Correlation between aggregated molecular cancer subtypes and selected clinical features
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19P30BC
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 8 different clustering approaches and 43 clinical features across 200 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 7 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE',  'NEOPLASMHISTOLOGICGRADE', and 'INITIAL_PATHOLOGIC_DX_YEAR'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 43 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 9 significant findings detected.

Clinical
Features
Statistical
Tests
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.478
(1.00)
0.241
(1.00)
0.353
(1.00)
0.0267
(1.00)
0.499
(1.00)
0.647
(1.00)
0.829
(1.00)
0.147
(1.00)
AGE Kruskal-Wallis (anova) 0.14
(1.00)
0.0117
(1.00)
0.11
(1.00)
0.0489
(1.00)
0.221
(1.00)
0.166
(1.00)
0.0366
(1.00)
0.0118
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.945
(1.00)
0.983
(1.00)
0.723
(1.00)
0.762
(1.00)
0.89
(1.00)
0.205
(1.00)
0.785
(1.00)
0.436
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.105
(1.00)
0.331
(1.00)
0.0153
(1.00)
0.583
(1.00)
0.587
(1.00)
0.776
(1.00)
0.727
(1.00)
0.631
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.269
(1.00)
0.716
(1.00)
0.114
(1.00)
0.242
(1.00)
0.628
(1.00)
0.389
(1.00)
0.407
(1.00)
0.643
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 0.452
(1.00)
1e-05
(0.00341)
1e-05
(0.00341)
1e-05
(0.00341)
1e-05
(0.00341)
1e-05
(0.00341)
1e-05
(0.00341)
1e-05
(0.00341)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.891
(1.00)
0.429
(1.00)
0.317
(1.00)
0.536
(1.00)
0.411
(1.00)
0.628
(1.00)
0.416
(1.00)
0.0401
(1.00)
NUMBERPACKYEARSSMOKED Kruskal-Wallis (anova) 0.454
(1.00)
0.544
(1.00)
0.0633
(1.00)
0.198
(1.00)
0.0665
(1.00)
0.213
(1.00)
0.395
(1.00)
0.491
(1.00)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.166
(1.00)
0.513
(1.00)
0.0587
(1.00)
0.557
(1.00)
0.443
(1.00)
0.667
(1.00)
0.654
(1.00)
0.485
(1.00)
RACE Fisher's exact test 0.536
(1.00)
0.287
(1.00)
0.238
(1.00)
0.387
(1.00)
0.386
(1.00)
0.421
(1.00)
0.156
(1.00)
0.632
(1.00)
ETHNICITY Fisher's exact test 0.925
(1.00)
0.164
(1.00)
0.477
(1.00)
0.436
(1.00)
0.506
(1.00)
0.0869
(1.00)
0.335
(1.00)
0.266
(1.00)
WEIGHT KG AT DIAGNOSIS Kruskal-Wallis (anova) 0.168
(1.00)
0.56
(1.00)
0.356
(1.00)
0.517
(1.00)
0.35
(1.00)
0.634
(1.00)
0.124
(1.00)
0.407
(1.00)
TUMOR STATUS Fisher's exact test 0.578
(1.00)
0.795
(1.00)
0.945
(1.00)
0.195
(1.00)
0.724
(1.00)
0.671
(1.00)
0.566
(1.00)
0.454
(1.00)
TUMOR SAMPLE PROCUREMENT COUNTRY Fisher's exact test 0.0209
(1.00)
0.477
(1.00)
0.662
(1.00)
0.535
(1.00)
0.579
(1.00)
0.274
(1.00)
0.311
(1.00)
0.572
(1.00)
NEOPLASMHISTOLOGICGRADE Fisher's exact test 0.0515
(1.00)
0.74
(1.00)
0.131
(1.00)
0.0169
(1.00)
0.0596
(1.00)
0.327
(1.00)
0.0581
(1.00)
0.00071
(0.236)
TOBACCO SMOKING YEAR STOPPED Kruskal-Wallis (anova) 0.426
(1.00)
0.355
(1.00)
0.799
(1.00)
0.794
(1.00)
0.317
(1.00)
0.16
(1.00)
0.737
(1.00)
0.388
(1.00)
TOBACCO SMOKING PACK YEARS SMOKED Kruskal-Wallis (anova) 0.454
(1.00)
0.544
(1.00)
0.0633
(1.00)
0.198
(1.00)
0.0665
(1.00)
0.213
(1.00)
0.395
(1.00)
0.491
(1.00)
TOBACCO SMOKING HISTORY Fisher's exact test 0.894
(1.00)
0.539
(1.00)
0.384
(1.00)
0.413
(1.00)
0.245
(1.00)
0.38
(1.00)
0.762
(1.00)
0.899
(1.00)
PATIENT AGEBEGANSMOKINGINYEARS Kruskal-Wallis (anova) 0.386
(1.00)
0.549
(1.00)
0.812
(1.00)
0.576
(1.00)
0.239
(1.00)
0.258
(1.00)
0.0729
(1.00)
0.105
(1.00)
RADIATION THERAPY TYPE Fisher's exact test 0.211
(1.00)
0.366
(1.00)
0.84
(1.00)
0.458
(1.00)
0.267
(1.00)
0.724
(1.00)
0.13
(1.00)
0.0239
(1.00)
RADIATION ADJUVANT UNITS Fisher's exact test 1
(1.00)
0.84
(1.00)
1
(1.00)
0.471
(1.00)
0.137
(1.00)
0.784
(1.00)
0.417
(1.00)
0.94
(1.00)
PREGNANCIES COUNT TOTAL Kruskal-Wallis (anova) 0.606
(1.00)
0.417
(1.00)
0.12
(1.00)
0.274
(1.00)
0.229
(1.00)
0.0722
(1.00)
0.464
(1.00)
0.375
(1.00)
PREGNANCIES COUNT STILLBIRTH Kruskal-Wallis (anova) 0.221
(1.00)
0.00866
(1.00)
0.159
(1.00)
0.468
(1.00)
0.791
(1.00)
0.939
(1.00)
0.951
(1.00)
0.26
(1.00)
PATIENT PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT Kruskal-Wallis (anova) 0.928
(1.00)
0.206
(1.00)
0.355
(1.00)
0.347
(1.00)
0.0365
(1.00)
0.102
(1.00)
0.217
(1.00)
0.269
(1.00)
PREGNANCIES COUNT LIVE BIRTH Kruskal-Wallis (anova) 0.528
(1.00)
0.258
(1.00)
0.142
(1.00)
0.155
(1.00)
0.167
(1.00)
0.0486
(1.00)
0.618
(1.00)
0.21
(1.00)
PATIENT PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT Kruskal-Wallis (anova) 0.883
(1.00)
0.0424
(1.00)
0.737
(1.00)
0.911
(1.00)
0.392
(1.00)
0.512
(1.00)
0.147
(1.00)
0.737
(1.00)
PREGNANCIES COUNT ECTOPIC Kruskal-Wallis (anova) 0.266
(1.00)
0.777
(1.00)
0.96
(1.00)
0.999
(1.00)
0.831
(1.00)
0.71
(1.00)
0.911
(1.00)
0.765
(1.00)
LYMPH NODE LOCATION Fisher's exact test 0.363
(1.00)
0.593
(1.00)
0.0318
(1.00)
0.521
(1.00)
0.343
(1.00)
0.562
(1.00)
0.131
(1.00)
0.309
(1.00)
LOCATION OF POSITIVE MARGINS Fisher's exact test 0.933
(1.00)
0.725
(1.00)
0.839
(1.00)
0.436
(1.00)
0.706
(1.00)
1
(1.00)
0.0317
(1.00)
0.984
(1.00)
MENOPAUSE STATUS Fisher's exact test 0.326
(1.00)
0.0169
(1.00)
0.275
(1.00)
0.0133
(1.00)
0.466
(1.00)
0.493
(1.00)
0.471
(1.00)
0.025
(1.00)
LYMPHOVASCULAR INVOLVEMENT Fisher's exact test 0.245
(1.00)
0.858
(1.00)
0.459
(1.00)
0.877
(1.00)
0.325
(1.00)
0.363
(1.00)
0.665
(1.00)
0.386
(1.00)
LYMPH NODES EXAMINED HE COUNT Kruskal-Wallis (anova) 0.166
(1.00)
0.513
(1.00)
0.0587
(1.00)
0.557
(1.00)
0.443
(1.00)
0.667
(1.00)
0.654
(1.00)
0.485
(1.00)
LYMPH NODES EXAMINED Kruskal-Wallis (anova) 0.593
(1.00)
0.829
(1.00)
0.79
(1.00)
0.78
(1.00)
0.528
(1.00)
0.856
(1.00)
0.179
(1.00)
0.24
(1.00)
KERATINIZATION SQUAMOUS CELL Fisher's exact test 0.628
(1.00)
0.0149
(1.00)
0.0222
(1.00)
0.115
(1.00)
0.031
(1.00)
0.0822
(1.00)
0.0205
(1.00)
0.021
(1.00)
INITIAL PATHOLOGIC DX YEAR Kruskal-Wallis (anova) 0.213
(1.00)
0.34
(1.00)
0.147
(1.00)
0.123
(1.00)
0.173
(1.00)
0.342
(1.00)
0.222
(1.00)
0.000681
(0.227)
HYSTERECTOMY TYPE Fisher's exact test 0.099
(1.00)
0.285
(1.00)
0.477
(1.00)
0.169
(1.00)
0.136
(1.00)
0.132
(1.00)
0.307
(1.00)
0.028
(1.00)
HISTORY HORMONAL CONTRACEPTIVES USE Fisher's exact test 0.404
(1.00)
0.647
(1.00)
0.114
(1.00)
0.0445
(1.00)
0.944
(1.00)
0.211
(1.00)
0.931
(1.00)
0.172
(1.00)
HEIGHT CM AT DIAGNOSIS Kruskal-Wallis (anova) 0.344
(1.00)
0.158
(1.00)
0.502
(1.00)
0.41
(1.00)
0.185
(1.00)
0.678
(1.00)
0.271
(1.00)
0.482
(1.00)
CORPUS INVOLVEMENT Fisher's exact test 0.487
(1.00)
0.536
(1.00)
0.111
(1.00)
0.877
(1.00)
0.322
(1.00)
0.659
(1.00)
0.124
(1.00)
0.899
(1.00)
CHEMO CONCURRENT TYPE Fisher's exact test 0.535
(1.00)
0.296
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
0.103
(1.00)
0.537
(1.00)
0.89
(1.00)
CERVIX SUV RESULTS Kruskal-Wallis (anova) 0.0809
(1.00)
0.456
(1.00)
0.362
(1.00)
0.0947
(1.00)
0.0809
(1.00)
AJCC TUMOR PATHOLOGIC PT Fisher's exact test 0.766
(1.00)
0.402
(1.00)
0.0624
(1.00)
0.00179
(0.594)
0.35
(1.00)
0.113
(1.00)
0.742
(1.00)
0.304
(1.00)
AGE AT DIAGNOSIS Kruskal-Wallis (anova) 0.112
(1.00)
0.0161
(1.00)
0.128
(1.00)
0.0454
(1.00)
0.249
(1.00)
0.156
(1.00)
0.0504
(1.00)
0.0105
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 55 39 82
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.478 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 169 32 0.0 - 182.9 (13.6)
subtype1 53 8 0.0 - 173.3 (14.9)
subtype2 35 9 0.1 - 118.0 (13.1)
subtype3 81 15 0.0 - 182.9 (10.1)

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 'AGE'

P value = 0.14 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 175 47.5 (13.1)
subtype1 54 49.4 (10.4)
subtype2 39 46.7 (15.0)
subtype3 82 46.5 (13.7)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.945 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 97 36 3
subtype1 30 11 1
subtype2 19 5 0
subtype3 48 20 2

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 = 0.105 (Fisher's exact test), Q value = 1

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

nPatients 0 1
ALL 87 40
subtype1 25 14
subtype2 13 11
subtype3 49 15

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 = 0.269 (Fisher's exact test), Q value = 1

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

nPatients M0 M1 MX
ALL 72 3 65
subtype1 28 1 16
subtype2 14 0 11
subtype3 30 2 38

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.452 (Fisher's exact test), Q value = 1

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

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 144 4 20 2 4
subtype1 0 48 1 3 1 2
subtype2 0 35 1 3 0 0
subtype3 2 61 2 14 1 2

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.891 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 27 149
subtype1 8 47
subtype2 7 32
subtype3 12 70

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.454 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 53 18.6 (12.4)
subtype1 15 18.1 (15.0)
subtype2 11 21.0 (10.2)
subtype3 27 17.9 (11.9)

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 1

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 107 0.9 (2.2)
subtype1 31 0.8 (1.3)
subtype2 19 0.9 (1.3)
subtype3 57 1.0 (2.8)

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 = 0.536 (Fisher's exact test), Q value = 1

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 7 15 18 1 130
subtype1 2 8 5 0 38
subtype2 1 1 6 0 31
subtype3 4 6 7 1 61

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 = 0.925 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 127
subtype1 3 40
subtype2 2 29
subtype3 6 58

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 = 0.168 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 162 75.6 (20.9)
subtype1 52 73.1 (25.4)
subtype2 37 76.9 (16.0)
subtype3 73 76.8 (19.7)

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 = 0.578 (Fisher's exact test), Q value = 1

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

nPatients TUMOR FREE WITH TUMOR
ALL 48 19
subtype1 17 5
subtype2 10 6
subtype3 21 8

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.0209 (Fisher's exact test), Q value = 1

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

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 10 6 147 12
subtype1 0 6 1 40 8
subtype2 1 1 1 36 0
subtype3 0 3 4 71 4

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.0515 (Fisher's exact test), Q value = 1

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

nPatients G1 G2 G3 G4 GX
ALL 14 83 72 1 5
subtype1 6 30 18 0 1
subtype2 0 14 23 1 1
subtype3 8 39 31 0 3

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.426 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 25 1997.7 (11.7)
subtype1 8 1996.9 (11.1)
subtype2 4 1991.0 (12.8)
subtype3 13 2000.3 (11.8)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.454 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 53 18.6 (12.4)
subtype1 15 18.1 (15.0)
subtype2 11 21.0 (10.2)
subtype3 27 17.9 (11.9)

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.894 (Fisher's exact test), Q value = 1

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

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 21 8 1 36 83
subtype1 7 4 1 10 26
subtype2 3 1 0 8 14
subtype3 11 3 0 18 43

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

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

P value = 0.386 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 48 20.5 (6.3)
subtype1 14 21.9 (7.1)
subtype2 10 18.6 (5.0)
subtype3 24 20.5 (6.3)

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.211 (Fisher's exact test), Q value = 1

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

nPatients COMBINATION EXTERNAL EXTERNAL BEAM INTERNAL
ALL 17 37 11 5
subtype1 6 12 3 0
subtype2 4 6 3 4
subtype3 7 19 5 1

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

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

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

nPatients CGY GY
ALL 11 3
subtype1 2 0
subtype2 5 1
subtype3 4 2

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.606 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 152 3.4 (2.4)
subtype1 46 3.7 (2.6)
subtype2 34 3.5 (2.8)
subtype3 72 3.2 (2.1)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.221 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 93 0.1 (0.4)
subtype1 28 0.1 (0.6)
subtype2 19 0.0 (0.0)
subtype3 46 0.1 (0.3)

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

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

P value = 0.928 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 105 0.3 (0.6)
subtype1 31 0.3 (0.5)
subtype2 21 0.3 (0.6)
subtype3 53 0.3 (0.6)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.528 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 157 2.4 (1.7)
subtype1 48 2.4 (1.3)
subtype2 35 2.8 (2.4)
subtype3 74 2.2 (1.6)

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

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

P value = 0.883 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 100 0.8 (1.7)
subtype1 28 1.3 (2.8)
subtype2 22 0.6 (1.0)
subtype3 50 0.7 (1.1)

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 94 0.1 (0.3)
subtype1 29 0.2 (0.5)
subtype2 20 0.1 (0.4)
subtype3 45 0.0 (0.2)

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODE_LOCATION'

P value = 0.363 (Fisher's exact test), Q value = 1

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

nPatients 2003 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 38
subtype1 0 0 15
subtype2 1 0 7
subtype3 0 1 16

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

'Copy Number Ratio CNMF subtypes' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.933 (Fisher's exact test), Q value = 1

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

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 6 9 1 5
subtype1 1 2 4 0 2
subtype2 0 3 3 0 1
subtype3 1 1 2 1 2

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

'Copy Number Ratio CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.326 (Fisher's exact test), Q value = 1

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

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 54 77
subtype1 2 5 19 20
subtype2 0 1 12 16
subtype3 0 4 23 41

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.245 (Fisher's exact test), Q value = 1

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

nPatients ABSENT PRESENT
ALL 54 59
subtype1 18 19
subtype2 8 16
subtype3 28 24

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 0.9 (2.2)
subtype1 31 0.8 (1.3)
subtype2 19 0.9 (1.3)
subtype3 57 1.0 (2.8)

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.593 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 126 22.2 (13.0)
subtype1 37 21.1 (14.2)
subtype2 25 24.1 (14.0)
subtype3 64 22.2 (11.9)

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

'Copy Number Ratio CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.628 (Fisher's exact test), Q value = 1

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

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 35 81
subtype1 12 26
subtype2 9 16
subtype3 14 39

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

'Copy Number Ratio CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.213 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 176 2007.6 (5.1)
subtype1 55 2007.5 (5.3)
subtype2 39 2006.9 (4.4)
subtype3 82 2008.0 (5.2)

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

'Copy Number Ratio CNMF subtypes' versus 'HYSTERECTOMY_TYPE'

P value = 0.099 (Fisher's exact test), Q value = 1

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

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 113 5
subtype1 0 37 0
subtype2 0 24 0
subtype3 3 52 5

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

'Copy Number Ratio CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.404 (Fisher's exact test), Q value = 1

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

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 40
subtype1 2 10 16
subtype2 3 11 6
subtype3 3 19 18

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

'Copy Number Ratio CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.344 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 156 162.3 (6.7)
subtype1 50 161.4 (6.3)
subtype2 35 163.2 (5.8)
subtype3 71 162.5 (7.2)

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

'Copy Number Ratio CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.487 (Fisher's exact test), Q value = 1

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

nPatients ABSENT PRESENT
ALL 73 14
subtype1 26 7
subtype2 13 1
subtype3 34 6

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

'Copy Number Ratio CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.535 (Fisher's exact test), Q value = 1

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

nPatients CARBOPLATIN CISPLATIN
ALL 1 14
subtype1 0 3
subtype2 0 7
subtype3 1 4

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

'Copy Number Ratio CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.766 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TX
ALL 2 1 22 51 21 3 6 6 8 13 2 1 7
subtype1 2 0 8 14 6 1 3 2 0 5 1 0 4
subtype2 0 0 4 12 3 0 0 2 1 2 0 0 1
subtype3 0 1 10 25 12 2 3 2 7 6 1 1 2

Figure S41.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'Copy Number Ratio CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 1

Table S43.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 176 47.6 (13.1)
subtype1 55 49.7 (10.5)
subtype2 39 46.7 (15.0)
subtype3 82 46.5 (13.7)

Figure S42.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #2: 'METHLYATION CNMF'

Table S44.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4 5 6
Number of samples 45 32 38 42 31 12
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.241 (logrank test), Q value = 1

Table S45.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 193 38 0.0 - 182.9 (13.9)
subtype1 44 7 0.1 - 137.2 (8.8)
subtype2 32 4 0.1 - 173.3 (13.1)
subtype3 38 9 0.0 - 182.9 (14.4)
subtype4 39 13 0.0 - 118.7 (15.5)
subtype5 28 4 0.0 - 134.3 (20.7)
subtype6 12 1 1.4 - 53.2 (6.0)

Figure S43.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0117 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 199 47.5 (13.4)
subtype1 45 44.5 (10.5)
subtype2 32 48.7 (14.6)
subtype3 38 48.1 (14.1)
subtype4 41 42.7 (12.0)
subtype5 31 54.3 (13.5)
subtype6 12 52.1 (14.6)

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

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.983 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 108 39 4
subtype1 25 12 1
subtype2 18 5 0
subtype3 23 9 1
subtype4 18 7 1
subtype5 18 4 1
subtype6 6 2 0

Figure S45.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.331 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 100 44
subtype1 26 9
subtype2 11 11
subtype3 21 11
subtype4 20 5
subtype5 16 6
subtype6 6 2

Figure S46.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.716 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 83 4 70
subtype1 20 3 15
subtype2 13 0 12
subtype3 19 0 14
subtype4 16 1 11
subtype5 12 0 12
subtype6 3 0 6

Figure S47.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S50.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 168 4 20 2 4
subtype1 2 14 4 19 2 4
subtype2 0 31 0 1 0 0
subtype3 0 38 0 0 0 0
subtype4 0 42 0 0 0 0
subtype5 0 31 0 0 0 0
subtype6 0 12 0 0 0 0

Figure S48.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.429 (Fisher's exact test), Q value = 1

Table S51.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 32 168
subtype1 4 41
subtype2 3 29
subtype3 8 30
subtype4 9 33
subtype5 6 25
subtype6 2 10

Figure S49.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.544 (Kruskal-Wallis (anova)), Q value = 1

Table S52.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 13 15.0 (11.8)
subtype2 11 21.5 (18.5)
subtype3 12 16.1 (7.6)
subtype4 12 21.3 (13.4)
subtype5 10 22.8 (12.4)
subtype6 4 24.5 (22.3)

Figure S50.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.513 (Kruskal-Wallis (anova)), Q value = 1

Table S53.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 29 1.2 (3.3)
subtype2 19 1.4 (2.0)
subtype3 23 1.0 (2.1)
subtype4 21 0.7 (1.5)
subtype5 23 1.3 (3.5)
subtype6 8 0.4 (0.7)

Figure S51.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.287 (Fisher's exact test), Q value = 1

Table S54.  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 7 16 23 1 148
subtype1 0 3 3 0 38
subtype2 1 3 3 0 24
subtype3 1 6 6 0 25
subtype4 4 3 6 1 28
subtype5 1 0 2 0 25
subtype6 0 1 3 0 8

Figure S52.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.164 (Fisher's exact test), Q value = 1

Table S55.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 144
subtype1 5 32
subtype2 2 22
subtype3 0 33
subtype4 5 28
subtype5 1 20
subtype6 0 9

Figure S53.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'ETHNICITY'

'METHLYATION CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.56 (Kruskal-Wallis (anova)), Q value = 1

Table S56.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 182 75.2 (20.9)
subtype1 41 75.7 (15.3)
subtype2 32 74.8 (19.3)
subtype3 33 71.3 (21.8)
subtype4 36 78.5 (19.5)
subtype5 29 77.0 (30.5)
subtype6 11 70.6 (15.0)

Figure S54.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'TUMOR_STATUS'

P value = 0.795 (Fisher's exact test), Q value = 1

Table S57.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 51 22
subtype1 9 6
subtype2 8 4
subtype3 12 4
subtype4 12 6
subtype5 6 2
subtype6 4 0

Figure S55.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'METHLYATION CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.477 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 7 169 12
subtype1 1 2 1 39 2
subtype2 0 2 2 26 2
subtype3 0 3 0 31 4
subtype4 0 0 3 36 3
subtype5 0 4 1 26 0
subtype6 0 0 0 11 1

Figure S56.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'METHLYATION CNMF' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.74 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 94 83 1 5
subtype1 6 20 18 0 1
subtype2 1 15 15 0 1
subtype3 2 20 16 0 0
subtype4 1 17 20 1 1
subtype5 3 17 9 0 1
subtype6 1 5 5 0 1

Figure S57.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.355 (Kruskal-Wallis (anova)), Q value = 1

Table S60.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 27 1998.4 (11.5)
subtype1 4 1993.8 (14.8)
subtype2 4 1990.2 (16.0)
subtype3 5 1997.8 (3.3)
subtype4 4 2002.0 (5.6)
subtype5 6 1999.2 (14.0)
subtype6 4 2007.0 (8.3)

Figure S58.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.544 (Kruskal-Wallis (anova)), Q value = 1

Table S61.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 13 15.0 (11.8)
subtype2 11 21.5 (18.5)
subtype3 12 16.1 (7.6)
subtype4 12 21.3 (13.4)
subtype5 10 22.8 (12.4)
subtype6 4 24.5 (22.3)

Figure S59.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.539 (Fisher's exact test), Q value = 1

Table S62.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 25 8 3 43 89
subtype1 3 1 0 10 25
subtype2 3 2 1 6 16
subtype3 5 2 0 8 19
subtype4 5 0 1 10 16
subtype5 5 3 1 6 9
subtype6 4 0 0 3 4

Figure S60.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'METHLYATION CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.549 (Kruskal-Wallis (anova)), Q value = 1

Table S63.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 54 20.5 (6.2)
subtype1 12 18.7 (4.2)
subtype2 10 19.3 (6.7)
subtype3 9 19.9 (5.8)
subtype4 10 20.3 (5.2)
subtype5 9 24.7 (8.5)
subtype6 4 21.0 (6.2)

Figure S61.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'METHLYATION CNMF' versus 'RADIATION_THERAPY_TYPE'

P value = 0.366 (Fisher's exact test), Q value = 1

Table S64.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 18 44 14 1 8
subtype1 3 13 1 0 1
subtype2 3 6 0 1 2
subtype3 5 7 3 0 0
subtype4 5 10 4 0 3
subtype5 1 5 5 0 1
subtype6 1 3 1 0 1

Figure S62.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'METHLYATION CNMF' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.84 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 16 4
subtype1 4 0
subtype2 1 0
subtype3 3 1
subtype4 5 2
subtype5 2 0
subtype6 1 1

Figure S63.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.417 (Kruskal-Wallis (anova)), Q value = 1

Table S66.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 175 3.5 (2.4)
subtype1 42 3.0 (2.2)
subtype2 27 3.2 (2.1)
subtype3 35 3.8 (2.6)
subtype4 34 3.9 (2.7)
subtype5 27 3.9 (2.7)
subtype6 10 3.0 (2.1)

Figure S64.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.00866 (Kruskal-Wallis (anova)), Q value = 1

Table S67.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 100 0.1 (0.4)
subtype1 22 0.0 (0.2)
subtype2 20 0.0 (0.0)
subtype3 21 0.3 (0.7)
subtype4 20 0.0 (0.0)
subtype5 13 0.0 (0.0)
subtype6 4 0.0 (0.0)

Figure S65.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'METHLYATION CNMF' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.206 (Kruskal-Wallis (anova)), Q value = 1

Table S68.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 116 0.4 (0.6)
subtype1 23 0.5 (0.6)
subtype2 21 0.1 (0.3)
subtype3 27 0.5 (0.8)
subtype4 24 0.4 (0.6)
subtype5 16 0.2 (0.4)
subtype6 5 0.4 (0.5)

Figure S66.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.258 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 179 2.5 (1.8)
subtype1 41 2.1 (1.9)
subtype2 28 2.9 (1.9)
subtype3 37 2.5 (1.7)
subtype4 34 2.8 (1.9)
subtype5 28 2.3 (1.3)
subtype6 11 2.5 (1.6)

Figure S67.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'METHLYATION CNMF' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.0424 (Kruskal-Wallis (anova)), Q value = 1

Table S70.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 109 0.9 (1.9)
subtype1 24 0.8 (1.0)
subtype2 21 0.3 (0.9)
subtype3 23 1.1 (2.3)
subtype4 22 0.9 (1.6)
subtype5 15 1.9 (3.3)
subtype6 4 0.0 (0.0)

Figure S68.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.777 (Kruskal-Wallis (anova)), Q value = 1

Table S71.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 101 0.1 (0.3)
subtype1 21 0.1 (0.3)
subtype2 21 0.0 (0.2)
subtype3 21 0.0 (0.2)
subtype4 20 0.1 (0.4)
subtype5 13 0.2 (0.6)
subtype6 5 0.2 (0.4)

Figure S69.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'METHLYATION CNMF' versus 'LYMPH_NODE_LOCATION'

P value = 0.593 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 41
subtype1 0 0 1 8
subtype2 1 0 0 8
subtype3 0 0 0 8
subtype4 0 1 0 5
subtype5 0 0 0 8
subtype6 0 0 0 4

Figure S70.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'METHLYATION CNMF' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.725 (Fisher's exact test), Q value = 1

Table S73.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

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

Figure S71.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'METHLYATION CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.0169 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 58 85
subtype1 0 3 10 24
subtype2 1 2 12 11
subtype3 0 3 14 16
subtype4 0 1 3 21
subtype5 1 0 13 9
subtype6 0 1 6 4

Figure S72.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'METHLYATION CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.858 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 56 66
subtype1 16 18
subtype2 6 12
subtype3 10 13
subtype4 10 8
subtype5 11 12
subtype6 3 3

Figure S73.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.513 (Kruskal-Wallis (anova)), Q value = 1

Table S76.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 29 1.2 (3.3)
subtype2 19 1.4 (2.0)
subtype3 23 1.0 (2.1)
subtype4 21 0.7 (1.5)
subtype5 23 1.3 (3.5)
subtype6 8 0.4 (0.7)

Figure S74.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.829 (Kruskal-Wallis (anova)), Q value = 1

Table S77.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 143 22.4 (12.7)
subtype1 34 21.2 (9.6)
subtype2 21 25.4 (16.2)
subtype3 28 24.3 (13.7)
subtype4 26 21.0 (12.2)
subtype5 25 21.8 (11.0)
subtype6 9 19.2 (16.8)

Figure S75.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'METHLYATION CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0149 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 43 88
subtype1 0 13
subtype2 5 18
subtype3 10 20
subtype4 14 17
subtype5 12 13
subtype6 2 7

Figure S76.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'METHLYATION CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.34 (Kruskal-Wallis (anova)), Q value = 1

Table S79.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 200 2007.3 (5.2)
subtype1 45 2008.4 (4.4)
subtype2 32 2006.8 (6.0)
subtype3 38 2006.3 (5.6)
subtype4 42 2006.6 (5.2)
subtype5 31 2007.5 (5.2)
subtype6 12 2009.2 (3.7)

Figure S77.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'METHLYATION CNMF' versus 'HYSTERECTOMY_TYPE'

P value = 0.285 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 129 5
subtype1 2 31 2
subtype2 0 21 0
subtype3 0 28 1
subtype4 0 20 2
subtype5 0 23 0
subtype6 1 6 0

Figure S78.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'METHLYATION CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.647 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 47
subtype1 5 13 10
subtype2 1 7 8
subtype3 1 6 10
subtype4 1 8 7
subtype5 0 3 9
subtype6 0 3 3

Figure S79.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'METHLYATION CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.158 (Kruskal-Wallis (anova)), Q value = 1

Table S82.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 162.1 (7.2)
subtype1 40 163.3 (7.0)
subtype2 30 163.3 (6.9)
subtype3 32 159.1 (8.2)
subtype4 33 161.7 (6.4)
subtype5 27 162.2 (7.2)
subtype6 11 164.1 (6.5)

Figure S80.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'METHLYATION CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.536 (Fisher's exact test), Q value = 1

Table S83.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 81 15
subtype1 24 4
subtype2 14 1
subtype3 13 4
subtype4 13 1
subtype5 14 5
subtype6 3 0

Figure S81.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'METHLYATION CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.296 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 3 17
subtype1 0 2
subtype2 1 1
subtype3 1 5
subtype4 0 7
subtype5 1 2
subtype6 0 0

Figure S82.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'METHLYATION CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.0809 (Kruskal-Wallis (anova)), Q value = 1

Table S85.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 10 12.8 (6.1)
subtype2 1 11.1 (NA)
subtype3 3 8.4 (1.8)
subtype4 3 20.1 (5.2)
subtype5 2 9.0 (4.2)
subtype6 1 13.8 (NA)

Figure S83.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

'METHLYATION CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.402 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TIS TX
ALL 2 1 26 55 24 3 6 7 9 14 2 2 1 8
subtype1 0 0 1 18 6 0 2 3 3 4 1 0 0 0
subtype2 1 0 4 8 5 1 1 0 1 2 0 0 0 3
subtype3 1 0 7 13 2 2 1 2 1 3 0 1 0 0
subtype4 0 0 8 7 3 0 1 1 2 3 0 1 1 3
subtype5 0 1 6 6 5 0 1 1 1 1 1 0 0 1
subtype6 0 0 0 3 3 0 0 0 1 1 0 0 0 1

Figure S84.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'METHLYATION CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0161 (Kruskal-Wallis (anova)), Q value = 1

Table S87.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 200 47.6 (13.4)
subtype1 45 44.5 (10.5)
subtype2 32 48.7 (14.6)
subtype3 38 48.1 (14.1)
subtype4 42 43.2 (12.3)
subtype5 31 54.3 (13.5)
subtype6 12 52.1 (14.6)

Figure S85.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S88.  Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 77 47 56
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.353 (logrank test), Q value = 1

Table S89.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 173 33 0.0 - 182.9 (13.6)
subtype1 75 13 0.0 - 182.9 (13.9)
subtype2 44 12 0.0 - 118.7 (14.5)
subtype3 54 8 0.1 - 147.4 (9.9)

Figure S86.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.11 (Kruskal-Wallis (anova)), Q value = 1

Table S90.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 179 47.4 (12.9)
subtype1 77 50.1 (13.7)
subtype2 46 45.6 (13.9)
subtype3 56 45.2 (10.1)

Figure S87.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.723 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 100 37 3
subtype1 40 19 2
subtype2 25 7 0
subtype3 35 11 1

Figure S88.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0153 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 89 42
subtype1 31 26
subtype2 23 7
subtype3 35 9

Figure S89.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.114 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 76 3 65
subtype1 29 0 33
subtype2 20 0 14
subtype3 27 3 18

Figure S90.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S94.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 148 4 20 2 4
subtype1 0 77 0 0 0 0
subtype2 0 47 0 0 0 0
subtype3 2 24 4 20 2 4

Figure S91.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.317 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 30 150
subtype1 14 63
subtype2 10 37
subtype3 6 50

Figure S92.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0633 (Kruskal-Wallis (anova)), Q value = 1

Table S96.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 54 18.6 (12.3)
subtype1 28 21.2 (13.3)
subtype2 11 18.8 (9.9)
subtype3 15 13.6 (11.0)

Figure S93.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0587 (Kruskal-Wallis (anova)), Q value = 1

Table S97.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 111 1.1 (2.6)
subtype1 49 1.2 (1.9)
subtype2 25 1.0 (3.2)
subtype3 37 0.9 (2.9)

Figure S94.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.238 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #3: '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 6 15 18 1 135
subtype1 2 8 8 0 56
subtype2 4 3 6 1 32
subtype3 0 4 4 0 47

Figure S95.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.477 (Fisher's exact test), Q value = 1

Table S99.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 129
subtype1 3 56
subtype2 4 32
subtype3 5 41

Figure S96.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RNAseq CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.356 (Kruskal-Wallis (anova)), Q value = 1

Table S100.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 166 75.3 (20.5)
subtype1 72 74.7 (25.0)
subtype2 41 74.9 (18.0)
subtype3 53 76.5 (15.2)

Figure S97.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.945 (Fisher's exact test), Q value = 1

Table S101.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 50 20
subtype1 22 8
subtype2 13 6
subtype3 15 6

Figure S98.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.662 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 6 150 12
subtype1 0 7 2 61 7
subtype2 0 1 2 42 2
subtype3 1 3 2 47 3

Figure S99.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RNAseq CNMF subtypes' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.131 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 85 74 1 5
subtype1 6 44 26 0 1
subtype2 2 17 25 1 1
subtype3 6 24 23 0 3

Figure S100.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.799 (Kruskal-Wallis (anova)), Q value = 1

Table S104.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 25 1997.7 (11.7)
subtype1 14 1997.9 (12.6)
subtype2 3 1996.0 (3.6)
subtype3 8 1998.1 (13.2)

Figure S101.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0633 (Kruskal-Wallis (anova)), Q value = 1

Table S105.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 54 18.6 (12.3)
subtype1 28 21.2 (13.3)
subtype2 11 18.8 (9.9)
subtype3 15 13.6 (11.0)

Figure S102.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.384 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 23 8 1 36 83
subtype1 11 7 1 16 33
subtype2 4 0 0 10 21
subtype3 8 1 0 10 29

Figure S103.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.812 (Kruskal-Wallis (anova)), Q value = 1

Table S107.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 48 20.5 (6.3)
subtype1 25 21.9 (7.7)
subtype2 9 19.1 (4.2)
subtype3 14 19.0 (3.9)

Figure S104.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.84 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM INTERNAL
ALL 17 37 14 5
subtype1 8 13 7 2
subtype2 5 11 5 2
subtype3 4 13 2 1

Figure S105.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

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

Table S109.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 12 3
subtype1 4 1
subtype2 5 1
subtype3 3 1

Figure S106.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 1

Table S110.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 156 3.5 (2.5)
subtype1 67 3.8 (2.7)
subtype2 40 3.6 (2.3)
subtype3 49 2.9 (2.1)

Figure S107.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.159 (Kruskal-Wallis (anova)), Q value = 1

Table S111.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 96 0.1 (0.4)
subtype1 45 0.2 (0.5)
subtype2 23 0.0 (0.0)
subtype3 28 0.0 (0.2)

Figure S108.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.355 (Kruskal-Wallis (anova)), Q value = 1

Table S112.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 108 0.3 (0.6)
subtype1 52 0.3 (0.6)
subtype2 27 0.2 (0.4)
subtype3 29 0.4 (0.6)

Figure S109.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 1

Table S113.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 161 2.4 (1.7)
subtype1 71 2.4 (1.5)
subtype2 41 2.7 (1.9)
subtype3 49 2.1 (1.8)

Figure S110.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.737 (Kruskal-Wallis (anova)), Q value = 1

Table S114.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 103 0.9 (1.9)
subtype1 49 1.2 (2.6)
subtype2 24 0.6 (1.1)
subtype3 30 0.7 (1.1)

Figure S111.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.96 (Kruskal-Wallis (anova)), Q value = 1

Table S115.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 97 0.1 (0.3)
subtype1 46 0.1 (0.4)
subtype2 23 0.1 (0.3)
subtype3 28 0.1 (0.3)

Figure S112.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq CNMF subtypes' versus 'LYMPH_NODE_LOCATION'

P value = 0.0318 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 39
subtype1 0 0 0 23
subtype2 1 1 0 6
subtype3 0 0 1 10

Figure S113.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'RNAseq CNMF subtypes' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.839 (Fisher's exact test), Q value = 1

Table S117.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 6 12 1 5
subtype1 1 2 6 1 2
subtype2 0 2 5 0 2
subtype3 1 2 1 0 1

Figure S114.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'RNAseq CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.275 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 53 81
subtype1 2 4 30 30
subtype2 0 2 9 22
subtype3 0 4 14 29

Figure S115.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'RNAseq CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.459 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 54 62
subtype1 21 31
subtype2 12 10
subtype3 21 21

Figure S116.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0587 (Kruskal-Wallis (anova)), Q value = 1

Table S120.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 111 1.1 (2.6)
subtype1 49 1.2 (1.9)
subtype2 25 1.0 (3.2)
subtype3 37 0.9 (2.9)

Figure S117.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.79 (Kruskal-Wallis (anova)), Q value = 1

Table S121.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 130 22.3 (12.8)
subtype1 56 24.3 (15.5)
subtype2 31 21.2 (10.7)
subtype3 43 20.5 (9.9)

Figure S118.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'RNAseq CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0222 (Fisher's exact test), Q value = 1

Table S122.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 81
subtype1 21 38
subtype2 16 23
subtype3 2 20

Figure S119.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 1

Table S123.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 180 2007.5 (5.1)
subtype1 77 2007.2 (5.7)
subtype2 47 2006.9 (4.7)
subtype3 56 2008.5 (4.4)

Figure S120.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq CNMF subtypes' versus 'HYSTERECTOMY_TYPE'

P value = 0.477 (Fisher's exact test), Q value = 1

Table S124.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 117 5
subtype1 1 55 1
subtype2 0 23 2
subtype3 2 39 2

Figure S121.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'RNAseq CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.114 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 43
subtype1 1 12 22
subtype2 2 11 10
subtype3 5 17 11

Figure S122.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.502 (Kruskal-Wallis (anova)), Q value = 1

Table S126.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 160 162.1 (6.9)
subtype1 70 161.7 (7.3)
subtype2 38 161.3 (5.6)
subtype3 52 163.1 (7.1)

Figure S123.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.111 (Fisher's exact test), Q value = 1

Table S127.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 78 14
subtype1 30 8
subtype2 18 0
subtype3 30 6

Figure S124.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

Table S128.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 2 16
subtype1 1 6
subtype2 1 7
subtype3 0 3

Figure S125.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'RNAseq CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.0624 (Fisher's exact test), Q value = 1

Table S129.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TX
ALL 2 1 23 52 22 3 6 6 9 13 2 1 7
subtype1 2 1 14 16 7 2 3 2 4 8 1 1 2
subtype2 0 0 8 11 6 1 1 2 2 1 0 0 4
subtype3 0 0 1 25 9 0 2 2 3 4 1 0 1

Figure S126.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.128 (Kruskal-Wallis (anova)), Q value = 1

Table S130.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 180 47.5 (12.9)
subtype1 77 50.1 (13.7)
subtype2 47 46.0 (14.0)
subtype3 56 45.2 (10.1)

Figure S127.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S131.  Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 91 25 17 47
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0267 (logrank test), Q value = 1

Table S132.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 173 33 0.0 - 182.9 (13.6)
subtype1 86 16 0.1 - 182.9 (15.5)
subtype2 25 3 0.0 - 147.4 (13.1)
subtype3 17 7 0.1 - 78.7 (11.7)
subtype4 45 7 0.1 - 137.2 (9.7)

Figure S128.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0489 (Kruskal-Wallis (anova)), Q value = 1

Table S133.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 179 47.4 (12.9)
subtype1 90 49.0 (14.4)
subtype2 25 50.6 (9.8)
subtype3 17 40.9 (12.2)
subtype4 47 45.1 (10.4)

Figure S129.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.762 (Fisher's exact test), Q value = 1

Table S134.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 100 37 3
subtype1 45 19 2
subtype2 19 3 0
subtype3 9 4 0
subtype4 27 11 1

Figure S130.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.583 (Fisher's exact test), Q value = 1

Table S135.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 89 42
subtype1 39 22
subtype2 13 8
subtype3 10 3
subtype4 27 9

Figure S131.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.242 (Fisher's exact test), Q value = 1

Table S136.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 76 3 65
subtype1 33 0 34
subtype2 15 0 8
subtype3 8 0 6
subtype4 20 3 17

Figure S132.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S137.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 148 4 20 2 4
subtype1 0 91 0 0 0 0
subtype2 0 25 0 0 0 0
subtype3 0 17 0 0 0 0
subtype4 2 15 4 20 2 4

Figure S133.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.536 (Fisher's exact test), Q value = 1

Table S138.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 30 150
subtype1 17 74
subtype2 4 21
subtype3 4 13
subtype4 5 42

Figure S134.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.198 (Kruskal-Wallis (anova)), Q value = 1

Table S139.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 54 18.6 (12.3)
subtype1 30 21.0 (12.9)
subtype2 4 16.2 (12.5)
subtype3 7 17.8 (9.5)
subtype4 13 14.2 (11.7)

Figure S135.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.557 (Kruskal-Wallis (anova)), Q value = 1

Table S140.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 111 1.1 (2.6)
subtype1 49 1.3 (2.8)
subtype2 20 0.8 (1.2)
subtype3 12 0.3 (0.7)
subtype4 30 1.1 (3.2)

Figure S136.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.387 (Fisher's exact test), Q value = 1

Table S141.  Clustering Approach #4: '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 6 15 18 1 135
subtype1 4 7 9 1 67
subtype2 0 4 3 0 17
subtype3 2 1 3 0 11
subtype4 0 3 3 0 40

Figure S137.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.436 (Fisher's exact test), Q value = 1

Table S142.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 12 129
subtype1 6 63
subtype2 0 20
subtype3 1 12
subtype4 5 34

Figure S138.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RNAseq cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.517 (Kruskal-Wallis (anova)), Q value = 1

Table S143.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 166 75.3 (20.5)
subtype1 84 75.9 (24.3)
subtype2 24 71.0 (16.8)
subtype3 14 75.6 (16.2)
subtype4 44 76.5 (15.3)

Figure S139.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.195 (Fisher's exact test), Q value = 1

Table S144.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 50 20
subtype1 23 9
subtype2 12 1
subtype3 5 4
subtype4 10 6

Figure S140.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.535 (Fisher's exact test), Q value = 1

Table S145.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 6 150 12
subtype1 0 7 2 76 6
subtype2 0 2 0 20 3
subtype3 0 0 2 14 1
subtype4 1 2 2 40 2

Figure S141.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RNAseq cHierClus subtypes' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.0169 (Fisher's exact test), Q value = 1

Table S146.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 85 74 1 5
subtype1 6 52 30 0 2
subtype2 1 6 17 0 1
subtype3 1 5 10 1 0
subtype4 6 22 17 0 2

Figure S142.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.794 (Kruskal-Wallis (anova)), Q value = 1

Table S147.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 25 1997.7 (11.7)
subtype1 12 1997.6 (13.1)
subtype2 6 2001.2 (10.9)
subtype3 2 1997.5 (3.5)
subtype4 5 1994.0 (12.8)

Figure S143.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.198 (Kruskal-Wallis (anova)), Q value = 1

Table S148.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 54 18.6 (12.3)
subtype1 30 21.0 (12.9)
subtype2 4 16.2 (12.5)
subtype3 7 17.8 (9.5)
subtype4 13 14.2 (11.7)

Figure S144.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.413 (Fisher's exact test), Q value = 1

Table S149.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 23 8 1 36 83
subtype1 10 6 1 20 37
subtype2 7 1 0 2 12
subtype3 2 0 0 5 8
subtype4 4 1 0 9 26

Figure S145.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.576 (Kruskal-Wallis (anova)), Q value = 1

Table S150.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 48 20.5 (6.3)
subtype1 25 21.4 (7.1)
subtype2 5 22.6 (8.2)
subtype3 6 18.0 (4.0)
subtype4 12 19.0 (4.1)

Figure S146.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.458 (Fisher's exact test), Q value = 1

Table S151.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM INTERNAL
ALL 17 37 14 5
subtype1 10 14 7 3
subtype2 1 5 4 1
subtype3 2 6 2 1
subtype4 4 12 1 0

Figure S147.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.471 (Fisher's exact test), Q value = 1

Table S152.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 12 3
subtype1 3 2
subtype2 2 1
subtype3 4 0
subtype4 3 0

Figure S148.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.274 (Kruskal-Wallis (anova)), Q value = 1

Table S153.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 156 3.5 (2.5)
subtype1 79 3.7 (2.7)
subtype2 20 3.2 (1.8)
subtype3 16 3.7 (2.1)
subtype4 41 2.9 (2.2)

Figure S149.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.468 (Kruskal-Wallis (anova)), Q value = 1

Table S154.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 96 0.1 (0.4)
subtype1 52 0.1 (0.5)
subtype2 11 0.0 (0.0)
subtype3 10 0.0 (0.0)
subtype4 23 0.0 (0.2)

Figure S150.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.347 (Kruskal-Wallis (anova)), Q value = 1

Table S155.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 108 0.3 (0.6)
subtype1 61 0.3 (0.6)
subtype2 12 0.2 (0.4)
subtype3 11 0.3 (0.5)
subtype4 24 0.5 (0.6)

Figure S151.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.155 (Kruskal-Wallis (anova)), Q value = 1

Table S156.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 161 2.4 (1.7)
subtype1 82 2.4 (1.6)
subtype2 22 2.4 (1.4)
subtype3 16 2.9 (2.0)
subtype4 41 2.0 (1.9)

Figure S152.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.911 (Kruskal-Wallis (anova)), Q value = 1

Table S157.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 103 0.9 (1.9)
subtype1 54 1.0 (2.4)
subtype2 13 1.0 (1.5)
subtype3 11 0.7 (1.1)
subtype4 25 0.7 (1.0)

Figure S153.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.999 (Kruskal-Wallis (anova)), Q value = 1

Table S158.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 97 0.1 (0.3)
subtype1 53 0.1 (0.4)
subtype2 12 0.1 (0.3)
subtype3 10 0.1 (0.3)
subtype4 22 0.1 (0.3)

Figure S154.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODE_LOCATION'

P value = 0.521 (Fisher's exact test), Q value = 1

Table S159.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 39
subtype1 0 1 0 21
subtype2 1 0 0 7
subtype3 0 0 0 3
subtype4 0 0 1 8

Figure S155.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'RNAseq cHierClus subtypes' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.436 (Fisher's exact test), Q value = 1

Table S160.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 6 12 1 5
subtype1 1 3 9 1 1
subtype2 0 1 2 0 2
subtype3 0 1 1 0 1
subtype4 1 1 0 0 1

Figure S156.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'RNAseq cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0133 (Fisher's exact test), Q value = 1

Table S161.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 53 81
subtype1 2 3 30 37
subtype2 0 3 12 8
subtype3 0 1 0 11
subtype4 0 3 11 25

Figure S157.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'RNAseq cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.877 (Fisher's exact test), Q value = 1

Table S162.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 54 62
subtype1 23 28
subtype2 8 12
subtype3 5 5
subtype4 18 17

Figure S158.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.557 (Kruskal-Wallis (anova)), Q value = 1

Table S163.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 111 1.1 (2.6)
subtype1 49 1.3 (2.8)
subtype2 20 0.8 (1.2)
subtype3 12 0.3 (0.7)
subtype4 30 1.1 (3.2)

Figure S159.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.78 (Kruskal-Wallis (anova)), Q value = 1

Table S164.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 130 22.3 (12.8)
subtype1 60 23.1 (13.9)
subtype2 21 20.5 (14.6)
subtype3 13 22.9 (13.2)
subtype4 36 21.7 (9.8)

Figure S160.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'RNAseq cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.115 (Fisher's exact test), Q value = 1

Table S165.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 39 81
subtype1 27 46
subtype2 6 13
subtype3 5 8
subtype4 1 14

Figure S161.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.123 (Kruskal-Wallis (anova)), Q value = 1

Table S166.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 180 2007.5 (5.1)
subtype1 91 2007.1 (5.2)
subtype2 25 2008.8 (4.5)
subtype3 17 2005.3 (6.1)
subtype4 47 2008.3 (4.4)

Figure S162.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq cHierClus subtypes' versus 'HYSTERECTOMY_TYPE'

P value = 0.169 (Fisher's exact test), Q value = 1

Table S167.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 117 5
subtype1 1 56 1
subtype2 0 19 0
subtype3 0 9 2
subtype4 2 33 2

Figure S163.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'RNAseq cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S168.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 43
subtype1 0 15 22
subtype2 1 8 6
subtype3 2 3 6
subtype4 5 14 9

Figure S164.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.41 (Kruskal-Wallis (anova)), Q value = 1

Table S169.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 160 162.1 (6.9)
subtype1 81 161.0 (7.2)
subtype2 24 163.5 (6.6)
subtype3 12 162.6 (4.2)
subtype4 43 163.0 (6.8)

Figure S165.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.877 (Fisher's exact test), Q value = 1

Table S170.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 78 14
subtype1 35 8
subtype2 11 2
subtype3 6 0
subtype4 26 4

Figure S166.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

Table S171.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 2 16
subtype1 2 8
subtype2 0 3
subtype3 0 3
subtype4 0 2

Figure S167.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'RNAseq cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.00179 (Fisher's exact test), Q value = 0.59

Table S172.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TX
ALL 2 1 23 52 22 3 6 6 9 13 2 1 7
subtype1 1 0 16 16 12 2 3 1 5 8 1 1 3
subtype2 1 1 0 15 2 1 0 1 0 1 0 0 2
subtype3 0 0 6 2 1 0 1 2 1 0 0 0 1
subtype4 0 0 1 19 7 0 2 2 3 4 1 0 1

Figure S168.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0454 (Kruskal-Wallis (anova)), Q value = 1

Table S173.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 180 47.5 (12.9)
subtype1 91 49.2 (14.4)
subtype2 25 50.6 (9.8)
subtype3 17 40.9 (12.2)
subtype4 47 45.1 (10.4)

Figure S169.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S174.  Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 62 60 78
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.499 (logrank test), Q value = 1

Table S175.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 193 38 0.0 - 182.9 (13.9)
subtype1 61 10 0.1 - 182.9 (13.1)
subtype2 57 9 0.0 - 147.4 (14.9)
subtype3 75 19 0.0 - 177.0 (14.4)

Figure S170.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.221 (Kruskal-Wallis (anova)), Q value = 1

Table S176.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 47.5 (13.4)
subtype1 62 46.4 (10.2)
subtype2 60 50.1 (14.4)
subtype3 77 46.3 (14.7)

Figure S171.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.89 (Fisher's exact test), Q value = 1

Table S177.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 108 39 4
subtype1 38 16 1
subtype2 34 12 1
subtype3 36 11 2

Figure S172.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.587 (Fisher's exact test), Q value = 1

Table S178.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 100 44
subtype1 38 13
subtype2 30 16
subtype3 32 15

Figure S173.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.628 (Fisher's exact test), Q value = 1

Table S179.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 83 4 70
subtype1 28 3 25
subtype2 28 0 21
subtype3 27 1 24

Figure S174.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S180.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 168 4 20 2 4
subtype1 2 36 3 16 2 3
subtype2 0 54 1 4 0 1
subtype3 0 78 0 0 0 0

Figure S175.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.411 (Fisher's exact test), Q value = 1

Table S181.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 32 168
subtype1 8 54
subtype2 8 52
subtype3 16 62

Figure S176.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0665 (Kruskal-Wallis (anova)), Q value = 1

Table S182.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 14 12.5 (9.4)
subtype2 21 22.0 (14.4)
subtype3 27 21.0 (14.0)

Figure S177.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.443 (Kruskal-Wallis (anova)), Q value = 1

Table S183.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 40 0.9 (2.6)
subtype2 46 1.3 (2.5)
subtype3 37 0.9 (2.7)

Figure S178.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.386 (Fisher's exact test), Q value = 1

Table S184.  Clustering Approach #5: '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 7 16 23 1 148
subtype1 0 5 8 0 48
subtype2 1 6 6 0 44
subtype3 6 5 9 1 56

Figure S179.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.506 (Fisher's exact test), Q value = 1

Table S185.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 144
subtype1 4 47
subtype2 2 42
subtype3 7 55

Figure S180.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.35 (Kruskal-Wallis (anova)), Q value = 1

Table S186.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 182 75.2 (20.9)
subtype1 60 76.7 (16.3)
subtype2 52 74.1 (26.3)
subtype3 70 74.8 (20.0)

Figure S181.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'TUMOR_STATUS'

P value = 0.724 (Fisher's exact test), Q value = 1

Table S187.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 51 22
subtype1 19 6
subtype2 12 6
subtype3 20 10

Figure S182.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.579 (Fisher's exact test), Q value = 1

Table S188.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 7 169 12
subtype1 1 2 3 53 3
subtype2 0 6 2 47 5
subtype3 0 3 2 69 4

Figure S183.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CNMF' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.0596 (Fisher's exact test), Q value = 1

Table S189.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 94 83 1 5
subtype1 6 24 30 0 2
subtype2 6 34 18 1 0
subtype3 2 36 35 0 3

Figure S184.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.317 (Kruskal-Wallis (anova)), Q value = 1

Table S190.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 27 1998.4 (11.5)
subtype1 6 1997.0 (13.6)
subtype2 9 1995.1 (11.5)
subtype3 12 2001.5 (10.6)

Figure S185.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0665 (Kruskal-Wallis (anova)), Q value = 1

Table S191.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 14 12.5 (9.4)
subtype2 21 22.0 (14.4)
subtype3 27 21.0 (14.0)

Figure S186.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.245 (Fisher's exact test), Q value = 1

Table S192.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 25 8 3 43 89
subtype1 7 2 0 8 35
subtype2 7 4 2 15 26
subtype3 11 2 1 20 28

Figure S187.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.239 (Kruskal-Wallis (anova)), Q value = 1

Table S193.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 54 20.5 (6.2)
subtype1 12 20.3 (5.7)
subtype2 19 22.1 (6.5)
subtype3 23 19.2 (6.0)

Figure S188.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_TYPE'

P value = 0.267 (Fisher's exact test), Q value = 1

Table S194.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 18 44 14 1 8
subtype1 5 16 3 0 0
subtype2 3 13 5 1 3
subtype3 10 15 6 0 5

Figure S189.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CNMF' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.137 (Fisher's exact test), Q value = 1

Table S195.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 16 4
subtype1 4 0
subtype2 6 0
subtype3 6 4

Figure S190.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.229 (Kruskal-Wallis (anova)), Q value = 1

Table S196.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 175 3.5 (2.4)
subtype1 53 3.2 (2.5)
subtype2 55 3.7 (2.4)
subtype3 67 3.5 (2.5)

Figure S191.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.791 (Kruskal-Wallis (anova)), Q value = 1

Table S197.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 100 0.1 (0.4)
subtype1 28 0.1 (0.3)
subtype2 30 0.1 (0.5)
subtype3 42 0.1 (0.3)

Figure S192.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CNMF' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.0365 (Kruskal-Wallis (anova)), Q value = 1

Table S198.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 116 0.4 (0.6)
subtype1 31 0.5 (0.7)
subtype2 37 0.4 (0.5)
subtype3 48 0.2 (0.6)

Figure S193.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.167 (Kruskal-Wallis (anova)), Q value = 1

Table S199.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 179 2.5 (1.8)
subtype1 54 2.3 (1.9)
subtype2 56 2.7 (1.7)
subtype3 69 2.5 (1.7)

Figure S194.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CNMF' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.392 (Kruskal-Wallis (anova)), Q value = 1

Table S200.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 109 0.9 (1.9)
subtype1 30 0.9 (1.5)
subtype2 34 1.2 (2.4)
subtype3 45 0.7 (1.7)

Figure S195.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.831 (Kruskal-Wallis (anova)), Q value = 1

Table S201.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 101 0.1 (0.3)
subtype1 28 0.1 (0.3)
subtype2 30 0.1 (0.4)
subtype3 43 0.1 (0.3)

Figure S196.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CNMF' versus 'LYMPH_NODE_LOCATION'

P value = 0.343 (Fisher's exact test), Q value = 1

Table S202.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 41
subtype1 0 0 1 12
subtype2 0 0 0 16
subtype3 1 1 0 13

Figure S197.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'MIRSEQ CNMF' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.706 (Fisher's exact test), Q value = 1

Table S203.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 13 1 6
subtype1 1 2 1 0 1
subtype2 1 2 5 1 3
subtype3 0 3 7 0 2

Figure S198.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'MIRSEQ CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.466 (Fisher's exact test), Q value = 1

Table S204.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 58 85
subtype1 1 4 14 28
subtype2 1 3 23 22
subtype3 0 3 21 35

Figure S199.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'MIRSEQ CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.325 (Fisher's exact test), Q value = 1

Table S205.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 56 66
subtype1 17 28
subtype2 20 22
subtype3 19 16

Figure S200.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.443 (Kruskal-Wallis (anova)), Q value = 1

Table S206.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 40 0.9 (2.6)
subtype2 46 1.3 (2.5)
subtype3 37 0.9 (2.7)

Figure S201.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.528 (Kruskal-Wallis (anova)), Q value = 1

Table S207.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 143 22.4 (12.7)
subtype1 49 21.0 (10.7)
subtype2 49 25.0 (14.8)
subtype3 45 21.2 (12.0)

Figure S202.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.031 (Fisher's exact test), Q value = 1

Table S208.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 43 88
subtype1 4 25
subtype2 14 27
subtype3 25 36

Figure S203.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.173 (Kruskal-Wallis (anova)), Q value = 1

Table S209.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 200 2007.3 (5.2)
subtype1 62 2008.0 (5.3)
subtype2 60 2007.4 (5.0)
subtype3 78 2006.6 (5.3)

Figure S204.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CNMF' versus 'HYSTERECTOMY_TYPE'

P value = 0.136 (Fisher's exact test), Q value = 1

Table S210.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 129 5
subtype1 3 46 1
subtype2 0 47 1
subtype3 0 36 3

Figure S205.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'MIRSEQ CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.944 (Fisher's exact test), Q value = 1

Table S211.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 47
subtype1 4 13 16
subtype2 2 13 15
subtype3 2 14 16

Figure S206.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.185 (Kruskal-Wallis (anova)), Q value = 1

Table S212.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 162.1 (7.2)
subtype1 57 163.6 (6.6)
subtype2 48 162.1 (7.8)
subtype3 68 160.8 (7.1)

Figure S207.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.322 (Fisher's exact test), Q value = 1

Table S213.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 81 15
subtype1 28 6
subtype2 27 7
subtype3 26 2

Figure S208.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'MIRSEQ CNMF' versus 'CHEMO_CONCURRENT_TYPE'

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

Table S214.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 3 17
subtype1 0 4
subtype2 1 3
subtype3 2 10

Figure S209.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.456 (Kruskal-Wallis (anova)), Q value = 1

Table S215.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 10 12.8 (6.1)
subtype2 4 10.2 (3.3)
subtype3 6 14.6 (7.1)

Figure S210.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

'MIRSEQ CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.35 (Fisher's exact test), Q value = 1

Table S216.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TIS TX
ALL 2 1 26 55 24 3 6 7 9 14 2 2 1 8
subtype1 0 0 6 21 11 1 2 4 5 4 1 0 0 1
subtype2 1 1 5 20 7 0 3 2 2 5 0 1 0 2
subtype3 1 0 15 14 6 2 1 1 2 5 1 1 1 5

Figure S211.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.249 (Kruskal-Wallis (anova)), Q value = 1

Table S217.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 200 47.6 (13.4)
subtype1 62 46.4 (10.2)
subtype2 60 50.1 (14.4)
subtype3 78 46.5 (14.7)

Figure S212.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S218.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 37 123 40
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.647 (logrank test), Q value = 1

Table S219.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 193 38 0.0 - 182.9 (13.9)
subtype1 36 7 0.1 - 137.2 (7.6)
subtype2 118 23 0.0 - 182.9 (14.5)
subtype3 39 8 0.0 - 177.0 (15.0)

Figure S213.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.166 (Kruskal-Wallis (anova)), Q value = 1

Table S220.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 47.5 (13.4)
subtype1 37 43.6 (10.6)
subtype2 122 48.0 (13.6)
subtype3 40 49.4 (14.6)

Figure S214.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.205 (Fisher's exact test), Q value = 1

Table S221.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 108 39 4
subtype1 22 7 1
subtype2 56 27 3
subtype3 30 5 0

Figure S215.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.776 (Fisher's exact test), Q value = 1

Table S222.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 100 44
subtype1 21 7
subtype2 57 26
subtype3 22 11

Figure S216.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.389 (Fisher's exact test), Q value = 1

Table S223.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 83 4 70
subtype1 13 2 16
subtype2 49 2 39
subtype3 21 0 15

Figure S217.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S224.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 168 4 20 2 4
subtype1 2 7 4 19 1 4
subtype2 0 121 0 1 1 0
subtype3 0 40 0 0 0 0

Figure S218.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.628 (Fisher's exact test), Q value = 1

Table S225.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 32 168
subtype1 4 33
subtype2 22 101
subtype3 6 34

Figure S219.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.213 (Kruskal-Wallis (anova)), Q value = 1

Table S226.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 12 14.5 (12.2)
subtype2 40 20.8 (13.9)
subtype3 10 20.0 (13.9)

Figure S220.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.667 (Kruskal-Wallis (anova)), Q value = 1

Table S227.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 25 1.3 (3.5)
subtype2 68 0.9 (2.3)
subtype3 30 1.2 (2.1)

Figure S221.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.421 (Fisher's exact test), Q value = 1

Table S228.  Clustering Approach #6: '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 7 16 23 1 148
subtype1 0 2 2 0 32
subtype2 7 9 16 1 88
subtype3 0 5 5 0 28

Figure S222.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0869 (Fisher's exact test), Q value = 1

Table S229.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 144
subtype1 4 25
subtype2 9 87
subtype3 0 32

Figure S223.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CHIERARCHICAL' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.634 (Kruskal-Wallis (anova)), Q value = 1

Table S230.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 182 75.2 (20.9)
subtype1 34 76.1 (15.6)
subtype2 110 74.7 (18.8)
subtype3 38 75.9 (29.6)

Figure S224.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_STATUS'

P value = 0.671 (Fisher's exact test), Q value = 1

Table S231.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 51 22
subtype1 7 5
subtype2 32 12
subtype3 12 5

Figure S225.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.274 (Fisher's exact test), Q value = 1

Table S232.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 7 169 12
subtype1 0 0 2 34 1
subtype2 1 6 4 105 7
subtype3 0 5 1 30 4

Figure S226.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.327 (Fisher's exact test), Q value = 1

Table S233.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 94 83 1 5
subtype1 5 18 12 0 2
subtype2 5 56 55 1 3
subtype3 4 20 16 0 0

Figure S227.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.16 (Kruskal-Wallis (anova)), Q value = 1

Table S234.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 27 1998.4 (11.5)
subtype1 4 1993.8 (14.8)
subtype2 18 2001.4 (9.8)
subtype3 5 1991.2 (12.9)

Figure S228.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.213 (Kruskal-Wallis (anova)), Q value = 1

Table S235.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 12 14.5 (12.2)
subtype2 40 20.8 (13.9)
subtype3 10 20.0 (13.9)

Figure S229.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.38 (Fisher's exact test), Q value = 1

Table S236.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 25 8 3 43 89
subtype1 3 1 0 9 21
subtype2 19 3 2 26 48
subtype3 3 4 1 8 20

Figure S230.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.258 (Kruskal-Wallis (anova)), Q value = 1

Table S237.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 54 20.5 (6.2)
subtype1 11 18.7 (4.4)
subtype2 35 20.2 (6.1)
subtype3 8 24.1 (7.8)

Figure S231.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_TYPE'

P value = 0.724 (Fisher's exact test), Q value = 1

Table S238.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 18 44 14 1 8
subtype1 3 10 1 0 1
subtype2 12 27 10 0 6
subtype3 3 7 3 1 1

Figure S232.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.784 (Fisher's exact test), Q value = 1

Table S239.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 16 4
subtype1 4 0
subtype2 9 3
subtype3 3 1

Figure S233.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0722 (Kruskal-Wallis (anova)), Q value = 1

Table S240.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 175 3.5 (2.4)
subtype1 33 2.8 (2.0)
subtype2 106 3.6 (2.8)
subtype3 36 3.7 (1.6)

Figure S234.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.939 (Kruskal-Wallis (anova)), Q value = 1

Table S241.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 100 0.1 (0.4)
subtype1 17 0.1 (0.2)
subtype2 61 0.1 (0.4)
subtype3 22 0.0 (0.2)

Figure S235.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.102 (Kruskal-Wallis (anova)), Q value = 1

Table S242.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 116 0.4 (0.6)
subtype1 17 0.6 (0.6)
subtype2 73 0.3 (0.6)
subtype3 26 0.3 (0.5)

Figure S236.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0486 (Kruskal-Wallis (anova)), Q value = 1

Table S243.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 179 2.5 (1.8)
subtype1 33 2.0 (1.8)
subtype2 107 2.6 (1.9)
subtype3 39 2.7 (1.4)

Figure S237.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.512 (Kruskal-Wallis (anova)), Q value = 1

Table S244.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 109 0.9 (1.9)
subtype1 19 0.6 (0.8)
subtype2 65 1.0 (2.3)
subtype3 25 1.0 (1.3)

Figure S238.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.71 (Kruskal-Wallis (anova)), Q value = 1

Table S245.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 101 0.1 (0.3)
subtype1 16 0.1 (0.3)
subtype2 63 0.1 (0.3)
subtype3 22 0.1 (0.4)

Figure S239.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODE_LOCATION'

P value = 0.562 (Fisher's exact test), Q value = 1

Table S246.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 41
subtype1 0 0 1 7
subtype2 1 1 0 24
subtype3 0 0 0 10

Figure S240.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'MIRSEQ CHIERARCHICAL' versus 'LOCATION_OF_POSITIVE_MARGINS'

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

Table S247.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 13 1 6
subtype1 1 0 0 0 1
subtype2 1 5 9 1 4
subtype3 0 2 4 0 1

Figure S241.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'MIRSEQ CHIERARCHICAL' versus 'MENOPAUSE_STATUS'

P value = 0.493 (Fisher's exact test), Q value = 1

Table S248.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 58 85
subtype1 0 1 9 21
subtype2 1 5 35 48
subtype3 1 4 14 16

Figure S242.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.363 (Fisher's exact test), Q value = 1

Table S249.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 56 66
subtype1 15 13
subtype2 30 33
subtype3 11 20

Figure S243.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.667 (Kruskal-Wallis (anova)), Q value = 1

Table S250.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 25 1.3 (3.5)
subtype2 68 0.9 (2.3)
subtype3 30 1.2 (2.1)

Figure S244.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED'

P value = 0.856 (Kruskal-Wallis (anova)), Q value = 1

Table S251.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 143 22.4 (12.7)
subtype1 28 21.6 (9.4)
subtype2 82 21.8 (12.7)
subtype3 33 24.5 (15.0)

Figure S245.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CHIERARCHICAL' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0822 (Fisher's exact test), Q value = 1

Table S252.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 43 88
subtype1 0 9
subtype2 32 59
subtype3 11 20

Figure S246.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CHIERARCHICAL' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.342 (Kruskal-Wallis (anova)), Q value = 1

Table S253.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 200 2007.3 (5.2)
subtype1 37 2008.2 (4.7)
subtype2 123 2007.0 (5.2)
subtype3 40 2007.2 (5.5)

Figure S247.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CHIERARCHICAL' versus 'HYSTERECTOMY_TYPE'

P value = 0.132 (Fisher's exact test), Q value = 1

Table S254.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 129 5
subtype1 2 24 2
subtype2 1 69 3
subtype3 0 36 0

Figure S248.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.211 (Fisher's exact test), Q value = 1

Table S255.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 47
subtype1 4 11 7
subtype2 3 23 29
subtype3 1 6 11

Figure S249.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CHIERARCHICAL' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.678 (Kruskal-Wallis (anova)), Q value = 1

Table S256.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 162.1 (7.2)
subtype1 33 163.2 (7.3)
subtype2 105 161.8 (7.3)
subtype3 35 161.8 (7.0)

Figure S250.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CORPUS_INVOLVEMENT'

P value = 0.659 (Fisher's exact test), Q value = 1

Table S257.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 81 15
subtype1 21 2
subtype2 40 9
subtype3 20 4

Figure S251.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.103 (Fisher's exact test), Q value = 1

Table S258.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 3 17
subtype1 0 2
subtype2 1 13
subtype3 2 2

Figure S252.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'CERVIX_SUV_RESULTS'

P value = 0.362 (Kruskal-Wallis (anova)), Q value = 1

Table S259.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 10 12.8 (6.1)
subtype2 7 14.2 (6.6)
subtype3 3 9.7 (3.8)

Figure S253.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

'MIRSEQ CHIERARCHICAL' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.113 (Fisher's exact test), Q value = 1

Table S260.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TIS TX
ALL 2 1 26 55 24 3 6 7 9 14 2 2 1 8
subtype1 0 0 1 16 5 0 2 3 1 1 1 0 0 1
subtype2 1 0 17 23 15 3 2 3 8 11 1 2 1 6
subtype3 1 1 8 16 4 0 2 1 0 2 0 0 0 1

Figure S254.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CHIERARCHICAL' versus 'AGE_AT_DIAGNOSIS'

P value = 0.156 (Kruskal-Wallis (anova)), Q value = 1

Table S261.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 200 47.6 (13.4)
subtype1 37 43.6 (10.6)
subtype2 123 48.1 (13.6)
subtype3 40 49.4 (14.6)

Figure S255.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S262.  Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 64 65 71
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.829 (logrank test), Q value = 1

Table S263.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 193 38 0.0 - 182.9 (13.9)
subtype1 63 10 0.1 - 182.9 (13.1)
subtype2 61 13 0.0 - 147.4 (13.9)
subtype3 69 15 0.0 - 177.0 (14.5)

Figure S256.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0366 (Kruskal-Wallis (anova)), Q value = 1

Table S264.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 47.5 (13.4)
subtype1 64 47.8 (11.1)
subtype2 65 50.4 (14.3)
subtype3 70 44.5 (14.0)

Figure S257.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.785 (Fisher's exact test), Q value = 1

Table S265.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 108 39 4
subtype1 37 17 1
subtype2 35 12 2
subtype3 36 10 1

Figure S258.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.727 (Fisher's exact test), Q value = 1

Table S266.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 100 44
subtype1 37 14
subtype2 32 17
subtype3 31 13

Figure S259.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.407 (Fisher's exact test), Q value = 1

Table S267.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 83 4 70
subtype1 26 3 28
subtype2 30 0 22
subtype3 27 1 20

Figure S260.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S268.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 168 4 20 2 4
subtype1 2 37 4 16 2 3
subtype2 0 60 0 4 0 1
subtype3 0 71 0 0 0 0

Figure S261.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.416 (Fisher's exact test), Q value = 1

Table S269.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 32 168
subtype1 7 57
subtype2 11 54
subtype3 14 57

Figure S262.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 1

Table S270.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 18 17.2 (15.5)
subtype2 22 20.3 (11.2)
subtype3 22 20.4 (14.5)

Figure S263.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.654 (Kruskal-Wallis (anova)), Q value = 1

Table S271.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 40 0.9 (2.6)
subtype2 48 1.3 (2.4)
subtype3 35 0.9 (2.7)

Figure S264.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.156 (Fisher's exact test), Q value = 1

Table S272.  Clustering Approach #7: '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 16 23 1 148
subtype1 0 4 9 0 50
subtype2 1 7 5 0 49
subtype3 6 5 9 1 49

Figure S265.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.335 (Fisher's exact test), Q value = 1

Table S273.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 144
subtype1 4 47
subtype2 2 47
subtype3 7 50

Figure S266.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.124 (Kruskal-Wallis (anova)), Q value = 1

Table S274.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 182 75.2 (20.9)
subtype1 61 76.3 (16.1)
subtype2 58 72.0 (25.7)
subtype3 63 77.1 (19.9)

Figure S267.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.566 (Fisher's exact test), Q value = 1

Table S275.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 51 22
subtype1 18 6
subtype2 14 9
subtype3 19 7

Figure S268.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.311 (Fisher's exact test), Q value = 1

Table S276.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 7 169 12
subtype1 1 2 3 55 3
subtype2 0 7 2 50 6
subtype3 0 2 2 64 3

Figure S269.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.0581 (Fisher's exact test), Q value = 1

Table S277.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 94 83 1 5
subtype1 6 25 29 0 3
subtype2 7 35 22 1 0
subtype3 1 34 32 0 2

Figure S270.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.737 (Kruskal-Wallis (anova)), Q value = 1

Table S278.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 27 1998.4 (11.5)
subtype1 7 1998.7 (13.2)
subtype2 10 1996.8 (12.1)
subtype3 10 1999.7 (10.8)

Figure S271.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 1

Table S279.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 18 17.2 (15.5)
subtype2 22 20.3 (11.2)
subtype3 22 20.4 (14.5)

Figure S272.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.762 (Fisher's exact test), Q value = 1

Table S280.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 25 8 3 43 89
subtype1 7 2 0 11 34
subtype2 9 4 2 16 27
subtype3 9 2 1 16 28

Figure S273.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.0729 (Kruskal-Wallis (anova)), Q value = 1

Table S281.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 54 20.5 (6.2)
subtype1 16 21.3 (6.7)
subtype2 19 22.1 (6.5)
subtype3 19 18.1 (4.8)

Figure S274.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.13 (Fisher's exact test), Q value = 1

Table S282.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 18 44 14 1 8
subtype1 5 19 2 0 0
subtype2 5 10 6 1 4
subtype3 8 15 6 0 4

Figure S275.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.417 (Fisher's exact test), Q value = 1

Table S283.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 16 4
subtype1 5 0
subtype2 5 1
subtype3 6 3

Figure S276.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.464 (Kruskal-Wallis (anova)), Q value = 1

Table S284.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 175 3.5 (2.4)
subtype1 55 3.3 (2.5)
subtype2 60 3.6 (2.3)
subtype3 60 3.6 (2.6)

Figure S277.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.951 (Kruskal-Wallis (anova)), Q value = 1

Table S285.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 100 0.1 (0.4)
subtype1 29 0.1 (0.3)
subtype2 32 0.1 (0.6)
subtype3 39 0.1 (0.2)

Figure S278.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.217 (Kruskal-Wallis (anova)), Q value = 1

Table S286.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 116 0.4 (0.6)
subtype1 31 0.5 (0.6)
subtype2 41 0.3 (0.5)
subtype3 44 0.3 (0.6)

Figure S279.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.618 (Kruskal-Wallis (anova)), Q value = 1

Table S287.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 179 2.5 (1.8)
subtype1 56 2.4 (1.9)
subtype2 61 2.5 (1.7)
subtype3 62 2.5 (1.8)

Figure S280.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 1

Table S288.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 109 0.9 (1.9)
subtype1 31 0.8 (1.5)
subtype2 36 1.2 (2.3)
subtype3 42 0.7 (1.7)

Figure S281.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.911 (Kruskal-Wallis (anova)), Q value = 1

Table S289.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 101 0.1 (0.3)
subtype1 29 0.1 (0.3)
subtype2 32 0.1 (0.4)
subtype3 40 0.1 (0.3)

Figure S282.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODE_LOCATION'

P value = 0.131 (Fisher's exact test), Q value = 1

Table S290.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 41
subtype1 0 0 1 13
subtype2 0 0 0 17
subtype3 1 1 0 11

Figure S283.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'MIRseq Mature CNMF subtypes' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.0317 (Fisher's exact test), Q value = 1

Table S291.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 13 1 6
subtype1 1 1 0 0 3
subtype2 1 3 5 1 3
subtype3 0 3 8 0 0

Figure S284.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'MIRseq Mature CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.471 (Fisher's exact test), Q value = 1

Table S292.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 58 85
subtype1 1 4 18 26
subtype2 1 3 24 24
subtype3 0 3 16 35

Figure S285.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'MIRseq Mature CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.665 (Fisher's exact test), Q value = 1

Table S293.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 56 66
subtype1 19 27
subtype2 20 23
subtype3 17 16

Figure S286.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.654 (Kruskal-Wallis (anova)), Q value = 1

Table S294.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 40 0.9 (2.6)
subtype2 48 1.3 (2.4)
subtype3 35 0.9 (2.7)

Figure S287.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.179 (Kruskal-Wallis (anova)), Q value = 1

Table S295.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 143 22.4 (12.7)
subtype1 49 20.7 (11.0)
subtype2 51 25.6 (14.2)
subtype3 43 20.5 (12.0)

Figure S288.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0205 (Fisher's exact test), Q value = 1

Table S296.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 43 88
subtype1 4 26
subtype2 20 27
subtype3 19 35

Figure S289.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.222 (Kruskal-Wallis (anova)), Q value = 1

Table S297.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 200 2007.3 (5.2)
subtype1 64 2007.9 (5.2)
subtype2 65 2007.4 (5.1)
subtype3 71 2006.7 (5.3)

Figure S290.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature CNMF subtypes' versus 'HYSTERECTOMY_TYPE'

P value = 0.307 (Fisher's exact test), Q value = 1

Table S298.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 129 5
subtype1 3 46 2
subtype2 0 49 1
subtype3 0 34 2

Figure S291.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'MIRseq Mature CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.931 (Fisher's exact test), Q value = 1

Table S299.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 47
subtype1 4 14 15
subtype2 2 13 16
subtype3 2 13 16

Figure S292.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.271 (Kruskal-Wallis (anova)), Q value = 1

Table S300.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 162.1 (7.2)
subtype1 58 163.4 (6.4)
subtype2 53 161.1 (8.5)
subtype3 62 161.7 (6.6)

Figure S293.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.124 (Fisher's exact test), Q value = 1

Table S301.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 81 15
subtype1 29 6
subtype2 27 8
subtype3 25 1

Figure S294.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.537 (Fisher's exact test), Q value = 1

Table S302.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 3 17
subtype1 0 3
subtype2 2 4
subtype3 1 10

Figure S295.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0947 (Kruskal-Wallis (anova)), Q value = 1

Table S303.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 10 12.8 (6.1)
subtype2 5 9.4 (3.4)
subtype3 5 16.3 (6.4)

Figure S296.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

'MIRseq Mature CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.742 (Fisher's exact test), Q value = 1

Table S304.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TIS TX
ALL 2 1 26 55 24 3 6 7 9 14 2 2 1 8
subtype1 0 0 6 20 11 2 2 4 5 4 1 0 0 2
subtype2 1 1 7 19 7 1 3 2 2 4 0 2 0 3
subtype3 1 0 13 16 6 0 1 1 2 6 1 0 1 3

Figure S297.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0504 (Kruskal-Wallis (anova)), Q value = 1

Table S305.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 200 47.6 (13.4)
subtype1 64 47.8 (11.1)
subtype2 65 50.4 (14.3)
subtype3 71 44.7 (14.0)

Figure S298.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S306.  Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 23 40 17 31 29 27 33
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.147 (logrank test), Q value = 1

Table S307.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 193 38 0.0 - 182.9 (13.9)
subtype1 22 3 0.5 - 97.0 (7.6)
subtype2 38 7 0.1 - 182.9 (20.0)
subtype3 17 6 2.4 - 177.0 (36.8)
subtype4 30 11 0.0 - 118.0 (14.0)
subtype5 29 4 0.0 - 173.3 (8.3)
subtype6 26 4 0.1 - 137.2 (4.3)
subtype7 31 3 0.0 - 147.4 (15.0)

Figure S299.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0118 (Kruskal-Wallis (anova)), Q value = 1

Table S308.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 199 47.5 (13.4)
subtype1 23 42.6 (11.2)
subtype2 39 48.9 (11.2)
subtype3 17 49.8 (16.2)
subtype4 31 41.1 (13.7)
subtype5 29 51.4 (13.9)
subtype6 27 46.1 (9.0)
subtype7 33 51.7 (15.3)

Figure S300.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.436 (Fisher's exact test), Q value = 1

Table S309.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3+T4
ALL 108 39 4
subtype1 14 5 0
subtype2 18 9 2
subtype3 12 2 0
subtype4 14 3 2
subtype5 14 8 0
subtype6 14 7 0
subtype7 22 5 0

Figure S301.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.631 (Fisher's exact test), Q value = 1

Table S310.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 100 44
subtype1 13 4
subtype2 19 10
subtype3 7 7
subtype4 14 5
subtype5 13 7
subtype6 16 4
subtype7 18 7

Figure S302.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.643 (Fisher's exact test), Q value = 1

Table S311.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 83 4 70
subtype1 7 1 11
subtype2 18 1 14
subtype3 9 0 5
subtype4 10 1 9
subtype5 9 0 13
subtype6 14 1 7
subtype7 16 0 11

Figure S303.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0034

Table S312.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

nPatients ADENOSQUAMOUS CERVICAL SQUAMOUS CELL CARCINOMA ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE ENDOCERVICAL TYPE OF ADENOCARCINOMA ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL 2 168 4 20 2 4
subtype1 0 1 4 14 0 4
subtype2 0 39 0 1 0 0
subtype3 0 17 0 0 0 0
subtype4 0 30 0 1 0 0
subtype5 0 29 0 0 0 0
subtype6 2 19 0 4 2 0
subtype7 0 33 0 0 0 0

Figure S304.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0401 (Fisher's exact test), Q value = 1

Table S313.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 32 168
subtype1 1 22
subtype2 9 31
subtype3 7 10
subtype4 6 25
subtype5 3 26
subtype6 2 25
subtype7 4 29

Figure S305.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.491 (Kruskal-Wallis (anova)), Q value = 1

Table S314.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 8 13.1 (12.3)
subtype2 11 18.9 (16.1)
subtype3 6 16.3 (5.8)
subtype4 9 17.2 (9.7)
subtype5 11 27.5 (18.1)
subtype6 6 19.2 (10.6)
subtype7 11 20.3 (13.0)

Figure S306.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 1

Table S315.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 16 1.1 (2.8)
subtype2 29 1.6 (3.4)
subtype3 13 1.7 (2.7)
subtype4 15 1.3 (3.6)
subtype5 14 0.7 (1.1)
subtype6 13 0.2 (0.4)
subtype7 23 0.7 (1.4)

Figure S307.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.632 (Fisher's exact test), Q value = 1

Table S316.  Clustering Approach #8: '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 16 23 1 148
subtype1 0 1 1 0 21
subtype2 1 4 4 0 30
subtype3 0 2 3 0 12
subtype4 3 1 5 1 21
subtype5 2 1 6 0 20
subtype6 0 3 2 0 21
subtype7 1 4 2 0 23

Figure S308.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.266 (Fisher's exact test), Q value = 1

Table S317.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 13 144
subtype1 3 16
subtype2 1 29
subtype3 0 14
subtype4 3 20
subtype5 4 19
subtype6 1 22
subtype7 1 24

Figure S309.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.407 (Kruskal-Wallis (anova)), Q value = 1

Table S318.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 182 75.2 (20.9)
subtype1 21 76.8 (13.7)
subtype2 38 75.7 (18.9)
subtype3 14 64.9 (11.3)
subtype4 27 74.2 (19.2)
subtype5 27 75.0 (20.7)
subtype6 25 77.0 (17.0)
subtype7 30 77.9 (32.5)

Figure S310.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.454 (Fisher's exact test), Q value = 1

Table S319.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients TUMOR FREE WITH TUMOR
ALL 51 22
subtype1 4 1
subtype2 14 3
subtype3 8 5
subtype4 7 5
subtype5 6 3
subtype6 4 4
subtype7 8 1

Figure S311.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.572 (Fisher's exact test), Q value = 1

Table S320.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients NIGERIA RUSSIA UKRAINE UNITED STATES VIETNAM
ALL 1 11 7 169 12
subtype1 0 0 0 23 0
subtype2 0 3 1 33 3
subtype3 0 1 0 16 0
subtype4 0 0 2 28 1
subtype5 0 2 1 25 1
subtype6 1 2 2 19 3
subtype7 0 3 1 25 4

Figure S312.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASMHISTOLOGICGRADE'

P value = 0.00071 (Fisher's exact test), Q value = 0.24

Table S321.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

nPatients G1 G2 G3 G4 GX
ALL 14 94 83 1 5
subtype1 5 14 3 0 1
subtype2 1 22 12 1 3
subtype3 0 8 9 0 0
subtype4 2 8 19 0 0
subtype5 2 16 11 0 0
subtype6 0 8 18 0 1
subtype7 4 18 11 0 0

Figure S313.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASMHISTOLOGICGRADE'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.388 (Kruskal-Wallis (anova)), Q value = 1

Table S322.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

nPatients Mean (Std.Dev)
ALL 27 1998.4 (11.5)
subtype1 4 1993.8 (14.8)
subtype2 6 2001.2 (13.4)
subtype3 3 1998.3 (4.2)
subtype4 2 2001.0 (8.5)
subtype5 4 2002.5 (11.3)
subtype6 2 2009.0 (4.2)
subtype7 6 1991.5 (11.6)

Figure S314.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.491 (Kruskal-Wallis (anova)), Q value = 1

Table S323.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

nPatients Mean (Std.Dev)
ALL 62 19.4 (13.6)
subtype1 8 13.1 (12.3)
subtype2 11 18.9 (16.1)
subtype3 6 16.3 (5.8)
subtype4 9 17.2 (9.7)
subtype5 11 27.5 (18.1)
subtype6 6 19.2 (10.6)
subtype7 11 20.3 (13.0)

Figure S315.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.899 (Fisher's exact test), Q value = 1

Table S324.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients CURRENT REFORMED SMOKER FOR < OR = 15 YEARS CURRENT REFORMED SMOKER FOR > 15 YEARS CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED CURRENT SMOKER LIFELONG NON-SMOKER
ALL 25 8 3 43 89
subtype1 3 1 0 5 12
subtype2 7 1 1 7 22
subtype3 3 2 0 3 8
subtype4 2 0 1 8 11
subtype5 5 1 0 6 10
subtype6 2 0 0 5 14
subtype7 3 3 1 9 12

Figure S316.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.105 (Kruskal-Wallis (anova)), Q value = 1

Table S325.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

nPatients Mean (Std.Dev)
ALL 54 20.5 (6.2)
subtype1 8 18.5 (3.8)
subtype2 9 23.4 (6.0)
subtype3 5 22.2 (7.0)
subtype4 6 16.7 (2.8)
subtype5 10 19.5 (7.9)
subtype6 6 18.2 (4.4)
subtype7 10 23.1 (6.7)

Figure S317.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.0239 (Fisher's exact test), Q value = 1

Table S326.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

nPatients COMBINATION EXTERNAL EXTERNAL BEAM IMPLANTS INTERNAL
ALL 18 44 14 1 8
subtype1 1 7 0 0 0
subtype2 4 12 5 0 2
subtype3 6 0 1 1 0
subtype4 3 6 3 0 2
subtype5 2 7 1 0 1
subtype6 2 6 0 0 1
subtype7 0 6 4 0 2

Figure S318.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.94 (Fisher's exact test), Q value = 1

Table S327.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

nPatients CGY GY
ALL 16 4
subtype1 2 0
subtype2 4 2
subtype3 0 0
subtype4 3 1
subtype5 1 0
subtype6 3 0
subtype7 3 1

Figure S319.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.375 (Kruskal-Wallis (anova)), Q value = 1

Table S328.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

nPatients Mean (Std.Dev)
ALL 175 3.5 (2.4)
subtype1 21 3.0 (2.2)
subtype2 34 3.4 (2.8)
subtype3 17 3.9 (1.9)
subtype4 26 3.7 (2.6)
subtype5 25 4.0 (3.3)
subtype6 24 2.9 (2.2)
subtype7 28 3.5 (1.5)

Figure S320.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.26 (Kruskal-Wallis (anova)), Q value = 1

Table S329.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

nPatients Mean (Std.Dev)
ALL 100 0.1 (0.4)
subtype1 13 0.1 (0.3)
subtype2 21 0.0 (0.2)
subtype3 15 0.3 (0.8)
subtype4 15 0.0 (0.0)
subtype5 15 0.1 (0.3)
subtype6 7 0.0 (0.0)
subtype7 14 0.0 (0.0)

Figure S321.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.269 (Kruskal-Wallis (anova)), Q value = 1

Table S330.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 116 0.4 (0.6)
subtype1 12 0.5 (0.5)
subtype2 25 0.3 (0.6)
subtype3 15 0.3 (0.6)
subtype4 18 0.2 (0.4)
subtype5 16 0.3 (0.8)
subtype6 11 0.7 (0.8)
subtype7 19 0.3 (0.5)

Figure S322.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'PATIENT.PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.21 (Kruskal-Wallis (anova)), Q value = 1

Table S331.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

nPatients Mean (Std.Dev)
ALL 179 2.5 (1.8)
subtype1 20 2.3 (2.1)
subtype2 34 2.2 (1.5)
subtype3 17 2.7 (1.8)
subtype4 26 2.9 (2.2)
subtype5 26 2.7 (1.9)
subtype6 25 2.0 (1.7)
subtype7 31 2.6 (1.3)

Figure S323.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.737 (Kruskal-Wallis (anova)), Q value = 1

Table S332.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

nPatients Mean (Std.Dev)
ALL 109 0.9 (1.9)
subtype1 14 0.4 (0.6)
subtype2 23 1.1 (2.7)
subtype3 14 0.7 (1.1)
subtype4 17 0.6 (0.9)
subtype5 16 1.4 (3.0)
subtype6 8 1.2 (1.4)
subtype7 17 0.9 (1.3)

Figure S324.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PATIENT.PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.765 (Kruskal-Wallis (anova)), Q value = 1

Table S333.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

nPatients Mean (Std.Dev)
ALL 101 0.1 (0.3)
subtype1 12 0.1 (0.3)
subtype2 21 0.1 (0.3)
subtype3 15 0.1 (0.3)
subtype4 15 0.0 (0.0)
subtype5 17 0.2 (0.4)
subtype6 7 0.1 (0.4)
subtype7 14 0.1 (0.5)

Figure S325.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODE_LOCATION'

P value = 0.309 (Fisher's exact test), Q value = 1

Table S334.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

nPatients 2003 2010 COMMON ILIAC PELVIC (EXTERNAL ILIAC, INTERNAL ILIAC, OBTURATOR)
ALL 1 1 1 41
subtype1 0 0 0 5
subtype2 0 0 0 13
subtype3 0 0 0 6
subtype4 1 0 0 4
subtype5 0 1 0 6
subtype6 0 0 1 1
subtype7 0 0 0 6

Figure S326.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'LYMPH_NODE_LOCATION'

'MIRseq Mature cHierClus subtypes' versus 'LOCATION_OF_POSITIVE_MARGINS'

P value = 0.984 (Fisher's exact test), Q value = 1

Table S335.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

nPatients MACROSCOPIC PARAMETRIAL INVOLVEMENT MICROSCOPIC PARAMETRIAL INVOLVEMENT OTHER LOCATION, SPECIFY POSITIVE BLADDER MARGIN POSITIVE VAGINAL MARGIN
ALL 2 7 13 1 6
subtype1 1 0 0 0 1
subtype2 0 3 4 1 2
subtype3 0 2 3 0 1
subtype4 0 1 2 0 1
subtype5 0 1 2 0 1
subtype6 0 0 0 0 0
subtype7 1 0 2 0 0

Figure S327.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'LOCATION_OF_POSITIVE_MARGINS'

'MIRseq Mature cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.025 (Fisher's exact test), Q value = 1

Table S336.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

nPatients INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL) PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD) POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY) PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL 2 10 58 85
subtype1 0 0 6 13
subtype2 1 2 10 16
subtype3 1 0 8 7
subtype4 0 1 2 16
subtype5 0 1 15 9
subtype6 0 2 4 12
subtype7 0 4 13 12

Figure S328.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'MENOPAUSE_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.386 (Fisher's exact test), Q value = 1

Table S337.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 56 66
subtype1 12 5
subtype2 12 12
subtype3 4 9
subtype4 6 8
subtype5 6 8
subtype6 6 11
subtype7 10 13

Figure S329.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.485 (Kruskal-Wallis (anova)), Q value = 1

Table S338.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

nPatients Mean (Std.Dev)
ALL 123 1.1 (2.6)
subtype1 16 1.1 (2.8)
subtype2 29 1.6 (3.4)
subtype3 13 1.7 (2.7)
subtype4 15 1.3 (3.6)
subtype5 14 0.7 (1.1)
subtype6 13 0.2 (0.4)
subtype7 23 0.7 (1.4)

Figure S330.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.24 (Kruskal-Wallis (anova)), Q value = 1

Table S339.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

nPatients Mean (Std.Dev)
ALL 143 22.4 (12.7)
subtype1 18 23.2 (10.7)
subtype2 31 18.0 (11.3)
subtype3 13 26.5 (17.3)
subtype4 18 23.1 (11.3)
subtype5 19 20.7 (13.7)
subtype6 18 21.3 (10.1)
subtype7 26 26.5 (13.9)

Figure S331.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.021 (Fisher's exact test), Q value = 1

Table S340.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients KERATINIZING SQUAMOUS CELL CARCINOMA NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL 43 88
subtype1 0 5
subtype2 8 19
subtype3 5 9
subtype4 12 12
subtype5 7 16
subtype6 0 13
subtype7 11 14

Figure S332.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.000681 (Kruskal-Wallis (anova)), Q value = 0.23

Table S341.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

nPatients Mean (Std.Dev)
ALL 200 2007.3 (5.2)
subtype1 23 2008.3 (4.7)
subtype2 40 2007.8 (4.9)
subtype3 17 2002.6 (5.4)
subtype4 31 2005.4 (5.2)
subtype5 29 2007.7 (5.6)
subtype6 27 2008.9 (4.7)
subtype7 33 2008.5 (4.2)

Figure S333.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature cHierClus subtypes' versus 'HYSTERECTOMY_TYPE'

P value = 0.028 (Fisher's exact test), Q value = 1

Table S342.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

nPatients OTHER RADICAL HYSTERECTOMY SIMPLE HYSTERECTOMY
ALL 3 129 5
subtype1 2 14 1
subtype2 0 26 0
subtype3 0 13 0
subtype4 0 13 2
subtype5 1 15 0
subtype6 0 20 2
subtype7 0 28 0

Figure S334.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'HYSTERECTOMY_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.172 (Fisher's exact test), Q value = 1

Table S343.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients CURRENT USER FORMER USER NEVER USED
ALL 8 40 47
subtype1 3 7 3
subtype2 0 10 10
subtype3 0 1 7
subtype4 2 6 6
subtype5 0 4 9
subtype6 2 6 6
subtype7 1 6 6

Figure S335.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.482 (Kruskal-Wallis (anova)), Q value = 1

Table S344.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 173 162.1 (7.2)
subtype1 21 164.8 (8.4)
subtype2 37 162.6 (7.5)
subtype3 13 161.0 (7.0)
subtype4 25 161.9 (7.4)
subtype5 27 160.6 (7.9)
subtype6 23 161.7 (4.5)
subtype7 27 161.8 (6.8)

Figure S336.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.899 (Fisher's exact test), Q value = 1

Table S345.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

nPatients ABSENT PRESENT
ALL 81 15
subtype1 14 1
subtype2 16 3
subtype3 9 3
subtype4 10 1
subtype5 9 2
subtype6 10 2
subtype7 13 3

Figure S337.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'CORPUS_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.89 (Fisher's exact test), Q value = 1

Table S346.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

nPatients CARBOPLATIN CISPLATIN
ALL 3 17
subtype1 0 0
subtype2 1 4
subtype3 2 3
subtype4 0 5
subtype5 0 2
subtype6 0 1
subtype7 0 2

Figure S338.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0809 (Kruskal-Wallis (anova)), Q value = 1

Table S347.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

nPatients Mean (Std.Dev)
ALL 10 12.8 (6.1)
subtype2 1 6.0 (NA)
subtype3 2 9.3 (0.9)
subtype4 3 20.1 (5.2)
subtype5 1 11.1 (NA)
subtype7 3 10.8 (3.8)

Figure S339.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CERVIX_SUV_RESULTS'

'MIRseq Mature cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.304 (Fisher's exact test), Q value = 1

Table S348.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients T1A T1A1 T1B T1B1 T1B2 T2 T2A T2A1 T2A2 T2B T3B T4 TIS TX
ALL 2 1 26 55 24 3 6 7 9 14 2 2 1 8
subtype1 0 0 1 11 2 0 1 2 1 1 0 0 0 0
subtype2 0 0 5 10 3 1 1 2 2 3 1 1 0 4
subtype3 0 0 6 5 1 0 0 0 0 2 0 0 0 0
subtype4 0 0 6 6 2 0 1 1 1 0 1 1 1 2
subtype5 1 0 4 3 6 1 0 0 2 5 0 0 0 1
subtype6 0 0 1 8 5 1 1 1 3 1 0 0 0 1
subtype7 1 1 3 12 5 0 2 1 0 2 0 0 0 0

Figure S340.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.0105 (Kruskal-Wallis (anova)), Q value = 1

Table S349.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

nPatients Mean (Std.Dev)
ALL 200 47.6 (13.4)
subtype1 23 42.6 (11.2)
subtype2 40 49.2 (11.3)
subtype3 17 49.8 (16.2)
subtype4 31 41.1 (13.7)
subtype5 29 51.4 (13.9)
subtype6 27 46.1 (9.0)
subtype7 33 51.7 (15.3)

Figure S341.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AGE_AT_DIAGNOSIS'

Methods & Data
Input
  • Cluster data file = CESC-TP.mergedcluster.txt

  • Clinical data file = CESC-TP.merged_data.txt

  • Number of patients = 200

  • Number of clustering approaches = 8

  • Number of selected clinical features = 43

  • 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)