Correlation between aggregated molecular cancer subtypes and selected clinical features

Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WS8S7R

Overview

Introduction

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

Summary

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

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'NEOPLASM_HISTOLOGIC_GRADE', 'MENOPAUSE_STATUS', and 'AGE_AT_DIAGNOSIS'.
6 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'PREGNANCIES_COUNT_STILLBIRTH', 'PREGNANCIES_COUNT_LIVE_BIRTH', 'KERATINIZATION_SQUAMOUS_CELL', and 'AGE_AT_DIAGNOSIS'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'PREGNANCIES_COUNT_LIVE_BIRTH', 'LYMPH_NODES_EXAMINED', 'KERATINIZATION_SQUAMOUS_CELL', and 'CERVIX_SUV_RESULTS'.
Consensus hierarchical clustering analysis on RPPA data identified 5 subtypes that correlate to 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', 'RACE', 'NEOPLASM_HISTOLOGIC_GRADE', 'MENOPAUSE_STATUS', 'LYMPH_NODES_EXAMINED_HE_COUNT', 'KERATINIZATION_SQUAMOUS_CELL', and 'AGE_AT_DIAGNOSIS'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'NUMBER_OF_LYMPH_NODES', 'PREGNANCIES_COUNT_LIVE_BIRTH', 'LYMPH_NODES_EXAMINED_HE_COUNT', 'KERATINIZATION_SQUAMOUS_CELL', 'INITIAL_PATHOLOGIC_DX_YEAR', 'HISTORY_HORMONAL_CONTRACEPTIVES_USE', and 'AGE_AT_DIAGNOSIS'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL_TYPE', 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY', 'NEOPLASM_HISTOLOGIC_GRADE', 'RADIATION_THERAPY_SITE', 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT', 'INITIAL_PATHOLOGIC_DX_YEAR', and 'STAGE_EVENT.CLINICAL_STAGE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'NEOPLASM_HISTOLOGIC_GRADE', 'PREGNANCIES_COUNT_LIVE_BIRTH', 'MENOPAUSE_STATUS', 'AJCC_TUMOR_PATHOLOGIC_PT', 'AGE_AT_DIAGNOSIS', and 'STAGE_EVENT.CLINICAL_STAGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY', 'NEOPLASM_HISTOLOGIC_GRADE', 'RADIATION_TOTAL_DOSE', 'RADIATION_THERAPY_TYPE', 'KERATINIZATION_SQUAMOUS_CELL', 'INITIAL_PATHOLOGIC_DX_YEAR', and 'STAGE_EVENT.CLINICAL_STAGE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY', 'RADIATION_THERAPY_TYPE', 'PREGNANCIES_COUNT_LIVE_BIRTH', 'KERATINIZATION_SQUAMOUS_CELL', and 'INITIAL_PATHOLOGIC_DX_YEAR'.

Results

Overview of the results

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


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

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	128	71	93

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_STATUS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

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

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

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

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

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

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'MENOPAUSE_STATUS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

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

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

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

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

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

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

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

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

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3	4	5	6
Number of samples	70	58	59	61	11	45

'METHLYATION CNMF' versus 'Time to Death'

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

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

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

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

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

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

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

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

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

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

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

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

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

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

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

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

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

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

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

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

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

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

'METHLYATION CNMF' versus 'RACE'

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

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

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

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

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

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

'METHLYATION CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

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

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

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

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

'METHLYATION CNMF' versus 'TUMOR_STATUS'

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

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

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

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

'METHLYATION CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

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

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

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

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

'METHLYATION CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

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

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

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

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

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

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

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

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

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

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

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

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

'METHLYATION CNMF' versus 'TOBACCO_SMOKING_HISTORY'

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

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

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

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

'METHLYATION CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_TOTAL_DOSE'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY_TYPE'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY_STATUS'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY_SITE'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_ADJUVANT_UNITS'

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

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

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

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

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

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

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

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

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

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

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

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

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

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

'METHLYATION CNMF' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

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

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

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

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

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

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

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

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

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

'METHLYATION CNMF' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

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

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

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

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

'METHLYATION CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

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

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

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

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

'METHLYATION CNMF' versus 'POS_LYMPH_NODE_LOCATION'

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

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

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

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

'METHLYATION CNMF' versus 'MENOPAUSE_STATUS'

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

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

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

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

'METHLYATION CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

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

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

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

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

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

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

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

'METHLYATION CNMF' versus 'LYMPH_NODES_EXAMINED'

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

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

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

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

'METHLYATION CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

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

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

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

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

'METHLYATION CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

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

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

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

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

'METHLYATION CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

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

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

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

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

'METHLYATION CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

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

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

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

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

'METHLYATION CNMF' versus 'CORPUS_INVOLVEMENT'

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

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

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

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

'METHLYATION CNMF' versus 'CHEMO_CONCURRENT_TYPE'

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

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

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

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

'METHLYATION CNMF' versus 'CERVIX_SUV_RESULTS'

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

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

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

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

'METHLYATION CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

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

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

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

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

'METHLYATION CNMF' versus 'AGE_AT_DIAGNOSIS'

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

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

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

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

'METHLYATION CNMF' versus 'STAGE_EVENT.CLINICAL_STAGE'

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

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

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

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

Clustering Approach #3: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	52	46	74

'RPPA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

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

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

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

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

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

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

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

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

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

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

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

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

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

'RPPA CNMF subtypes' versus 'RACE'

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

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

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

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

'RPPA CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

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

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

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

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

'RPPA CNMF subtypes' versus 'TUMOR_STATUS'

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

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

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

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

'RPPA CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

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

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

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

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

'RPPA CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

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

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

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

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

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

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

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

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

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

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

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

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

'RPPA CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

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

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

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

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

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

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

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

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

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

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

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

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

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

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

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

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

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

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

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

'RPPA CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

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

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

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

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

'RPPA CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

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

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

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

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

'RPPA CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

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

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

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

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

'RPPA CNMF subtypes' versus 'MENOPAUSE_STATUS'

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

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

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

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

'RPPA CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

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

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

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

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

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

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

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

'RPPA CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

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

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

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

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

'RPPA CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

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

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

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

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

'RPPA CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

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

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

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

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

'RPPA CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

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

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

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

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

'RPPA CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

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

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

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

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

'RPPA CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

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

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

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

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

'RPPA CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

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

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

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

'RPPA CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

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

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

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

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

'RPPA CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

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

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

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

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

'RPPA CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

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

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

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

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

'RPPA CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

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

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

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

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

Clustering Approach #4: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	31	49	51	23	18

'RPPA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

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

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

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RACE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TUMOR_STATUS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

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

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

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

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

'RPPA cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

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

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

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

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

'RPPA cHierClus subtypes' versus 'MENOPAUSE_STATUS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

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

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

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

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

'RPPA cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

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

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

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

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

'RPPA cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

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

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

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

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

'RPPA cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

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

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

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

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

'RPPA cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

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

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

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

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

'RPPA cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

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

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

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

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	56	104	69	72

'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

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

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

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

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

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

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

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

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

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

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

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

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

'RNAseq CNMF subtypes' versus 'RACE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

'RNAseq CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

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

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

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

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

'RNAseq CNMF subtypes' versus 'TUMOR_STATUS'

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

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

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

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

'RNAseq CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

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

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

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

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

'RNAseq CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

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

Table S199. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	18	135	116	1	24
subtype1	2	22	28	0	2
subtype2	7	62	28	0	6
subtype3	7	30	25	0	7
subtype4	2	21	35	1	9

Figure S194. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.871 (Kruskal-Wallis (anova)), Q value = 0.94

Table S200. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	42	1999.7 (13.8)
subtype1	6	1999.0 (13.9)
subtype2	17	1997.5 (16.6)
subtype3	11	2001.5 (12.6)
subtype4	8	2002.2 (9.9)

Figure S195. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

Table S201. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

Figure S196. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.355 (Fisher's exact test), Q value = 0.67

Table S202. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	39	9	4	64	143
subtype1	6	3	1	14	21
subtype2	14	5	2	25	48
subtype3	11	1	0	9	39
subtype4	8	0	1	16	35

Figure S197. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

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

Table S203. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	85	21.1 (7.7)
subtype1	15	22.9 (7.9)
subtype2	37	21.6 (8.3)
subtype3	15	19.9 (4.4)
subtype4	18	19.7 (8.5)

Figure S198. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.636 (Kruskal-Wallis (anova)), Q value = 0.83

Table S204. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	148	3643.7 (1713.4)
subtype1	28	3862.9 (1628.2)
subtype2	46	3506.0 (1746.5)
subtype3	34	3686.8 (1737.8)
subtype4	40	3612.1 (1759.3)

Figure S199. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.436 (Fisher's exact test), Q value = 0.73

Table S205. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	20	104	15	1	24
subtype1	3	20	5	0	2
subtype2	8	31	4	1	10
subtype3	3	29	1	0	4
subtype4	6	24	5	0	8

Figure S200. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.736 (Fisher's exact test), Q value = 0.89

Table S206. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients	COMPLETED AS PLANNED	TREATMENT NOT COMPLETED
ALL	29	3
subtype1	7	0
subtype2	10	1
subtype3	4	0
subtype4	8	2

Figure S201. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

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

Table S207. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	38	32
subtype1	0	0	7	6
subtype2	0	1	12	13
subtype3	1	0	5	6
subtype4	1	1	14	7

Figure S202. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RNAseq CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.492 (Fisher's exact test), Q value = 0.76

Table S208. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	57	5
subtype1	10	0
subtype2	21	1
subtype3	11	1
subtype4	15	3

Figure S203. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0416 (Kruskal-Wallis (anova)), Q value = 0.25

Table S209. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	262	3.6 (2.6)
subtype1	47	3.6 (2.6)
subtype2	92	4.1 (2.7)
subtype3	61	3.0 (2.1)
subtype4	62	3.7 (2.8)

Figure S204. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.171 (Kruskal-Wallis (anova)), Q value = 0.51

Table S210. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.3)
subtype1	17	0.0 (0.0)
subtype2	46	0.2 (0.5)
subtype3	26	0.0 (0.2)
subtype4	22	0.0 (0.0)

Figure S205. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.858 (Kruskal-Wallis (anova)), Q value = 0.94

Table S211. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	146	0.5 (0.9)
subtype1	22	0.5 (1.1)
subtype2	63	0.5 (1.0)
subtype3	28	0.5 (0.8)
subtype4	33	0.6 (0.9)

Figure S206. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0533 (Kruskal-Wallis (anova)), Q value = 0.29

Table S212. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	258	2.9 (2.1)
subtype1	47	2.7 (2.1)
subtype2	91	3.0 (1.8)
subtype3	59	2.4 (2.1)
subtype4	61	3.1 (2.4)

Figure S207. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.805 (Kruskal-Wallis (anova)), Q value = 0.92

Table S213. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	121	0.9 (1.8)
subtype1	19	0.8 (1.4)
subtype2	51	1.2 (2.5)
subtype3	27	0.6 (0.8)
subtype4	24	0.5 (0.9)

Figure S208. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.586 (Kruskal-Wallis (anova)), Q value = 0.81

Table S214. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	115	0.1 (0.3)
subtype1	18	0.2 (0.4)
subtype2	49	0.1 (0.4)
subtype3	26	0.1 (0.3)
subtype4	22	0.0 (0.2)

Figure S209. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.499 (Fisher's exact test), Q value = 0.76

Table S215. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	2	8	39	1	10
subtype1	1	1	3	0	3
subtype2	0	2	17	1	4
subtype3	1	2	10	0	2
subtype4	0	3	9	0	1

Figure S210. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RNAseq CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0246 (Fisher's exact test), Q value = 0.19

Table S216. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	81	123
subtype1	0	6	20	20
subtype2	2	8	35	38
subtype3	0	7	19	30
subtype4	0	4	7	35

Figure S211. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RNAseq CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.616 (Fisher's exact test), Q value = 0.82

Table S217. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	70	78
subtype1	12	18
subtype2	26	29
subtype3	18	21
subtype4	14	10

Figure S212. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

Table S218. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

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

Figure S213. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

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

Table S219. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	176	22.4 (12.6)
subtype1	31	18.7 (12.0)
subtype2	63	23.0 (14.6)
subtype3	49	24.0 (11.1)
subtype4	33	22.6 (11.2)

Figure S214. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RNAseq CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0206 (Fisher's exact test), Q value = 0.19

Table S220. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	54	118
subtype1	15	20
subtype2	20	54
subtype3	1	16
subtype4	18	28

Figure S215. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.354 (Kruskal-Wallis (anova)), Q value = 0.67

Table S221. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	299	2008.3 (4.8)
subtype1	56	2009.0 (4.0)
subtype2	103	2008.0 (5.4)
subtype3	68	2008.7 (4.1)
subtype4	72	2007.6 (4.9)

Figure S216. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.128 (Fisher's exact test), Q value = 0.47

Table S222. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	52	88
subtype1	0	11	19
subtype2	3	15	31
subtype3	8	15	18
subtype4	4	11	20

Figure S217. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.424 (Kruskal-Wallis (anova)), Q value = 0.73

Table S223. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	259	161.1 (7.1)
subtype1	48	160.1 (6.5)
subtype2	89	160.4 (7.3)
subtype3	61	162.2 (7.3)
subtype4	61	161.6 (6.9)

Figure S218. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.0661 (Fisher's exact test), Q value = 0.33

Table S224. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

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

Figure S219. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RNAseq CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.406 (Fisher's exact test), Q value = 0.71

Table S225. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	18	1
subtype1	1	4	0
subtype2	2	5	0
subtype3	0	1	0
subtype4	0	8	1

Figure S220. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RNAseq CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.231 (Kruskal-Wallis (anova)), Q value = 0.57

Table S226. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	17	13.2 (7.2)
subtype1	3	13.4 (1.3)
subtype2	9	11.7 (8.4)
subtype3	1	7.1 (NA)
subtype4	4	18.0 (6.0)

Figure S221. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'RNAseq CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.514 (Fisher's exact test), Q value = 0.76

Table S227. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1A1	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	1	36	71	31	6	10	7	10	36	1	2	17	10	1	17
subtype1	1	2	13	6	2	3	1	3	4	0	1	5	3	0	1
subtype2	0	16	21	11	2	4	2	4	14	1	0	6	4	0	7
subtype3	0	7	26	7	0	2	2	1	9	0	0	2	1	0	3
subtype4	0	11	11	7	2	1	2	2	9	0	1	4	2	1	6

Figure S222. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 2.72e-05 (Kruskal-Wallis (anova)), Q value = 0.00082

Table S228. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	301	48.2 (13.8)
subtype1	56	53.1 (12.6)
subtype2	104	50.8 (14.9)
subtype3	69	46.2 (12.1)
subtype4	72	42.3 (12.5)

Figure S223. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RNAseq CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0537 (Fisher's exact test), Q value = 0.29

Table S229. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA1	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	1	37	77	38	5	9	5	7	42	1	2	42	9	12
subtype1	1	1	1	0	4	13	7	1	1	1	4	6	0	1	9	2	3
subtype2	1	0	0	1	15	22	10	4	2	2	2	12	1	0	19	4	5
subtype3	1	0	0	0	5	31	10	0	2	0	0	10	0	0	5	1	3
subtype4	2	0	0	0	13	11	11	0	4	2	1	14	0	1	9	2	1

Figure S224. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S230. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	70	194	37

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.013 (logrank test), Q value = 0.14

Table S231. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	287	69	0.0 - 210.7 (20.1)
subtype1	68	17	0.1 - 137.2 (18.4)
subtype2	184	39	0.1 - 210.7 (20.9)
subtype3	35	13	0.0 - 99.9 (15.4)

Figure S225. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

Table S232. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	297	48.1 (13.9)
subtype1	68	47.1 (11.5)
subtype2	192	49.7 (14.5)
subtype3	37	41.4 (12.7)

Figure S226. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.716 (Fisher's exact test), Q value = 0.88

Table S233. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	139	69	20	10
subtype1	38	19	3	2
subtype2	86	41	16	8
subtype3	15	9	1	0

Figure S227. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.28 (Fisher's exact test), Q value = 0.62

Table S234. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	131	59
subtype1	37	12
subtype2	77	42
subtype3	17	5

Figure S228. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0507 (Fisher's exact test), Q value = 0.28

Table S235. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	115	10
subtype1	26	5
subtype2	77	3
subtype3	12	2

Figure S229. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

Table S236. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ADENOSQUAMOUS	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE	ENDOCERVICAL TYPE OF ADENOCARCINOMA	ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX	MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL	4	251	6	22	3	15
subtype1	4	22	5	22	3	14
subtype2	0	192	1	0	0	1
subtype3	0	37	0	0	0	0

Figure S230. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

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

Table S237. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	35	266
subtype1	3	67
subtype2	25	169
subtype3	7	30

Figure S231. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

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

Table S238. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	18	14.2 (12.0)
subtype2	63	19.0 (15.2)
subtype3	12	13.8 (9.9)

Figure S232. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0281 (Kruskal-Wallis (anova)), Q value = 0.2

Table S239. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	42	0.7 (2.2)
subtype2	97	1.4 (2.6)
subtype3	18	0.2 (0.5)

Figure S233. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.351 (Fisher's exact test), Q value = 0.67

Table S240. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	7	19	30	2	208
subtype1	1	5	3	0	52
subtype2	4	13	20	2	130
subtype3	2	1	7	0	26

Figure S234. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.85 (Fisher's exact test), Q value = 0.94

Table S241. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	167
subtype1	6	37
subtype2	16	108
subtype3	2	22

Figure S235. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'RNAseq cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.528 (Kruskal-Wallis (anova)), Q value = 0.77

Table S242. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	273	73.1 (21.4)
subtype1	64	73.4 (16.3)
subtype2	177	72.9 (23.6)
subtype3	32	73.6 (17.6)

Figure S236. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.856 (Fisher's exact test), Q value = 0.94

Table S243. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients	TUMOR FREE	WITH TUMOR
ALL	137	44
subtype1	32	12
subtype2	93	28
subtype3	12	4

Figure S237. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'RNAseq cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.372 (Fisher's exact test), Q value = 0.68

Table S244. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients	BRAZIL	CANADA	NIGERIA	RUSSIA	UKRAINE	UNITED STATES	VIETNAM
ALL	53	5	1	12	7	209	14
subtype1	15	3	1	2	2	44	3
subtype2	32	2	0	10	3	137	10
subtype3	6	0	0	0	2	28	1

Figure S238. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.152 (Fisher's exact test), Q value = 0.47

Table S245. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	18	135	116	1	24
subtype1	7	30	27	0	6
subtype2	10	93	69	0	16
subtype3	1	12	20	1	2

Figure S239. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.536 (Kruskal-Wallis (anova)), Q value = 0.77

Table S246. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	42	1999.7 (13.8)
subtype1	8	2002.4 (13.5)
subtype2	30	1999.1 (14.8)
subtype3	4	1999.0 (5.2)

Figure S240. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

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

Table S247. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	18	14.2 (12.0)
subtype2	63	19.0 (15.2)
subtype3	12	13.8 (9.9)

Figure S241. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

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

Table S248. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	39	9	4	64	143
subtype1	8	1	0	11	43
subtype2	27	8	3	43	85
subtype3	4	0	1	10	15

Figure S242. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'RNAseq cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.655 (Kruskal-Wallis (anova)), Q value = 0.84

Table S249. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	85	21.1 (7.7)
subtype1	17	20.8 (5.7)
subtype2	56	21.5 (8.1)
subtype3	12	19.8 (8.3)

Figure S243. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'RNAseq cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.992 (Kruskal-Wallis (anova)), Q value = 1

Table S250. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	148	3643.7 (1713.4)
subtype1	36	3649.1 (1798.7)
subtype2	88	3687.6 (1633.7)
subtype3	24	3475.0 (1925.1)

Figure S244. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 0.298 (Fisher's exact test), Q value = 0.63

Table S251. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	20	104	15	1	24
subtype1	2	31	1	0	5
subtype2	13	59	12	1	14
subtype3	5	14	2	0	5

Figure S245. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

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

Table S252. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

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

Figure S246. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.116 (Fisher's exact test), Q value = 0.46

Table S253. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	38	32
subtype1	1	0	6	6
subtype2	0	1	22	23
subtype3	1	1	10	3

Figure S247. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'RNAseq cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.369 (Fisher's exact test), Q value = 0.68

Table S254. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	57	5
subtype1	12	1
subtype2	35	2
subtype3	10	2

Figure S248. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.0652 (Kruskal-Wallis (anova)), Q value = 0.33

Table S255. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	262	3.6 (2.6)
subtype1	63	3.0 (2.0)
subtype2	167	3.8 (2.7)
subtype3	32	4.0 (2.8)

Figure S249. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.583 (Kruskal-Wallis (anova)), Q value = 0.81

Table S256. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.3)
subtype1	24	0.0 (0.2)
subtype2	74	0.1 (0.4)
subtype3	13	0.0 (0.0)

Figure S250. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.46

Table S257. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	146	0.5 (0.9)
subtype1	28	0.6 (0.9)
subtype2	98	0.5 (0.9)
subtype3	20	0.8 (1.1)

Figure S251. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0221 (Kruskal-Wallis (anova)), Q value = 0.19

Table S258. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	258	2.9 (2.1)
subtype1	61	2.3 (1.9)
subtype2	165	3.0 (2.0)
subtype3	32	3.2 (2.4)

Figure S252. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.535 (Kruskal-Wallis (anova)), Q value = 0.77

Table S259. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	121	0.9 (1.8)
subtype1	26	0.8 (1.0)
subtype2	80	0.9 (2.1)
subtype3	15	0.6 (1.0)

Figure S253. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'RNAseq cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.873 (Kruskal-Wallis (anova)), Q value = 0.94

Table S260. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	115	0.1 (0.3)
subtype1	23	0.1 (0.3)
subtype2	79	0.1 (0.4)
subtype3	13	0.1 (0.3)

Figure S254. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'RNAseq cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.913 (Fisher's exact test), Q value = 0.96

Table S261. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	2	8	39	1	10
subtype1	1	1	8	0	2
subtype2	1	5	26	1	7
subtype3	0	2	5	0	1

Figure S255. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'RNAseq cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.0424 (Fisher's exact test), Q value = 0.25

Table S262. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	81	123
subtype1	0	8	18	33
subtype2	2	15	61	72
subtype3	0	2	2	18

Figure S256. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'RNAseq cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.635 (Fisher's exact test), Q value = 0.83

Table S263. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	70	78
subtype1	22	20
subtype2	39	50
subtype3	9	8

Figure S257. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0281 (Kruskal-Wallis (anova)), Q value = 0.2

Table S264. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	42	0.7 (2.2)
subtype2	97	1.4 (2.6)
subtype3	18	0.2 (0.5)

Figure S258. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'RNAseq cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.347 (Kruskal-Wallis (anova)), Q value = 0.67

Table S265. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	176	22.4 (12.6)
subtype1	46	21.5 (10.4)
subtype2	109	22.2 (13.4)
subtype3	21	25.7 (13.0)

Figure S259. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'RNAseq cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.00558 (Fisher's exact test), Q value = 0.081

Table S266. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	54	118
subtype1	0	16
subtype2	46	89
subtype3	8	13

Figure S260. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'RNAseq cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0251 (Kruskal-Wallis (anova)), Q value = 0.19

Table S267. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	299	2008.3 (4.8)
subtype1	69	2009.4 (3.5)
subtype2	193	2008.2 (4.9)
subtype3	37	2006.4 (5.8)

Figure S261. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'RNAseq cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.0311 (Fisher's exact test), Q value = 0.22

Table S268. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	52	88
subtype1	7	16	20
subtype2	4	32	59
subtype3	4	4	9

Figure S262. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'RNAseq cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.143 (Kruskal-Wallis (anova)), Q value = 0.47

Table S269. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	259	161.1 (7.1)
subtype1	62	161.4 (6.6)
subtype2	167	160.5 (7.2)
subtype3	30	163.5 (6.8)

Figure S263. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.283 (Fisher's exact test), Q value = 0.62

Table S270. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	99	18
subtype1	31	5
subtype2	56	13
subtype3	12	0

Figure S264. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'RNAseq cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.367 (Fisher's exact test), Q value = 0.68

Table S271. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	18	1
subtype1	0	2	0
subtype2	3	12	0
subtype3	0	4	1

Figure S265. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'RNAseq cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.226 (Kruskal-Wallis (anova)), Q value = 0.57

Table S272. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	17	13.2 (7.2)
subtype1	1	7.1 (NA)
subtype2	13	12.5 (7.0)
subtype3	3	18.4 (7.3)

Figure S266. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'RNAseq cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.134 (Fisher's exact test), Q value = 0.47

Table S273. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1A1	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	1	36	71	31	6	10	7	10	36	1	2	17	10	1	17
subtype1	0	6	25	7	1	4	3	3	8	0	0	3	2	0	2
subtype2	1	21	44	20	5	5	2	6	23	1	2	13	8	0	12
subtype3	0	9	2	4	0	1	2	1	5	0	0	1	0	1	3

Figure S267. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'RNAseq cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.00591 (Kruskal-Wallis (anova)), Q value = 0.083

Table S274. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	301	48.2 (13.8)
subtype1	70	47.2 (11.4)
subtype2	194	49.8 (14.5)
subtype3	37	41.4 (12.7)

Figure S268. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'RNAseq cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0902 (Fisher's exact test), Q value = 0.4

Table S275. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA1	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	1	37	77	38	5	9	5	7	42	1	2	42	9	12
subtype1	1	0	0	0	5	28	9	1	3	1	2	8	0	0	6	1	5
subtype2	4	1	1	1	24	44	20	4	3	2	4	29	1	2	33	8	7
subtype3	0	0	0	0	8	5	9	0	3	2	1	5	0	0	3	0	0

Figure S269. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S276. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	102	79	123

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.377 (logrank test), Q value = 0.69

Table S277. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	290	69	0.0 - 210.7 (20.0)
subtype1	99	25	0.1 - 160.4 (19.7)
subtype2	75	14	0.1 - 210.7 (20.4)
subtype3	116	30	0.0 - 195.8 (20.8)

Figure S270. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.539 (Kruskal-Wallis (anova)), Q value = 0.77

Table S278. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	300	48.1 (13.8)
subtype1	100	47.8 (12.6)
subtype2	79	49.4 (13.3)
subtype3	121	47.6 (15.2)

Figure S271. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.516 (Fisher's exact test), Q value = 0.76

Table S279. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	140	70	21	10
subtype1	50	28	10	5
subtype2	44	16	3	1
subtype3	46	26	8	4

Figure S272. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.819 (Fisher's exact test), Q value = 0.93

Table S280. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	133	59
subtype1	45	19
subtype2	41	21
subtype3	47	19

Figure S273. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.197 (Fisher's exact test), Q value = 0.53

Table S281. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	115	10
subtype1	37	6
subtype2	35	1
subtype3	43	3

Figure S274. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S282. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ADENOSQUAMOUS	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE	ENDOCERVICAL TYPE OF ADENOCARCINOMA	ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX	MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL	5	253	6	22	3	15
subtype1	4	61	5	17	3	12
subtype2	1	70	1	5	0	2
subtype3	0	122	0	0	0	1

Figure S275. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.199 (Fisher's exact test), Q value = 0.53

Table S283. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	35	269
subtype1	8	94
subtype2	8	71
subtype3	19	104

Figure S276. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S284. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	25	13.2 (10.5)
subtype2	27	22.2 (14.5)
subtype3	41	16.8 (15.1)

Figure S277. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.66

Table S285. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	54	0.8 (2.3)
subtype2	55	1.4 (2.5)
subtype3	48	0.9 (2.4)

Figure S278. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.322 (Fisher's exact test), Q value = 0.65

Table S286. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	8	19	30	2	210
subtype1	1	8	9	0	67
subtype2	1	7	7	0	55
subtype3	6	4	14	2	88

Figure S279. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.601 (Fisher's exact test), Q value = 0.81

Table S287. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	169
subtype1	7	52
subtype2	5	50
subtype3	12	67

Figure S280. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CNMF' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.736 (Kruskal-Wallis (anova)), Q value = 0.89

Table S288. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	276	73.3 (21.5)
subtype1	96	73.9 (20.3)
subtype2	69	73.9 (27.6)
subtype3	111	72.3 (18.0)

Figure S281. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'TUMOR_STATUS'

P value = 0.622 (Fisher's exact test), Q value = 0.82

Table S289. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients	TUMOR FREE	WITH TUMOR
ALL	139	44
subtype1	52	16
subtype2	37	9
subtype3	50	19

Figure S282. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CNMF' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00038 (Fisher's exact test), Q value = 0.0095

Table S290. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients	BRAZIL	CANADA	NIGERIA	RUSSIA	UKRAINE	UNITED STATES	VIETNAM
ALL	54	5	1	12	7	211	14
subtype1	30	3	1	2	3	58	5
subtype2	5	2	0	6	2	58	6
subtype3	19	0	0	4	2	95	3

Figure S283. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CNMF' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.00701 (Fisher's exact test), Q value = 0.095

Table S291. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	18	136	118	1	24
subtype1	8	40	42	0	11
subtype2	8	41	27	1	1
subtype3	2	55	49	0	12

Figure S284. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.313 (Kruskal-Wallis (anova)), Q value = 0.65

Table S292. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	42	1999.7 (13.8)
subtype1	11	2002.7 (11.8)
subtype2	11	1996.2 (11.8)
subtype3	20	2000.0 (15.9)

Figure S285. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.28

Table S293. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	25	13.2 (10.5)
subtype2	27	22.2 (14.5)
subtype3	41	16.8 (15.1)

Figure S286. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.0886 (Fisher's exact test), Q value = 0.4

Table S294. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	39	9	4	64	145
subtype1	12	1	0	17	61
subtype2	9	4	3	19	34
subtype3	18	4	1	28	50

Figure S287. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CNMF' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.846 (Kruskal-Wallis (anova)), Q value = 0.94

Table S295. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	85	21.1 (7.7)
subtype1	23	20.7 (7.2)
subtype2	24	21.0 (6.2)
subtype3	38	21.5 (8.9)

Figure S288. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CNMF' versus 'RADIATION_TOTAL_DOSE'

P value = 0.941 (Kruskal-Wallis (anova)), Q value = 0.98

Table S296. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	149	3635.4 (1710.6)
subtype1	58	3649.5 (1721.1)
subtype2	30	3667.4 (1787.3)
subtype3	61	3606.2 (1690.6)

Figure S289. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_TYPE'

P value = 0.117 (Fisher's exact test), Q value = 0.46

Table S297. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	20	104	15	1	25
subtype1	6	44	2	0	10
subtype2	2	22	6	1	5
subtype3	12	38	7	0	10

Figure S290. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_STATUS'

P value = 0.397 (Fisher's exact test), Q value = 0.71

Table S298. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients	COMPLETED AS PLANNED	TREATMENT NOT COMPLETED
ALL	29	3
subtype1	8	0
subtype2	6	0
subtype3	15	3

Figure S291. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY_SITE'

P value = 0.0273 (Fisher's exact test), Q value = 0.2

Table S299. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	38	33
subtype1	1	0	7	17
subtype2	0	1	8	4
subtype3	1	1	23	12

Figure S292. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRSEQ CNMF' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.354 (Fisher's exact test), Q value = 0.67

Table S300. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	58	5
subtype1	24	1
subtype2	9	0
subtype3	25	4

Figure S293. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.89

Table S301. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	265	3.6 (2.6)
subtype1	90	3.7 (2.9)
subtype2	71	3.6 (2.3)
subtype3	104	3.6 (2.5)

Figure S294. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.86 (Kruskal-Wallis (anova)), Q value = 0.94

Table S302. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.3)
subtype1	29	0.0 (0.2)
subtype2	34	0.1 (0.5)
subtype3	48	0.1 (0.2)

Figure S295. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CNMF' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.0036 (Kruskal-Wallis (anova)), Q value = 0.062

Table S303. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	146	0.5 (0.9)
subtype1	40	0.9 (1.2)
subtype2	44	0.4 (0.8)
subtype3	62	0.4 (0.8)

Figure S296. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.847 (Kruskal-Wallis (anova)), Q value = 0.94

Table S304. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	260	2.9 (2.1)
subtype1	86	3.0 (2.5)
subtype2	70	2.7 (1.7)
subtype3	104	2.8 (1.9)

Figure S297. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CNMF' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.464 (Kruskal-Wallis (anova)), Q value = 0.75

Table S305. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	121	0.9 (1.8)
subtype1	32	0.8 (1.5)
subtype2	38	1.1 (2.3)
subtype3	51	0.7 (1.6)

Figure S298. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CNMF' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.32 (Kruskal-Wallis (anova)), Q value = 0.65

Table S306. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	115	0.1 (0.3)
subtype1	29	0.1 (0.3)
subtype2	36	0.2 (0.5)
subtype3	50	0.1 (0.3)

Figure S299. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CNMF' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.864 (Fisher's exact test), Q value = 0.94

Table S307. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	2	8	39	1	10
subtype1	1	2	8	0	2
subtype2	1	2	15	1	5
subtype3	0	4	16	0	3

Figure S300. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CNMF' versus 'MENOPAUSE_STATUS'

P value = 0.507 (Fisher's exact test), Q value = 0.76

Table S308. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	83	124
subtype1	1	12	26	43
subtype2	1	6	25	29
subtype3	0	7	32	52

Figure S301. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRSEQ CNMF' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.631 (Fisher's exact test), Q value = 0.83

Table S309. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	71	79
subtype1	24	29
subtype2	22	28
subtype3	25	22

Figure S302. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.332 (Kruskal-Wallis (anova)), Q value = 0.66

Table S310. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	54	0.8 (2.3)
subtype2	55	1.4 (2.5)
subtype3	48	0.9 (2.4)

Figure S303. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CNMF' versus 'LYMPH_NODES_EXAMINED'

P value = 0.605 (Kruskal-Wallis (anova)), Q value = 0.81

Table S311. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	178	22.3 (12.6)
subtype1	59	20.7 (11.0)
subtype2	61	24.5 (14.3)
subtype3	58	21.7 (12.0)

Figure S304. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CNMF' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.131 (Fisher's exact test), Q value = 0.47

Table S312. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	54	119
subtype1	6	28
subtype2	19	32
subtype3	29	59

Figure S305. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CNMF' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.00401 (Kruskal-Wallis (anova)), Q value = 0.065

Table S313. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	302	2008.3 (4.8)
subtype1	101	2009.4 (4.3)
subtype2	78	2008.1 (4.7)
subtype3	123	2007.5 (5.0)

Figure S306. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CNMF' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.593 (Fisher's exact test), Q value = 0.81

Table S314. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	53	89
subtype1	8	19	34
subtype2	3	16	19
subtype3	4	18	36

Figure S307. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CNMF' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.907 (Kruskal-Wallis (anova)), Q value = 0.96

Table S315. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	262	161.0 (7.3)
subtype1	91	161.2 (7.3)
subtype2	64	161.6 (7.6)
subtype3	107	160.6 (7.0)

Figure S308. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'CORPUS_INVOLVEMENT'

P value = 0.734 (Fisher's exact test), Q value = 0.89

Table S316. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	99	18
subtype1	34	7
subtype2	33	7
subtype3	32	4

Figure S309. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRSEQ CNMF' versus 'CHEMO_CONCURRENT_TYPE'

P value = 1 (Fisher's exact test), Q value = 1

Table S317. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	18	1
subtype1	0	3	0
subtype2	1	3	0
subtype3	2	12	1

Figure S310. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CNMF' versus 'CERVIX_SUV_RESULTS'

P value = 0.141 (Kruskal-Wallis (anova)), Q value = 0.47

Table S318. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	17	13.2 (7.2)
subtype1	2	10.8 (5.2)
subtype2	6	9.5 (5.7)
subtype3	9	16.3 (7.5)

Figure S311. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRSEQ CNMF' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.665 (Fisher's exact test), Q value = 0.84

Table S319. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1A1	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	1	36	72	31	6	10	7	10	37	2	2	17	10	1	17
subtype1	0	11	29	10	2	5	3	6	12	1	1	8	5	0	3
subtype2	1	9	25	9	1	3	2	2	8	0	0	3	1	0	4
subtype3	0	16	18	12	3	2	2	2	17	1	1	6	4	1	10

Figure S312. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CNMF' versus 'AGE_AT_DIAGNOSIS'

P value = 0.603 (Kruskal-Wallis (anova)), Q value = 0.81

Table S320. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	304	48.2 (13.8)
subtype1	102	47.8 (12.5)
subtype2	79	49.4 (13.3)
subtype3	123	47.8 (15.1)

Figure S313. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CNMF' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.0259 (Fisher's exact test), Q value = 0.19

Table S321. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA1	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	1	37	78	38	5	9	5	7	43	1	3	42	9	12
subtype1	2	0	0	0	8	30	14	1	3	1	5	11	0	2	13	4	8
subtype2	0	1	1	1	8	28	10	1	3	2	2	7	0	0	10	2	1
subtype3	3	0	0	0	21	20	14	3	3	2	0	25	1	1	19	3	3

Figure S314. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S322. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	58	101	74	26	45

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.475 (logrank test), Q value = 0.76

Table S323. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	290	69	0.0 - 210.7 (20.0)
subtype1	57	15	0.1 - 137.2 (17.5)
subtype2	97	21	0.0 - 160.4 (19.8)
subtype3	69	15	0.1 - 210.7 (29.9)
subtype4	24	6	0.1 - 195.8 (21.0)
subtype5	43	12	0.1 - 144.2 (17.8)

Figure S315. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 8.6e-05 (Kruskal-Wallis (anova)), Q value = 0.0023

Table S324. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	300	48.1 (13.8)
subtype1	56	45.9 (12.4)
subtype2	100	50.4 (14.4)
subtype3	74	51.2 (14.1)
subtype4	25	49.9 (11.3)
subtype5	45	39.7 (11.6)

Figure S316. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.00436 (Fisher's exact test), Q value = 0.068

Table S325. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	140	70	21	10
subtype1	34	12	2	1
subtype2	32	28	12	6
subtype3	47	11	2	1
subtype4	8	9	2	1
subtype5	19	10	3	1

Figure S317. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.183 (Fisher's exact test), Q value = 0.51

Table S326. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	133	59
subtype1	31	9
subtype2	32	22
subtype3	38	20
subtype4	11	2
subtype5	21	6

Figure S318. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.34 (Fisher's exact test), Q value = 0.66

Table S327. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	115	10
subtype1	17	2
subtype2	29	2
subtype3	38	1
subtype4	12	2
subtype5	19	3

Figure S319. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S328. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ADENOSQUAMOUS	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE	ENDOCERVICAL TYPE OF ADENOCARCINOMA	ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX	MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL	5	253	6	22	3	15
subtype1	4	13	4	20	3	14
subtype2	1	98	1	1	0	0
subtype3	0	74	0	0	0	0
subtype4	0	24	1	0	0	1
subtype5	0	44	0	1	0	0

Figure S320. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0268 (Fisher's exact test), Q value = 0.2

Table S329. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	35	269
subtype1	3	55
subtype2	7	94
subtype3	12	62
subtype4	3	23
subtype5	10	35

Figure S321. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S330. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	18	14.7 (11.6)
subtype2	36	19.9 (17.8)
subtype3	21	20.2 (10.9)
subtype4	5	7.7 (8.3)
subtype5	13	13.4 (9.5)

Figure S322. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.46

Table S331. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	38	0.6 (1.9)
subtype2	40	1.7 (3.1)
subtype3	50	1.0 (1.9)
subtype4	8	0.4 (0.7)
subtype5	21	0.9 (3.1)

Figure S323. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.376 (Fisher's exact test), Q value = 0.69

Table S332. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	8	19	30	2	210
subtype1	0	4	2	0	44
subtype2	3	5	13	0	66
subtype3	1	6	7	0	51
subtype4	1	2	3	1	17
subtype5	3	2	5	1	32

Figure S324. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.401 (Fisher's exact test), Q value = 0.71

Table S333. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	169
subtype1	4	30
subtype2	10	52
subtype3	3	50
subtype4	3	16
subtype5	4	21

Figure S325. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'ETHNICITY'

'MIRSEQ CHIERARCHICAL' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.905 (Kruskal-Wallis (anova)), Q value = 0.96

Table S334. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	276	73.3 (21.5)
subtype1	52	72.8 (16.0)
subtype2	96	72.8 (20.3)
subtype3	65	75.2 (28.7)
subtype4	24	68.6 (17.0)
subtype5	39	74.6 (19.2)

Figure S326. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_STATUS'

P value = 0.569 (Fisher's exact test), Q value = 0.79

Table S335. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients	TUMOR FREE	WITH TUMOR
ALL	139	44
subtype1	28	8
subtype2	45	13
subtype3	36	11
subtype4	10	7
subtype5	20	5

Figure S327. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.118 (Fisher's exact test), Q value = 0.46

Table S336. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients	BRAZIL	CANADA	NIGERIA	RUSSIA	UKRAINE	UNITED STATES	VIETNAM
ALL	54	5	1	12	7	211	14
subtype1	13	3	0	1	2	37	2
subtype2	23	0	0	5	2	67	4
subtype3	5	2	0	5	1	57	4
subtype4	5	0	1	1	0	17	2
subtype5	8	0	0	0	2	33	2

Figure S328. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0181 (Fisher's exact test), Q value = 0.17

Table S337. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	18	136	118	1	24
subtype1	6	27	19	0	6
subtype2	4	46	38	0	8
subtype3	6	39	28	0	1
subtype4	0	14	9	0	3
subtype5	2	10	24	1	6

Figure S329. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.147 (Kruskal-Wallis (anova)), Q value = 0.47

Table S338. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	42	1999.7 (13.8)
subtype1	8	2002.4 (13.5)
subtype2	16	2001.1 (17.6)
subtype3	8	1992.9 (10.7)
subtype4	3	1998.0 (10.5)
subtype5	7	2002.0 (7.7)

Figure S330. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.157 (Kruskal-Wallis (anova)), Q value = 0.48

Table S339. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	17.4 (14.1)
subtype1	18	14.7 (11.6)
subtype2	36	19.9 (17.8)
subtype3	21	20.2 (10.9)
subtype4	5	7.7 (8.3)
subtype5	13	13.4 (9.5)

Figure S331. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.386 (Fisher's exact test), Q value = 0.7

Table S340. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	39	9	4	64	145
subtype1	8	1	0	11	33
subtype2	16	2	1	21	45
subtype3	4	6	2	17	34
subtype4	3	0	0	5	14
subtype5	8	0	1	10	19

Figure S332. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.764 (Kruskal-Wallis (anova)), Q value = 0.91

Table S341. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	85	21.1 (7.7)
subtype1	17	19.8 (4.7)
subtype2	32	21.9 (8.9)
subtype3	16	21.6 (6.9)
subtype4	5	24.8 (10.1)
subtype5	15	19.5 (8.0)

Figure S333. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_TOTAL_DOSE'

P value = 0.395 (Kruskal-Wallis (anova)), Q value = 0.71

Table S342. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	149	3635.4 (1710.6)
subtype1	29	3839.8 (1694.7)
subtype2	51	3307.3 (1775.1)
subtype3	27	4180.7 (1304.9)
subtype4	13	3702.6 (1454.8)
subtype5	29	3470.0 (1980.7)

Figure S334. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_TYPE'

P value = 0.0459 (Fisher's exact test), Q value = 0.26

Table S343. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	20	104	15	1	25
subtype1	3	23	0	0	5
subtype2	4	34	3	0	14
subtype3	7	19	5	1	1
subtype4	1	10	2	0	1
subtype5	5	18	5	0	4

Figure S335. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_STATUS'

P value = 0.859 (Fisher's exact test), Q value = 0.94

Table S344. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients	COMPLETED AS PLANNED	TREATMENT NOT COMPLETED
ALL	29	3
subtype1	3	0
subtype2	7	0
subtype3	9	1
subtype4	2	0
subtype5	8	2

Figure S336. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY_SITE'

P value = 0.12 (Fisher's exact test), Q value = 0.46

Table S345. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	38	33
subtype1	1	0	4	6
subtype2	0	0	16	19
subtype3	0	1	6	1
subtype4	0	0	5	3
subtype5	1	1	7	4

Figure S337. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.353 (Fisher's exact test), Q value = 0.67

Table S346. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	58	5
subtype1	10	1
subtype2	26	1
subtype3	5	1
subtype4	7	0
subtype5	10	2

Figure S338. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.088 (Kruskal-Wallis (anova)), Q value = 0.4

Table S347. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	265	3.6 (2.6)
subtype1	52	2.9 (1.9)
subtype2	88	4.2 (3.2)
subtype3	63	3.6 (2.0)
subtype4	20	3.7 (2.2)
subtype5	42	3.2 (2.6)

Figure S339. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.487 (Kruskal-Wallis (anova)), Q value = 0.76

Table S348. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.3)
subtype1	19	0.1 (0.2)
subtype2	27	0.0 (0.2)
subtype3	39	0.2 (0.5)
subtype4	9	0.0 (0.0)
subtype5	17	0.0 (0.0)

Figure S340. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.138 (Kruskal-Wallis (anova)), Q value = 0.47

Table S349. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	146	0.5 (0.9)
subtype1	22	0.8 (0.9)
subtype2	41	0.7 (1.3)
subtype3	47	0.4 (0.7)
subtype4	12	0.3 (0.7)
subtype5	24	0.5 (0.7)

Figure S341. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0185 (Kruskal-Wallis (anova)), Q value = 0.17

Table S350. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	260	2.9 (2.1)
subtype1	50	2.3 (1.8)
subtype2	83	3.3 (2.4)
subtype3	66	2.7 (1.6)
subtype4	21	3.2 (1.9)
subtype5	40	2.6 (2.2)

Figure S342. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.933 (Kruskal-Wallis (anova)), Q value = 0.97

Table S351. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	121	0.9 (1.8)
subtype1	21	0.6 (0.7)
subtype2	31	1.5 (3.1)
subtype3	41	0.7 (1.1)
subtype4	9	0.7 (1.4)
subtype5	19	0.6 (0.9)

Figure S343. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRSEQ CHIERARCHICAL' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.957 (Kruskal-Wallis (anova)), Q value = 0.98

Table S352. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	115	0.1 (0.3)
subtype1	18	0.1 (0.3)
subtype2	30	0.1 (0.3)
subtype3	41	0.1 (0.4)
subtype4	9	0.1 (0.3)
subtype5	17	0.1 (0.2)

Figure S344. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRSEQ CHIERARCHICAL' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.758 (Fisher's exact test), Q value = 0.9

Table S353. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	2	8	39	1	10
subtype1	1	0	6	0	2
subtype2	0	3	8	1	4
subtype3	1	3	16	0	3
subtype4	0	0	2	0	0
subtype5	0	2	7	0	1

Figure S345. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRSEQ CHIERARCHICAL' versus 'MENOPAUSE_STATUS'

P value = 0.0142 (Fisher's exact test), Q value = 0.15

Table S354. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	83	124
subtype1	0	5	15	29
subtype2	1	9	29	37
subtype3	1	5	30	23
subtype4	0	4	5	9
subtype5	0	2	4	26

Figure S346. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.37 (Fisher's exact test), Q value = 0.68

Table S355. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	71	79
subtype1	20	15
subtype2	17	24
subtype3	19	29
subtype4	5	3
subtype5	10	8

Figure S347. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.125 (Kruskal-Wallis (anova)), Q value = 0.46

Table S356. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

	nPatients	Mean (Std.Dev)
ALL	157	1.1 (2.4)
subtype1	38	0.6 (1.9)
subtype2	40	1.7 (3.1)
subtype3	50	1.0 (1.9)
subtype4	8	0.4 (0.7)
subtype5	21	0.9 (3.1)

Figure S348. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH_NODES_EXAMINED'

P value = 0.311 (Kruskal-Wallis (anova)), Q value = 0.65

Table S357. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	178	22.3 (12.6)
subtype1	41	22.1 (10.8)
subtype2	48	20.6 (12.0)
subtype3	55	24.6 (14.6)
subtype4	10	15.9 (8.3)
subtype5	24	23.8 (12.7)

Figure S349. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRSEQ CHIERARCHICAL' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.154 (Fisher's exact test), Q value = 0.48

Table S358. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	54	119
subtype1	0	10
subtype2	20	46
subtype3	20	34
subtype4	4	12
subtype5	10	17

Figure S350. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRSEQ CHIERARCHICAL' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 0.0391 (Kruskal-Wallis (anova)), Q value = 0.25

Table S359. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	302	2008.3 (4.8)
subtype1	57	2009.3 (4.0)
subtype2	101	2008.9 (4.3)
subtype3	73	2007.3 (5.0)
subtype4	26	2008.0 (5.9)
subtype5	45	2007.5 (5.1)

Figure S351. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRSEQ CHIERARCHICAL' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.298 (Fisher's exact test), Q value = 0.63

Table S360. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	53	89
subtype1	6	13	17
subtype2	4	17	33
subtype3	1	14	19
subtype4	0	5	7
subtype5	4	4	13

Figure S352. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRSEQ CHIERARCHICAL' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.693 (Kruskal-Wallis (anova)), Q value = 0.86

Table S361. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	262	161.0 (7.3)
subtype1	50	161.4 (6.9)
subtype2	92	160.7 (7.6)
subtype3	62	160.5 (7.5)
subtype4	21	160.1 (7.4)
subtype5	37	162.7 (6.3)

Figure S353. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'CORPUS_INVOLVEMENT'

P value = 0.259 (Fisher's exact test), Q value = 0.6

Table S362. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	99	18
subtype1	27	3
subtype2	22	5
subtype3	32	8
subtype4	5	2
subtype5	13	0

Figure S354. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRSEQ CHIERARCHICAL' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.446 (Fisher's exact test), Q value = 0.74

Table S363. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	18	1
subtype1	0	1	0
subtype2	1	2	0
subtype3	2	7	0
subtype4	0	1	0
subtype5	0	7	1

Figure S355. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'CERVIX_SUV_RESULTS'

P value = 0.242 (Kruskal-Wallis (anova)), Q value = 0.58

Table S364. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	17	13.2 (7.2)
subtype1	1	7.1 (NA)
subtype2	5	15.8 (8.6)
subtype3	8	10.5 (5.5)
subtype5	3	18.4 (7.3)

Figure S356. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRSEQ CHIERARCHICAL' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.022 (Fisher's exact test), Q value = 0.19

Table S365. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1A1	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	1	36	72	31	6	10	7	10	37	2	2	17	10	1	17
subtype1	0	6	23	5	1	3	3	1	4	0	0	2	1	0	2
subtype2	0	7	14	11	3	2	0	6	17	1	2	9	6	0	7
subtype3	1	13	26	7	0	3	1	0	7	0	0	2	1	0	4
subtype4	0	2	3	3	1	1	1	2	4	1	0	1	1	0	2
subtype5	0	8	6	5	1	1	2	1	5	0	0	3	1	1	2

Figure S357. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRSEQ CHIERARCHICAL' versus 'AGE_AT_DIAGNOSIS'

P value = 6.89e-05 (Kruskal-Wallis (anova)), Q value = 0.0019

Table S366. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	304	48.2 (13.8)
subtype1	58	46.0 (12.2)
subtype2	101	50.5 (14.3)
subtype3	74	51.2 (14.1)
subtype4	26	50.4 (11.4)
subtype5	45	39.7 (11.6)

Figure S358. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRSEQ CHIERARCHICAL' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.00073 (Fisher's exact test), Q value = 0.016

Table S367. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA1	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	1	37	78	38	5	9	5	7	43	1	3	42	9	12
subtype1	1	0	0	0	4	26	8	1	2	1	0	5	0	0	6	1	3
subtype2	2	0	0	0	7	17	12	3	1	0	3	17	0	3	19	5	6
subtype3	0	1	1	1	13	25	8	1	2	1	1	5	0	0	11	2	1
subtype4	2	0	0	0	6	1	2	0	1	1	2	5	1	0	3	0	2
subtype5	0	0	0	0	7	9	8	0	3	2	1	11	0	0	3	1	0

Figure S359. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S368. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	127	77	87

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.957 (logrank test), Q value = 0.98

Table S369. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	279	66	0.0 - 210.7 (20.4)
subtype1	123	25	0.0 - 210.7 (19.6)
subtype2	72	16	0.1 - 147.4 (17.3)
subtype3	84	25	0.1 - 177.0 (30.0)

Figure S360. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.48 (Kruskal-Wallis (anova)), Q value = 0.76

Table S370. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	288	48.1 (13.9)
subtype1	125	49.0 (13.4)
subtype2	76	48.6 (14.0)
subtype3	87	46.4 (14.4)

Figure S361. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.463 (Fisher's exact test), Q value = 0.75

Table S371. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	135	66	19	9
subtype1	53	31	12	6
subtype2	38	15	3	1
subtype3	44	20	4	2

Figure S362. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.177 (Fisher's exact test), Q value = 0.51

Table S372. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	128	56
subtype1	50	16
subtype2	41	16
subtype3	37	24

Figure S363. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.065 (Fisher's exact test), Q value = 0.33

Table S373. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	111	9
subtype1	39	7
subtype2	32	1
subtype3	40	1

Figure S364. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S374. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ADENOSQUAMOUS	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE	ENDOCERVICAL TYPE OF ADENOCARCINOMA	ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX	MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL	5	241	6	21	3	15
subtype1	4	89	4	14	3	13
subtype2	1	65	2	7	0	2
subtype3	0	87	0	0	0	0

Figure S365. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S375. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	33	258
subtype1	3	124
subtype2	6	71
subtype3	24	63

Figure S366. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.53

Table S376. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	17.2 (14.0)
subtype1	31	14.5 (14.3)
subtype2	23	19.6 (12.2)
subtype3	34	18.0 (14.9)

Figure S367. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.54

Table S377. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	153	1.0 (2.4)
subtype1	51	0.7 (2.1)
subtype2	48	1.2 (2.3)
subtype3	54	1.2 (2.7)

Figure S368. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.139 (Fisher's exact test), Q value = 0.47

Table S378. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	7	18	27	2	204
subtype1	5	9	15	0	81
subtype2	1	6	3	0	58
subtype3	1	3	9	2	65

Figure S369. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.131 (Fisher's exact test), Q value = 0.47

Table S379. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	160
subtype1	14	63
subtype2	3	48
subtype3	7	49

Figure S370. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature CNMF subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.744 (Kruskal-Wallis (anova)), Q value = 0.89

Table S380. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	264	73.2 (21.4)
subtype1	117	72.3 (19.3)
subtype2	69	74.3 (27.8)
subtype3	78	73.5 (17.6)

Figure S371. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_STATUS'

P value = 0.782 (Fisher's exact test), Q value = 0.91

Table S381. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients	TUMOR FREE	WITH TUMOR
ALL	135	42
subtype1	60	20
subtype2	30	7
subtype3	45	15

Figure S372. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature CNMF subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00149 (Fisher's exact test), Q value = 0.029

Table S382. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients	BRAZIL	CANADA	NIGERIA	RUSSIA	UKRAINE	UNITED STATES	VIETNAM
ALL	53	5	1	12	6	201	13
subtype1	35	3	1	2	4	76	6
subtype2	7	1	0	6	2	55	6
subtype3	11	1	0	4	0	70	1

Figure S373. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0169 (Fisher's exact test), Q value = 0.17

Table S383. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	16	128	116	1	23
subtype1	6	47	59	0	13
subtype2	8	42	24	0	2
subtype3	2	39	33	1	8

Figure S374. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.288 (Kruskal-Wallis (anova)), Q value = 0.62

Table S384. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	40	2000.0 (14.1)
subtype1	16	2002.6 (16.0)
subtype2	9	1996.2 (16.2)
subtype3	15	1999.5 (10.5)

Figure S375. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.193 (Kruskal-Wallis (anova)), Q value = 0.53

Table S385. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	17.2 (14.0)
subtype1	31	14.5 (14.3)
subtype2	23	19.6 (12.2)
subtype3	34	18.0 (14.9)

Figure S376. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.0416 (Fisher's exact test), Q value = 0.25

Table S386. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	38	8	4	61	142
subtype1	16	2	1	18	71
subtype2	7	3	3	18	34
subtype3	15	3	0	25	37

Figure S377. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature CNMF subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.685 (Kruskal-Wallis (anova)), Q value = 0.86

Table S387. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	80	20.9 (7.6)
subtype1	31	21.5 (7.4)
subtype2	19	20.7 (7.2)
subtype3	30	20.4 (8.2)

Figure S378. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.00376 (Kruskal-Wallis (anova)), Q value = 0.063

Table S388. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	145	3646.2 (1702.8)
subtype1	70	3255.7 (1854.7)
subtype2	29	3579.7 (1809.6)
subtype3	46	4282.3 (1145.8)

Figure S379. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S389. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	19	99	15	1	25
subtype1	2	57	1	0	14
subtype2	2	20	5	0	6
subtype3	15	22	9	1	5

Figure S380. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 1 (Fisher's exact test), Q value = 1

Table S390. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients	COMPLETED AS PLANNED	TREATMENT NOT COMPLETED
ALL	27	3
subtype1	3	0
subtype2	5	0
subtype3	19	3

Figure S381. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.54 (Fisher's exact test), Q value = 0.77

Table S391. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	36	33
subtype1	1	0	17	19
subtype2	1	1	9	6
subtype3	0	1	10	8

Figure S382. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 1 (Fisher's exact test), Q value = 1

Table S392. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	56	5
subtype1	31	3
subtype2	10	1
subtype3	15	1

Figure S383. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.775 (Kruskal-Wallis (anova)), Q value = 0.91

Table S393. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	255	3.6 (2.5)
subtype1	109	3.6 (2.7)
subtype2	68	3.6 (2.2)
subtype3	78	3.6 (2.6)

Figure S384. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.202 (Kruskal-Wallis (anova)), Q value = 0.53

Table S394. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	106	0.1 (0.3)
subtype1	31	0.0 (0.2)
subtype2	29	0.0 (0.0)
subtype3	46	0.1 (0.5)

Figure S385. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.236 (Kruskal-Wallis (anova)), Q value = 0.57

Table S395. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	142	0.6 (1.0)
subtype1	48	0.8 (1.2)
subtype2	40	0.4 (0.7)
subtype3	54	0.5 (0.8)

Figure S386. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.687 (Kruskal-Wallis (anova)), Q value = 0.86

Table S396. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	250	2.8 (2.0)
subtype1	107	3.0 (2.3)
subtype2	67	2.7 (1.7)
subtype3	76	2.6 (1.9)

Figure S387. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.695 (Kruskal-Wallis (anova)), Q value = 0.86

Table S397. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	115	0.8 (1.8)
subtype1	34	0.5 (0.9)
subtype2	34	1.1 (2.4)
subtype3	47	0.8 (1.7)

Figure S388. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature CNMF subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.981 (Kruskal-Wallis (anova)), Q value = 1

Table S398. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.4)
subtype1	34	0.1 (0.3)
subtype2	30	0.1 (0.4)
subtype3	47	0.1 (0.3)

Figure S389. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature CNMF subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.865 (Fisher's exact test), Q value = 0.94

Table S399. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	1	7	38	1	10
subtype1	0	1	8	0	2
subtype2	1	2	11	1	4
subtype3	0	4	19	0	4

Figure S390. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature CNMF subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.261 (Fisher's exact test), Q value = 0.6

Table S400. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	77	119
subtype1	0	15	30	51
subtype2	0	6	22	30
subtype3	2	4	25	38

Figure S391. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRseq Mature CNMF subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.85 (Fisher's exact test), Q value = 0.94

Table S401. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	68	73
subtype1	23	23
subtype2	23	23
subtype3	22	27

Figure S392. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.209 (Kruskal-Wallis (anova)), Q value = 0.54

Table S402. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

	nPatients	Mean (Std.Dev)
ALL	153	1.0 (2.4)
subtype1	51	0.7 (2.1)
subtype2	48	1.2 (2.3)
subtype3	54	1.2 (2.7)

Figure S393. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature CNMF subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.246 (Kruskal-Wallis (anova)), Q value = 0.58

Table S403. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	170	22.8 (12.7)
subtype1	59	22.0 (12.8)
subtype2	54	25.1 (12.8)
subtype3	57	21.4 (12.4)

Figure S394. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature CNMF subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0211 (Fisher's exact test), Q value = 0.19

Table S404. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	51	113
subtype1	9	44
subtype2	18	30
subtype3	24	39

Figure S395. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature CNMF subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 4.31e-06 (Kruskal-Wallis (anova)), Q value = 0.00035

Table S405. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	289	2008.3 (4.8)
subtype1	126	2009.5 (4.2)
subtype2	76	2008.3 (4.7)
subtype3	87	2006.5 (5.2)

Figure S396. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature CNMF subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.564 (Fisher's exact test), Q value = 0.79

Table S406. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	53	84
subtype1	6	21	44
subtype2	4	16	17
subtype3	5	16	23

Figure S397. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature CNMF subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.511 (Kruskal-Wallis (anova)), Q value = 0.76

Table S407. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	251	160.9 (7.2)
subtype1	114	160.5 (6.8)
subtype2	64	161.5 (7.1)
subtype3	73	160.9 (8.1)

Figure S398. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.652 (Fisher's exact test), Q value = 0.84

Table S408. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	95	18
subtype1	29	4
subtype2	30	5
subtype3	36	9

Figure S399. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRseq Mature CNMF subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.206 (Fisher's exact test), Q value = 0.54

Table S409. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	17	1
subtype1	0	2	0
subtype2	0	2	1
subtype3	3	13	0

Figure S400. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature CNMF subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.479 (Kruskal-Wallis (anova)), Q value = 0.76

Table S410. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	16	13.0 (7.3)
subtype1	4	18.3 (10.8)
subtype2	6	10.5 (4.0)
subtype3	6	11.9 (6.6)

Figure S401. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRseq Mature CNMF subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.315 (Fisher's exact test), Q value = 0.65

Table S411. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	35	70	30	6	10	7	9	34	1	2	16	9	1	17
subtype1	12	27	14	3	6	2	5	15	1	2	9	6	1	8
subtype2	6	22	10	0	4	3	2	6	0	0	3	1	0	6
subtype3	17	21	6	3	0	2	2	13	0	0	4	2	0	3

Figure S402. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature CNMF subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.439 (Kruskal-Wallis (anova)), Q value = 0.73

Table S412. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	291	48.2 (13.8)
subtype1	127	49.0 (13.2)
subtype2	77	48.8 (14.0)
subtype3	87	46.4 (14.4)

Figure S403. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature CNMF subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.00554 (Fisher's exact test), Q value = 0.081

Table S413. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	36	76	36	5	8	5	7	41	3	41	8	11
subtype1	3	0	0	9	35	17	2	4	0	4	18	3	16	3	9
subtype2	0	1	1	5	24	12	2	3	3	2	8	0	9	3	1
subtype3	2	0	0	22	17	7	1	1	2	1	15	0	16	2	1

Figure S404. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S414. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	62	50	31	148

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.793 (logrank test), Q value = 0.92

Table S415. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	279	66	0.0 - 210.7 (20.4)
subtype1	60	15	0.1 - 137.2 (17.7)
subtype2	46	9	0.1 - 147.4 (18.7)
subtype3	31	12	1.2 - 177.0 (35.6)
subtype4	142	30	0.0 - 210.7 (19.6)

Figure S405. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.857 (Kruskal-Wallis (anova)), Q value = 0.94

Table S416. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	288	48.1 (13.9)
subtype1	60	47.1 (12.1)
subtype2	50	49.4 (14.2)
subtype3	31	48.9 (14.4)
subtype4	147	47.9 (14.4)

Figure S406. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.512 (Fisher's exact test), Q value = 0.76

Table S417. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	135	66	19	9
subtype1	32	18	2	2
subtype2	26	8	3	1
subtype3	18	5	1	0
subtype4	59	35	13	6

Figure S407. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.149 (Fisher's exact test), Q value = 0.47

Table S418. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	128	56
subtype1	34	8
subtype2	25	11
subtype3	13	11
subtype4	56	26

Figure S408. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.336 (Fisher's exact test), Q value = 0.66

Table S419. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	111	9
subtype1	21	4
subtype2	24	1
subtype3	15	0
subtype4	51	4

Figure S409. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S420. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ADENOSQUAMOUS	CERVICAL SQUAMOUS CELL CARCINOMA	ENDOCERVICAL ADENOCARCINOMA OF THE USUAL TYPE	ENDOCERVICAL TYPE OF ADENOCARCINOMA	ENDOMETRIOID ADENOCARCINOMA OF ENDOCERVIX	MUCINOUS ADENOCARCINOMA OF ENDOCERVICAL TYPE
ALL	5	241	6	21	3	15
subtype1	3	16	5	20	3	15
subtype2	1	49	0	0	0	0
subtype3	0	30	0	1	0	0
subtype4	1	146	1	0	0	0

Figure S410. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S421. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	33	258
subtype1	1	61
subtype2	3	47
subtype3	19	12
subtype4	10	138

Figure S411. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.47

Table S422. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	17.2 (14.0)
subtype1	17	13.1 (11.1)
subtype2	13	21.9 (12.2)
subtype3	10	19.5 (9.9)
subtype4	48	16.9 (15.8)

Figure S412. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0694 (Kruskal-Wallis (anova)), Q value = 0.34

Table S423. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	153	1.0 (2.4)
subtype1	37	0.6 (1.9)
subtype2	28	1.1 (1.9)
subtype3	25	2.4 (4.5)
subtype4	63	0.6 (1.1)

Figure S413. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.563 (Fisher's exact test), Q value = 0.79

Table S424. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	7	18	27	2	204
subtype1	1	5	3	0	44
subtype2	1	5	2	0	35
subtype3	0	2	3	0	26
subtype4	5	6	19	2	99

Figure S414. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.455 (Fisher's exact test), Q value = 0.75

Table S425. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	24	160
subtype1	6	32
subtype2	3	31
subtype3	1	22
subtype4	14	75

Figure S415. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'ETHNICITY'

'MIRseq Mature cHierClus subtypes' versus 'WEIGHT_KG_AT_DIAGNOSIS'

P value = 0.808 (Kruskal-Wallis (anova)), Q value = 0.92

Table S426. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	264	73.2 (21.4)
subtype1	56	73.5 (16.1)
subtype2	45	75.3 (32.9)
subtype3	28	71.8 (17.7)
subtype4	135	72.6 (19.2)

Figure S416. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'WEIGHT_KG_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_STATUS'

P value = 0.254 (Fisher's exact test), Q value = 0.6

Table S427. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

nPatients	TUMOR FREE	WITH TUMOR
ALL	135	42
subtype1	29	9
subtype2	22	4
subtype3	16	10
subtype4	68	19

Figure S417. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'TUMOR_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

P value = 0.00051 (Fisher's exact test), Q value = 0.012

Table S428. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

nPatients	BRAZIL	CANADA	NIGERIA	RUSSIA	UKRAINE	UNITED STATES	VIETNAM
ALL	53	5	1	12	6	201	13
subtype1	15	3	1	2	1	37	3
subtype2	5	1	0	6	2	31	5
subtype3	0	0	0	1	0	30	0
subtype4	33	1	0	3	3	103	5

Figure S418. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #14: 'TUMOR_SAMPLE_PROCUREMENT_COUNTRY'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_HISTOLOGIC_GRADE'

P value = 0.0444 (Fisher's exact test), Q value = 0.26

Table S429. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

nPatients	G1	G2	G3	G4	GX
ALL	16	128	116	1	23
subtype1	6	28	22	0	6
subtype2	5	28	14	0	2
subtype3	0	17	14	0	0
subtype4	5	55	66	1	15

Figure S419. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #15: 'NEOPLASM_HISTOLOGIC_GRADE'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_YEAR_STOPPED'

P value = 0.266 (Kruskal-Wallis (anova)), Q value = 0.61

Table S430. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

	nPatients	Mean (Std.Dev)
ALL	40	2000.0 (14.1)
subtype1	7	2001.0 (13.9)
subtype2	5	1995.4 (15.9)
subtype3	5	1995.6 (5.4)
subtype4	23	2001.6 (15.3)

Figure S420. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #16: 'TOBACCO_SMOKING_YEAR_STOPPED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

P value = 0.142 (Kruskal-Wallis (anova)), Q value = 0.47

Table S431. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	88	17.2 (14.0)
subtype1	17	13.1 (11.1)
subtype2	13	21.9 (12.2)
subtype3	10	19.5 (9.9)
subtype4	48	16.9 (15.8)

Figure S421. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #17: 'TOBACCO_SMOKING_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'TOBACCO_SMOKING_HISTORY'

P value = 0.288 (Fisher's exact test), Q value = 0.62

Table S432. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

nPatients	CURRENT REFORMED SMOKER FOR < OR = 15 YEARS	CURRENT REFORMED SMOKER FOR > 15 YEARS	CURRENT REFORMED SMOKER, DURATION NOT SPECIFIED	CURRENT SMOKER	LIFELONG NON-SMOKER
ALL	38	8	4	61	142
subtype1	7	1	0	11	37
subtype2	3	2	2	11	22
subtype3	8	2	0	5	14
subtype4	20	3	2	34	69

Figure S422. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #18: 'TOBACCO_SMOKING_HISTORY'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT.AGEBEGANSMOKINGINYEARS'

P value = 0.899 (Kruskal-Wallis (anova)), Q value = 0.96

Table S433. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

	nPatients	Mean (Std.Dev)
ALL	80	20.9 (7.6)
subtype1	16	21.0 (5.8)
subtype2	10	19.9 (5.0)
subtype3	8	19.9 (5.2)
subtype4	46	21.2 (8.9)

Figure S423. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #19: 'PATIENT.AGEBEGANSMOKINGINYEARS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_TOTAL_DOSE'

P value = 0.048 (Kruskal-Wallis (anova)), Q value = 0.27

Table S434. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

	nPatients	Mean (Std.Dev)
ALL	145	3646.2 (1702.8)
subtype1	31	3621.0 (1777.9)
subtype2	17	3381.6 (2017.6)
subtype3	19	4549.5 (914.6)
subtype4	78	3493.8 (1705.5)

Figure S424. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #20: 'RADIATION_TOTAL_DOSE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00035

Table S435. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

nPatients	COMBINATION	EXTERNAL	EXTERNAL BEAM	IMPLANTS	INTERNAL
ALL	19	99	15	1	25
subtype1	1	27	0	0	4
subtype2	0	14	3	0	4
subtype3	13	1	7	1	0
subtype4	5	57	5	0	17

Figure S425. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #21: 'RADIATION_THERAPY_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_STATUS'

P value = 0.457 (Fisher's exact test), Q value = 0.75

Table S436. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

nPatients	COMPLETED AS PLANNED	TREATMENT NOT COMPLETED
ALL	27	3
subtype1	1	0
subtype2	2	0
subtype3	17	1
subtype4	7	2

Figure S426. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #22: 'RADIATION_THERAPY_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY_SITE'

P value = 0.589 (Fisher's exact test), Q value = 0.81

Table S437. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

nPatients	DISTANT RECURRENCE	LOCAL RECURRENCE	PRIMARY TUMOR FIELD	REGIONAL SITE
ALL	2	2	36	33
subtype1	1	0	6	5
subtype2	0	1	5	4
subtype3	0	0	2	0
subtype4	1	1	23	24

Figure S427. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #23: 'RADIATION_THERAPY_SITE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_ADJUVANT_UNITS'

P value = 0.458 (Fisher's exact test), Q value = 0.75

Table S438. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

nPatients	CGY	GY
ALL	56	5
subtype1	12	0
subtype2	6	1
subtype3	2	0
subtype4	36	4

Figure S428. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #24: 'RADIATION_ADJUVANT_UNITS'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_TOTAL'

P value = 0.172 (Kruskal-Wallis (anova)), Q value = 0.51

Table S439. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

	nPatients	Mean (Std.Dev)
ALL	255	3.6 (2.5)
subtype1	56	3.0 (2.0)
subtype2	42	3.5 (2.5)
subtype3	30	3.3 (1.9)
subtype4	127	4.0 (2.8)

Figure S429. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #25: 'PREGNANCIES_COUNT_TOTAL'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_STILLBIRTH'

P value = 0.201 (Kruskal-Wallis (anova)), Q value = 0.53

Table S440. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

	nPatients	Mean (Std.Dev)
ALL	106	0.1 (0.3)
subtype1	20	0.0 (0.0)
subtype2	19	0.0 (0.0)
subtype3	26	0.2 (0.6)
subtype4	41	0.0 (0.2)

Figure S430. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #26: 'PREGNANCIES_COUNT_STILLBIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 0.46

Table S441. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	142	0.6 (1.0)
subtype1	25	0.6 (0.9)
subtype2	26	0.3 (0.5)
subtype3	27	0.4 (0.7)
subtype4	64	0.7 (1.2)

Figure S431. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #27: 'PATIENT_PATIENTPREGNANCYSPONTANEOUSABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_LIVE_BIRTH'

P value = 0.0231 (Kruskal-Wallis (anova)), Q value = 0.19

Table S442. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	250	2.8 (2.0)
subtype1	53	2.5 (1.9)
subtype2	42	2.6 (1.3)
subtype3	31	2.2 (1.6)
subtype4	124	3.2 (2.3)

Figure S432. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #28: 'PREGNANCIES_COUNT_LIVE_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

P value = 0.781 (Kruskal-Wallis (anova)), Q value = 0.91

Table S443. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

	nPatients	Mean (Std.Dev)
ALL	115	0.8 (1.8)
subtype1	22	0.7 (1.0)
subtype2	23	1.3 (2.8)
subtype3	25	0.6 (1.0)
subtype4	45	0.7 (1.7)

Figure S433. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #29: 'PATIENT_PATIENTPREGNANCYTHERAPEUTICABORTIONCOUNT'

'MIRseq Mature cHierClus subtypes' versus 'PREGNANCIES_COUNT_ECTOPIC'

P value = 0.653 (Kruskal-Wallis (anova)), Q value = 0.84

Table S444. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

	nPatients	Mean (Std.Dev)
ALL	111	0.1 (0.4)
subtype1	20	0.1 (0.2)
subtype2	21	0.2 (0.5)
subtype3	26	0.1 (0.3)
subtype4	44	0.1 (0.3)

Figure S434. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #30: 'PREGNANCIES_COUNT_ECTOPIC'

'MIRseq Mature cHierClus subtypes' versus 'POS_LYMPH_NODE_LOCATION'

P value = 0.721 (Fisher's exact test), Q value = 0.88

Table S445. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

nPatients	MACROSCOPIC PARAMETRIAL INVOLVEMENT	MICROSCOPIC PARAMETRIAL INVOLVEMENT	OTHER LOCATION, SPECIFY	POSITIVE BLADDER MARGIN	POSITIVE VAGINAL MARGIN
ALL	1	7	38	1	10
subtype1	0	1	6	0	2
subtype2	1	1	6	1	1
subtype3	0	2	14	0	2
subtype4	0	3	12	0	5

Figure S435. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #31: 'POS_LYMPH_NODE_LOCATION'

'MIRseq Mature cHierClus subtypes' versus 'MENOPAUSE_STATUS'

P value = 0.288 (Fisher's exact test), Q value = 0.62

Table S446. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

nPatients	INDETERMINATE (NEITHER PRE OR POSTMENOPAUSAL)	PERI (6-12 MONTHS SINCE LAST MENSTRUAL PERIOD)	POST (PRIOR BILATERAL OVARIECTOMY OR >12 MO SINCE LMP WITH NO PRIOR HYSTERECTOMY)	PRE (<6 MONTHS SINCE LMP AND NO PRIOR BILATERAL OVARIECTOMY AND NOT ON ESTROGEN REPLACEMENT)
ALL	2	25	77	119
subtype1	0	7	16	29
subtype2	0	4	15	18
subtype3	2	1	12	14
subtype4	0	13	34	58

Figure S436. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #32: 'MENOPAUSE_STATUS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPHOVASCULAR_INVOLVEMENT'

P value = 0.0782 (Fisher's exact test), Q value = 0.38

Table S447. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	68	73
subtype1	20	15
subtype2	14	15
subtype3	6	18
subtype4	28	25

Figure S437. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #33: 'LYMPHOVASCULAR_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED_HE_COUNT'

P value = 0.0694 (Kruskal-Wallis (anova)), Q value = 0.34

Table S448. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

	nPatients	Mean (Std.Dev)
ALL	153	1.0 (2.4)
subtype1	37	0.6 (1.9)
subtype2	28	1.1 (1.9)
subtype3	25	2.4 (4.5)
subtype4	63	0.6 (1.1)

Figure S438. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #34: 'LYMPH_NODES_EXAMINED_HE_COUNT'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH_NODES_EXAMINED'

P value = 0.769 (Kruskal-Wallis (anova)), Q value = 0.91

Table S449. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

	nPatients	Mean (Std.Dev)
ALL	170	22.8 (12.7)
subtype1	41	21.6 (10.8)
subtype2	33	21.7 (12.2)
subtype3	25	26.1 (15.2)
subtype4	71	22.8 (13.0)

Figure S439. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #35: 'LYMPH_NODES_EXAMINED'

'MIRseq Mature cHierClus subtypes' versus 'KERATINIZATION_SQUAMOUS_CELL'

P value = 0.0321 (Fisher's exact test), Q value = 0.22

Table S450. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

nPatients	KERATINIZING SQUAMOUS CELL CARCINOMA	NON-KERATINIZING SQUAMOUS CELL CARCINOMA
ALL	51	113
subtype1	0	11
subtype2	14	21
subtype3	9	12
subtype4	28	69

Figure S440. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #36: 'KERATINIZATION_SQUAMOUS_CELL'

'MIRseq Mature cHierClus subtypes' versus 'INITIAL_PATHOLOGIC_DX_YEAR'

P value = 5.03e-08 (Kruskal-Wallis (anova)), Q value = 2.3e-05

Table S451. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

	nPatients	Mean (Std.Dev)
ALL	289	2008.3 (4.8)
subtype1	61	2009.5 (3.6)
subtype2	49	2009.3 (3.9)
subtype3	31	2003.0 (5.1)
subtype4	148	2008.5 (4.8)

Figure S441. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #37: 'INITIAL_PATHOLOGIC_DX_YEAR'

'MIRseq Mature cHierClus subtypes' versus 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

P value = 0.314 (Fisher's exact test), Q value = 0.65

Table S452. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

nPatients	CURRENT USER	FORMER USER	NEVER USED
ALL	15	53	84
subtype1	7	13	20
subtype2	0	10	11
subtype3	0	5	10
subtype4	8	25	43

Figure S442. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #38: 'HISTORY_HORMONAL_CONTRACEPTIVES_USE'

'MIRseq Mature cHierClus subtypes' versus 'HEIGHT_CM_AT_DIAGNOSIS'

P value = 0.949 (Kruskal-Wallis (anova)), Q value = 0.98

Table S453. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	251	160.9 (7.2)
subtype1	55	161.2 (6.8)
subtype2	42	161.7 (7.9)
subtype3	25	160.8 (8.0)
subtype4	129	160.5 (7.1)

Figure S443. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #39: 'HEIGHT_CM_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'CORPUS_INVOLVEMENT'

P value = 0.46 (Fisher's exact test), Q value = 0.75

Table S454. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

nPatients	ABSENT	PRESENT
ALL	95	18
subtype1	27	4
subtype2	18	4
subtype3	17	6
subtype4	33	4

Figure S444. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #40: 'CORPUS_INVOLVEMENT'

'MIRseq Mature cHierClus subtypes' versus 'CHEMO_CONCURRENT_TYPE'

P value = 0.138 (Fisher's exact test), Q value = 0.47

Table S455. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

nPatients	CARBOPLATIN	CISPLATIN	OTHER
ALL	3	17	1
subtype1	0	0	0
subtype2	0	1	0
subtype3	3	9	0
subtype4	0	7	1

Figure S445. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #41: 'CHEMO_CONCURRENT_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'CERVIX_SUV_RESULTS'

P value = 0.0797 (Kruskal-Wallis (anova)), Q value = 0.38

Table S456. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

	nPatients	Mean (Std.Dev)
ALL	16	13.0 (7.3)
subtype1	1	7.1 (NA)
subtype2	6	9.0 (5.9)
subtype3	3	11.0 (3.0)
subtype4	6	18.8 (7.3)

Figure S446. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #42: 'CERVIX_SUV_RESULTS'

'MIRseq Mature cHierClus subtypes' versus 'AJCC_TUMOR_PATHOLOGIC_PT'

P value = 0.932 (Fisher's exact test), Q value = 0.97

Table S457. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

nPatients	T1B	T1B1	T1B2	T2	T2A	T2A1	T2A2	T2B	T3	T3A	T3B	T4	TIS	TX
ALL	35	70	30	6	10	7	9	34	1	2	16	9	1	17
subtype1	6	20	6	1	4	2	3	8	0	0	2	2	0	2
subtype2	7	14	5	0	3	1	1	3	0	0	3	1	0	3
subtype3	6	9	3	1	0	1	0	3	0	0	1	0	0	0
subtype4	16	27	16	4	3	3	5	20	1	2	10	6	1	12

Figure S447. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #43: 'AJCC_TUMOR_PATHOLOGIC_PT'

'MIRseq Mature cHierClus subtypes' versus 'AGE_AT_DIAGNOSIS'

P value = 0.885 (Kruskal-Wallis (anova)), Q value = 0.95

Table S458. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

	nPatients	Mean (Std.Dev)
ALL	291	48.2 (13.8)
subtype1	62	47.2 (11.9)
subtype2	50	49.4 (14.2)
subtype3	31	48.9 (14.4)
subtype4	148	48.0 (14.3)

Figure S448. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #44: 'AGE_AT_DIAGNOSIS'

'MIRseq Mature cHierClus subtypes' versus 'STAGE_EVENT.CLINICAL_STAGE'

P value = 0.163 (Fisher's exact test), Q value = 0.49

Table S459. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

nPatients	STAGE I	STAGE IA	STAGE IA2	STAGE IB	STAGE IB1	STAGE IB2	STAGE II	STAGE IIA	STAGE IIA1	STAGE IIA2	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IVA	STAGE IVB
ALL	5	1	1	36	76	36	5	8	5	7	41	3	41	8	11
subtype1	1	0	0	5	23	9	1	3	1	2	7	0	5	1	4
subtype2	0	1	1	4	17	5	1	2	1	1	6	0	7	2	1
subtype3	0	0	0	12	5	4	0	1	1	0	2	0	6	0	0
subtype4	4	0	0	15	31	18	3	2	2	4	26	3	23	5	6

Figure S449. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #45: 'STAGE_EVENT.CLINICAL_STAGE'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/CESC-TP/15095897/CESC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CESC-TP/15076797/CESC-TP.merged_data.txt
Number of patients = 304
Number of clustering approaches = 10
Number of selected clinical features = 45
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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