fisher's exact test), Q value = 0.8

Table S91.  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 1 125
subtype1 0 2 3 1 19
subtype2 0 1 4 0 21
subtype3 0 1 2 0 15
subtype4 0 1 0 0 16
subtype5 0 4 1 0 23
subtype6 2 4 1 0 18
subtype7 0 1 1 0 5
subtype8 1 1 0 0 8

Figure S88.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.0445 (Fisher's exact test), Q value = 0.27

Table S92.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 113
subtype1 1 22
subtype2 2 15
subtype3 0 15
subtype4 3 11
subtype5 0 23
subtype6 4 17
subtype7 1 3
subtype8 2 7

Figure S89.  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 = 0.534 (Kruskal-Wallis (anova)), Q value = 0.77

Table S93.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 158 74.7 (23.1)
subtype1 23 71.0 (18.8)
subtype2 25 73.7 (21.3)
subtype3 16 66.9 (14.0)
subtype4 20 83.0 (33.2)
subtype5 31 78.3 (27.6)
subtype6 27 70.9 (18.1)
subtype7 6 76.3 (22.0)
subtype8 10 78.9 (20.4)

Figure S90.  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 = 0.552 (Fisher's exact test), Q value = 0.77

Table S94.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 111 35
subtype1 17 7
subtype2 15 9
subtype3 11 4
subtype4 15 2
subtype5 21 7
subtype6 20 3
subtype7 6 1
subtype8 6 2

Figure S91.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RPPA CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.206 (Fisher's exact test), Q value = 0.53

Table S95.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 10 75 74 1 8
subtype1 3 7 13 0 3
subtype2 1 9 15 1 0
subtype3 1 11 5 0 2
subtype4 0 9 12 0 0
subtype5 1 14 16 0 1
subtype6 4 13 7 0 1
subtype7 0 4 2 0 0
subtype8 0 8 4 0 1

Figure S92.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.196 (Kruskal-Wallis (anova)), Q value = 0.52

Table S96.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 31 2001.5 (10.7)
subtype1 5 2003.0 (8.2)
subtype2 6 2009.0 (5.1)
subtype3 3 2006.7 (8.5)
subtype4 2 1997.5 (13.4)
subtype5 8 1996.0 (14.7)
subtype6 4 1996.8 (7.0)
subtype8 3 2003.0 (11.3)

Figure S93.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 0.15

Table S97.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 45 17.7 (13.7)
subtype1 4 7.2 (6.6)
subtype2 10 21.8 (14.0)
subtype3 5 11.4 (8.8)
subtype4 4 11.9 (9.3)
subtype5 10 15.7 (11.9)
subtype6 9 29.9 (14.8)
subtype8 3 6.3 (3.8)

Figure S94.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.679 (Fisher's exact test), Q value = 0.84

Table S98.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: '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 28 6 2 23 86
subtype1 2 2 0 2 15
subtype2 6 0 0 4 10
subtype3 5 0 0 3 7
subtype4 4 0 0 3 11
subtype5 6 2 0 5 18
subtype6 2 2 1 4 15
subtype7 0 0 1 0 5
subtype8 3 0 0 2 5

Figure S95.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'RPPA CNMF subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.887 (Kruskal-Wallis (anova)), Q value = 0.96

Table S99.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 44 21.5 (7.9)
subtype1 3 28.7 (13.6)
subtype2 9 22.1 (9.1)
subtype3 6 18.3 (4.4)
subtype4 4 18.8 (5.7)
subtype5 11 20.8 (5.2)
subtype6 7 20.1 (6.8)
subtype8 4 26.8 (13.3)

Figure S96.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.857 (Fisher's exact test), Q value = 0.94

Table S100.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 21 2
subtype1 3 1
subtype2 5 1
subtype3 2 0
subtype4 4 0
subtype5 6 0
subtype6 1 0
subtype7 0 0
subtype8 0 0

Figure S97.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0312 (Kruskal-Wallis (anova)), Q value = 0.21

Table S101.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 151 3.6 (2.6)
subtype1 20 3.3 (1.9)
subtype2 25 2.6 (2.1)
subtype3 15 2.9 (2.1)
subtype4 20 3.6 (3.1)
subtype5 30 3.6 (1.8)
subtype6 24 4.1 (2.3)
subtype7 7 6.1 (3.8)
subtype8 10 5.3 (4.2)

Figure S98.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.58 (Kruskal-Wallis (anova)), Q value = 0.79

Table S102.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 72 0.0 (0.2)
subtype1 10 0.1 (0.3)
subtype2 11 0.0 (0.0)
subtype3 8 0.1 (0.4)
subtype4 10 0.1 (0.3)
subtype5 14 0.0 (0.0)
subtype6 14 0.0 (0.0)
subtype7 2 0.0 (0.0)
subtype8 3 0.0 (0.0)

Figure S99.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'RPPA CNMF subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.0872 (Kruskal-Wallis (anova)), Q value = 0.39

Table S103.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 89 0.4 (0.8)
subtype1 10 0.2 (0.4)
subtype2 16 0.4 (0.5)
subtype3 9 0.1 (0.3)
subtype4 12 0.2 (0.5)
subtype5 16 0.6 (1.1)
subtype6 16 0.2 (0.6)
subtype7 5 1.6 (1.9)
subtype8 5 0.8 (0.8)

Figure S100.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 0.15

Table S104.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 151 2.6 (1.9)
subtype1 21 2.2 (1.8)
subtype2 24 2.0 (1.9)
subtype3 16 2.1 (1.7)
subtype4 19 2.8 (2.3)
subtype5 28 2.4 (1.4)
subtype6 26 3.2 (1.6)
subtype7 7 4.4 (2.4)
subtype8 10 3.3 (2.5)

Figure S101.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RPPA CNMF subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.152 (Kruskal-Wallis (anova)), Q value = 0.48

Table S105.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 81 1.1 (2.2)
subtype1 12 1.5 (1.4)
subtype2 11 0.3 (0.6)
subtype3 8 0.8 (1.4)
subtype4 10 1.0 (3.2)
subtype5 17 1.0 (1.2)
subtype6 15 1.1 (1.9)
subtype7 3 1.3 (1.5)
subtype8 5 3.2 (5.6)

Figure S102.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.568 (Kruskal-Wallis (anova)), Q value = 0.78

Table S106.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 74 0.1 (0.4)
subtype1 11 0.2 (0.4)
subtype2 11 0.1 (0.3)
subtype3 8 0.0 (0.0)
subtype4 11 0.3 (0.6)
subtype5 14 0.1 (0.4)
subtype6 14 0.0 (0.0)
subtype7 2 0.0 (0.0)
subtype8 3 0.0 (0.0)

Figure S103.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'RPPA CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.42 (Fisher's exact test), Q value = 0.73

Table S107.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 7 25 1 5
subtype1 2 1 2 0 0
subtype2 0 3 5 1 1
subtype3 0 0 2 0 1
subtype4 0 0 5 0 0
subtype5 0 3 5 0 2
subtype6 1 0 5 0 1
subtype7 0 0 1 0 0
subtype8 0 0 0 0 0

Figure S104.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'RPPA CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.541 (Fisher's exact test), Q value = 0.77

Table S108.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #27: '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 79
subtype1 0 2 8 14
subtype2 0 2 4 13
subtype3 0 0 2 13
subtype4 0 1 7 10
subtype5 1 5 9 14
subtype6 1 2 10 9
subtype7 0 1 2 2
subtype8 0 0 4 4

Figure S105.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RPPA CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.0813 (Fisher's exact test), Q value = 0.37

Table S109.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 52 60
subtype1 9 7
subtype2 5 11
subtype3 7 5
subtype4 6 8
subtype5 7 19
subtype6 9 7
subtype7 1 1
subtype8 8 2

Figure S106.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.52 (Kruskal-Wallis (anova)), Q value = 0.76

Table S110.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 105 1.1 (2.7)
subtype1 13 0.8 (1.5)
subtype2 16 2.6 (5.1)
subtype3 11 1.6 (3.6)
subtype4 16 1.0 (1.5)
subtype5 25 0.7 (1.1)
subtype6 14 1.1 (2.3)
subtype7 3 0.0 (0.0)
subtype8 7 0.0 (0.0)

Figure S107.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.0598 (Kruskal-Wallis (anova)), Q value = 0.31

Table S111.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 122 20.8 (11.9)
subtype1 17 18.6 (8.5)
subtype2 20 23.9 (11.2)
subtype3 13 25.1 (14.2)
subtype4 19 24.1 (13.4)
subtype5 26 21.0 (12.6)
subtype6 15 15.6 (9.4)
subtype7 3 9.3 (8.1)
subtype8 9 16.3 (9.6)

Figure S108.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'RPPA CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.896 (Fisher's exact test), Q value = 0.96

Table S112.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 76
subtype1 9 14
subtype2 7 11
subtype3 3 7
subtype4 4 9
subtype5 5 11
subtype6 9 14
subtype7 1 2
subtype8 1 8

Figure S109.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'RPPA CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.879 (Kruskal-Wallis (anova)), Q value = 0.96

Table S113.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 171 2008.3 (5.0)
subtype1 26 2009.3 (4.4)
subtype2 26 2007.6 (5.4)
subtype3 19 2007.2 (6.0)
subtype4 21 2007.3 (5.6)
subtype5 33 2008.6 (4.5)
subtype6 26 2008.4 (5.1)
subtype7 7 2008.4 (5.0)
subtype8 13 2009.2 (3.8)

Figure S110.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RPPA CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.804 (Fisher's exact test), Q value = 0.92

Table S114.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 5 32 51
subtype1 0 4 7
subtype2 1 7 8
subtype3 2 3 3
subtype4 1 4 8
subtype5 1 9 10
subtype6 0 4 9
subtype7 0 0 3
subtype8 0 1 3

Figure S111.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RPPA CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.879 (Kruskal-Wallis (anova)), Q value = 0.96

Table S115.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 153 161.7 (7.1)
subtype1 21 162.5 (6.4)
subtype2 25 162.0 (6.2)
subtype3 16 164.1 (6.4)
subtype4 20 161.3 (7.0)
subtype5 28 161.0 (7.5)
subtype6 26 161.1 (8.5)
subtype7 6 161.7 (5.5)
subtype8 11 159.6 (8.8)

Figure S112.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'RPPA CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.985 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 84 12
subtype1 11 1
subtype2 10 1
subtype3 10 1
subtype4 11 2
subtype5 20 3
subtype6 13 2
subtype7 2 0
subtype8 7 2

Figure S113.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'RPPA CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

Table S117.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 48 1
subtype1 0 9 1
subtype2 1 8 0
subtype3 0 3 0
subtype4 0 10 0
subtype5 1 8 0
subtype6 1 6 0
subtype7 0 3 0
subtype8 0 1 0

Figure S114.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'RPPA CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0592 (Kruskal-Wallis (anova)), Q value = 0.31

Table S118.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 47.7 (13.5)
subtype1 26 49.3 (15.5)
subtype2 26 44.9 (12.3)
subtype3 20 39.0 (9.5)
subtype4 21 47.4 (9.4)
subtype5 33 50.2 (12.1)
subtype6 27 50.5 (15.2)
subtype7 7 51.0 (20.3)
subtype8 13 50.1 (14.8)

Figure S115.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'RPPA CNMF subtypes' versus 'CLINICAL_STAGE'

P value = 0.507 (Fisher's exact test), Q value = 0.75

Table S119.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #39: '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 28 4 5 4 4 16 1 1 19 4 2
subtype1 2 1 1 0 3 5 8 0 0 0 0 2 0 0 3 0 1
subtype2 0 0 0 0 5 5 6 0 0 1 2 3 0 0 2 1 0
subtype3 0 0 0 0 3 7 3 0 1 0 0 3 0 0 2 0 0
subtype4 0 0 0 1 5 9 0 2 0 0 0 3 0 0 1 0 0
subtype5 1 0 0 0 4 10 6 0 1 1 1 1 0 0 5 1 1
subtype6 0 0 0 0 3 7 3 1 1 2 0 3 1 0 5 1 0
subtype7 0 0 0 0 3 1 0 0 1 0 0 0 0 1 0 1 0
subtype8 1 0 0 0 1 4 2 1 1 0 1 1 0 0 1 0 0

Figure S116.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S120.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 32 49 51 23 18
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.35 (logrank test), Q value = 0.67

Table S121.  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 (23.8)
subtype1 30 6 0.4 - 144.2 (18.6)
subtype2 46 7 0.1 - 210.7 (22.8)
subtype3 48 8 0.3 - 209.6 (26.7)
subtype4 20 6 0.0 - 78.7 (25.3)
subtype5 17 3 0.1 - 147.4 (24.8)

Figure S117.  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 = 0.0879 (Kruskal-Wallis (anova)), Q value = 0.39

Table S122.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 171 47.5 (13.5)
subtype1 31 44.2 (14.4)
subtype2 48 47.5 (13.5)
subtype3 51 48.9 (13.4)
subtype4 23 44.2 (12.3)
subtype5 18 53.8 (12.1)

Figure S118.  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 = 0.649 (Fisher's exact test), Q value = 0.83

Table S123.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 99 32 7 3
subtype1 19 4 0 0
subtype2 31 9 2 0
subtype3 24 11 4 2
subtype4 13 3 1 1
subtype5 12 5 0 0

Figure S119.  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 = 0.21 (Fisher's exact test), Q value = 0.53

Table S124.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 93 38
subtype1 17 4
subtype2 30 12
subtype3 19 14
subtype4 13 6
subtype5 14 2

Figure S120.  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 = 1 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 84 4
subtype1 12 1
subtype2 24 1
subtype3 26 1
subtype4 12 1
subtype5 10 0

Figure S121.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.422 (Fisher's exact test), Q value = 0.73

Table S126.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 28 58
subtype1 7 10
subtype2 10 15
subtype3 6 24
subtype4 3 6
subtype5 2 3

Figure S122.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00417 (Fisher's exact test), Q value = 0.074

Table S127.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: '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 4 144 3 16 2 4
subtype1 2 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 5 0 1

Figure S123.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

Table S128.  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 S124.  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 = 0.177 (Kruskal-Wallis (anova)), Q value = 0.51

Table S129.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 105 1.1 (2.7)
subtype1 16 1.3 (2.9)
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 S125.  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 = 0.208 (Fisher's exact test), Q value = 0.53

Table S130.  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 1 125
subtype1 0 4 3 1 23
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 S126.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.474 (Fisher's exact test), Q value = 0.74

Table S131.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 113
subtype1 1 28
subtype2 3 29
subtype3 6 29
subtype4 1 14
subtype5 2 13

Figure S127.  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 = 0.542 (Kruskal-Wallis (anova)), Q value = 0.77

Table S132.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 158 74.7 (23.1)
subtype1 27 68.4 (19.0)
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 S128.  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 = 0.455 (Fisher's exact test), Q value = 0.74

Table S133.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 111 35
subtype1 23 6
subtype2 33 8
subtype3 34 9
subtype4 12 7
subtype5 9 5

Figure S129.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RPPA cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.554 (Fisher's exact test), Q value = 0.77

Table S134.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 10 75 74 1 8
subtype1 2 15 12 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 S130.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.706 (Kruskal-Wallis (anova)), Q value = 0.85

Table S135.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 31 2001.5 (10.7)
subtype1 6 2002.2 (8.0)
subtype2 11 2000.3 (14.3)
subtype3 10 2004.3 (8.2)
subtype4 1 2000.0 (NA)
subtype5 3 1996.3 (11.8)

Figure S131.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #15: '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 S136.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: '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 S132.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.701 (Fisher's exact test), Q value = 0.85

Table S137.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: '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 28 6 2 23 86
subtype1 3 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 S133.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'RPPA cHierClus subtypes' versus 'AGEBEGANSMOKINGINYEARS'

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

Table S138.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #18: '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 S134.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

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

Table S139.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #19: '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 S135.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.622 (Kruskal-Wallis (anova)), Q value = 0.83

Table S140.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 151 3.6 (2.6)
subtype1 25 3.3 (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 S136.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.224 (Kruskal-Wallis (anova)), Q value = 0.55

Table S141.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 72 0.0 (0.2)
subtype1 13 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 S137.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'RPPA cHierClus subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.809 (Kruskal-Wallis (anova)), Q value = 0.92

Table S142.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 89 0.4 (0.8)
subtype1 15 0.3 (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 S138.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.114 (Kruskal-Wallis (anova)), Q value = 0.46

Table S143.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 151 2.6 (1.9)
subtype1 26 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 S139.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RPPA cHierClus subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.655 (Kruskal-Wallis (anova)), Q value = 0.83

Table S144.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 81 1.1 (2.2)
subtype1 16 1.1 (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 S140.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.638 (Kruskal-Wallis (anova)), Q value = 0.83

Table S145.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 74 0.1 (0.4)
subtype1 12 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 S141.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'RPPA cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.368 (Fisher's exact test), Q value = 0.69

Table S146.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 7 25 1 5
subtype1 2 1 3 0 0
subtype2 1 4 10 0 2
subtype3 0 2 7 0 1
subtype4 0 0 3 1 2
subtype5 0 0 2 0 0

Figure S142.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'RPPA cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.763 (Fisher's exact test), Q value = 0.89

Table S147.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #27: '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 79
subtype1 0 2 7 19
subtype2 0 3 15 23
subtype3 2 6 13 18
subtype4 0 1 5 12
subtype5 0 1 6 7

Figure S143.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RPPA cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

Table S148.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 52 60
subtype1 13 8
subtype2 13 22
subtype3 12 17
subtype4 6 6
subtype5 8 7

Figure S144.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.177 (Kruskal-Wallis (anova)), Q value = 0.51

Table S149.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 105 1.1 (2.7)
subtype1 16 1.3 (2.9)
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 S145.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.236 (Kruskal-Wallis (anova)), Q value = 0.56

Table S150.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 122 20.8 (11.9)
subtype1 22 18.4 (9.4)
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 S146.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'RPPA cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0395 (Fisher's exact test), Q value = 0.25

Table S151.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 76
subtype1 7 18
subtype2 16 14
subtype3 10 25
subtype4 6 11
subtype5 0 8

Figure S147.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'RPPA cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.841 (Kruskal-Wallis (anova)), Q value = 0.94

Table S152.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 171 2008.3 (5.0)
subtype1 32 2008.3 (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 S148.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RPPA cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.553 (Fisher's exact test), Q value = 0.77

Table S153.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 5 32 51
subtype1 0 4 8
subtype2 3 12 13
subtype3 1 6 18
subtype4 0 6 8
subtype5 1 4 4

Figure S149.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RPPA cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.167 (Kruskal-Wallis (anova)), Q value = 0.5

Table S154.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 153 161.7 (7.1)
subtype1 26 162.4 (6.5)
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 S150.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'RPPA cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.456 (Fisher's exact test), Q value = 0.74

Table S155.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 84 12
subtype1 16 1
subtype2 24 3
subtype3 25 3
subtype4 9 1
subtype5 10 4

Figure S151.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'RPPA cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.00356 (Fisher's exact test), Q value = 0.068

Table S156.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 3 48 1
subtype1 0 9 1
subtype2 1 10 0
subtype3 0 22 0
subtype4 0 6 0
subtype5 2 1 0

Figure S152.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'RPPA cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.117 (Kruskal-Wallis (anova)), Q value = 0.46

Table S157.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 47.7 (13.5)
subtype1 32 44.8 (14.5)
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 S153.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'RPPA cHierClus subtypes' versus 'CLINICAL_STAGE'

P value = 0.127 (Fisher's exact test), Q value = 0.47

Table S158.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #39: '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 28 4 5 4 4 16 1 1 19 4 2
subtype1 1 1 1 0 4 7 9 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 S154.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S159.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 58 105 69 72
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.429 (logrank test), Q value = 0.73

Table S160.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 287 71 0.0 - 210.7 (23.8)
subtype1 54 12 0.1 - 146.9 (21.8)
subtype2 99 23 0.2 - 210.7 (26.1)
subtype3 68 15 0.4 - 147.4 (25.0)
subtype4 66 21 0.0 - 154.3 (19.4)

Figure S155.  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 = 1.03e-05 (Kruskal-Wallis (anova)), Q value = 0.00044

Table S161.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 302 48.2 (13.9)
subtype1 58 53.4 (12.7)
subtype2 104 50.7 (14.9)
subtype3 69 46.2 (12.1)
subtype4 71 42.0 (12.3)

Figure S156.  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 = 0.613 (Fisher's exact test), Q value = 0.82

Table S162.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 71 20 10
subtype1 23 14 6 3
subtype2 48 27 7 4
subtype3 40 14 2 1
subtype4 29 16 5 2

Figure S157.  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 = 0.361 (Fisher's exact test), Q value = 0.69

Table S163.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 60
subtype1 23 12
subtype2 46 27
subtype3 35 10
subtype4 29 11

Figure S158.  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 = 0.248 (Fisher's exact test), Q value = 0.58

Table S164.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 24 4
subtype2 43 1
subtype3 23 2
subtype4 26 3

Figure S159.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.955 (Fisher's exact test), Q value = 0.99

Table S165.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 125
subtype1 9 24
subtype2 18 38
subtype3 15 30
subtype4 15 33

Figure S160.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S166.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: '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 5 252 6 21 3 17
subtype1 3 47 2 3 1 2
subtype2 0 105 0 0 0 0
subtype3 2 28 4 18 2 15
subtype4 0 72 0 0 0 0

Figure S161.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

Table S167.  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 S162.  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 = 0.0109 (Kruskal-Wallis (anova)), Q value = 0.14

Table S168.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 28 1.4 (3.1)
subtype2 60 1.4 (2.3)
subtype3 44 0.8 (2.7)
subtype4 27 0.3 (0.6)

Figure S163.  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 = 0.008 (Fisher's exact test), Q value = 0.11

Table S169.  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 20 30 2 209
subtype1 2 7 7 0 36
subtype2 1 7 11 0 71
subtype3 0 2 1 0 55
subtype4 4 4 11 2 47

Figure S164.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.251 (Fisher's exact test), Q value = 0.58

Table S170.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 168
subtype1 2 39
subtype2 8 58
subtype3 5 32
subtype4 9 39

Figure S165.  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 = 0.651 (Kruskal-Wallis (anova)), Q value = 0.83

Table S171.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 275 72.9 (21.5)
subtype1 53 72.1 (25.2)
subtype2 94 73.3 (24.9)
subtype3 63 73.3 (15.8)
subtype4 65 72.4 (17.9)

Figure S166.  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 = 0.288 (Fisher's exact test), Q value = 0.61

Table S172.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 189 78
subtype1 33 20
subtype2 70 21
subtype3 43 17
subtype4 43 20

Figure S167.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.00156 (Fisher's exact test), Q value = 0.036

Table S173.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 135 118 1 24
subtype1 2 22 30 0 2
subtype2 7 62 28 0 6
subtype3 7 30 25 0 7
subtype4 2 21 35 1 9

Figure S168.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.855 (Kruskal-Wallis (anova)), Q value = 0.94

Table S174.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 7 1999.1 (12.7)
subtype2 17 1997.5 (16.6)
subtype3 11 2001.5 (12.6)
subtype4 8 2002.2 (9.9)

Figure S169.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: '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 S175.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: '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 S170.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.397 (Fisher's exact test), Q value = 0.71

Table S176.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: '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 40 9 4 64 144
subtype1 7 3 1 14 22
subtype2 14 5 2 25 48
subtype3 11 1 0 9 39
subtype4 8 0 1 16 35

Figure S171.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'RNAseq CNMF subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.431 (Kruskal-Wallis (anova)), Q value = 0.73

Table S177.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: '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 S172.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.735 (Fisher's exact test), Q value = 0.87

Table S178.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: '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 S173.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0401 (Kruskal-Wallis (anova)), Q value = 0.25

Table S179.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 264 3.6 (2.6)
subtype1 49 3.6 (2.6)
subtype2 92 4.1 (2.7)
subtype3 61 3.0 (2.1)
subtype4 62 3.7 (2.8)

Figure S174.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.164 (Kruskal-Wallis (anova)), Q value = 0.49

Table S180.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 18 0.0 (0.0)
subtype2 46 0.2 (0.5)
subtype3 26 0.0 (0.2)
subtype4 22 0.0 (0.0)

Figure S175.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq CNMF subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.815 (Kruskal-Wallis (anova)), Q value = 0.92

Table S181.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 23 0.5 (1.1)
subtype2 63 0.5 (1.0)
subtype3 28 0.5 (0.8)
subtype4 33 0.6 (0.9)

Figure S176.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0499 (Kruskal-Wallis (anova)), Q value = 0.28

Table S182.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 260 2.9 (2.1)
subtype1 49 2.7 (2.0)
subtype2 91 3.0 (1.8)
subtype3 59 2.4 (2.1)
subtype4 61 3.1 (2.4)

Figure S177.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq CNMF subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.792 (Kruskal-Wallis (anova)), Q value = 0.92

Table S183.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 20 0.8 (1.4)
subtype2 51 1.2 (2.5)
subtype3 27 0.6 (0.8)
subtype4 24 0.5 (0.9)

Figure S178.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.617 (Kruskal-Wallis (anova)), Q value = 0.82

Table S184.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 19 0.2 (0.4)
subtype2 49 0.1 (0.4)
subtype3 26 0.1 (0.3)
subtype4 22 0.0 (0.2)

Figure S179.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.43 (Fisher's exact test), Q value = 0.73

Table S185.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 8 40 1 10
subtype1 2 1 4 0 3
subtype2 0 2 17 1 4
subtype3 1 2 10 0 2
subtype4 0 3 9 0 1

Figure S180.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'RNAseq CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0243 (Fisher's exact test), Q value = 0.19

Table S186.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: '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 82 124
subtype1 0 6 21 21
subtype2 2 8 35 38
subtype3 0 7 19 30
subtype4 0 4 7 35

Figure S181.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RNAseq CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

Table S187.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 13 19
subtype2 26 29
subtype3 18 21
subtype4 14 10

Figure S182.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0109 (Kruskal-Wallis (anova)), Q value = 0.14

Table S188.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 28 1.4 (3.1)
subtype2 60 1.4 (2.3)
subtype3 44 0.8 (2.7)
subtype4 27 0.3 (0.6)

Figure S183.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.114 (Kruskal-Wallis (anova)), Q value = 0.46

Table S189.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.4 (12.6)
subtype1 33 18.5 (11.6)
subtype2 63 23.0 (14.6)
subtype3 49 24.0 (11.1)
subtype4 33 22.6 (11.2)

Figure S184.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'RNAseq CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0275 (Fisher's exact test), Q value = 0.2

Table S190.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 55 119
subtype1 15 21
subtype2 21 54
subtype3 1 16
subtype4 18 28

Figure S185.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 0.59

Table S191.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 302 2008.3 (4.8)
subtype1 58 2009.2 (4.0)
subtype2 104 2008.1 (5.4)
subtype3 68 2008.7 (4.1)
subtype4 72 2007.6 (4.9)

Figure S186.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.11 (Fisher's exact test), Q value = 0.45

Table S192.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 89
subtype1 0 12 20
subtype2 3 15 31
subtype3 8 15 18
subtype4 4 11 20

Figure S187.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.369 (Kruskal-Wallis (anova)), Q value = 0.69

Table S193.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 261 161.0 (7.1)
subtype1 50 159.8 (6.7)
subtype2 89 160.4 (7.3)
subtype3 61 162.2 (7.3)
subtype4 61 161.6 (6.9)

Figure S188.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.055 (Fisher's exact test), Q value = 0.29

Table S194.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 18 5
subtype2 32 10
subtype3 28 4
subtype4 21 0

Figure S189.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.397 (Fisher's exact test), Q value = 0.71

Table S195.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 3 16 0
subtype2 2 34 0
subtype3 1 20 1
subtype4 1 32 1

Figure S190.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'RNAseq CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.231 (Kruskal-Wallis (anova)), Q value = 0.55

Table S196.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: '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 S191.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'RNAseq CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 1.58e-05 (Kruskal-Wallis (anova)), Q value = 0.00062

Table S197.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 58 53.4 (12.7)
subtype2 105 50.8 (14.8)
subtype3 69 46.2 (12.1)
subtype4 72 42.3 (12.5)

Figure S192.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'CLINICAL_STAGE'

P value = 0.0688 (Fisher's exact test), Q value = 0.34

Table S198.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: '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 38 77 39 5 9 5 7 43 1 2 42 9 12
subtype1 1 1 1 0 5 13 8 1 1 1 4 6 0 1 9 2 3
subtype2 1 0 0 1 15 22 10 4 2 2 2 13 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 S193.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S199.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 72 195 37
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0258 (logrank test), Q value = 0.2

Table S200.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 287 71 0.0 - 210.7 (23.8)
subtype1 70 17 0.4 - 137.2 (20.6)
subtype2 182 41 0.1 - 210.7 (24.8)
subtype3 35 13 0.0 - 99.9 (17.2)

Figure S194.  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 = 0.00802 (Kruskal-Wallis (anova)), Q value = 0.11

Table S201.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 302 48.2 (13.9)
subtype1 72 47.6 (11.6)
subtype2 193 49.7 (14.5)
subtype3 37 41.4 (12.7)

Figure S195.  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 = 0.705 (Fisher's exact test), Q value = 0.85

Table S202.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 71 20 10
subtype1 39 20 3 2
subtype2 86 42 16 8
subtype3 15 9 1 0

Figure S196.  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 = 0.351 (Fisher's exact test), Q value = 0.67

Table S203.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 60
subtype1 38 13
subtype2 78 42
subtype3 17 5

Figure S197.  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 = 0.0512 (Fisher's exact test), Q value = 0.28

Table S204.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 27 5
subtype2 77 3
subtype3 12 2

Figure S198.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.917 (Fisher's exact test), Q value = 0.96

Table S205.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 125
subtype1 14 33
subtype2 37 77
subtype3 6 15

Figure S199.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S206.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: '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 5 252 6 21 3 17
subtype1 5 22 5 21 3 16
subtype2 0 193 1 0 0 1
subtype3 0 37 0 0 0 0

Figure S200.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.437 (Kruskal-Wallis (anova)), Q value = 0.73

Table S207.  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 S201.  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 = 0.0308 (Kruskal-Wallis (anova)), Q value = 0.21

Table S208.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 44 0.7 (2.2)
subtype2 97 1.4 (2.6)
subtype3 18 0.2 (0.5)

Figure S202.  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 = 0.307 (Fisher's exact test), Q value = 0.64

Table S209.  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 20 30 2 209
subtype1 1 6 3 0 53
subtype2 4 13 20 2 130
subtype3 2 1 7 0 26

Figure S203.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.895 (Fisher's exact test), Q value = 0.96

Table S210.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 168
subtype1 6 38
subtype2 16 108
subtype3 2 22

Figure S204.  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 = 0.677 (Kruskal-Wallis (anova)), Q value = 0.84

Table S211.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 275 72.9 (21.5)
subtype1 66 72.5 (17.0)
subtype2 177 72.9 (23.6)
subtype3 32 73.6 (17.6)

Figure S205.  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 = 0.274 (Fisher's exact test), Q value = 0.59

Table S212.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 189 78
subtype1 42 22
subtype2 126 44
subtype3 21 12

Figure S206.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.153 (Fisher's exact test), Q value = 0.48

Table S213.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 135 118 1 24
subtype1 7 30 29 0 6
subtype2 10 93 69 0 16
subtype3 1 12 20 1 2

Figure S207.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.569 (Kruskal-Wallis (anova)), Q value = 0.78

Table S214.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 9 2002.1 (12.6)
subtype2 30 1999.1 (14.8)
subtype3 4 1999.0 (5.2)

Figure S208.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: '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 S215.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: '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 S209.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.313 (Fisher's exact test), Q value = 0.64

Table S216.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: '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 40 9 4 64 144
subtype1 9 1 0 11 44
subtype2 27 8 3 43 85
subtype3 4 0 1 10 15

Figure S210.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'RNAseq cHierClus subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.655 (Kruskal-Wallis (anova)), Q value = 0.83

Table S217.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: '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 S211.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

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

Table S218.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 3 0
subtype2 20 2
subtype3 6 1

Figure S212.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0475 (Kruskal-Wallis (anova)), Q value = 0.28

Table S219.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 264 3.6 (2.6)
subtype1 65 3.0 (2.0)
subtype2 167 3.8 (2.7)
subtype3 32 4.0 (2.8)

Figure S213.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.574 (Kruskal-Wallis (anova)), Q value = 0.78

Table S220.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 25 0.0 (0.2)
subtype2 74 0.1 (0.4)
subtype3 13 0.0 (0.0)

Figure S214.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq cHierClus subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

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

Table S221.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 29 0.6 (0.9)
subtype2 98 0.5 (0.9)
subtype3 20 0.8 (1.1)

Figure S215.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0188 (Kruskal-Wallis (anova)), Q value = 0.17

Table S222.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 260 2.9 (2.1)
subtype1 63 2.3 (1.8)
subtype2 165 3.0 (2.0)
subtype3 32 3.2 (2.4)

Figure S216.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq cHierClus subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.616 (Kruskal-Wallis (anova)), Q value = 0.82

Table S223.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 27 0.7 (1.0)
subtype2 80 0.9 (2.1)
subtype3 15 0.6 (1.0)

Figure S217.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.855 (Kruskal-Wallis (anova)), Q value = 0.94

Table S224.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 24 0.1 (0.3)
subtype2 79 0.1 (0.4)
subtype3 13 0.1 (0.3)

Figure S218.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.76 (Fisher's exact test), Q value = 0.89

Table S225.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 8 40 1 10
subtype1 2 1 9 0 2
subtype2 1 5 26 1 7
subtype3 0 2 5 0 1

Figure S219.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'RNAseq cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0463 (Fisher's exact test), Q value = 0.27

Table S226.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: '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 82 124
subtype1 0 8 19 34
subtype2 2 15 61 72
subtype3 0 2 2 18

Figure S220.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RNAseq cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

Table S227.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 23 21
subtype2 39 50
subtype3 9 8

Figure S221.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0308 (Kruskal-Wallis (anova)), Q value = 0.21

Table S228.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 44 0.7 (2.2)
subtype2 97 1.4 (2.6)
subtype3 18 0.2 (0.5)

Figure S222.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.342 (Kruskal-Wallis (anova)), Q value = 0.67

Table S229.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.4 (12.6)
subtype1 48 21.2 (10.2)
subtype2 109 22.2 (13.4)
subtype3 21 25.7 (13.0)

Figure S223.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'RNAseq cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.00364 (Fisher's exact test), Q value = 0.068

Table S230.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 55 119
subtype1 0 17
subtype2 47 89
subtype3 8 13

Figure S224.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0135 (Kruskal-Wallis (anova)), Q value = 0.15

Table S231.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 302 2008.3 (4.8)
subtype1 71 2009.5 (3.5)
subtype2 194 2008.2 (4.8)
subtype3 37 2006.4 (5.8)

Figure S225.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.0343 (Fisher's exact test), Q value = 0.22

Table S232.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 53 89
subtype1 7 17 21
subtype2 4 32 59
subtype3 4 4 9

Figure S226.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.146 (Kruskal-Wallis (anova)), Q value = 0.47

Table S233.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 261 161.0 (7.1)
subtype1 64 161.1 (6.9)
subtype2 167 160.5 (7.2)
subtype3 30 163.5 (6.8)

Figure S227.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.337 (Fisher's exact test), Q value = 0.67

Table S234.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 31 6
subtype2 56 13
subtype3 12 0

Figure S228.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.117 (Fisher's exact test), Q value = 0.46

Table S235.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 3 22 1
subtype2 4 67 0
subtype3 0 13 1

Figure S229.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'RNAseq cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.226 (Kruskal-Wallis (anova)), Q value = 0.55

Table S236.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: '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 S230.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'RNAseq cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.00655 (Kruskal-Wallis (anova)), Q value = 0.11

Table S237.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 72 47.6 (11.6)
subtype2 195 49.8 (14.4)
subtype3 37 41.4 (12.7)

Figure S231.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'CLINICAL_STAGE'

P value = 0.105 (Fisher's exact test), Q value = 0.44

Table S238.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: '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 38 77 39 5 9 5 7 43 1 2 42 9 12
subtype1 1 0 0 0 6 28 10 1 3 1 2 8 0 0 6 1 5
subtype2 4 1 1 1 24 44 20 4 3 2 4 30 1 2 33 8 7
subtype3 0 0 0 0 8 5 9 0 3 2 1 5 0 0 3 0 0

Figure S232.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S239.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 103 80 124
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.499 (logrank test), Q value = 0.75

Table S240.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 71 0.0 - 210.7 (23.8)
subtype1 99 25 0.3 - 160.4 (24.3)
subtype2 77 15 0.1 - 210.7 (21.0)
subtype3 114 31 0.0 - 209.6 (24.3)

Figure S233.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.456 (Kruskal-Wallis (anova)), Q value = 0.74

Table S241.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 305 48.2 (13.8)
subtype1 103 47.8 (12.4)
subtype2 80 49.7 (13.5)
subtype3 122 47.5 (15.1)

Figure S234.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.446 (Fisher's exact test), Q value = 0.74

Table S242.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 141 72 21 10
subtype1 50 29 10 5
subtype2 45 16 3 1
subtype3 46 27 8 4

Figure S235.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.688 (Fisher's exact test), Q value = 0.85

Table S243.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 135 60
subtype1 46 19
subtype2 41 22
subtype3 48 19

Figure S236.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.219 (Fisher's exact test), Q value = 0.54

Table S244.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 38 6
subtype2 35 1
subtype3 43 3

Figure S237.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.979 (Fisher's exact test), Q value = 1

Table S245.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 127
subtype1 23 49
subtype2 13 30
subtype3 21 48

Figure S238.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S246.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: '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 6 254 6 21 3 17
subtype1 5 61 5 17 3 12
subtype2 1 70 1 4 0 4
subtype3 0 123 0 0 0 1

Figure S239.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S247.  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 S240.  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 = 0.269 (Kruskal-Wallis (anova)), Q value = 0.59

Table S248.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 55 0.8 (2.2)
subtype2 56 1.4 (2.5)
subtype3 48 0.9 (2.4)

Figure S241.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.257 (Fisher's exact test), Q value = 0.58

Table S249.  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 20 30 2 211
subtype1 1 8 9 0 68
subtype2 1 8 7 0 55
subtype3 6 4 14 2 88

Figure S242.  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 S250.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 170
subtype1 7 53
subtype2 5 50
subtype3 12 67

Figure S243.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.729 (Kruskal-Wallis (anova)), Q value = 0.87

Table S251.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 278 73.0 (21.5)
subtype1 97 73.6 (20.4)
subtype2 70 73.4 (27.6)
subtype3 111 72.3 (18.0)

Figure S244.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'TUMOR_STATUS'

P value = 0.213 (Fisher's exact test), Q value = 0.53

Table S252.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 191 78
subtype1 62 31
subtype2 56 15
subtype3 73 32

Figure S245.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.00758 (Fisher's exact test), Q value = 0.11

Table S253.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 120 1 24
subtype1 8 40 43 0 11
subtype2 8 41 28 1 1
subtype3 2 55 49 0 12

Figure S246.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.313 (Kruskal-Wallis (anova)), Q value = 0.64

Table S254.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 12 2002.5 (11.3)
subtype2 11 1996.2 (11.8)
subtype3 20 2000.0 (15.9)

Figure S247.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S255.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: '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 S248.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.103 (Fisher's exact test), Q value = 0.44

Table S256.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: '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 40 9 4 64 146
subtype1 13 1 0 17 61
subtype2 9 4 3 19 35
subtype3 18 4 1 28 50

Figure S249.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CNMF' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.846 (Kruskal-Wallis (anova)), Q value = 0.94

Table S257.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: '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 S250.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.393 (Fisher's exact test), Q value = 0.71

Table S258.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 8 0
subtype2 6 0
subtype3 15 3

Figure S251.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.698 (Kruskal-Wallis (anova)), Q value = 0.85

Table S259.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 267 3.6 (2.6)
subtype1 91 3.6 (2.9)
subtype2 72 3.6 (2.3)
subtype3 104 3.6 (2.5)

Figure S252.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.844 (Kruskal-Wallis (anova)), Q value = 0.94

Table S260.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 30 0.0 (0.2)
subtype2 34 0.1 (0.5)
subtype3 48 0.1 (0.2)

Figure S253.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CNMF' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.00548 (Kruskal-Wallis (anova)), Q value = 0.093

Table S261.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 41 0.9 (1.2)
subtype2 44 0.4 (0.8)
subtype3 62 0.4 (0.8)

Figure S254.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.787 (Kruskal-Wallis (anova)), Q value = 0.91

Table S262.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 262 2.8 (2.0)
subtype1 87 2.9 (2.5)
subtype2 71 2.7 (1.7)
subtype3 104 2.8 (1.9)

Figure S255.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CNMF' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.497 (Kruskal-Wallis (anova)), Q value = 0.75

Table S263.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 33 0.8 (1.4)
subtype2 38 1.1 (2.3)
subtype3 51 0.7 (1.6)

Figure S256.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.306 (Kruskal-Wallis (anova)), Q value = 0.64

Table S264.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 30 0.1 (0.3)
subtype2 36 0.2 (0.5)
subtype3 50 0.1 (0.3)

Figure S257.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CNMF' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.707 (Fisher's exact test), Q value = 0.85

Table S265.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 8 40 1 10
subtype1 2 2 9 0 2
subtype2 1 2 15 1 5
subtype3 0 4 16 0 3

Figure S258.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.465 (Fisher's exact test), Q value = 0.74

Table S266.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: '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 84 125
subtype1 1 12 26 44
subtype2 1 6 26 29
subtype3 0 7 32 52

Figure S259.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'MIRSEQ CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.635 (Fisher's exact test), Q value = 0.83

Table S267.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 72 80
subtype1 24 30
subtype2 23 28
subtype3 25 22

Figure S260.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.269 (Kruskal-Wallis (anova)), Q value = 0.59

Table S268.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 55 0.8 (2.2)
subtype2 56 1.4 (2.5)
subtype3 48 0.9 (2.4)

Figure S261.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.645 (Kruskal-Wallis (anova)), Q value = 0.83

Table S269.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 180 22.3 (12.6)
subtype1 60 20.7 (11.0)
subtype2 62 24.3 (14.3)
subtype3 58 21.7 (12.0)

Figure S262.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.107 (Fisher's exact test), Q value = 0.45

Table S270.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 55 120
subtype1 6 29
subtype2 19 32
subtype3 30 59

Figure S263.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0032 (Kruskal-Wallis (anova)), Q value = 0.068

Table S271.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 305 2008.3 (4.8)
subtype1 102 2009.4 (4.3)
subtype2 79 2008.1 (4.7)
subtype3 124 2007.6 (5.0)

Figure S264.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.653 (Fisher's exact test), Q value = 0.83

Table S272.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 90
subtype1 8 20 34
subtype2 3 16 20
subtype3 4 18 36

Figure S265.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.963 (Kruskal-Wallis (anova)), Q value = 0.99

Table S273.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 161.0 (7.3)
subtype1 92 161.1 (7.3)
subtype2 65 161.3 (7.8)
subtype3 107 160.6 (7.0)

Figure S266.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.633 (Fisher's exact test), Q value = 0.83

Table S274.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 34 8
subtype2 33 7
subtype3 32 4

Figure S267.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRSEQ CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.891 (Fisher's exact test), Q value = 0.96

Table S275.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 104 2
subtype1 3 36 0
subtype2 1 19 0
subtype3 3 49 2

Figure S268.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.47

Table S276.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: '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 S269.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'MIRSEQ CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.514 (Kruskal-Wallis (anova)), Q value = 0.76

Table S277.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 307 48.3 (13.8)
subtype1 103 47.8 (12.4)
subtype2 80 49.7 (13.5)
subtype3 124 47.8 (15.1)

Figure S270.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CLINICAL_STAGE'

P value = 0.0215 (Fisher's exact test), Q value = 0.18

Table S278.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: '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 38 78 39 5 9 5 7 44 1 3 42 9 12
subtype1 2 0 0 0 8 30 15 1 3 1 5 11 0 2 13 4 8
subtype2 0 1 1 1 9 28 10 1 3 2 2 7 0 0 10 2 1
subtype3 3 0 0 0 21 20 14 3 3 2 0 26 1 1 19 3 3

Figure S271.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S279.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5
Number of samples 60 102 74 26 45
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.607 (logrank test), Q value = 0.82

Table S280.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 290 71 0.0 - 210.7 (23.8)
subtype1 59 15 0.5 - 137.2 (18.8)
subtype2 96 21 0.0 - 160.4 (20.9)
subtype3 69 16 0.1 - 210.7 (31.4)
subtype4 22 7 3.0 - 209.6 (26.8)
subtype5 44 12 0.1 - 144.2 (17.9)

Figure S272.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.000125 (Kruskal-Wallis (anova)), Q value = 0.0038

Table S281.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 305 48.2 (13.8)
subtype1 60 46.5 (12.5)
subtype2 101 50.4 (14.3)
subtype3 74 51.2 (14.1)
subtype4 25 49.9 (11.3)
subtype5 45 39.7 (11.6)

Figure S273.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00365 (Fisher's exact test), Q value = 0.068

Table S282.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 141 72 21 10
subtype1 35 13 2 1
subtype2 32 29 12 6
subtype3 47 11 2 1
subtype4 8 9 2 1
subtype5 19 10 3 1

Figure S274.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.229 (Fisher's exact test), Q value = 0.55

Table S283.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 135 60
subtype1 32 10
subtype2 33 22
subtype3 38 20
subtype4 11 2
subtype5 21 6

Figure S275.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.35 (Fisher's exact test), Q value = 0.67

Table S284.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 18 2
subtype2 29 2
subtype3 38 1
subtype4 12 2
subtype5 19 3

Figure S276.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.417 (Fisher's exact test), Q value = 0.73

Table S285.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 57 127
subtype1 11 28
subtype2 18 50
subtype3 14 19
subtype4 7 10
subtype5 7 20

Figure S277.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S286.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: '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 6 254 6 21 3 17
subtype1 5 13 4 19 3 16
subtype2 1 99 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 S278.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S287.  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 S279.  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 = 0.134 (Kruskal-Wallis (anova)), Q value = 0.47

Table S288.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 40 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 S280.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.331 (Fisher's exact test), Q value = 0.67

Table S289.  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 20 30 2 211
subtype1 0 5 2 0 45
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 S281.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.397 (Fisher's exact test), Q value = 0.71

Table S290.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 170
subtype1 4 31
subtype2 10 52
subtype3 3 50
subtype4 3 16
subtype5 4 21

Figure S282.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CHIERARCHICAL' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.933 (Kruskal-Wallis (anova)), Q value = 0.97

Table S291.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 278 73.0 (21.5)
subtype1 54 71.7 (16.8)
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 S283.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_STATUS'

P value = 0.508 (Fisher's exact test), Q value = 0.75

Table S292.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 191 78
subtype1 38 17
subtype2 64 25
subtype3 50 15
subtype4 12 9
subtype5 27 12

Figure S284.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0223 (Fisher's exact test), Q value = 0.18

Table S293.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 18 136 120 1 24
subtype1 6 27 21 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 S285.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 0.47

Table S294.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 43 1999.7 (13.6)
subtype1 9 2002.1 (12.6)
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 S286.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S295.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: '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 S287.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.367 (Fisher's exact test), Q value = 0.69

Table S296.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: '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 40 9 4 64 146
subtype1 9 1 0 11 34
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 S288.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CHIERARCHICAL' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.764 (Kruskal-Wallis (anova)), Q value = 0.89

Table S297.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: '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 S289.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_STATUS'

P value = 0.858 (Fisher's exact test), Q value = 0.94

Table S298.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: '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 S290.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0673 (Kruskal-Wallis (anova)), Q value = 0.33

Table S299.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 267 3.6 (2.6)
subtype1 54 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 S291.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.481 (Kruskal-Wallis (anova)), Q value = 0.74

Table S300.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 20 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 S292.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.179 (Kruskal-Wallis (anova)), Q value = 0.51

Table S301.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 23 0.7 (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 S293.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.016 (Kruskal-Wallis (anova)), Q value = 0.16

Table S302.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 262 2.8 (2.0)
subtype1 52 2.2 (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 S294.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.936 (Kruskal-Wallis (anova)), Q value = 0.97

Table S303.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 22 0.5 (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 S295.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.958 (Kruskal-Wallis (anova)), Q value = 0.99

Table S304.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 19 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 S296.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CHIERARCHICAL' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.568 (Fisher's exact test), Q value = 0.78

Table S305.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 3 8 40 1 10
subtype1 2 0 7 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 S297.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CHIERARCHICAL' versus 'MENOPAUSE_STATUS'

P value = 0.0141 (Fisher's exact test), Q value = 0.15

Table S306.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: '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 84 125
subtype1 0 5 16 30
subtype2 1 9 29 37
subtype3 1 5 30 23
subtype4 0 4 5 9
subtype5 0 2 4 26

Figure S298.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.378 (Fisher's exact test), Q value = 0.7

Table S307.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 72 80
subtype1 21 16
subtype2 17 24
subtype3 19 29
subtype4 5 3
subtype5 10 8

Figure S299.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.134 (Kruskal-Wallis (anova)), Q value = 0.47

Table S308.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 159 1.0 (2.4)
subtype1 40 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 S300.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED'

P value = 0.319 (Kruskal-Wallis (anova)), Q value = 0.65

Table S309.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 180 22.3 (12.6)
subtype1 43 21.8 (10.6)
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 S301.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CHIERARCHICAL' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.122 (Fisher's exact test), Q value = 0.46

Table S310.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 55 120
subtype1 0 11
subtype2 21 46
subtype3 20 34
subtype4 4 12
subtype5 10 17

Figure S302.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CHIERARCHICAL' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0235 (Kruskal-Wallis (anova)), Q value = 0.19

Table S311.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 305 2008.3 (4.8)
subtype1 59 2009.4 (4.0)
subtype2 102 2008.9 (4.3)
subtype3 73 2007.3 (5.0)
subtype4 26 2008.0 (5.9)
subtype5 45 2007.5 (5.1)

Figure S303.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CHIERARCHICAL' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.313 (Fisher's exact test), Q value = 0.64

Table S312.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 90
subtype1 6 14 18
subtype2 4 17 33
subtype3 1 14 19
subtype4 0 5 7
subtype5 4 4 13

Figure S304.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CHIERARCHICAL' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.686 (Kruskal-Wallis (anova)), Q value = 0.85

Table S313.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 161.0 (7.3)
subtype1 52 161.1 (7.2)
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 S305.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CORPUS_INVOLVEMENT'

P value = 0.34 (Fisher's exact test), Q value = 0.67

Table S314.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 27 4
subtype2 22 5
subtype3 32 8
subtype4 5 2
subtype5 13 0

Figure S306.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.46 (Fisher's exact test), Q value = 0.74

Table S315.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 104 2
subtype1 2 18 1
subtype2 2 36 0
subtype3 2 20 0
subtype4 1 8 0
subtype5 0 22 1

Figure S307.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'CERVIX_SUV_RESULTS'

P value = 0.242 (Kruskal-Wallis (anova)), Q value = 0.57

Table S316.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: '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 S308.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'MIRSEQ CHIERARCHICAL' versus 'AGE_AT_DIAGNOSIS'

P value = 9.35e-05 (Kruskal-Wallis (anova)), Q value = 0.003

Table S317.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 307 48.3 (13.8)
subtype1 60 46.5 (12.5)
subtype2 102 50.4 (14.3)
subtype3 74 51.2 (14.1)
subtype4 26 50.4 (11.4)
subtype5 45 39.7 (11.6)

Figure S309.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CLINICAL_STAGE'

P value = 0.00066 (Fisher's exact test), Q value = 0.017

Table S318.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: '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 38 78 39 5 9 5 7 44 1 3 42 9 12
subtype1 1 0 0 0 5 26 9 1 2 1 0 5 0 0 6 1 3
subtype2 2 0 0 0 7 17 12 3 1 0 3 18 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 S310.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S319.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 129 78 87
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.908 (logrank test), Q value = 0.96

Table S320.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 280 68 0.0 - 210.7 (24.0)
subtype1 123 25 0.0 - 210.7 (20.5)
subtype2 74 17 0.1 - 147.4 (20.8)
subtype3 83 26 0.1 - 177.0 (33.3)

Figure S311.  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 = 0.43 (Kruskal-Wallis (anova)), Q value = 0.73

Table S321.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 293 48.2 (13.8)
subtype1 129 49.0 (13.2)
subtype2 77 48.9 (14.2)
subtype3 87 46.4 (14.4)

Figure S312.  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 = 0.402 (Fisher's exact test), Q value = 0.71

Table S322.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 136 68 19 9
subtype1 53 33 12 6
subtype2 39 15 3 1
subtype3 44 20 4 2

Figure S313.  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 = 0.153 (Fisher's exact test), Q value = 0.48

Table S323.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 130 57
subtype1 52 16
subtype2 41 17
subtype3 37 24

Figure S314.  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 = 0.0637 (Fisher's exact test), Q value = 0.32

Table S324.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 112 9
subtype1 40 7
subtype2 32 1
subtype3 40 1

Figure S315.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.882 (Fisher's exact test), Q value = 0.96

Table S325.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 54 123
subtype1 31 65
subtype2 11 27
subtype3 12 31

Figure S316.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00071

Table S326.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: '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 6 242 6 20 3 17
subtype1 5 90 4 14 3 13
subtype2 1 65 2 6 0 4
subtype3 0 87 0 0 0 0

Figure S317.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.52

Table S327.  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 S318.  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 = 0.178 (Kruskal-Wallis (anova)), Q value = 0.51

Table S328.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 155 1.0 (2.3)
subtype1 52 0.7 (2.1)
subtype2 49 1.2 (2.3)
subtype3 54 1.2 (2.7)

Figure S319.  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 = 0.119 (Fisher's exact test), Q value = 0.46

Table S329.  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 19 27 2 205
subtype1 5 9 15 0 82
subtype2 1 7 3 0 58
subtype3 1 3 9 2 65

Figure S320.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.143 (Fisher's exact test), Q value = 0.47

Table S330.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 161
subtype1 14 64
subtype2 3 48
subtype3 7 49

Figure S321.  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 = 0.669 (Kruskal-Wallis (anova)), Q value = 0.84

Table S331.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 266 72.9 (21.4)
subtype1 118 72.0 (19.4)
subtype2 70 73.9 (27.9)
subtype3 78 73.5 (17.6)

Figure S322.  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 = 0.464 (Fisher's exact test), Q value = 0.74

Table S332.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 186 74
subtype1 77 36
subtype2 53 16
subtype3 56 22

Figure S323.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0181 (Fisher's exact test), Q value = 0.17

Table S333.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 16 128 118 1 23
subtype1 6 47 60 0 13
subtype2 8 42 25 0 2
subtype3 2 39 33 1 8

Figure S324.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.312 (Kruskal-Wallis (anova)), Q value = 0.64

Table S334.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 41 2000.0 (13.9)
subtype1 17 2002.4 (15.5)
subtype2 9 1996.2 (16.2)
subtype3 15 1999.5 (10.5)

Figure S325.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.52

Table S335.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: '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 S326.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.0428 (Fisher's exact test), Q value = 0.26

Table S336.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: '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 8 4 61 143
subtype1 17 2 1 18 71
subtype2 7 3 3 18 35
subtype3 15 3 0 25 37

Figure S327.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature CNMF subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.85

Table S337.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: '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 S328.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

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

Table S338.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 27 3
subtype1 3 0
subtype2 5 0
subtype3 19 3

Figure S329.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.728 (Kruskal-Wallis (anova)), Q value = 0.87

Table S339.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 257 3.6 (2.5)
subtype1 110 3.5 (2.7)
subtype2 69 3.6 (2.2)
subtype3 78 3.6 (2.6)

Figure S330.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.196 (Kruskal-Wallis (anova)), Q value = 0.52

Table S340.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 107 0.1 (0.3)
subtype1 32 0.0 (0.2)
subtype2 29 0.0 (0.0)
subtype3 46 0.1 (0.5)

Figure S331.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.277 (Kruskal-Wallis (anova)), Q value = 0.6

Table S341.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 143 0.6 (1.0)
subtype1 49 0.8 (1.2)
subtype2 40 0.4 (0.7)
subtype3 54 0.5 (0.8)

Figure S332.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.72 (Kruskal-Wallis (anova)), Q value = 0.86

Table S342.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 252 2.8 (2.0)
subtype1 108 3.0 (2.3)
subtype2 68 2.8 (1.7)
subtype3 76 2.6 (1.9)

Figure S333.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.668 (Kruskal-Wallis (anova)), Q value = 0.84

Table S343.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 116 0.8 (1.8)
subtype1 35 0.5 (0.9)
subtype2 34 1.1 (2.4)
subtype3 47 0.8 (1.7)

Figure S334.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.989 (Kruskal-Wallis (anova)), Q value = 1

Table S344.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 35 0.1 (0.3)
subtype2 30 0.1 (0.4)
subtype3 47 0.1 (0.3)

Figure S335.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

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

Table S345.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 39 1 10
subtype1 1 1 9 0 2
subtype2 1 2 11 1 4
subtype3 0 4 19 0 4

Figure S336.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.241 (Fisher's exact test), Q value = 0.57

Table S346.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: '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 78 120
subtype1 0 15 30 52
subtype2 0 6 23 30
subtype3 2 4 25 38

Figure S337.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'MIRseq Mature CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.871 (Fisher's exact test), Q value = 0.95

Table S347.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 69 74
subtype1 23 24
subtype2 24 23
subtype3 22 27

Figure S338.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.178 (Kruskal-Wallis (anova)), Q value = 0.51

Table S348.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 155 1.0 (2.3)
subtype1 52 0.7 (2.1)
subtype2 49 1.2 (2.3)
subtype3 54 1.2 (2.7)

Figure S339.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.28 (Kruskal-Wallis (anova)), Q value = 0.6

Table S349.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 172 22.7 (12.6)
subtype1 60 21.9 (12.7)
subtype2 55 24.9 (12.8)
subtype3 57 21.4 (12.4)

Figure S340.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0337 (Fisher's exact test), Q value = 0.22

Table S350.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 52 114
subtype1 10 45
subtype2 18 30
subtype3 24 39

Figure S341.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 3.4e-06 (Kruskal-Wallis (anova)), Q value = 0.00044

Table S351.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 292 2008.3 (4.8)
subtype1 128 2009.5 (4.1)
subtype2 77 2008.4 (4.7)
subtype3 87 2006.5 (5.2)

Figure S342.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.676 (Fisher's exact test), Q value = 0.84

Table S352.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 85
subtype1 6 22 44
subtype2 4 16 18
subtype3 5 16 23

Figure S343.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.554 (Kruskal-Wallis (anova)), Q value = 0.77

Table S353.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 253 160.8 (7.3)
subtype1 115 160.5 (6.8)
subtype2 65 161.3 (7.4)
subtype3 73 160.9 (8.1)

Figure S344.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.815 (Fisher's exact test), Q value = 0.92

Table S354.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 19
subtype1 29 5
subtype2 30 5
subtype3 36 9

Figure S345.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.535 (Fisher's exact test), Q value = 0.77

Table S355.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 4 52 1
subtype2 0 17 1
subtype3 3 33 0

Figure S346.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.479 (Kruskal-Wallis (anova)), Q value = 0.74

Table S356.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: '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 S347.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'MIRseq Mature CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.406 (Kruskal-Wallis (anova)), Q value = 0.71

Table S357.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 294 48.2 (13.8)
subtype1 129 49.0 (13.2)
subtype2 78 49.1 (14.1)
subtype3 87 46.4 (14.4)

Figure S348.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'CLINICAL_STAGE'

P value = 0.00682 (Fisher's exact test), Q value = 0.11

Table S358.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: '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 37 76 37 5 8 5 7 42 3 41 8 11
subtype1 3 0 0 9 35 18 2 4 0 4 19 3 16 3 9
subtype2 0 1 1 6 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 S349.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S359.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 64 50 31 149
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.938 (logrank test), Q value = 0.97

Table S360.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 280 68 0.0 - 210.7 (24.0)
subtype1 62 15 0.4 - 137.2 (20.2)
subtype2 46 10 0.1 - 147.4 (21.3)
subtype3 31 12 1.2 - 177.0 (36.8)
subtype4 141 31 0.0 - 210.7 (21.4)

Figure S350.  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 = 0.894 (Kruskal-Wallis (anova)), Q value = 0.96

Table S361.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 293 48.2 (13.8)
subtype1 64 47.6 (12.1)
subtype2 50 49.4 (14.2)
subtype3 31 48.9 (14.4)
subtype4 148 47.9 (14.3)

Figure S351.  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 = 0.465 (Fisher's exact test), Q value = 0.74

Table S362.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 136 68 19 9
subtype1 33 19 2 2
subtype2 26 8 3 1
subtype3 18 5 1 0
subtype4 59 36 13 6

Figure S352.  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 = 0.189 (Fisher's exact test), Q value = 0.52

Table S363.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 130 57
subtype1 35 9
subtype2 25 11
subtype3 13 11
subtype4 57 26

Figure S353.  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 = 0.369 (Fisher's exact test), Q value = 0.69

Table S364.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 112 9
subtype1 22 4
subtype2 24 1
subtype3 15 0
subtype4 51 4

Figure S354.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.25 (Fisher's exact test), Q value = 0.58

Table S365.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 54 123
subtype1 14 29
subtype2 8 18
subtype3 4 2
subtype4 28 74

Figure S355.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00044

Table S366.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: '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 6 242 6 20 3 17
subtype1 4 16 5 19 3 17
subtype2 1 49 0 0 0 0
subtype3 0 30 0 1 0 0
subtype4 1 147 1 0 0 0

Figure S356.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.47

Table S367.  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 S357.  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 = 0.0805 (Kruskal-Wallis (anova)), Q value = 0.37

Table S368.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

nPatients Mean (Std.Dev)
ALL 155 1.0 (2.3)
subtype1 39 0.6 (1.9)
subtype2 28 1.1 (1.9)
subtype3 25 2.4 (4.5)
subtype4 63 0.6 (1.1)

Figure S358.  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 = 0.484 (Fisher's exact test), Q value = 0.74

Table S369.  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 19 27 2 205
subtype1 1 6 3 0 45
subtype2 1 5 2 0 35
subtype3 0 2 3 0 26
subtype4 5 6 19 2 99

Figure S359.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.468 (Fisher's exact test), Q value = 0.74

Table S370.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 161
subtype1 6 33
subtype2 3 31
subtype3 1 22
subtype4 14 75

Figure S360.  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 = 0.925 (Kruskal-Wallis (anova)), Q value = 0.97

Table S371.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 266 72.9 (21.4)
subtype1 58 72.4 (16.8)
subtype2 45 75.3 (32.9)
subtype3 28 71.8 (17.7)
subtype4 135 72.6 (19.2)

Figure S361.  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 = 0.493 (Fisher's exact test), Q value = 0.75

Table S372.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 186 74
subtype1 39 19
subtype2 33 10
subtype3 19 11
subtype4 95 34

Figure S362.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0523 (Fisher's exact test), Q value = 0.28

Table S373.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients G1 G2 G3 G4 GX
ALL 16 128 118 1 23
subtype1 6 28 24 0 6
subtype2 5 28 14 0 2
subtype3 0 17 14 0 0
subtype4 5 55 66 1 15

Figure S363.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.254 (Kruskal-Wallis (anova)), Q value = 0.58

Table S374.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 41 2000.0 (13.9)
subtype1 8 2000.9 (12.9)
subtype2 5 1995.4 (15.9)
subtype3 5 1995.6 (5.4)
subtype4 23 2001.6 (15.3)

Figure S364.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: '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 S375.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: '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 S365.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.287 (Fisher's exact test), Q value = 0.61

Table S376.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: '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 8 4 61 143
subtype1 8 1 0 11 38
subtype2 3 2 2 11 22
subtype3 8 2 0 5 14
subtype4 20 3 2 34 69

Figure S366.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature cHierClus subtypes' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.899 (Kruskal-Wallis (anova)), Q value = 0.96

Table S377.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: '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 S367.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.459 (Fisher's exact test), Q value = 0.74

Table S378.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: '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 S368.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.135 (Kruskal-Wallis (anova)), Q value = 0.47

Table S379.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 257 3.6 (2.5)
subtype1 58 3.0 (2.0)
subtype2 42 3.5 (2.5)
subtype3 30 3.3 (1.9)
subtype4 127 4.0 (2.8)

Figure S369.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.52

Table S380.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 107 0.1 (0.3)
subtype1 21 0.0 (0.0)
subtype2 19 0.0 (0.0)
subtype3 26 0.2 (0.6)
subtype4 41 0.0 (0.2)

Figure S370.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

P value = 0.128 (Kruskal-Wallis (anova)), Q value = 0.47

Table S381.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 143 0.6 (1.0)
subtype1 26 0.6 (0.9)
subtype2 26 0.3 (0.5)
subtype3 27 0.4 (0.7)
subtype4 64 0.7 (1.2)

Figure S371.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0205 (Kruskal-Wallis (anova)), Q value = 0.18

Table S382.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 252 2.8 (2.0)
subtype1 55 2.4 (1.9)
subtype2 42 2.6 (1.3)
subtype3 31 2.2 (1.6)
subtype4 124 3.2 (2.3)

Figure S372.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

P value = 0.804 (Kruskal-Wallis (anova)), Q value = 0.92

Table S383.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 116 0.8 (1.8)
subtype1 23 0.7 (1.0)
subtype2 23 1.3 (2.8)
subtype3 25 0.6 (1.0)
subtype4 45 0.7 (1.7)

Figure S373.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.629 (Kruskal-Wallis (anova)), Q value = 0.83

Table S384.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 21 0.0 (0.2)
subtype2 21 0.2 (0.5)
subtype3 26 0.1 (0.3)
subtype4 44 0.1 (0.3)

Figure S374.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.693 (Fisher's exact test), Q value = 0.85

Table S385.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 39 1 10
subtype1 1 1 7 0 2
subtype2 1 1 6 1 1
subtype3 0 2 14 0 2
subtype4 0 3 12 0 5

Figure S375.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.301 (Fisher's exact test), Q value = 0.64

Table S386.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: '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 78 120
subtype1 0 7 17 30
subtype2 0 4 15 18
subtype3 2 1 12 14
subtype4 0 13 34 58

Figure S376.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.0807 (Fisher's exact test), Q value = 0.37

Table S387.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 69 74
subtype1 21 16
subtype2 14 15
subtype3 6 18
subtype4 28 25

Figure S377.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0805 (Kruskal-Wallis (anova)), Q value = 0.37

Table S388.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 155 1.0 (2.3)
subtype1 39 0.6 (1.9)
subtype2 28 1.1 (1.9)
subtype3 25 2.4 (4.5)
subtype4 63 0.6 (1.1)

Figure S378.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.746 (Kruskal-Wallis (anova)), Q value = 0.88

Table S389.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 172 22.7 (12.6)
subtype1 43 21.3 (10.6)
subtype2 33 21.7 (12.2)
subtype3 25 26.1 (15.2)
subtype4 71 22.8 (13.0)

Figure S379.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0268 (Fisher's exact test), Q value = 0.2

Table S390.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 52 114
subtype1 0 12
subtype2 14 21
subtype3 9 12
subtype4 29 69

Figure S380.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 3.14e-08 (Kruskal-Wallis (anova)), Q value = 1.2e-05

Table S391.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 292 2008.3 (4.8)
subtype1 63 2009.6 (3.6)
subtype2 49 2009.3 (3.9)
subtype3 31 2003.0 (5.1)
subtype4 149 2008.5 (4.7)

Figure S381.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.347 (Fisher's exact test), Q value = 0.67

Table S392.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 15 54 85
subtype1 7 14 21
subtype2 0 10 11
subtype3 0 5 10
subtype4 8 25 43

Figure S382.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.929 (Kruskal-Wallis (anova)), Q value = 0.97

Table S393.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 253 160.8 (7.3)
subtype1 57 160.9 (7.1)
subtype2 42 161.7 (7.9)
subtype3 25 160.8 (8.0)
subtype4 129 160.5 (7.1)

Figure S383.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.484 (Fisher's exact test), Q value = 0.74

Table S394.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 19
subtype1 27 5
subtype2 18 4
subtype3 17 6
subtype4 33 4

Figure S384.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.0215 (Fisher's exact test), Q value = 0.18

Table S395.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 3 19 1
subtype2 0 12 0
subtype3 3 9 0
subtype4 1 62 1

Figure S385.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0797 (Kruskal-Wallis (anova)), Q value = 0.37

Table S396.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: '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 S386.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'MIRseq Mature cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.906 (Kruskal-Wallis (anova)), Q value = 0.96

Table S397.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 294 48.2 (13.8)
subtype1 64 47.6 (12.1)
subtype2 50 49.4 (14.2)
subtype3 31 48.9 (14.4)
subtype4 149 48.0 (14.3)

Figure S387.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'CLINICAL_STAGE'

P value = 0.163 (Fisher's exact test), Q value = 0.49

Table S398.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: '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 37 76 37 5 8 5 7 42 3 41 8 11
subtype1 1 0 0 6 23 10 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 27 3 23 5 6

Figure S388.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/CESC-TP/20124378/CESC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CESC-TP/19775059/CESC-TP.merged_data.txt

  • Number of patients = 307

  • Number of clustering approaches = 10

  • Number of selected clinical features = 39

  • 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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