Correlation between aggregated molecular cancer subtypes and selected clinical features
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PC31Q1
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, 76 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'PATHOLOGY_T_STAGE',  'RADIATION_THERAPY',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES',  'NEOPLASM_HISTOLOGIC_GRADE',  'LYMPH_NODES_EXAMINED_HE_COUNT',  'AGE_AT_DIAGNOSIS', and 'CLINICAL_STAGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES',  'PREGNANCIES_COUNT_TOTAL',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'LYMPH_NODES_EXAMINED_HE_COUNT', and 'KERATINIZATION_SQUAMOUS_CELL'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'LYMPHOVASCULAR_INVOLVEMENT' and 'LYMPH_NODES_EXAMINED'.

  • 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',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'MENOPAUSE_STATUS',  'LYMPH_NODES_EXAMINED_HE_COUNT',  'KERATINIZATION_SQUAMOUS_CELL',  'AGE_AT_DIAGNOSIS', and 'CLINICAL_STAGE'.

  • 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_TOTAL',  'PREGNANCIES_COUNT_LIVE_BIRTH',  'MENOPAUSE_STATUS',  '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',  'TOBACCO_SMOKING_HISTORY',  'PREGNANCY_SPONTANEOUS_ABORTION_COUNT',  'KERATINIZATION_SQUAMOUS_CELL',  '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 'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'NEOPLASM_HISTOLOGIC_GRADE',  'MENOPAUSE_STATUS',  'KERATINIZATION_SQUAMOUS_CELL',  'INITIAL_PATHOLOGIC_DX_YEAR',  'AGE_AT_DIAGNOSIS', and 'CLINICAL_STAGE'.

  • 5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH',  'HISTOLOGICAL_TYPE',  'NUMBER_OF_LYMPH_NODES',  'NEOPLASM_HISTOLOGIC_GRADE',  'RADIATION_THERAPY_STATUS',  'LYMPH_NODES_EXAMINED_HE_COUNT',  'INITIAL_PATHOLOGIC_DX_YEAR', and 'AGE_AT_DIAGNOSIS'.

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, 76 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 0.682
(0.852)
0.967
(0.992)
0.0599
(0.285)
0.183
(0.498)
0.44
(0.723)
0.0302
(0.199)
0.889
(0.957)
0.692
(0.856)
0.779
(0.893)
0.928
(0.98)
YEARS TO BIRTH Kruskal-Wallis (anova) 0.00931
(0.114)
0.174
(0.493)
0.0786
(0.325)
0.0879
(0.346)
8.51e-06
(0.000433)
0.00802
(0.114)
0.619
(0.844)
0.000125
(0.00376)
0.0366
(0.207)
0.00654
(0.102)
PATHOLOGY T STAGE Fisher's exact test 0.0336
(0.199)
0.422
(0.72)
0.891
(0.957)
0.645
(0.844)
0.661
(0.844)
0.704
(0.857)
0.172
(0.493)
0.00365
(0.0729)
0.211
(0.541)
0.262
(0.567)
PATHOLOGY N STAGE Fisher's exact test 0.35
(0.657)
0.232
(0.549)
0.661
(0.844)
0.213
(0.543)
0.377
(0.675)
0.349
(0.657)
0.609
(0.844)
0.23
(0.549)
0.547
(0.794)
0.0778
(0.325)
PATHOLOGY M STAGE Fisher's exact test 0.736
(0.872)
0.36
(0.661)
0.55
(0.794)
1
(1.00)
0.267
(0.576)
0.0524
(0.266)
0.0987
(0.37)
0.354
(0.66)
0.114
(0.398)
0.282
(0.601)
RADIATION THERAPY Fisher's exact test 0.0136
(0.144)
0.773
(0.891)
0.611
(0.844)
0.303
(0.624)
0.888
(0.957)
1
(1.00)
0.955
(0.986)
0.445
(0.726)
0.976
(1)
0.927
(0.98)
HISTOLOGICAL TYPE Fisher's exact test 1e-05
(0.000433)
1e-05
(0.000433)
0.311
(0.628)
0.00394
(0.0732)
1e-05
(0.000433)
1e-05
(0.000433)
1e-05
(0.000433)
1e-05
(0.000433)
1e-05
(0.000433)
1e-05
(0.000433)
NUMBER PACK YEARS SMOKED Kruskal-Wallis (anova) 0.662
(0.844)
0.112
(0.398)
0.198
(0.515)
0.54
(0.794)
0.106
(0.385)
0.437
(0.723)
0.0659
(0.29)
0.157
(0.474)
0.302
(0.624)
0.0646
(0.29)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.048
(0.247)
0.0366
(0.207)
0.632
(0.844)
0.177
(0.493)
0.0146
(0.146)
0.0308
(0.199)
0.242
(0.549)
0.134
(0.421)
0.478
(0.742)
0.0318
(0.199)
RACE Fisher's exact test 0.239
(0.549)
0.273
(0.586)
0.307
(0.628)
0.209
(0.539)
0.00901
(0.114)
0.307
(0.628)
0.217
(0.547)
0.33
(0.647)
0.0823
(0.327)
0.0739
(0.313)
ETHNICITY Fisher's exact test 0.225
(0.549)
0.701
(0.857)
0.432
(0.723)
0.473
(0.742)
0.255
(0.566)
0.896
(0.957)
0.674
(0.848)
0.412
(0.715)
0.24
(0.549)
0.217
(0.547)
WEIGHT KG AT DIAGNOSIS Kruskal-Wallis (anova) 0.324
(0.638)
0.244
(0.55)
0.0556
(0.278)
0.542
(0.794)
0.694
(0.856)
0.677
(0.849)
0.551
(0.794)
0.933
(0.984)
0.476
(0.742)
0.479
(0.742)
TUMOR STATUS Fisher's exact test 0.949
(0.986)
0.561
(0.801)
0.674
(0.848)
0.53
(0.789)
0.38
(0.676)
0.425
(0.72)
0.58
(0.814)
0.713
(0.861)
0.556
(0.797)
0.642
(0.844)
NEOPLASM HISTOLOGIC GRADE Fisher's exact test 0.0298
(0.199)
0.375
(0.675)
0.34
(0.65)
0.551
(0.794)
0.00049
(0.0136)
0.157
(0.474)
0.00417
(0.0739)
0.0228
(0.199)
0.00354
(0.0729)
6e-05
(0.00213)
TOBACCO SMOKING YEAR STOPPED Kruskal-Wallis (anova) 0.401
(0.698)
0.497
(0.759)
0.838
(0.93)
0.706
(0.857)
0.836
(0.93)
0.569
(0.81)
0.125
(0.415)
0.147
(0.455)
0.422
(0.72)
0.479
(0.742)
TOBACCO SMOKING PACK YEARS SMOKED Kruskal-Wallis (anova) 0.662
(0.844)
0.112
(0.398)
0.198
(0.515)
0.54
(0.794)
0.106
(0.385)
0.437
(0.723)
0.0659
(0.29)
0.157
(0.474)
0.302
(0.624)
0.0646
(0.29)
TOBACCO SMOKING HISTORY Kruskal-Wallis (anova) 0.338
(0.65)
0.0617
(0.29)
0.802
(0.91)
0.471
(0.742)
0.255
(0.566)
0.0944
(0.364)
0.043
(0.233)
0.669
(0.848)
0.444
(0.726)
0.31
(0.628)
AGEBEGANSMOKINGINYEARS Kruskal-Wallis (anova) 0.527
(0.787)
0.715
(0.861)
0.747
(0.88)
0.808
(0.91)
0.226
(0.549)
0.655
(0.844)
0.892
(0.957)
0.764
(0.89)
0.95
(0.986)
0.4
(0.698)
RADIATION THERAPY STATUS Fisher's exact test 1
(1.00)
1
(1.00)
0.725
(0.87)
1
(1.00)
0.733
(0.871)
1
(1.00)
0.551
(0.794)
0.858
(0.935)
1
(1.00)
0.0332
(0.199)
PREGNANCIES COUNT TOTAL Kruskal-Wallis (anova) 0.669
(0.848)
0.0168
(0.16)
0.472
(0.742)
0.622
(0.844)
0.0261
(0.199)
0.0475
(0.247)
0.506
(0.771)
0.0673
(0.292)
0.494
(0.759)
0.0979
(0.37)
PREGNANCIES COUNT STILLBIRTH Kruskal-Wallis (anova) 0.0563
(0.278)
0.793
(0.904)
0.458
(0.737)
0.224
(0.549)
0.164
(0.487)
0.574
(0.81)
0.756
(0.886)
0.481
(0.742)
0.756
(0.886)
0.657
(0.844)
PREGNANCY SPONTANEOUS ABORTION COUNT Kruskal-Wallis (anova) 0.951
(0.986)
0.629
(0.844)
0.806
(0.91)
0.809
(0.91)
0.772
(0.891)
0.13
(0.415)
0.0206
(0.187)
0.179
(0.493)
0.194
(0.512)
0.159
(0.477)
PREGNANCIES COUNT LIVE BIRTH Kruskal-Wallis (anova) 0.26
(0.567)
0.0333
(0.199)
0.124
(0.415)
0.114
(0.398)
0.0381
(0.209)
0.0188
(0.174)
0.649
(0.844)
0.016
(0.156)
0.957
(0.986)
0.235
(0.549)
PREGNANCY THERAPEUTIC ABORTION COUNT Kruskal-Wallis (anova) 0.854
(0.935)
0.76
(0.887)
0.786
(0.898)
0.655
(0.844)
0.815
(0.913)
0.616
(0.844)
0.462
(0.739)
0.936
(0.984)
0.188
(0.506)
0.323
(0.638)
PREGNANCIES COUNT ECTOPIC Kruskal-Wallis (anova) 0.6
(0.839)
0.291
(0.61)
0.919
(0.976)
0.638
(0.844)
0.192
(0.51)
0.855
(0.935)
0.338
(0.65)
0.958
(0.986)
0.982
(1.00)
0.426
(0.72)
POS LYMPH NODE LOCATION Fisher's exact test 0.177
(0.493)
0.366
(0.666)
0.366
(0.666)
0.251
(0.562)
0.359
(0.661)
0.426
(0.72)
0.0901
(0.352)
0.428
(0.72)
0.397
(0.697)
0.261
(0.567)
MENOPAUSE STATUS Fisher's exact test 0.241
(0.549)
0.0814
(0.327)
0.128
(0.415)
0.873
(0.949)
0.0481
(0.247)
0.0234
(0.199)
0.727
(0.87)
0.0134
(0.144)
0.012
(0.137)
0.29
(0.61)
LYMPHOVASCULAR INVOLVEMENT Fisher's exact test 0.172
(0.493)
0.224
(0.549)
0.0301
(0.199)
0.425
(0.72)
0.819
(0.915)
0.639
(0.844)
0.607
(0.844)
0.378
(0.675)
0.695
(0.856)
0.112
(0.398)
LYMPH NODES EXAMINED HE COUNT Kruskal-Wallis (anova) 0.048
(0.247)
0.0366
(0.207)
0.632
(0.844)
0.177
(0.493)
0.0146
(0.146)
0.0308
(0.199)
0.242
(0.549)
0.134
(0.421)
0.478
(0.742)
0.0318
(0.199)
LYMPH NODES EXAMINED Kruskal-Wallis (anova) 0.742
(0.877)
0.804
(0.91)
0.00935
(0.114)
0.236
(0.549)
0.174
(0.493)
0.342
(0.65)
0.847
(0.933)
0.319
(0.635)
0.642
(0.844)
0.316
(0.634)
KERATINIZATION SQUAMOUS CELL Fisher's exact test 0.524
(0.786)
0.0271
(0.199)
0.0795
(0.325)
0.0376
(0.209)
0.0321
(0.199)
0.00374
(0.0729)
0.0335
(0.199)
0.123
(0.415)
0.00884
(0.114)
0.0641
(0.29)
INITIAL PATHOLOGIC DX YEAR Kruskal-Wallis (anova) 0.63
(0.844)
0.947
(0.986)
0.524
(0.786)
0.841
(0.93)
0.347
(0.657)
0.0135
(0.144)
0.000899
(0.0219)
0.0235
(0.199)
0.00872
(0.114)
0.0245
(0.199)
HISTORY HORMONAL CONTRACEPTIVES USE Fisher's exact test 0.776
(0.892)
0.235
(0.549)
0.697
(0.856)
0.552
(0.794)
0.0953
(0.364)
0.0336
(0.199)
0.731
(0.871)
0.317
(0.634)
0.625
(0.844)
0.662
(0.844)
HEIGHT CM AT DIAGNOSIS Kruskal-Wallis (anova) 0.461
(0.739)
0.573
(0.81)
0.126
(0.415)
0.167
(0.493)
0.291
(0.61)
0.146
(0.455)
0.904
(0.963)
0.686
(0.854)
0.708
(0.857)
0.437
(0.723)
CORPUS INVOLVEMENT Fisher's exact test 0.95
(0.986)
0.17
(0.493)
0.127
(0.415)
0.455
(0.737)
0.0691
(0.296)
0.342
(0.65)
0.577
(0.812)
0.339
(0.65)
0.857
(0.935)
0.359
(0.661)
CHEMO CONCURRENT TYPE Fisher's exact test 0.259
(0.567)
0.628
(0.844)
0.509
(0.772)
0.00359
(0.0729)
0.396
(0.697)
0.117
(0.402)
0.841
(0.93)
0.458
(0.737)
0.895
(0.957)
0.08
(0.325)
CERVIX SUV RESULTS Kruskal-Wallis (anova) 0.768
(0.891)
0.385
(0.683)
0.0662
(0.29)
0.231
(0.549)
0.226
(0.549)
0.367
(0.666)
0.242
(0.549)
0.18
(0.493)
0.19
(0.509)
AGE AT DIAGNOSIS Kruskal-Wallis (anova) 0.0112
(0.132)
0.184
(0.498)
0.0597
(0.285)
0.117
(0.402)
1.34e-05
(0.000522)
0.00655
(0.102)
0.698
(0.856)
9.35e-05
(0.00304)
0.0326
(0.199)
0.00599
(0.102)
CLINICAL STAGE Fisher's exact test 0.0246
(0.199)
0.0591
(0.285)
0.645
(0.844)
0.13
(0.415)
0.0304
(0.199)
0.106
(0.385)
0.00923
(0.114)
0.00075
(0.0195)
0.0295
(0.199)
0.514
(0.778)
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 4 5
Number of samples 15 112 56 59 53
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.682 (logrank test), Q value = 0.85

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

nPatients nDeath Duration Range (Median), Month
ALL 282 68 0.1 - 210.7 (22.6)
subtype1 14 1 1.4 - 101.8 (26.4)
subtype2 108 29 0.4 - 155.8 (20.7)
subtype3 53 11 0.2 - 210.7 (23.0)
subtype4 58 15 0.1 - 177.0 (22.8)
subtype5 49 12 0.1 - 173.3 (25.0)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00931 (Kruskal-Wallis (anova)), Q value = 0.11

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 15 45.8 (12.3)
subtype2 111 45.6 (14.0)
subtype3 56 53.4 (12.4)
subtype4 59 48.0 (12.8)
subtype5 52 48.5 (15.2)

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 = 0.0336 (Fisher's exact test), Q value = 0.2

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 3 8 2 0
subtype2 50 25 6 2
subtype3 21 12 8 4
subtype4 32 17 2 1
subtype5 29 8 3 1

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 = 0.35 (Fisher's exact test), Q value = 0.66

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

nPatients 0 1
ALL 129 56
subtype1 7 3
subtype2 42 22
subtype3 19 13
subtype4 34 9
subtype5 27 9

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 = 0.736 (Fisher's exact test), Q value = 0.87

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

nPatients 0 1
ALL 108 10
subtype1 5 0
subtype2 42 4
subtype3 22 1
subtype4 22 4
subtype5 17 1

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 = 0.0136 (Fisher's exact test), Q value = 0.14

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

nPatients NO YES
ALL 53 125
subtype1 1 8
subtype2 15 52
subtype3 8 31
subtype4 17 19
subtype5 12 15

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 = 1e-05 (Fisher's exact test), Q value = 0.00043

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 1 14 0 0 0 0
subtype2 3 96 2 3 1 7
subtype3 0 56 0 0 0 0
subtype4 2 31 4 13 2 7
subtype5 0 45 0 5 0 3

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

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 6 14.2 (10.2)
subtype2 32 16.7 (12.2)
subtype3 20 22.4 (17.9)
subtype4 14 16.0 (10.8)
subtype5 18 15.7 (16.6)

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 = 0.048 (Kruskal-Wallis (anova)), Q value = 0.25

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 6 0.3 (0.8)
subtype2 48 0.8 (1.3)
subtype3 23 1.7 (2.3)
subtype4 41 0.9 (2.8)
subtype5 32 0.8 (1.8)

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 = 0.239 (Fisher's exact test), Q value = 0.55

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 1 0 0 0 13
subtype2 1 7 11 1 82
subtype3 0 4 9 0 29
subtype4 4 3 4 0 42
subtype5 2 5 4 1 36

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 = 0.225 (Fisher's exact test), Q value = 0.55

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 23 163
subtype1 2 9
subtype2 7 63
subtype3 2 28
subtype4 9 31
subtype5 3 32

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 = 0.324 (Kruskal-Wallis (anova)), Q value = 0.64

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 14 66.3 (15.6)
subtype2 101 71.5 (18.3)
subtype3 48 75.5 (29.4)
subtype4 53 76.3 (16.9)
subtype5 50 73.2 (24.6)

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 = 0.949 (Fisher's exact test), Q value = 0.99

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

nPatients TUMOR FREE WITH TUMOR
ALL 193 76
subtype1 11 3
subtype2 71 31
subtype3 34 14
subtype4 42 14
subtype5 35 14

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 = 0.0298 (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 2 6 7 0 0
subtype2 2 40 56 1 9
subtype3 4 25 22 0 5
subtype4 8 30 16 0 5
subtype5 2 28 14 0 5

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 = 0.401 (Kruskal-Wallis (anova)), Q value = 0.7

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 2 1991.0 (12.7)
subtype2 18 2003.5 (10.6)
subtype3 9 2000.2 (10.3)
subtype4 7 2000.3 (14.4)
subtype5 7 1991.1 (21.3)

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

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 6 14.2 (10.2)
subtype2 32 16.7 (12.2)
subtype3 20 22.4 (17.9)
subtype4 14 16.0 (10.8)
subtype5 18 15.7 (16.6)

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 = 0.338 (Kruskal-Wallis (anova)), Q value = 0.65

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

nPatients Mean (Std.Dev)
ALL 255 1.8 (1.1)
subtype1 13 1.8 (1.1)
subtype2 92 1.9 (1.2)
subtype3 50 1.9 (1.1)
subtype4 55 1.6 (1.0)
subtype5 45 1.9 (1.1)

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 = 0.527 (Kruskal-Wallis (anova)), Q value = 0.79

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 6 21.3 (3.3)
subtype2 31 21.6 (9.4)
subtype3 18 22.8 (8.0)
subtype4 14 18.3 (5.2)
subtype5 15 21.2 (6.5)

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

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 1 0
subtype2 10 2
subtype3 4 1
subtype4 5 0
subtype5 5 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 = 0.669 (Kruskal-Wallis (anova)), Q value = 0.85

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 14 3.9 (2.7)
subtype2 93 3.8 (2.9)
subtype3 51 3.8 (2.5)
subtype4 51 3.0 (1.9)
subtype5 46 3.5 (2.3)

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 = 0.0563 (Kruskal-Wallis (anova)), Q value = 0.28

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 6 0.0 (0.0)
subtype2 35 0.0 (0.0)
subtype3 19 0.3 (0.7)
subtype4 25 0.1 (0.3)
subtype5 21 0.0 (0.0)

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 = 0.951 (Kruskal-Wallis (anova)), Q value = 0.99

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 8 0.4 (0.7)
subtype2 47 0.5 (0.9)
subtype3 28 0.6 (1.2)
subtype4 30 0.6 (1.0)
subtype5 27 0.5 (0.8)

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 = 0.26 (Kruskal-Wallis (anova)), Q value = 0.57

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 14 3.4 (2.6)
subtype2 96 3.0 (2.1)
subtype3 47 3.1 (2.2)
subtype4 47 2.3 (1.4)
subtype5 47 2.5 (1.7)

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 = 0.854 (Kruskal-Wallis (anova)), Q value = 0.93

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 6 0.3 (0.5)
subtype2 39 1.0 (2.4)
subtype3 20 0.7 (1.1)
subtype4 28 0.7 (1.1)
subtype5 23 0.9 (1.2)

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 = 0.6 (Kruskal-Wallis (anova)), Q value = 0.84

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 6 0.0 (0.0)
subtype2 37 0.1 (0.3)
subtype3 20 0.2 (0.5)
subtype4 25 0.0 (0.2)
subtype5 22 0.1 (0.4)

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 = 0.177 (Fisher's exact test), Q value = 0.49

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 2 7 36 1 10
subtype1 1 0 1 0 0
subtype2 0 4 11 0 1
subtype3 0 1 8 0 3
subtype4 1 2 9 0 1
subtype5 0 0 7 1 5

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 = 0.241 (Fisher's exact test), Q value = 0.55

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 3 25 82 121
subtype1 0 0 3 10
subtype2 1 11 24 46
subtype3 0 4 25 17
subtype4 1 5 19 28
subtype5 1 5 11 20

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 = 0.172 (Fisher's exact test), Q value = 0.49

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

nPatients ABSENT PRESENT
ALL 71 74
subtype1 2 3
subtype2 25 23
subtype3 12 15
subtype4 23 14
subtype5 9 19

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 = 0.048 (Kruskal-Wallis (anova)), Q value = 0.25

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 6 0.3 (0.8)
subtype2 48 0.8 (1.3)
subtype3 23 1.7 (2.3)
subtype4 41 0.9 (2.8)
subtype5 32 0.8 (1.8)

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 = 0.742 (Kruskal-Wallis (anova)), Q value = 0.88

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 8 26.0 (13.0)
subtype2 56 21.3 (12.4)
subtype3 25 23.3 (15.1)
subtype4 43 23.5 (12.9)
subtype5 38 21.1 (11.9)

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 = 0.524 (Fisher's exact test), Q value = 0.79

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 5 6
subtype2 16 50
subtype3 12 21
subtype4 8 18
subtype5 8 22

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 = 0.63 (Kruskal-Wallis (anova)), Q value = 0.84

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 15 2009.7 (3.9)
subtype2 111 2008.3 (4.6)
subtype3 55 2009.1 (4.2)
subtype4 59 2008.6 (4.8)
subtype5 53 2007.9 (5.2)

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 = 0.776 (Fisher's exact test), Q value = 0.89

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 0 4 4
subtype2 5 18 33
subtype3 1 10 17
subtype4 6 12 20
subtype5 3 10 11

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 = 0.461 (Kruskal-Wallis (anova)), Q value = 0.74

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 14 158.7 (10.6)
subtype2 97 161.3 (6.7)
subtype3 46 160.7 (6.2)
subtype4 50 160.3 (6.4)
subtype5 46 162.8 (7.3)

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 = 0.95 (Fisher's exact test), Q value = 0.99

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

nPatients ABSENT PRESENT
ALL 92 18
subtype1 3 1
subtype2 30 6
subtype3 15 2
subtype4 25 5
subtype5 19 4

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 = 0.259 (Fisher's exact test), Q value = 0.57

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 0 8 0
subtype2 1 45 1
subtype3 1 21 0
subtype4 3 16 0
subtype5 0 10 1

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 = 0.768 (Kruskal-Wallis (anova)), Q value = 0.89

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 1 18.5 (NA)
subtype2 7 13.9 (7.9)
subtype3 4 12.1 (3.3)
subtype4 3 15.6 (11.8)
subtype5 2 6.8 (0.4)

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 = 0.0112 (Kruskal-Wallis (anova)), Q value = 0.13

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 15 45.8 (12.3)
subtype2 112 45.8 (14.0)
subtype3 56 53.4 (12.4)
subtype4 59 48.0 (12.8)
subtype5 53 48.8 (15.1)

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 = 0.0246 (Fisher's exact test), Q value = 0.2

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 0 0 0 0 1 2 2 0 2 0 0 5 0 1 2 0 0
subtype2 2 1 0 0 16 23 14 1 3 4 2 22 0 1 15 2 3
subtype3 1 0 0 1 3 14 3 0 1 0 1 7 1 1 15 4 2
subtype4 1 0 0 0 5 17 13 1 2 0 3 7 0 0 6 0 4
subtype5 1 0 1 0 9 21 5 2 0 1 1 3 0 0 4 1 3

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
Number of samples 72 122 113
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.967 (logrank test), Q value = 0.99

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

nPatients nDeath Duration Range (Median), Month
ALL 294 72 0.1 - 210.7 (23.2)
subtype1 70 17 0.1 - 147.4 (20.6)
subtype2 117 30 0.1 - 210.7 (20.9)
subtype3 107 25 0.4 - 177.0 (24.8)

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 = 0.174 (Kruskal-Wallis (anova)), Q value = 0.49

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 72 47.9 (12.1)
subtype2 121 50.1 (14.5)
subtype3 112 46.4 (13.9)

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 = 0.422 (Fisher's exact test), Q value = 0.72

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 39 18 4 1
subtype2 47 24 12 5
subtype3 55 30 5 4

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 = 0.232 (Fisher's exact test), Q value = 0.55

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

nPatients 0 1
ALL 135 60
subtype1 36 13
subtype2 41 26
subtype3 58 21

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 = 0.36 (Fisher's exact test), Q value = 0.66

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

nPatients 0 1
ALL 116 10
subtype1 24 4
subtype2 37 2
subtype3 55 4

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 = 0.773 (Fisher's exact test), Q value = 0.89

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

nPatients NO YES
ALL 55 129
subtype1 16 31
subtype2 22 53
subtype3 17 45

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 = 0.00043

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 3 28 6 19 2 14
subtype2 0 122 0 0 0 0
subtype3 3 104 0 2 1 3

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 = 0.112 (Kruskal-Wallis (anova)), Q value = 0.4

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 19 16.4 (15.4)
subtype2 43 20.9 (15.8)
subtype3 31 13.0 (9.1)

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 = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.21

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 42 1.0 (2.8)
subtype2 54 1.4 (2.8)
subtype3 63 0.7 (1.7)

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

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 7 4 0 52
subtype2 4 4 11 2 85
subtype3 3 9 15 0 74

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.701 (Fisher's exact test), Q value = 0.86

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 171
subtype1 7 37
subtype2 9 68
subtype3 8 66

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 = 0.244 (Kruskal-Wallis (anova)), Q value = 0.55

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 76.2 (22.7)
subtype2 116 73.0 (20.1)
subtype3 97 70.9 (22.4)

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 = 0.561 (Fisher's exact test), Q value = 0.8

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

nPatients TUMOR FREE WITH TUMOR
ALL 201 80
subtype1 46 21
subtype2 76 33
subtype3 79 26

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 = 0.375 (Fisher's exact test), Q value = 0.68

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 27 31 0 6
subtype2 6 62 40 0 10
subtype3 5 47 49 1 8

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 = 0.497 (Kruskal-Wallis (anova)), Q value = 0.76

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 1997.8 (11.8)
subtype2 21 1998.3 (16.4)
subtype3 13 2003.3 (9.5)

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 = 0.112 (Kruskal-Wallis (anova)), Q value = 0.4

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 19 16.4 (15.4)
subtype2 43 20.9 (15.8)
subtype3 31 13.0 (9.1)

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 = 0.0617 (Kruskal-Wallis (anova)), Q value = 0.29

Table S58.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 263 1.8 (1.1)
subtype1 64 1.6 (1.1)
subtype2 99 2.0 (1.2)
subtype3 100 1.8 (1.2)

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

'METHLYATION CNMF' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.715 (Kruskal-Wallis (anova)), Q value = 0.86

Table S59.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 17 21.4 (6.4)
subtype2 39 21.4 (9.1)
subtype3 29 20.6 (6.5)

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY_STATUS'

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

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 7 0
subtype2 8 1
subtype3 14 2

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 = 0.0168 (Kruskal-Wallis (anova)), Q value = 0.16

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 64 3.0 (2.0)
subtype2 103 4.2 (3.0)
subtype3 100 3.4 (2.3)

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 = 0.793 (Kruskal-Wallis (anova)), Q value = 0.9

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 26 0.0 (0.2)
subtype2 44 0.0 (0.2)
subtype3 42 0.1 (0.5)

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 = 0.629 (Kruskal-Wallis (anova)), Q value = 0.84

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 30 0.6 (0.9)
subtype2 60 0.6 (1.2)
subtype3 57 0.4 (0.6)

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 = 0.0333 (Kruskal-Wallis (anova)), Q value = 0.2

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 2.4 (1.9)
subtype2 102 3.2 (2.2)
subtype3 99 2.7 (1.9)

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 = 0.76 (Kruskal-Wallis (anova)), Q value = 0.89

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 28 0.5 (0.7)
subtype2 48 0.9 (2.2)
subtype3 46 1.0 (1.8)

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 = 0.291 (Kruskal-Wallis (anova)), Q value = 0.61

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 25 0.1 (0.3)
subtype2 47 0.1 (0.2)
subtype3 44 0.2 (0.4)

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 = 0.366 (Fisher's exact test), Q value = 0.67

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 2 8 40 1 12
subtype1 2 2 10 0 2
subtype2 0 3 13 1 7
subtype3 0 3 17 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 = 0.0814 (Fisher's exact test), Q value = 0.33

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 3 25 84 125
subtype1 0 10 20 30
subtype2 2 10 37 39
subtype3 1 5 27 56

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

'METHLYATION CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.224 (Fisher's exact test), Q value = 0.55

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

nPatients ABSENT PRESENT
ALL 72 80
subtype1 20 22
subtype2 20 32
subtype3 32 26

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 = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.21

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 42 1.0 (2.8)
subtype2 54 1.4 (2.8)
subtype3 63 0.7 (1.7)

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 = 0.804 (Kruskal-Wallis (anova)), Q value = 0.91

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 46 22.2 (10.2)
subtype2 62 22.8 (14.1)
subtype3 72 21.9 (12.7)

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 = 0.0271 (Fisher's exact test), Q value = 0.2

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 1 17
subtype2 31 55
subtype3 23 48

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 = 0.947 (Kruskal-Wallis (anova)), Q value = 0.99

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 71 2008.7 (4.1)
subtype2 121 2008.2 (4.9)
subtype3 113 2008.2 (5.0)

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 = 0.235 (Fisher's exact test), Q value = 0.55

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 17 19
subtype2 3 20 37
subtype3 5 17 34

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 = 0.573 (Kruskal-Wallis (anova)), Q value = 0.81

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 62 161.4 (7.1)
subtype2 110 161.4 (7.0)
subtype3 92 160.1 (7.8)

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 = 0.17 (Fisher's exact test), Q value = 0.49

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

nPatients ABSENT PRESENT
ALL 99 19
subtype1 33 4
subtype2 32 4
subtype3 34 11

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 = 0.628 (Fisher's exact test), Q value = 0.84

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

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 104 2
subtype1 2 20 1
subtype2 3 40 0
subtype3 2 44 1

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 = 0.385 (Kruskal-Wallis (anova)), Q value = 0.68

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 2 10.8 (5.2)
subtype2 7 15.3 (7.2)
subtype3 8 12.0 (7.9)

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 = 0.184 (Kruskal-Wallis (anova)), Q value = 0.5

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 72 47.9 (12.1)
subtype2 122 50.1 (14.5)
subtype3 113 46.5 (13.9)

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 = 0.0591 (Fisher's exact test), Q value = 0.29

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 28 12 1 3 1 0 9 0 0 8 0 4
subtype2 4 0 1 1 12 23 13 3 2 0 4 17 1 2 22 6 5
subtype3 0 1 0 0 21 27 14 1 4 4 3 18 0 1 12 3 3

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
Number of samples 60 45 68
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0599 (logrank test), Q value = 0.29

Table S82.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 164 31 0.1 - 210.7 (22.8)
subtype1 57 14 0.1 - 144.2 (18.0)
subtype2 41 6 0.4 - 173.3 (27.2)
subtype3 66 11 0.1 - 210.7 (24.3)

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 = 0.0786 (Kruskal-Wallis (anova)), Q value = 0.32

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 60 44.4 (12.5)
subtype2 44 49.4 (14.0)
subtype3 67 49.1 (13.8)

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 = 0.891 (Fisher's exact test), Q value = 0.96

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 34 10 1 1
subtype2 26 7 3 1
subtype3 39 15 3 1

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 = 0.661 (Fisher's exact test), Q value = 0.84

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

nPatients 0 1
ALL 93 38
subtype1 33 11
subtype2 22 8
subtype3 38 19

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 = 0.55 (Fisher's exact test), Q value = 0.79

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

nPatients 0 1
ALL 84 4
subtype1 28 2
subtype2 25 0
subtype3 31 2

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 = 0.611 (Fisher's exact test), Q value = 0.84

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

nPatients NO YES
ALL 26 60
subtype1 7 22
subtype2 7 17
subtype3 12 21

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 = 0.311 (Fisher's exact test), Q value = 0.63

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 3 51 1 3 0 2
subtype2 0 41 0 3 0 1
subtype3 1 52 2 10 2 1

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 = 0.198 (Kruskal-Wallis (anova)), Q value = 0.51

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 12 12.8 (8.3)
subtype2 12 14.2 (11.0)
subtype3 21 22.5 (16.1)

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 = 0.632 (Kruskal-Wallis (anova)), Q value = 0.84

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 33 0.9 (2.3)
subtype2 23 1.2 (2.1)
subtype3 49 1.3 (3.2)

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 = 0.307 (Fisher's exact test), Q value = 0.63

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 1 4 7 1 42
subtype2 1 7 1 0 32
subtype3 1 4 4 0 51

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.432 (Fisher's exact test), Q value = 0.72

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 3 43
subtype2 5 27
subtype3 5 43

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 = 0.0556 (Kruskal-Wallis (anova)), Q value = 0.28

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 52 68.7 (17.3)
subtype2 42 75.1 (17.5)
subtype3 64 79.2 (29.0)

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 = 0.674 (Fisher's exact test), Q value = 0.85

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

nPatients TUMOR FREE WITH TUMOR
ALL 119 36
subtype1 40 14
subtype2 32 7
subtype3 47 15

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 = 0.34 (Fisher's exact test), Q value = 0.65

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 26 25 1 4
subtype2 2 23 14 0 3
subtype3 5 26 35 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 = 0.838 (Kruskal-Wallis (anova)), Q value = 0.93

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 9 2003.9 (7.6)
subtype2 9 2001.0 (10.4)
subtype3 13 2000.3 (13.1)

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 = 0.198 (Kruskal-Wallis (anova)), Q value = 0.51

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 12 12.8 (8.3)
subtype2 12 14.2 (11.0)
subtype3 21 22.5 (16.1)

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 = 0.802 (Kruskal-Wallis (anova)), Q value = 0.91

Table S98.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 145 1.9 (1.2)
subtype1 49 1.9 (1.3)
subtype2 40 1.9 (1.3)
subtype3 56 1.9 (1.2)

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 = 0.747 (Kruskal-Wallis (anova)), Q value = 0.88

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

nPatients Mean (Std.Dev)
ALL 44 21.5 (7.9)
subtype1 11 21.9 (8.2)
subtype2 11 20.8 (8.1)
subtype3 22 21.7 (8.0)

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 = 0.725 (Fisher's exact test), Q value = 0.87

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 9 1
subtype2 5 1
subtype3 7 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 = 0.472 (Kruskal-Wallis (anova)), Q value = 0.74

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 48 3.4 (2.3)
subtype2 44 4.3 (3.4)
subtype3 59 3.3 (2.0)

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 = 0.458 (Kruskal-Wallis (anova)), Q value = 0.74

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 26 0.1 (0.3)
subtype2 17 0.0 (0.0)
subtype3 29 0.0 (0.2)

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 = 0.806 (Kruskal-Wallis (anova)), Q value = 0.91

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 29 0.3 (0.5)
subtype2 24 0.5 (1.1)
subtype3 36 0.5 (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 = 0.124 (Kruskal-Wallis (anova)), Q value = 0.41

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 50 2.4 (2.1)
subtype2 43 3.2 (2.2)
subtype3 58 2.4 (1.3)

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 = 0.786 (Kruskal-Wallis (anova)), Q value = 0.9

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 30 0.9 (1.3)
subtype2 19 1.8 (3.6)
subtype3 32 0.9 (1.6)

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 = 0.919 (Kruskal-Wallis (anova)), Q value = 0.98

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 26 0.1 (0.3)
subtype2 18 0.1 (0.3)
subtype3 30 0.1 (0.4)

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 = 0.366 (Fisher's exact test), Q value = 0.67

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 2 7 25 1 7
subtype1 2 1 5 1 2
subtype2 0 1 7 0 1
subtype3 0 5 13 0 4

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 = 0.128 (Fisher's exact test), Q value = 0.42

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 3 13 46 79
subtype1 0 2 13 35
subtype2 1 6 12 17
subtype3 2 5 21 27

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 = 0.0301 (Fisher's exact test), Q value = 0.2

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

nPatients ABSENT PRESENT
ALL 52 60
subtype1 22 15
subtype2 15 13
subtype3 15 32

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 = 0.632 (Kruskal-Wallis (anova)), Q value = 0.84

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 33 0.9 (2.3)
subtype2 23 1.2 (2.1)
subtype3 49 1.3 (3.2)

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 = 0.00935 (Kruskal-Wallis (anova)), Q value = 0.11

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 41 20.7 (11.3)
subtype2 25 14.8 (8.1)
subtype3 56 23.5 (12.8)

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 = 0.0795 (Fisher's exact test), Q value = 0.32

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 15 29
subtype2 6 25
subtype3 18 22

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 = 0.524 (Kruskal-Wallis (anova)), Q value = 0.79

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 59 2008.5 (5.1)
subtype2 45 2008.5 (4.7)
subtype3 67 2007.8 (5.1)

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 = 0.697 (Fisher's exact test), Q value = 0.86

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 2 10 16
subtype2 0 8 16
subtype3 3 14 19

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 = 0.126 (Kruskal-Wallis (anova)), Q value = 0.41

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 51 162.4 (6.6)
subtype2 43 160.2 (8.0)
subtype3 59 162.1 (6.8)

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 = 0.127 (Fisher's exact test), Q value = 0.41

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

nPatients ABSENT PRESENT
ALL 84 12
subtype1 27 3
subtype2 26 1
subtype3 31 8

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 = 0.509 (Fisher's exact test), Q value = 0.77

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 18 1
subtype2 1 15 0
subtype3 2 15 0

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

'RPPA CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0662 (Kruskal-Wallis (anova)), Q value = 0.29

Table S118.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 11 13.2 (8.7)
subtype1 4 16.1 (7.3)
subtype2 2 23.6 (7.3)
subtype3 5 6.6 (4.4)

Figure S115.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'RPPA CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0597 (Kruskal-Wallis (anova)), Q value = 0.29

Table S119.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 47.7 (13.5)
subtype1 60 44.4 (12.5)
subtype2 45 49.7 (14.0)
subtype3 68 49.2 (13.7)

Figure S116.  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 = 0.645 (Fisher's exact test), Q value = 0.84

Table S120.  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 1 1 1 0 8 15 13 0 2 1 1 8 0 0 6 1 1
subtype2 2 0 0 1 8 12 4 3 1 0 0 4 0 1 7 2 0
subtype3 1 0 0 0 11 21 11 1 2 3 3 4 1 0 6 1 1

Figure S117.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S121.  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 = 0.183 (logrank test), Q value = 0.5

Table S122.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 164 31 0.1 - 210.7 (22.8)
subtype1 31 6 0.4 - 144.2 (18.0)
subtype2 48 7 0.1 - 210.7 (22.6)
subtype3 48 8 0.4 - 209.6 (26.7)
subtype4 21 7 0.9 - 78.7 (24.6)
subtype5 16 3 0.1 - 147.4 (25.1)

Figure S118.  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 = 0.35

Table S123.  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 S119.  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 = 0.645 (Fisher's exact test), Q value = 0.84

Table S124.  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 S120.  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 = 0.213 (Fisher's exact test), Q value = 0.54

Table S125.  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 S121.  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 S126.  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 S122.  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 = 0.303 (Fisher's exact test), Q value = 0.62

Table S127.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 26 60
subtype1 7 10
subtype2 9 16
subtype3 5 25
subtype4 3 6
subtype5 2 3

Figure S123.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00394 (Fisher's exact test), Q value = 0.073

Table S128.  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 S124.  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 = 0.79

Table S129.  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 S125.  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 = 0.49

Table S130.  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 S126.  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 = 0.209 (Fisher's exact test), Q value = 0.54

Table S131.  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 S127.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.473 (Fisher's exact test), Q value = 0.74

Table S132.  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 S128.  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 = 0.79

Table S133.  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 S129.  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 = 0.53 (Fisher's exact test), Q value = 0.79

Table S134.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 119 36
subtype1 24 6
subtype2 36 9
subtype3 36 9
subtype4 13 7
subtype5 10 5

Figure S130.  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 = 0.551 (Fisher's exact test), Q value = 0.79

Table S135.  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 S131.  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 = 0.86

Table S136.  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 S132.  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 = 0.79

Table S137.  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 S133.  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 = 0.471 (Kruskal-Wallis (anova)), Q value = 0.74

Table S138.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 145 1.9 (1.2)
subtype1 27 1.6 (1.0)
subtype2 39 2.0 (1.3)
subtype3 44 2.0 (1.3)
subtype4 19 1.8 (1.2)
subtype5 16 1.6 (1.1)

Figure S134.  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 = 0.91

Table S139.  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 S135.  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 S140.  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 S136.  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 = 0.84

Table S141.  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 S137.  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 S142.  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 S138.  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 = 0.91

Table S143.  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 S139.  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 = 0.4

Table S144.  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 S140.  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 = 0.84

Table S145.  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 S141.  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 = 0.84

Table S146.  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 S142.  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 = 0.251 (Fisher's exact test), Q value = 0.56

Table S147.  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 2 7 25 1 7
subtype1 2 1 3 0 0
subtype2 0 4 10 0 3
subtype3 0 2 7 0 2
subtype4 0 0 3 1 2
subtype5 0 0 2 0 0

Figure S143.  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 = 0.873 (Fisher's exact test), Q value = 0.95

Table S148.  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 3 13 46 79
subtype1 0 2 7 19
subtype2 1 3 15 23
subtype3 2 6 13 18
subtype4 0 1 5 12
subtype5 0 1 6 7

Figure S144.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RPPA cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.425 (Fisher's exact test), Q value = 0.72

Table S149.  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 S145.  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 = 0.49

Table S150.  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 S146.  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 = 0.55

Table S151.  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 S147.  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 = 0.0376 (Fisher's exact test), Q value = 0.21

Table S152.  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 S148.  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 = 0.93

Table S153.  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 S149.  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 = 0.552 (Fisher's exact test), Q value = 0.79

Table S154.  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 S150.  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 = 0.49

Table S155.  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 S151.  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 = 0.455 (Fisher's exact test), Q value = 0.74

Table S156.  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 S152.  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 = 0.00359 (Fisher's exact test), Q value = 0.073

Table S157.  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 S153.  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 = 0.4

Table S158.  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 S154.  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 = 0.13 (Fisher's exact test), Q value = 0.42

Table S159.  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 S155.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S160.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 61 103 69 71
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.44 (logrank test), Q value = 0.72

Table S161.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 291 72 0.1 - 210.7 (23.5)
subtype1 58 12 0.1 - 146.9 (21.8)
subtype2 98 24 0.2 - 210.7 (26.7)
subtype3 68 15 0.4 - 147.4 (25.0)
subtype4 67 21 0.1 - 154.3 (18.7)

Figure S156.  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 = 8.51e-06 (Kruskal-Wallis (anova)), Q value = 0.00043

Table S162.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 302 48.2 (13.9)
subtype1 61 53.2 (12.6)
subtype2 102 50.7 (14.9)
subtype3 69 46.2 (12.1)
subtype4 70 41.9 (12.4)

Figure S157.  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 = 0.661 (Fisher's exact test), Q value = 0.84

Table S163.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 140 71 20 10
subtype1 24 17 5 3
subtype2 47 25 8 4
subtype3 40 14 2 1
subtype4 29 15 5 2

Figure S158.  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 = 0.377 (Fisher's exact test), Q value = 0.68

Table S164.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 133 60
subtype1 26 13
subtype2 44 26
subtype3 35 10
subtype4 28 11

Figure S159.  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 = 0.267 (Fisher's exact test), Q value = 0.58

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

nPatients 0 1
ALL 116 10
subtype1 25 4
subtype2 43 1
subtype3 23 2
subtype4 25 3

Figure S160.  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 = 0.888 (Fisher's exact test), Q value = 0.96

Table S166.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 127
subtype1 9 26
subtype2 16 39
subtype3 15 30
subtype4 15 32

Figure S161.  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 = 0.00043

Table S167.  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 50 2 3 1 2
subtype2 0 103 0 0 0 0
subtype3 2 28 4 18 2 15
subtype4 0 71 0 0 0 0

Figure S162.  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 = 0.106 (Kruskal-Wallis (anova)), Q value = 0.39

Table S168.  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 19 22.4 (17.0)
subtype2 37 19.7 (15.0)
subtype3 17 12.3 (10.6)
subtype4 20 12.6 (9.4)

Figure S163.  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 = 0.0146 (Kruskal-Wallis (anova)), Q value = 0.15

Table S169.  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 32 1.2 (2.9)
subtype2 57 1.5 (2.3)
subtype3 44 0.8 (2.7)
subtype4 26 0.3 (0.6)

Figure S164.  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 = 0.00901 (Fisher's exact test), Q value = 0.11

Table S170.  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 9 0 38
subtype2 1 7 10 0 69
subtype3 0 2 1 0 55
subtype4 4 4 10 2 47

Figure S165.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.255 (Fisher's exact test), Q value = 0.57

Table S171.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 169
subtype1 2 39
subtype2 8 59
subtype3 5 32
subtype4 9 39

Figure S166.  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 = 0.694 (Kruskal-Wallis (anova)), Q value = 0.86

Table S172.  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 55 72.3 (24.8)
subtype2 93 73.3 (25.0)
subtype3 63 73.3 (15.8)
subtype4 64 72.4 (18.1)

Figure S167.  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 = 0.38 (Fisher's exact test), Q value = 0.68

Table S173.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 198 80
subtype1 36 21
subtype2 69 22
subtype3 48 17
subtype4 45 20

Figure S168.  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 = 0.00049 (Fisher's exact test), Q value = 0.014

Table S174.  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 32 1 2
subtype2 7 62 26 0 6
subtype3 7 30 25 0 7
subtype4 2 21 35 0 9

Figure S169.  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 = 0.836 (Kruskal-Wallis (anova)), Q value = 0.93

Table S175.  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 8 1999.2 (11.7)
subtype2 17 1997.5 (16.6)
subtype3 11 2001.5 (12.6)
subtype4 7 2002.6 (10.6)

Figure S170.  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 = 0.106 (Kruskal-Wallis (anova)), Q value = 0.39

Table S176.  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 19 22.4 (17.0)
subtype2 37 19.7 (15.0)
subtype3 17 12.3 (10.6)
subtype4 20 12.6 (9.4)

Figure S171.  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 = 0.255 (Kruskal-Wallis (anova)), Q value = 0.57

Table S177.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 261 1.8 (1.1)
subtype1 50 2.0 (1.2)
subtype2 92 1.9 (1.2)
subtype3 60 1.7 (1.2)
subtype4 59 1.7 (1.1)

Figure S172.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'RNAseq CNMF subtypes' versus 'AGEBEGANSMOKINGINYEARS'

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

Table S178.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 17 23.3 (7.5)
subtype2 36 21.4 (8.3)
subtype3 15 19.9 (4.4)
subtype4 17 19.4 (8.7)

Figure S173.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.733 (Fisher's exact test), Q value = 0.87

Table S179.  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 S174.  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 = 0.0261 (Kruskal-Wallis (anova)), Q value = 0.2

Table S180.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 264 3.6 (2.6)
subtype1 51 3.4 (2.4)
subtype2 91 4.1 (2.7)
subtype3 61 3.0 (2.1)
subtype4 61 3.7 (2.8)

Figure S175.  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 S181.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 19 0.0 (0.0)
subtype2 46 0.2 (0.5)
subtype3 26 0.0 (0.2)
subtype4 21 0.0 (0.0)

Figure S176.  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 = 0.772 (Kruskal-Wallis (anova)), Q value = 0.89

Table S182.  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 25 0.5 (1.1)
subtype2 62 0.5 (1.0)
subtype3 28 0.5 (0.8)
subtype4 32 0.6 (0.9)

Figure S177.  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 = 0.0381 (Kruskal-Wallis (anova)), Q value = 0.21

Table S183.  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 52 2.6 (1.7)
subtype2 89 3.1 (1.9)
subtype3 59 2.4 (2.1)
subtype4 60 3.1 (2.4)

Figure S178.  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 = 0.815 (Kruskal-Wallis (anova)), Q value = 0.91

Table S184.  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 21 0.8 (1.4)
subtype2 51 1.2 (2.5)
subtype3 27 0.6 (0.8)
subtype4 23 0.5 (0.9)

Figure S179.  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 = 0.192 (Kruskal-Wallis (anova)), Q value = 0.51

Table S185.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 20 0.2 (0.4)
subtype2 49 0.1 (0.4)
subtype3 26 0.1 (0.3)
subtype4 21 0.0 (0.0)

Figure S180.  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 = 0.359 (Fisher's exact test), Q value = 0.66

Table S186.  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 2 8 40 1 12
subtype1 1 1 5 0 4
subtype2 0 2 16 1 5
subtype3 1 2 10 0 3
subtype4 0 3 9 0 0

Figure S181.  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 = 0.0481 (Fisher's exact test), Q value = 0.25

Table S187.  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 3 25 82 124
subtype1 0 6 22 23
subtype2 2 8 34 37
subtype3 1 7 19 30
subtype4 0 4 7 34

Figure S182.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'RNAseq CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.819 (Fisher's exact test), Q value = 0.92

Table S188.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 71 79
subtype1 15 19
subtype2 25 29
subtype3 18 21
subtype4 13 10

Figure S183.  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 = 0.0146 (Kruskal-Wallis (anova)), Q value = 0.15

Table S189.  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 32 1.2 (2.9)
subtype2 57 1.5 (2.3)
subtype3 44 0.8 (2.7)
subtype4 26 0.3 (0.6)

Figure S184.  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 = 0.174 (Kruskal-Wallis (anova)), Q value = 0.49

Table S190.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 178 22.4 (12.6)
subtype1 37 19.5 (12.6)
subtype2 60 23.0 (14.4)
subtype3 49 24.0 (11.1)
subtype4 32 22.0 (10.9)

Figure S185.  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 = 0.0321 (Fisher's exact test), Q value = 0.2

Table S191.  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 22
subtype2 21 53
subtype3 1 16
subtype4 18 28

Figure S186.  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 = 0.347 (Kruskal-Wallis (anova)), Q value = 0.66

Table S192.  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 61 2009.1 (4.0)
subtype2 102 2008.0 (5.4)
subtype3 68 2008.7 (4.1)
subtype4 71 2007.6 (4.9)

Figure S187.  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 = 0.0953 (Fisher's exact test), Q value = 0.36

Table S193.  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 13 20
subtype2 3 15 31
subtype3 8 15 18
subtype4 4 10 20

Figure S188.  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 = 0.291 (Kruskal-Wallis (anova)), Q value = 0.61

Table S194.  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 52 159.7 (6.7)
subtype2 88 160.5 (7.3)
subtype3 61 162.2 (7.3)
subtype4 60 161.7 (6.9)

Figure S189.  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 = 0.0691 (Fisher's exact test), Q value = 0.3

Table S195.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 19 6
subtype2 32 9
subtype3 28 4
subtype4 20 0

Figure S190.  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 = 0.396 (Fisher's exact test), Q value = 0.7

Table S196.  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 S191.  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 S197.  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 S192.  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 = 1.34e-05 (Kruskal-Wallis (anova)), Q value = 0.00052

Table S198.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 304 48.2 (13.8)
subtype1 61 53.2 (12.6)
subtype2 103 50.8 (14.9)
subtype3 69 46.2 (12.1)
subtype4 71 42.2 (12.5)

Figure S193.  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 = 0.0304 (Fisher's exact test), Q value = 0.2

Table S199.  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 9 1 1 3 4 7 0 1 8 2 3
subtype2 1 0 0 1 15 22 9 4 2 1 2 12 1 0 20 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 1 1 14 0 1 9 2 1

Figure S194.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S200.  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 = 0.0302 (logrank test), Q value = 0.2

Table S201.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 291 72 0.1 - 210.7 (23.5)
subtype1 70 17 0.5 - 137.2 (20.6)
subtype2 185 42 0.1 - 210.7 (24.6)
subtype3 36 13 0.4 - 99.9 (17.2)

Figure S195.  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 S202.  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 S196.  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 = 0.704 (Fisher's exact test), Q value = 0.86

Table S203.  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 S197.  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 = 0.349 (Fisher's exact test), Q value = 0.66

Table S204.  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 S198.  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 = 0.0524 (Fisher's exact test), Q value = 0.27

Table S205.  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 S199.  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 = 1 (Fisher's exact test), Q value = 1

Table S206.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 127
subtype1 14 33
subtype2 35 79
subtype3 6 15

Figure S200.  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 = 0.00043

Table S207.  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 S201.  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 = 0.72

Table S208.  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 S202.  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 = 0.2

Table S209.  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 S203.  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 = 0.63

Table S210.  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 S204.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

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

Table S211.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 169
subtype1 6 38
subtype2 16 109
subtype3 2 22

Figure S205.  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 = 0.85

Table S212.  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 S206.  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 = 0.425 (Fisher's exact test), Q value = 0.72

Table S213.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 198 80
subtype1 45 22
subtype2 130 46
subtype3 23 12

Figure S207.  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 = 0.157 (Fisher's exact test), Q value = 0.47

Table S214.  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 S208.  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 = 0.81

Table S215.  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 S209.  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 = 0.72

Table S216.  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 S210.  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 = 0.0944 (Kruskal-Wallis (anova)), Q value = 0.36

Table S217.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 261 1.8 (1.1)
subtype1 65 1.6 (1.1)
subtype2 166 1.9 (1.2)
subtype3 30 1.9 (1.2)

Figure S211.  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 = 0.84

Table S218.  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 S212.  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 S219.  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 S213.  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 = 0.25

Table S220.  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 S214.  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 = 0.81

Table S221.  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 S215.  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 = 0.42

Table S222.  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 S216.  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 S223.  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 S217.  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 = 0.84

Table S224.  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 S218.  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 = 0.93

Table S225.  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 S219.  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 = 0.426 (Fisher's exact test), Q value = 0.72

Table S226.  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 2 8 40 1 12
subtype1 2 1 9 0 2
subtype2 0 5 26 1 9
subtype3 0 2 5 0 1

Figure S220.  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 = 0.0234 (Fisher's exact test), Q value = 0.2

Table S227.  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 3 25 82 124
subtype1 0 8 19 34
subtype2 2 15 61 72
subtype3 1 2 2 18

Figure S221.  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 = 0.84

Table S228.  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 S222.  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 = 0.2

Table S229.  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 S223.  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 = 0.65

Table S230.  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 S224.  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 = 0.00374 (Fisher's exact test), Q value = 0.073

Table S231.  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 S225.  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 = 0.14

Table S232.  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 S226.  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 = 0.0336 (Fisher's exact test), Q value = 0.2

Table S233.  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 S227.  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 = 0.46

Table S234.  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 S228.  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 = 0.342 (Fisher's exact test), Q value = 0.65

Table S235.  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 S229.  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 = 0.4

Table S236.  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 S230.  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 S237.  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 S231.  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 = 0.1

Table S238.  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 S232.  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 = 0.106 (Fisher's exact test), Q value = 0.39

Table S239.  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 S233.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S240.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 108 78 121
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.889 (logrank test), Q value = 0.96

Table S241.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 294 72 0.1 - 210.7 (23.2)
subtype1 106 25 0.2 - 210.7 (21.8)
subtype2 75 17 0.1 - 155.8 (21.7)
subtype3 113 30 0.1 - 209.6 (24.0)

Figure S234.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.619 (Kruskal-Wallis (anova)), Q value = 0.84

Table S242.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 305 48.2 (13.8)
subtype1 108 48.1 (11.9)
subtype2 78 49.1 (13.7)
subtype3 119 47.7 (15.5)

Figure S235.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.172 (Fisher's exact test), Q value = 0.49

Table S243.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3 T4
ALL 141 72 21 10
subtype1 53 28 10 6
subtype2 46 14 3 1
subtype3 42 30 8 3

Figure S236.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.609 (Fisher's exact test), Q value = 0.84

Table S244.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 135 60
subtype1 48 18
subtype2 40 22
subtype3 47 20

Figure S237.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0987 (Fisher's exact test), Q value = 0.37

Table S245.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 116 10
subtype1 39 7
subtype2 36 1
subtype3 41 2

Figure S238.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

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

Table S246.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 129
subtype1 23 54
subtype2 13 28
subtype3 19 47

Figure S239.  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 = 0.00043

Table S247.  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 66 5 17 3 12
subtype2 1 68 1 4 0 4
subtype3 0 120 0 0 0 1

Figure S240.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0659 (Kruskal-Wallis (anova)), Q value = 0.29

Table S248.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 93 17.4 (14.1)
subtype1 28 14.2 (13.1)
subtype2 25 22.5 (14.9)
subtype3 40 16.4 (13.8)

Figure S241.  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 = 0.242 (Kruskal-Wallis (anova)), Q value = 0.55

Table S249.  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 55 1.4 (2.5)
subtype3 49 0.9 (2.5)

Figure S242.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.217 (Fisher's exact test), Q value = 0.55

Table S250.  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 10 0 73
subtype2 1 8 6 0 54
subtype3 6 4 14 2 84

Figure S243.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.674 (Fisher's exact test), Q value = 0.85

Table S251.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 171
subtype1 7 57
subtype2 5 46
subtype3 12 68

Figure S244.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.551 (Kruskal-Wallis (anova)), Q value = 0.79

Table S252.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 278 73.0 (21.5)
subtype1 102 73.7 (20.1)
subtype2 68 72.6 (27.4)
subtype3 108 72.6 (18.7)

Figure S245.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'TUMOR_STATUS'

P value = 0.58 (Fisher's exact test), Q value = 0.81

Table S253.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 201 80
subtype1 69 31
subtype2 54 17
subtype3 78 32

Figure S246.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

Table S254.  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 42 43 0 14
subtype2 8 41 27 1 1
subtype3 2 53 50 0 9

Figure S247.  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 = 0.125 (Kruskal-Wallis (anova)), Q value = 0.41

Table S255.  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 2002.9 (11.5)
subtype2 10 1994.6 (11.2)
subtype3 21 2000.3 (15.5)

Figure S248.  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 = 0.0659 (Kruskal-Wallis (anova)), Q value = 0.29

Table S256.  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 28 14.2 (13.1)
subtype2 25 22.5 (14.9)
subtype3 40 16.4 (13.8)

Figure S249.  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 = 0.043 (Kruskal-Wallis (anova)), Q value = 0.23

Table S257.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 263 1.8 (1.1)
subtype1 97 1.6 (1.0)
subtype2 68 1.9 (1.2)
subtype3 98 1.9 (1.2)

Figure S250.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CNMF' versus 'AGEBEGANSMOKINGINYEARS'

P value = 0.892 (Kruskal-Wallis (anova)), Q value = 0.96

Table S258.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 85 21.1 (7.7)
subtype1 25 20.8 (6.7)
subtype2 22 20.7 (5.8)
subtype3 38 21.6 (9.2)

Figure S251.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.551 (Fisher's exact test), Q value = 0.79

Table S259.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

nPatients COMPLETED AS PLANNED TREATMENT NOT COMPLETED
ALL 29 3
subtype1 7 0
subtype2 6 0
subtype3 16 3

Figure S252.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.506 (Kruskal-Wallis (anova)), Q value = 0.77

Table S260.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 267 3.6 (2.6)
subtype1 97 3.5 (2.9)
subtype2 70 3.6 (2.3)
subtype3 100 3.6 (2.5)

Figure S253.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.756 (Kruskal-Wallis (anova)), Q value = 0.89

Table S261.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 112 0.1 (0.3)
subtype1 31 0.1 (0.2)
subtype2 35 0.1 (0.5)
subtype3 46 0.1 (0.2)

Figure S254.  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 = 0.0206 (Kruskal-Wallis (anova)), Q value = 0.19

Table S262.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'PREGNANCY_SPONTANEOUS_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 147 0.5 (0.9)
subtype1 43 0.8 (1.2)
subtype2 45 0.4 (0.8)
subtype3 59 0.4 (0.8)

Figure S255.  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 = 0.649 (Kruskal-Wallis (anova)), Q value = 0.84

Table S263.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 262 2.8 (2.0)
subtype1 92 2.9 (2.5)
subtype2 69 2.7 (1.7)
subtype3 101 2.9 (1.9)

Figure S256.  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 = 0.462 (Kruskal-Wallis (anova)), Q value = 0.74

Table S264.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'PREGNANCY_THERAPEUTIC_ABORTION_COUNT'

nPatients Mean (Std.Dev)
ALL 122 0.9 (1.8)
subtype1 34 0.8 (1.4)
subtype2 39 1.1 (2.3)
subtype3 49 0.7 (1.7)

Figure S257.  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 = 0.338 (Kruskal-Wallis (anova)), Q value = 0.65

Table S265.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 116 0.1 (0.3)
subtype1 31 0.1 (0.2)
subtype2 37 0.2 (0.5)
subtype3 48 0.1 (0.3)

Figure S258.  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 = 0.0901 (Fisher's exact test), Q value = 0.35

Table S266.  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 2 8 40 1 12
subtype1 2 1 10 0 2
subtype2 0 1 16 1 7
subtype3 0 6 14 0 3

Figure S259.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.727 (Fisher's exact test), Q value = 0.87

Table S267.  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 3 25 84 125
subtype1 1 12 26 48
subtype2 1 6 26 30
subtype3 1 7 32 47

Figure S260.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'MENOPAUSE_STATUS'

'MIRSEQ CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.607 (Fisher's exact test), Q value = 0.84

Table S268.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 72 80
subtype1 25 29
subtype2 22 29
subtype3 25 22

Figure S261.  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 = 0.242 (Kruskal-Wallis (anova)), Q value = 0.55

Table S269.  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 55 1.4 (2.5)
subtype3 49 0.9 (2.5)

Figure S262.  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 = 0.847 (Kruskal-Wallis (anova)), Q value = 0.93

Table S270.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 180 22.3 (12.6)
subtype1 60 21.1 (11.6)
subtype2 61 23.6 (14.1)
subtype3 59 22.1 (11.8)

Figure S263.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0335 (Fisher's exact test), Q value = 0.2

Table S271.  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 34
subtype2 18 32
subtype3 31 54

Figure S264.  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 = 0.000899 (Kruskal-Wallis (anova)), Q value = 0.022

Table S272.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 305 2008.3 (4.8)
subtype1 107 2009.4 (4.5)
subtype2 77 2008.1 (4.7)
subtype3 121 2007.6 (4.9)

Figure S265.  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 = 0.731 (Fisher's exact test), Q value = 0.87

Table S273.  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 22 36
subtype2 3 15 19
subtype3 4 17 35

Figure S266.  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 = 0.904 (Kruskal-Wallis (anova)), Q value = 0.96

Table S274.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 264 161.0 (7.3)
subtype1 97 160.9 (7.0)
subtype2 63 161.0 (8.0)
subtype3 104 161.0 (7.2)

Figure S267.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.577 (Fisher's exact test), Q value = 0.81

Table S275.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 99 19
subtype1 35 7
subtype2 32 8
subtype3 32 4

Figure S268.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRSEQ CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.841 (Fisher's exact test), Q value = 0.93

Table S276.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 104 2
subtype1 3 39 0
subtype2 1 20 0
subtype3 3 45 2

Figure S269.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.367 (Kruskal-Wallis (anova)), Q value = 0.67

Table S277.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 17 13.2 (7.2)
subtype1 3 16.8 (11.0)
subtype2 6 9.5 (5.7)
subtype3 8 14.7 (6.3)

Figure S270.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'CERVIX_SUV_RESULTS'

'MIRSEQ CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.698 (Kruskal-Wallis (anova)), Q value = 0.86

Table S278.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 307 48.3 (13.8)
subtype1 108 48.1 (11.9)
subtype2 78 49.1 (13.7)
subtype3 121 47.9 (15.4)

Figure S271.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CLINICAL_STAGE'

P value = 0.00923 (Fisher's exact test), Q value = 0.11

Table S279.  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 3 0 0 0 10 30 17 1 2 1 5 11 0 2 13 4 9
subtype2 0 1 1 1 8 28 11 1 3 2 1 6 0 0 10 2 1
subtype3 2 0 0 0 20 20 11 3 4 2 1 27 1 1 19 3 2

Figure S272.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S280.  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 = 0.692 (logrank test), Q value = 0.86

Table S281.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 294 72 0.1 - 210.7 (23.2)
subtype1 59 15 0.5 - 137.2 (18.8)
subtype2 97 21 0.1 - 160.4 (20.9)
subtype3 70 17 0.1 - 210.7 (31.3)
subtype4 24 7 0.3 - 209.6 (25.1)
subtype5 44 12 0.1 - 144.2 (17.9)

Figure S273.  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 S282.  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 S274.  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 = 0.073

Table S283.  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 S275.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.23 (Fisher's exact test), Q value = 0.55

Table S284.  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 S276.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.354 (Fisher's exact test), Q value = 0.66

Table S285.  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 S277.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.445 (Fisher's exact test), Q value = 0.73

Table S286.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 55 129
subtype1 11 28
subtype2 17 51
subtype3 14 19
subtype4 6 11
subtype5 7 20

Figure S278.  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 = 0.00043

Table S287.  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 S279.  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 = 0.47

Table S288.  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 S280.  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 = 0.42

Table S289.  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 S281.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.33 (Fisher's exact test), Q value = 0.65

Table S290.  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 S282.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.412 (Fisher's exact test), Q value = 0.71

Table S291.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 171
subtype1 4 31
subtype2 10 53
subtype3 3 50
subtype4 3 16
subtype5 4 21

Figure S283.  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 = 0.98

Table S292.  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 S284.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_STATUS'

P value = 0.713 (Fisher's exact test), Q value = 0.86

Table S293.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 201 80
subtype1 40 17
subtype2 67 26
subtype3 51 16
subtype4 14 9
subtype5 29 12

Figure S285.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0228 (Fisher's exact test), Q value = 0.2

Table S294.  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 S286.  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 = 0.46

Table S295.  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 S287.  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 = 0.47

Table S296.  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 S288.  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 = 0.669 (Kruskal-Wallis (anova)), Q value = 0.85

Table S297.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 263 1.8 (1.1)
subtype1 55 1.7 (1.1)
subtype2 85 1.9 (1.2)
subtype3 63 1.8 (1.1)
subtype4 22 1.6 (1.0)
subtype5 38 2.0 (1.3)

Figure S289.  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 S298.  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 S290.  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 = 0.93

Table S299.  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 S291.  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 = 0.29

Table S300.  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 S292.  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 S301.  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 S293.  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 = 0.49

Table S302.  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 S294.  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 S303.  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 S295.  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 = 0.98

Table S304.  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 S296.  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 S305.  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 S297.  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 = 0.428 (Fisher's exact test), Q value = 0.72

Table S306.  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 2 8 40 1 12
subtype1 2 0 7 0 2
subtype2 0 3 8 1 4
subtype3 0 3 16 0 5
subtype4 0 0 2 0 0
subtype5 0 2 7 0 1

Figure S298.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CHIERARCHICAL' versus 'MENOPAUSE_STATUS'

P value = 0.0134 (Fisher's exact test), Q value = 0.14

Table S307.  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 3 25 84 125
subtype1 0 5 16 30
subtype2 1 9 29 37
subtype3 1 5 30 23
subtype4 0 4 5 9
subtype5 1 2 4 26

Figure S299.  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 = 0.68

Table S308.  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 S300.  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 = 0.42

Table S309.  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 S301.  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 = 0.63

Table S310.  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 S302.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CHIERARCHICAL' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.123 (Fisher's exact test), Q value = 0.41

Table S311.  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 S303.  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 = 0.2

Table S312.  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 S304.  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 = 0.317 (Fisher's exact test), Q value = 0.63

Table S313.  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 S305.  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 S314.  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 S306.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CORPUS_INVOLVEMENT'

P value = 0.339 (Fisher's exact test), Q value = 0.65

Table S315.  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 S307.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.458 (Fisher's exact test), Q value = 0.74

Table S316.  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 S308.  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 = 0.55

Table S317.  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 S309.  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 S318.  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 S310.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CLINICAL_STAGE'

P value = 0.00075 (Fisher's exact test), Q value = 0.019

Table S319.  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 S311.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'CLINICAL_STAGE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S320.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 104 82 108
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.779 (logrank test), Q value = 0.89

Table S321.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 283 69 0.1 - 210.7 (23.8)
subtype1 102 20 0.3 - 210.7 (20.4)
subtype2 78 19 0.1 - 155.8 (20.8)
subtype3 103 30 0.1 - 177.0 (28.5)

Figure S312.  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 = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.21

Table S322.  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 104 49.6 (13.1)
subtype2 81 50.0 (14.3)
subtype3 108 45.4 (13.8)

Figure S313.  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 = 0.211 (Fisher's exact test), Q value = 0.54

Table S323.  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 44 25 10 5
subtype2 44 13 3 2
subtype3 48 30 6 2

Figure S314.  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 = 0.547 (Fisher's exact test), Q value = 0.79

Table S324.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 130 57
subtype1 42 14
subtype2 42 20
subtype3 46 23

Figure S315.  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 = 0.114 (Fisher's exact test), Q value = 0.4

Table S325.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 112 9
subtype1 32 6
subtype2 36 1
subtype3 44 2

Figure S316.  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 = 0.976 (Fisher's exact test), Q value = 1

Table S326.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 53 124
subtype1 22 51
subtype2 12 26
subtype3 19 47

Figure S317.  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 = 1e-05 (Fisher's exact test), Q value = 0.00043

Table S327.  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 65 4 14 3 13
subtype2 1 69 2 6 0 4
subtype3 0 108 0 0 0 0

Figure S318.  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 = 0.302 (Kruskal-Wallis (anova)), Q value = 0.62

Table S328.  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 28 15.4 (13.6)
subtype2 26 19.4 (12.2)
subtype3 34 16.9 (15.7)

Figure S319.  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 = 0.478 (Kruskal-Wallis (anova)), Q value = 0.74

Table S329.  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 45 0.7 (2.2)
subtype2 54 1.2 (2.4)
subtype3 56 1.0 (2.5)

Figure S320.  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 = 0.0823 (Fisher's exact test), Q value = 0.33

Table S330.  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 1 7 13 0 69
subtype2 1 8 3 0 60
subtype3 5 4 11 2 76

Figure S321.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.24 (Fisher's exact test), Q value = 0.55

Table S331.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 161
subtype1 8 53
subtype2 4 52
subtype3 12 56

Figure S322.  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 = 0.476 (Kruskal-Wallis (anova)), Q value = 0.74

Table S332.  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 96 73.7 (20.6)
subtype2 74 72.3 (27.6)
subtype3 96 72.6 (16.5)

Figure S323.  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 = 0.556 (Fisher's exact test), Q value = 0.8

Table S333.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 194 75
subtype1 64 30
subtype2 55 20
subtype3 75 25

Figure S324.  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 = 0.00354 (Fisher's exact test), Q value = 0.073

Table S334.  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 5 37 47 0 13
subtype2 9 43 29 0 1
subtype3 2 48 42 1 9

Figure S325.  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 = 0.422 (Kruskal-Wallis (anova)), Q value = 0.72

Table S335.  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 16 2001.9 (15.9)
subtype2 9 1996.2 (16.2)
subtype3 16 2000.1 (10.5)

Figure S326.  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 = 0.302 (Kruskal-Wallis (anova)), Q value = 0.62

Table S336.  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 28 15.4 (13.6)
subtype2 26 19.4 (12.2)
subtype3 34 16.9 (15.7)

Figure S327.  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 = 0.444 (Kruskal-Wallis (anova)), Q value = 0.73

Table S337.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 255 1.8 (1.1)
subtype1 89 1.8 (1.2)
subtype2 70 1.9 (1.2)
subtype3 96 1.9 (1.2)

Figure S328.  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 = 0.95 (Kruskal-Wallis (anova)), Q value = 0.99

Table S338.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 80 20.9 (7.6)
subtype1 26 21.0 (6.9)
subtype2 22 20.2 (6.8)
subtype3 32 21.2 (8.7)

Figure S329.  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 S339.  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 5 0
subtype2 6 1
subtype3 16 2

Figure S330.  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 = 0.494 (Kruskal-Wallis (anova)), Q value = 0.76

Table S340.  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 90 3.4 (2.5)
subtype2 74 3.7 (2.3)
subtype3 93 3.7 (2.7)

Figure S331.  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 = 0.756 (Kruskal-Wallis (anova)), Q value = 0.89

Table S341.  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 24 0.0 (0.2)
subtype2 35 0.0 (0.2)
subtype3 48 0.1 (0.5)

Figure S332.  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 = 0.194 (Kruskal-Wallis (anova)), Q value = 0.51

Table S342.  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 37 0.8 (1.3)
subtype2 47 0.4 (0.7)
subtype3 59 0.5 (0.9)

Figure S333.  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 = 0.957 (Kruskal-Wallis (anova)), Q value = 0.99

Table S343.  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 87 2.8 (2.1)
subtype2 73 2.8 (1.9)
subtype3 92 2.9 (2.1)

Figure S334.  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 = 0.188 (Kruskal-Wallis (anova)), Q value = 0.51

Table S344.  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 27 0.7 (1.0)
subtype2 40 1.1 (2.2)
subtype3 49 0.6 (1.7)

Figure S335.  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 = 0.982 (Kruskal-Wallis (anova)), Q value = 1

Table S345.  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 26 0.1 (0.3)
subtype2 37 0.1 (0.4)
subtype3 49 0.1 (0.3)

Figure S336.  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 = 0.397 (Fisher's exact test), Q value = 0.7

Table S346.  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 1 7 39 1 12
subtype1 1 1 8 0 2
subtype2 0 3 12 1 7
subtype3 0 3 19 0 3

Figure S337.  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 = 0.012 (Fisher's exact test), Q value = 0.14

Table S347.  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 3 25 78 120
subtype1 1 15 26 39
subtype2 0 6 29 28
subtype3 2 4 23 53

Figure S338.  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 = 0.695 (Fisher's exact test), Q value = 0.86

Table S348.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 69 74
subtype1 20 25
subtype2 23 26
subtype3 26 23

Figure S339.  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 = 0.478 (Kruskal-Wallis (anova)), Q value = 0.74

Table S349.  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 45 0.7 (2.2)
subtype2 54 1.2 (2.4)
subtype3 56 1.0 (2.5)

Figure S340.  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 = 0.642 (Kruskal-Wallis (anova)), Q value = 0.84

Table S350.  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 52 20.8 (11.4)
subtype2 60 23.5 (12.8)
subtype3 60 23.5 (13.5)

Figure S341.  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 = 0.00884 (Fisher's exact test), Q value = 0.11

Table S351.  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 5 34
subtype2 21 30
subtype3 26 50

Figure S342.  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 = 0.00872 (Kruskal-Wallis (anova)), Q value = 0.11

Table S352.  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 103 2009.3 (4.4)
subtype2 81 2008.2 (4.7)
subtype3 108 2007.5 (5.1)

Figure S343.  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 = 0.625 (Fisher's exact test), Q value = 0.84

Table S353.  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 20 33
subtype2 3 18 19
subtype3 6 16 33

Figure S344.  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 = 0.708 (Kruskal-Wallis (anova)), Q value = 0.86

Table S354.  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 91 160.8 (6.5)
subtype2 69 160.9 (8.2)
subtype3 93 160.8 (7.4)

Figure S345.  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 = 0.857 (Fisher's exact test), Q value = 0.93

Table S355.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 19
subtype1 28 6
subtype2 31 7
subtype3 36 6

Figure S346.  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 = 0.895 (Fisher's exact test), Q value = 0.96

Table S356.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 3 37 1
subtype2 2 19 0
subtype3 2 46 1

Figure S347.  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 = 0.18 (Kruskal-Wallis (anova)), Q value = 0.49

Table S357.  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 3 16.8 (11.0)
subtype2 7 9.0 (5.4)
subtype3 6 15.7 (6.1)

Figure S348.  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 = 0.0326 (Kruskal-Wallis (anova)), Q value = 0.2

Table S358.  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 104 49.6 (13.1)
subtype2 82 50.1 (14.2)
subtype3 108 45.4 (13.8)

Figure S349.  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 = 0.0295 (Fisher's exact test), Q value = 0.2

Table S359.  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 8 29 16 2 2 1 3 11 2 14 2 7
subtype2 0 1 1 7 27 11 2 3 1 2 6 0 13 4 1
subtype3 2 0 0 22 20 10 1 3 3 2 25 1 14 2 3

Figure S350.  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 S360.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 29 81 109 42 33
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.928 (logrank test), Q value = 0.98

Table S361.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 283 69 0.1 - 210.7 (23.8)
subtype1 28 5 0.5 - 137.2 (19.3)
subtype2 78 20 0.1 - 177.0 (23.8)
subtype3 105 24 0.1 - 210.7 (24.3)
subtype4 40 11 0.4 - 154.3 (23.6)
subtype5 32 9 0.5 - 97.0 (19.6)

Figure S351.  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 = 0.00654 (Kruskal-Wallis (anova)), Q value = 0.1

Table S362.  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 29 48.7 (10.1)
subtype2 81 50.6 (14.5)
subtype3 108 49.9 (14.2)
subtype4 42 40.8 (11.7)
subtype5 33 45.9 (13.2)

Figure S352.  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 = 0.262 (Fisher's exact test), Q value = 0.57

Table S363.  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 13 12 1 1
subtype2 42 13 3 2
subtype3 43 31 10 4
subtype4 18 6 4 1
subtype5 20 6 1 1

Figure S353.  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 = 0.0778 (Fisher's exact test), Q value = 0.32

Table S364.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 130 57
subtype1 18 2
subtype2 42 17
subtype3 37 26
subtype4 15 7
subtype5 18 5

Figure S354.  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 = 0.282 (Fisher's exact test), Q value = 0.6

Table S365.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 112 9
subtype1 15 3
subtype2 40 1
subtype3 35 4
subtype4 14 1
subtype5 8 0

Figure S355.  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 = 0.927 (Fisher's exact test), Q value = 0.98

Table S366.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATION_THERAPY'

nPatients NO YES
ALL 53 124
subtype1 7 14
subtype2 11 23
subtype3 19 53
subtype4 9 19
subtype5 7 15

Figure S356.  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 = 0.00043

Table S367.  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 3 15 1 5 2 3
subtype2 1 78 0 1 0 1
subtype3 1 105 1 2 0 0
subtype4 0 42 0 0 0 0
subtype5 1 2 4 12 1 13

Figure S357.  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 = 0.0646 (Kruskal-Wallis (anova)), Q value = 0.29

Table S368.  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 5 12.2 (9.9)
subtype2 23 20.7 (10.4)
subtype3 36 19.4 (17.1)
subtype4 13 10.5 (9.9)
subtype5 11 12.9 (12.4)

Figure S358.  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 = 0.0318 (Kruskal-Wallis (anova)), Q value = 0.2

Table S369.  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 15 0.1 (0.5)
subtype2 51 0.9 (1.9)
subtype3 52 1.6 (3.1)
subtype4 15 0.3 (0.6)
subtype5 22 0.8 (2.4)

Figure S359.  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 = 0.0739 (Fisher's exact test), Q value = 0.31

Table S370.  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 4 2 0 17
subtype2 1 9 4 0 59
subtype3 3 4 13 1 74
subtype4 2 2 7 1 27
subtype5 0 0 1 0 28

Figure S360.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.217 (Fisher's exact test), Q value = 0.55

Table S371.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 24 161
subtype1 3 16
subtype2 3 55
subtype3 10 52
subtype4 5 22
subtype5 3 16

Figure S361.  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 = 0.479 (Kruskal-Wallis (anova)), Q value = 0.74

Table S372.  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 28 73.5 (18.5)
subtype2 72 72.0 (28.3)
subtype3 102 72.9 (19.9)
subtype4 36 74.8 (16.7)
subtype5 28 72.4 (14.8)

Figure S362.  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 = 0.642 (Fisher's exact test), Q value = 0.84

Table S373.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 194 75
subtype1 17 11
subtype2 54 21
subtype3 73 27
subtype4 29 8
subtype5 21 8

Figure S363.  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 = 6e-05 (Fisher's exact test), Q value = 0.0021

Table S374.  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 1 6 18 0 4
subtype2 7 39 32 0 2
subtype3 3 52 39 1 10
subtype4 0 10 24 0 5
subtype5 5 21 5 0 2

Figure S364.  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 = 0.479 (Kruskal-Wallis (anova)), Q value = 0.74

Table S375.  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 2 2003.5 (4.9)
subtype2 9 1996.1 (11.6)
subtype3 18 2000.8 (16.5)
subtype4 6 2002.0 (11.5)
subtype5 6 2000.0 (15.0)

Figure S365.  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 = 0.0646 (Kruskal-Wallis (anova)), Q value = 0.29

Table S376.  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 5 12.2 (9.9)
subtype2 23 20.7 (10.4)
subtype3 36 19.4 (17.1)
subtype4 13 10.5 (9.9)
subtype5 11 12.9 (12.4)

Figure S366.  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 = 0.31 (Kruskal-Wallis (anova)), Q value = 0.63

Table S377.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_HISTORY'

nPatients Mean (Std.Dev)
ALL 255 1.8 (1.1)
subtype1 25 1.4 (0.9)
subtype2 68 1.8 (1.1)
subtype3 98 1.9 (1.2)
subtype4 33 1.8 (1.2)
subtype5 31 1.8 (1.2)

Figure S367.  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 = 0.4 (Kruskal-Wallis (anova)), Q value = 0.7

Table S378.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 80 20.9 (7.6)
subtype1 4 26.5 (6.6)
subtype2 19 20.2 (4.8)
subtype3 34 21.3 (8.8)
subtype4 12 20.3 (9.8)
subtype5 11 19.5 (4.5)

Figure S368.  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 = 0.0332 (Fisher's exact test), Q value = 0.2

Table S379.  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 0 0
subtype2 8 1
subtype3 16 0
subtype4 2 2
subtype5 1 0

Figure S369.  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 = 0.0979 (Kruskal-Wallis (anova)), Q value = 0.37

Table S380.  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 27 2.7 (2.1)
subtype2 70 3.7 (2.1)
subtype3 96 3.9 (2.9)
subtype4 35 3.4 (2.6)
subtype5 29 3.1 (2.1)

Figure S370.  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 = 0.657 (Kruskal-Wallis (anova)), Q value = 0.84

Table S381.  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 7 0.0 (0.0)
subtype2 38 0.1 (0.5)
subtype3 38 0.1 (0.2)
subtype4 10 0.0 (0.0)
subtype5 14 0.0 (0.0)

Figure S371.  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 = 0.159 (Kruskal-Wallis (anova)), Q value = 0.48

Table S382.  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 9 0.4 (0.5)
subtype2 47 0.3 (0.5)
subtype3 54 0.8 (1.3)
subtype4 16 0.6 (0.7)
subtype5 17 0.7 (1.0)

Figure S372.  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 = 0.235 (Kruskal-Wallis (anova)), Q value = 0.55

Table S383.  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 24 2.4 (1.8)
subtype2 73 2.7 (1.4)
subtype3 93 3.1 (2.2)
subtype4 33 2.9 (2.4)
subtype5 29 2.4 (2.1)

Figure S373.  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 = 0.323 (Kruskal-Wallis (anova)), Q value = 0.64

Table S384.  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 8 1.0 (1.4)
subtype2 41 1.1 (2.2)
subtype3 40 0.6 (1.7)
subtype4 12 0.8 (1.0)
subtype5 15 0.4 (0.6)

Figure S374.  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 = 0.426 (Kruskal-Wallis (anova)), Q value = 0.72

Table S385.  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 7 0.0 (0.0)
subtype2 40 0.1 (0.4)
subtype3 41 0.2 (0.4)
subtype4 10 0.0 (0.0)
subtype5 14 0.1 (0.3)

Figure S375.  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 = 0.261 (Fisher's exact test), Q value = 0.57

Table S386.  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 1 7 39 1 12
subtype1 1 0 1 0 1
subtype2 0 1 15 1 5
subtype3 0 5 12 0 5
subtype4 0 1 5 0 0
subtype5 0 0 6 0 1

Figure S376.  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 = 0.29 (Fisher's exact test), Q value = 0.61

Table S387.  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 3 25 78 120
subtype1 0 3 6 16
subtype2 1 5 29 27
subtype3 1 11 30 43
subtype4 1 3 4 19
subtype5 0 3 9 15

Figure S377.  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 = 0.112 (Fisher's exact test), Q value = 0.4

Table S388.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 69 74
subtype1 6 12
subtype2 25 27
subtype3 19 25
subtype4 6 6
subtype5 13 4

Figure S378.  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 = 0.0318 (Kruskal-Wallis (anova)), Q value = 0.2

Table S389.  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 15 0.1 (0.5)
subtype2 51 0.9 (1.9)
subtype3 52 1.6 (3.1)
subtype4 15 0.3 (0.6)
subtype5 22 0.8 (2.4)

Figure S379.  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 = 0.316 (Kruskal-Wallis (anova)), Q value = 0.63

Table S390.  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 18 19.1 (9.9)
subtype2 57 24.1 (14.2)
subtype3 56 20.9 (12.2)
subtype4 18 24.9 (13.2)
subtype5 23 24.7 (10.7)

Figure S380.  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 = 0.0641 (Fisher's exact test), Q value = 0.29

Table S391.  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 10
subtype2 23 35
subtype3 19 46
subtype4 10 19
subtype5 0 4

Figure S381.  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 = 0.0245 (Kruskal-Wallis (anova)), Q value = 0.2

Table S392.  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 29 2010.7 (2.5)
subtype2 80 2007.4 (5.3)
subtype3 109 2008.7 (4.6)
subtype4 42 2007.3 (5.2)
subtype5 32 2008.6 (4.2)

Figure S382.  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 = 0.662 (Fisher's exact test), Q value = 0.84

Table S393.  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 4 6 8
subtype2 2 13 22
subtype3 4 21 35
subtype4 2 6 11
subtype5 3 8 9

Figure S383.  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 = 0.437 (Kruskal-Wallis (anova)), Q value = 0.72

Table S394.  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 28 160.1 (4.9)
subtype2 66 160.1 (7.8)
subtype3 98 160.6 (7.2)
subtype4 34 162.0 (7.7)
subtype5 27 162.4 (8.2)

Figure S384.  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 = 0.359 (Fisher's exact test), Q value = 0.66

Table S395.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 95 19
subtype1 10 4
subtype2 34 9
subtype3 28 5
subtype4 8 0
subtype5 15 1

Figure S385.  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 = 0.08 (Fisher's exact test), Q value = 0.32

Table S396.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN OTHER
ALL 7 102 2
subtype1 2 9 0
subtype2 2 19 0
subtype3 1 48 0
subtype4 1 16 1
subtype5 1 10 1

Figure S386.  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 = 0.19 (Kruskal-Wallis (anova)), Q value = 0.51

Table S397.  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 7 9.5 (5.2)
subtype3 5 15.8 (8.6)
subtype4 3 18.2 (7.3)

Figure S387.  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 = 0.00599 (Kruskal-Wallis (anova)), Q value = 0.1

Table S398.  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 29 48.7 (10.1)
subtype2 81 50.6 (14.5)
subtype3 109 49.9 (14.2)
subtype4 42 40.8 (11.7)
subtype5 33 45.9 (13.2)

Figure S388.  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 = 0.514 (Fisher's exact test), Q value = 0.78

Table S399.  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 2 8 5 0 2 1 2 4 0 2 0 2
subtype2 0 1 1 14 22 9 3 2 2 2 7 0 13 3 1
subtype3 3 0 0 11 24 10 2 1 2 3 20 1 18 3 6
subtype4 1 0 0 7 7 7 0 2 0 0 8 2 5 1 1
subtype5 0 0 0 3 15 6 0 1 0 0 3 0 3 1 1

Figure S389.  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/22541064/CESC-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CESC-TP/22489370/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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)