Correlation between aggregated molecular cancer subtypes and selected clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16D5RSC

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 12 different clustering approaches and 7 clinical features across 496 patients, 24 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 24 significant findings detected.


Clinical Features	Time to Death	AGE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.838 (1.00)	0.00418 (0.251)	0.00033 (0.0218)	0.0369 (1.00)	0.89 (1.00)	0.15 (1.00)	0.612 (1.00)
mRNA cHierClus subtypes	0.126 (1.00)	0.0314 (1.00)	1e-05 (0.00079)	0.103 (1.00)	0.491 (1.00)	0.571 (1.00)	1 (1.00)
Copy Number Ratio CNMF subtypes	0.00118 (0.0719)	6.6e-11 (5.55e-09)	1e-05 (0.00079)	0.00554 (0.327)	0.418 (1.00)	0.00051 (0.0331)	0.812 (1.00)
METHLYATION CNMF	0.0459 (1.00)	0.00114 (0.0705)	1e-05 (0.00079)	0.209 (1.00)	0.0138 (0.773)	0.0661 (1.00)	0.406 (1.00)
RPPA CNMF subtypes	0.0778 (1.00)	0.148 (1.00)	2e-05 (0.0014)	0.47 (1.00)	0.113 (1.00)	0.072 (1.00)	0.478 (1.00)
RPPA cHierClus subtypes	0.275 (1.00)	0.108 (1.00)	3e-05 (0.00207)	0.982 (1.00)	0.108 (1.00)	0.289 (1.00)	0.497 (1.00)
RNAseq CNMF subtypes	4.88e-05 (0.00332)	1.59e-06 (0.000127)	1e-05 (0.00079)	0.00624 (0.362)	0.0553 (1.00)	0.0435 (1.00)	0.939 (1.00)
RNAseq cHierClus subtypes	5.62e-05 (0.00377)	1.18e-09 (9.76e-08)	1e-05 (0.00079)	0.0965 (1.00)	0.0196 (1.00)	0.0111 (0.634)	0.192 (1.00)
MIRSEQ CNMF	0.000522 (0.0334)	7.1e-08 (5.75e-06)	1e-05 (0.00079)	0.648 (1.00)	0.156 (1.00)	0.316 (1.00)	0.147 (1.00)
MIRSEQ CHIERARCHICAL	0.000722 (0.0455)	5.59e-09 (4.58e-07)	1e-05 (0.00079)	0.199 (1.00)	0.402 (1.00)	0.0521 (1.00)	0.91 (1.00)
MIRseq Mature CNMF subtypes	0.0613 (1.00)	0.0708 (1.00)	1e-05 (0.00079)	0.646 (1.00)	0.103 (1.00)	0.233 (1.00)	0.0697 (1.00)
MIRseq Mature cHierClus subtypes	0.134 (1.00)	0.148 (1.00)	1e-05 (0.00079)	0.22 (1.00)	0.115 (1.00)	0.733 (1.00)	0.662 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	14	18	12	10

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.838 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	9	6.0 - 149.6 (37.4)
subtype1	14	2	13.6 - 149.6 (41.7)
subtype2	18	4	6.0 - 125.4 (46.2)
subtype3	12	1	19.8 - 105.4 (33.8)
subtype4	10	2	7.9 - 82.5 (28.3)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.00418 (Kruskal-Wallis (anova)), Q value = 0.25

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

	nPatients	Mean (Std.Dev)
ALL	54	62.9 (11.8)
subtype1	14	65.6 (11.7)
subtype2	18	68.2 (9.3)
subtype3	12	58.7 (11.9)
subtype4	10	54.9 (11.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00033 (Fisher's exact test), Q value = 0.022

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	41	1	12
subtype1	12	0	2
subtype2	8	0	10
subtype3	11	1	0
subtype4	10	0	0

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0369 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	25	29
subtype1	8	6
subtype2	12	6
subtype3	3	9
subtype4	2	8

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

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

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	41	4	2	1
subtype1	8	2	1	0
subtype2	14	1	1	1
subtype3	10	1	0	0
subtype4	9	0	0	0

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.15 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	6	40
subtype1	1	1	1	10
subtype2	0	0	4	13
subtype3	1	3	0	8
subtype4	0	0	1	9

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.612 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	24
subtype1	1	6
subtype2	1	6
subtype3	0	9
subtype4	0	3

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	14	10	7	7	16

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.126 (logrank test), Q value = 1

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	9	6.0 - 149.6 (37.4)
subtype1	14	1	10.2 - 149.6 (57.2)
subtype2	10	3	6.0 - 125.4 (42.5)
subtype3	7	3	13.6 - 68.3 (36.4)
subtype4	7	0	28.6 - 83.7 (49.2)
subtype5	16	2	7.9 - 105.4 (29.9)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0314 (Kruskal-Wallis (anova)), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	54	62.9 (11.8)
subtype1	14	62.4 (8.6)
subtype2	10	70.7 (9.3)
subtype3	7	63.4 (15.2)
subtype4	7	66.3 (7.1)
subtype5	16	56.9 (13.6)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	41	1	12
subtype1	11	0	3
subtype2	1	0	9
subtype3	6	1	0
subtype4	7	0	0
subtype5	16	0	0

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.103 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	25	29
subtype1	7	7
subtype2	7	3
subtype3	2	5
subtype4	5	2
subtype5	4	12

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.491 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	41	4	2	1
subtype1	11	1	0	1
subtype2	9	0	1	0
subtype3	4	1	1	0
subtype4	4	1	0	0
subtype5	13	1	0	0

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.571 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	4	6	40
subtype1	0	2	2	9
subtype2	0	0	3	7
subtype3	0	1	0	5
subtype4	1	0	0	6
subtype5	1	1	1	13

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

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

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	24
subtype1	1	9
subtype2	0	2
subtype3	1	5
subtype4	0	2
subtype5	0	6

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S17. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	286	45	138	11	5

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

P value = 0.00118 (logrank test), Q value = 0.072

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	483	60	0.0 - 191.8 (22.8)
subtype1	285	23	0.1 - 191.8 (25.0)
subtype2	45	9	0.2 - 149.6 (18.8)
subtype3	137	26	0.0 - 125.4 (18.2)
subtype4	11	2	8.7 - 73.2 (33.4)
subtype5	5	0	19.3 - 52.8 (31.3)

Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 6.6e-11 (Kruskal-Wallis (anova)), Q value = 5.5e-09

Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	484	63.7 (11.3)
subtype1	285	61.2 (11.2)
subtype2	45	62.5 (12.4)
subtype3	138	69.2 (8.7)
subtype4	11	63.3 (14.0)
subtype5	5	67.2 (12.7)

Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	367	20	98
subtype1	274	7	5
subtype2	41	0	4
subtype3	38	11	89
subtype4	10	1	0
subtype5	4	1	0

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.00554 (Fisher's exact test), Q value = 0.33

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	138	347
subtype1	95	191
subtype2	6	39
subtype3	30	108
subtype4	5	6
subtype5	2	3

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.418 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	22	17	29
subtype1	205	13	5	16
subtype2	31	1	2	3
subtype3	87	8	10	10
subtype4	8	0	0	0
subtype5	5	0	0	0

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00051 (Fisher's exact test), Q value = 0.033

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	4	19	73	9	355
subtype1	3	13	31	4	223
subtype2	0	4	2	1	37
subtype3	1	2	39	3	83
subtype4	0	0	1	1	7
subtype5	0	0	0	0	5

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.812 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	336
subtype1	7	196
subtype2	2	35
subtype3	4	93
subtype4	0	7
subtype5	0	5

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S25. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	143	77	160

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0459 (logrank test), Q value = 1

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	378	45	0.0 - 191.8 (18.0)
subtype1	141	24	0.0 - 191.8 (17.0)
subtype2	77	7	0.1 - 102.3 (24.3)
subtype3	160	14	0.1 - 185.8 (17.9)

Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00114 (Kruskal-Wallis (anova)), Q value = 0.07

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	379	63.9 (11.3)
subtype1	142	66.2 (10.3)
subtype2	77	63.1 (12.9)
subtype3	160	62.1 (11.0)

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	278	19	83
subtype1	59	15	69
subtype2	62	1	14
subtype3	157	3	0

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.209 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	87	293
subtype1	37	106
subtype2	12	65
subtype3	38	122

Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0138 (Fisher's exact test), Q value = 0.77

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	253	18	13	28
subtype1	91	9	8	10
subtype2	48	3	4	11
subtype3	114	6	1	7

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0661 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	7	68	7	275
subtype1	1	2	32	3	95
subtype2	0	2	13	4	55
subtype3	1	3	23	0	125

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.406 (Fisher's exact test), Q value = 1

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	275
subtype1	6	100
subtype2	1	58
subtype3	3	117

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S33. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	42	39	40	12	41	26

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0778 (logrank test), Q value = 1

Table S34. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	200	20	0.6 - 185.8 (29.2)
subtype1	42	4	0.6 - 106.9 (29.5)
subtype2	39	6	6.0 - 149.6 (28.1)
subtype3	40	1	1.8 - 185.8 (30.0)
subtype4	12	4	1.4 - 87.3 (35.6)
subtype5	41	2	0.7 - 113.4 (25.1)
subtype6	26	3	0.7 - 78.2 (29.6)

Figure S29. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 1

Table S35. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	62.7 (11.1)
subtype1	42	62.3 (12.7)
subtype2	39	64.2 (9.6)
subtype3	40	62.2 (11.2)
subtype4	12	69.8 (8.5)
subtype5	41	60.7 (9.4)
subtype6	26	62.0 (12.8)

Figure S30. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2e-05 (Fisher's exact test), Q value = 0.0014

Table S36. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	174	3	23
subtype1	35	1	6
subtype2	28	0	11
subtype3	39	1	0
subtype4	12	0	0
subtype5	41	0	0
subtype6	19	1	6

Figure S31. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.47 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	120
subtype1	20	22
subtype2	19	20
subtype3	13	27
subtype4	4	8
subtype5	13	28
subtype6	11	15

Figure S32. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.113 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	143	7	5	16
subtype1	24	3	3	6
subtype2	29	1	1	2
subtype3	26	0	0	5
subtype4	10	1	1	0
subtype5	32	2	0	1
subtype6	22	0	0	2

Figure S33. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.072 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	9	18	4	162
subtype1	0	1	6	2	30
subtype2	0	3	6	1	28
subtype3	0	0	2	0	38
subtype4	1	2	1	0	8
subtype5	1	2	3	1	33
subtype6	0	1	0	0	25

Figure S34. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.478 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	135
subtype1	2	29
subtype2	2	21
subtype3	0	30
subtype4	0	10
subtype5	1	23
subtype6	0	22

Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S41. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	70	67	44	19

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.275 (logrank test), Q value = 1

Table S42. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	200	20	0.6 - 185.8 (29.2)
subtype1	70	11	0.6 - 149.6 (28.6)
subtype2	67	4	0.7 - 113.4 (25.1)
subtype3	44	4	1.4 - 185.8 (35.0)
subtype4	19	1	0.7 - 78.2 (29.9)

Figure S36. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 1

Table S43. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	62.7 (11.1)
subtype1	70	64.9 (11.2)
subtype2	67	60.1 (10.9)
subtype3	44	62.9 (9.2)
subtype4	19	63.4 (13.8)

Figure S37. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0021

Table S44. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	174	3	23
subtype1	52	2	16
subtype2	64	1	2
subtype3	44	0	0
subtype4	14	0	5

Figure S38. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.982 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	120
subtype1	29	41
subtype2	26	41
subtype3	17	27
subtype4	8	11

Figure S39. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.108 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	143	7	5	16
subtype1	43	4	5	9
subtype2	50	1	0	3
subtype3	34	2	0	3
subtype4	16	0	0	1

Figure S40. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.289 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	9	18	4	162
subtype1	0	2	9	4	54
subtype2	1	3	7	0	52
subtype3	1	3	2	0	38
subtype4	0	1	0	0	18

Figure S41. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.497 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	135
subtype1	3	46
subtype2	2	37
subtype3	0	37
subtype4	0	15

Figure S42. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S49. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	205	144	139

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 4.88e-05 (logrank test), Q value = 0.0033

Table S50. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	486	60	0.0 - 191.8 (22.7)
subtype1	203	40	0.0 - 191.8 (18.7)
subtype2	144	12	0.3 - 185.8 (27.6)
subtype3	139	8	0.1 - 113.4 (23.9)

Figure S43. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 1.59e-06 (Kruskal-Wallis (anova)), Q value = 0.00013

Table S51. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	487	63.7 (11.3)
subtype1	204	66.3 (10.6)
subtype2	144	60.5 (11.4)
subtype3	139	63.2 (11.2)

Figure S44. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S52. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	370	19	99
subtype1	92	15	98
subtype2	143	1	0
subtype3	135	3	1

Figure S45. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.00624 (Fisher's exact test), Q value = 0.36

Table S53. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	137	351
subtype1	51	154
subtype2	55	89
subtype3	31	108

Figure S46. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0553 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	337	22	17	29
subtype1	131	13	11	14
subtype2	107	6	4	4
subtype3	99	3	2	11

Figure S47. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0435 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	4	19	74	9	357
subtype1	1	5	42	6	136
subtype2	2	6	14	2	114
subtype3	1	8	18	1	107

Figure S48. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.939 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	337
subtype1	6	141
subtype2	3	97
subtype3	4	99

Figure S49. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S57. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	149	200	139

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 5.62e-05 (logrank test), Q value = 0.0038

Table S58. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	486	60	0.0 - 191.8 (22.7)
subtype1	147	32	0.0 - 191.8 (19.3)
subtype2	200	20	0.2 - 185.8 (23.4)
subtype3	139	8	0.1 - 113.4 (24.3)

Figure S50. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 1.18e-09 (Kruskal-Wallis (anova)), Q value = 9.8e-08

Table S59. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	487	63.7 (11.3)
subtype1	148	68.2 (9.5)
subtype2	200	60.8 (11.5)
subtype3	139	63.1 (11.2)

Figure S51. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S60. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	370	19	99
subtype1	39	13	97
subtype2	195	4	1
subtype3	136	2	1

Figure S52. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0965 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	137	351
subtype1	40	109
subtype2	66	134
subtype3	31	108

Figure S53. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0196 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	337	22	17	29
subtype1	96	9	10	11
subtype2	141	11	5	7
subtype3	100	2	2	11

Figure S54. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0111 (Fisher's exact test), Q value = 0.63

Table S63. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	4	19	74	9	357
subtype1	1	6	34	2	93
subtype2	2	6	22	7	153
subtype3	1	7	18	0	111

Figure S55. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.192 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	337
subtype1	7	96
subtype2	4	138
subtype3	2	103

Figure S56. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S65. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	138	194	154

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000522 (logrank test), Q value = 0.033

Table S66. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	484	57	0.0 - 191.8 (22.8)
subtype1	138	14	0.1 - 185.8 (26.1)
subtype2	192	35	0.0 - 191.8 (18.8)
subtype3	154	8	0.1 - 113.4 (23.6)

Figure S57. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 7.1e-08 (Kruskal-Wallis (anova)), Q value = 5.7e-06

Table S67. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	485	63.6 (11.3)
subtype1	138	60.1 (11.9)
subtype2	193	67.0 (9.9)
subtype3	154	62.6 (11.1)

Figure S58. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S68. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	372	20	94
subtype1	134	3	1
subtype2	88	16	90
subtype3	150	1	3

Figure S59. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.648 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	134	352
subtype1	42	96
subtype2	50	144
subtype3	42	112

Figure S60. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.156 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	22	16	29
subtype1	99	7	3	6
subtype2	124	11	11	13
subtype3	113	4	2	10

Figure S61. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.316 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	4	19	76	9	353
subtype1	2	6	21	2	100
subtype2	1	7	37	6	129
subtype3	1	6	18	1	124

Figure S62. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.147 (Fisher's exact test), Q value = 1

Table S72. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	339
subtype1	2	92
subtype2	9	134
subtype3	2	113

Figure S63. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S73. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	54	147	115	170

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000722 (logrank test), Q value = 0.045

Table S74. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	484	57	0.0 - 191.8 (22.8)
subtype1	54	3	0.7 - 185.8 (29.2)
subtype2	146	29	0.0 - 191.8 (19.7)
subtype3	115	6	0.1 - 105.4 (23.9)
subtype4	169	19	0.1 - 118.0 (20.6)

Figure S64. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 5.59e-09 (Kruskal-Wallis (anova)), Q value = 4.6e-07

Table S75. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	485	63.6 (11.3)
subtype1	54	57.3 (11.9)
subtype2	146	67.7 (9.1)
subtype3	115	63.5 (10.3)
subtype4	170	62.3 (12.1)

Figure S65. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S76. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	372	20	94
subtype1	54	0	0
subtype2	45	13	89
subtype3	111	1	3
subtype4	162	6	2

Figure S66. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.199 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	134	352
subtype1	13	41
subtype2	33	114
subtype3	32	83
subtype4	56	114

Figure S67. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

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

Table S78. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	336	22	16	29
subtype1	36	4	1	4
subtype2	98	9	7	11
subtype3	84	2	2	8
subtype4	118	7	6	6

Figure S68. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.0521 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	4	19	76	9	353
subtype1	1	3	4	2	42
subtype2	1	6	35	2	94
subtype3	0	6	13	1	92
subtype4	2	4	24	4	125

Figure S69. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.91 (Fisher's exact test), Q value = 1

Table S80. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	339
subtype1	1	35
subtype2	5	104
subtype3	2	83
subtype4	5	117

Figure S70. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S81. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	118	156	85

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0613 (logrank test), Q value = 1

Table S82. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	357	46	0.0 - 191.8 (17.8)
subtype1	118	8	0.1 - 113.4 (19.8)
subtype2	154	25	0.0 - 191.8 (15.3)
subtype3	85	13	0.1 - 92.0 (24.3)

Figure S71. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0708 (Kruskal-Wallis (anova)), Q value = 1

Table S83. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	358	64.2 (11.2)
subtype1	118	63.1 (10.3)
subtype2	155	65.7 (11.0)
subtype3	85	63.0 (12.6)

Figure S72. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S84. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	259	18	82
subtype1	114	2	2
subtype2	89	8	59
subtype3	56	8	21

Figure S73. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.646 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	85	274
subtype1	28	90
subtype2	34	122
subtype3	23	62

Figure S74. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.103 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	243	17	13	23
subtype1	77	4	2	12
subtype2	101	9	10	7
subtype3	65	4	1	4

Figure S75. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.233 (Fisher's exact test), Q value = 1

Table S87. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	6	64	5	261
subtype1	1	1	19	1	93
subtype2	1	1	29	1	109
subtype3	0	4	16	3	59

Figure S76. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0697 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	258
subtype1	2	85
subtype2	8	110
subtype3	0	63

Figure S77. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S89. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	151	104	104

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.134 (logrank test), Q value = 1

Table S90. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	357	46	0.0 - 191.8 (17.8)
subtype1	150	25	0.0 - 113.4 (16.7)
subtype2	104	7	0.1 - 88.3 (19.4)
subtype3	103	14	0.1 - 191.8 (18.0)

Figure S78. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 1

Table S91. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	358	64.2 (11.2)
subtype1	151	65.5 (10.9)
subtype2	104	62.7 (11.0)
subtype3	103	63.9 (11.7)

Figure S79. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00079

Table S92. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	259	18	82
subtype1	89	10	52
subtype2	99	2	3
subtype3	71	6	27

Figure S80. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.22 (Fisher's exact test), Q value = 1

Table S93. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	85	274
subtype1	31	120
subtype2	23	81
subtype3	31	73

Figure S81. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

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

Table S94. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	243	17	13	23
subtype1	96	7	9	9
subtype2	71	2	2	10
subtype3	76	8	2	4

Figure S82. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.733 (Fisher's exact test), Q value = 1

Table S95. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	6	64	5	261
subtype1	1	1	31	2	105
subtype2	0	3	15	1	81
subtype3	1	2	18	2	75

Figure S83. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

Table S96. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	258
subtype1	4	109
subtype2	2	78
subtype3	4	71

Figure S84. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'ETHNICITY'

Methods & Data

Input

Cluster data file = UCEC-TP.mergedcluster.txt
Clinical data file = UCEC-TP.merged_data.txt
Number of patients = 496
Number of clustering approaches = 12
Number of selected clinical features = 7
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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