Correlation between aggregated molecular cancer subtypes and selected clinical features

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 7 clinical features across 536 patients, 38 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 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'YEARS_TO_BIRTH' and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', and 'COMPLETENESS_OF_RESECTION'.
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', 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', 'COMPLETENESS_OF_RESECTION', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH', 'HISTOLOGICAL_TYPE', 'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH' and '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, 38 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	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	0.836 (0.889)	0.00418 (0.0125)	0.0003 (0.00126)	0.0369 (0.0837)	0.891 (0.935)	0.148 (0.239)	0.614 (0.679)
mRNA cHierClus subtypes	0.095 (0.185)	0.0314 (0.0789)	1e-05 (5.25e-05)	0.103 (0.19)	0.488 (0.577)	0.571 (0.648)	1 (1.00)
Copy Number Ratio CNMF subtypes	2.19e-06 (3.68e-05)	3.09e-12 (2.59e-10)	1e-05 (5.25e-05)	0.0218 (0.059)	0.313 (0.404)	0.00269 (0.00837)	0.794 (0.855)
METHLYATION CNMF	0.0348 (0.0836)	0.00191 (0.00617)	1e-05 (5.25e-05)	0.248 (0.353)	0.0319 (0.0789)	0.0667 (0.144)	0.483 (0.577)
RPPA CNMF subtypes	0.228 (0.342)	0.148 (0.239)	3e-05 (0.000148)	0.471 (0.577)	0.113 (0.193)	0.0727 (0.149)	0.478 (0.577)
RPPA cHierClus subtypes	0.278 (0.377)	0.108 (0.19)	1e-05 (5.25e-05)	0.983 (1.00)	0.107 (0.19)	0.287 (0.377)	0.498 (0.581)
RNAseq CNMF subtypes	7.24e-05 (0.00032)	1.27e-06 (2.67e-05)	1e-05 (5.25e-05)	0.00432 (0.0125)	0.108 (0.19)	0.0827 (0.165)	1 (1.00)
RNAseq cHierClus subtypes	0.000338 (0.00132)	2.77e-08 (7.77e-07)	1e-05 (5.25e-05)	0.0441 (0.0975)	0.0276 (0.0726)	0.0359 (0.0837)	0.227 (0.342)
MIRSEQ CNMF	0.000656 (0.0023)	3.07e-06 (4.3e-05)	1e-05 (5.25e-05)	0.587 (0.657)	0.246 (0.353)	0.274 (0.377)	0.105 (0.19)
MIRSEQ CHIERARCHICAL	5.04e-05 (0.000235)	1.02e-09 (4.29e-08)	1e-05 (5.25e-05)	0.287 (0.377)	0.392 (0.491)	0.139 (0.234)	0.976 (1.00)
MIRseq Mature CNMF subtypes	0.0723 (0.149)	0.000821 (0.00276)	1e-05 (5.25e-05)	0.00572 (0.016)	0.694 (0.757)	0.00044 (0.00161)	0.168 (0.266)
MIRseq Mature cHierClus subtypes	0.239 (0.352)	0.000345 (0.00132)	1e-05 (5.25e-05)	0.282 (0.377)	0.181 (0.282)	0.366 (0.466)	0.548 (0.631)

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 = 0.836 (logrank test), Q value = 0.89

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 (47.9)
subtype3	12	1	19.8 - 105.4 (33.8)
subtype4	10	2	18.6 - 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 'YEARS_TO_BIRTH'

P value = 0.00418 (Kruskal-Wallis (anova)), Q value = 0.013

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

	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: 'YEARS_TO_BIRTH'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-04 (Fisher's exact test), Q value = 0.0013

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 = 0.084

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 = 0.891 (Fisher's exact test), Q value = 0.94

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 = 0.148 (Fisher's exact test), Q value = 0.24

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 = 0.614 (Fisher's exact test), Q value = 0.68

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 = 0.095 (logrank test), Q value = 0.19

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 (46.2)
subtype3	7	3	13.6 - 68.3 (36.4)
subtype4	7	0	28.6 - 83.7 (49.2)
subtype5	16	2	18.6 - 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 'YEARS_TO_BIRTH'

P value = 0.0314 (Kruskal-Wallis (anova)), Q value = 0.079

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

	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: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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 = 0.19

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 = 0.488 (Fisher's exact test), Q value = 0.58

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 = 0.65

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	164	299	19	9	37

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

P value = 2.19e-06 (logrank test), Q value = 3.7e-05

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	526	69	0.1 - 191.8 (23.3)
subtype1	163	37	0.1 - 125.4 (21.2)
subtype2	298	22	0.2 - 191.8 (24.8)
subtype3	19	3	0.2 - 149.6 (30.8)
subtype4	9	0	8.6 - 60.5 (21.2)
subtype5	37	7	0.1 - 73.2 (17.9)

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 'YEARS_TO_BIRTH'

P value = 3.09e-12 (Kruskal-Wallis (anova)), Q value = 2.6e-10

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

	nPatients	Mean (Std.Dev)
ALL	526	64.0 (11.2)
subtype1	163	69.2 (8.4)
subtype2	298	61.4 (11.2)
subtype3	19	61.9 (13.8)
subtype4	9	68.1 (12.9)
subtype5	37	61.6 (12.8)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	395	21	112
subtype1	48	11	105
subtype2	286	8	5
subtype3	17	0	2
subtype4	8	1	0
subtype5	36	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 = 0.0218 (Fisher's exact test), Q value = 0.059

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

nPatients	NO	YES
ALL	141	387
subtype1	32	132
subtype2	95	204
subtype3	2	17
subtype4	3	6
subtype5	9	28

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 = 0.313 (Fisher's exact test), Q value = 0.4

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

nPatients	R0	R1	R2	RX
ALL	363	23	17	35
subtype1	103	9	10	14
subtype2	211	14	5	19
subtype3	12	0	1	1
subtype4	9	0	0	0
subtype5	28	0	1	1

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 = 0.00269 (Fisher's exact test), Q value = 0.0084

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	20	101	9	366
subtype1	1	2	50	3	95
subtype2	3	15	43	5	219
subtype3	0	3	1	0	15
subtype4	0	0	1	0	8
subtype5	0	0	6	1	29

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 = 0.794 (Fisher's exact test), Q value = 0.85

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	367
subtype1	6	112
subtype2	8	201
subtype3	0	15
subtype4	0	9
subtype5	0	30

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	162	87	171

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0348 (logrank test), Q value = 0.084

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	418	54	0.1 - 191.8 (19.4)
subtype1	160	28	0.1 - 191.8 (18.8)
subtype2	87	9	0.3 - 116.2 (24.3)
subtype3	171	17	0.1 - 185.8 (18.9)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00191 (Kruskal-Wallis (anova)), Q value = 0.0062

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

	nPatients	Mean (Std.Dev)
ALL	418	64.2 (11.2)
subtype1	160	66.3 (10.1)
subtype2	87	63.2 (12.3)
subtype3	171	62.7 (11.4)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	303	20	97
subtype1	66	15	81
subtype2	69	2	16
subtype3	168	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 = 0.248 (Fisher's exact test), Q value = 0.35

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	90	330
subtype1	37	125
subtype2	13	74
subtype3	40	131

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 = 0.0319 (Fisher's exact test), Q value = 0.079

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	277	19	13	34
subtype1	102	10	8	12
subtype2	54	3	4	12
subtype3	121	6	1	10

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0667 (Fisher's exact test), Q value = 0.14

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	8	93	7	286
subtype1	1	2	42	3	101
subtype2	0	2	20	4	57
subtype3	1	4	31	0	128

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.483 (Fisher's exact test), Q value = 0.58

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	304
subtype1	6	114
subtype2	1	64
subtype3	4	126

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 = 0.228 (logrank test), Q value = 0.34

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 (29.5)
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	4.0 - 112.5 (48.8)
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 'YEARS_TO_BIRTH'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 0.24

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

	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: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00015

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 = 0.471 (Fisher's exact test), Q value = 0.58

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 = 0.19

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 = 0.0727 (Fisher's exact test), Q value = 0.15

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 = 0.58

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 = 0.278 (logrank test), Q value = 0.38

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 (29.5)
subtype1	70	11	0.6 - 149.6 (28.6)
subtype2	67	4	0.7 - 113.4 (25.1)
subtype3	44	4	1.8 - 185.8 (35.1)
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 'YEARS_TO_BIRTH'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 0.19

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

	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: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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 = 0.983 (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 = 0.107 (Fisher's exact test), Q value = 0.19

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 = 0.287 (Fisher's exact test), Q value = 0.38

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 = 0.498 (Fisher's exact test), Q value = 0.58

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	230	151	153

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 7.24e-05 (logrank test), Q value = 0.00032

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	532	69	0.1 - 191.8 (23.3)
subtype1	228	45	0.1 - 191.8 (20.1)
subtype2	151	12	0.1 - 185.8 (27.2)
subtype3	153	12	0.4 - 116.2 (24.9)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.27e-06 (Kruskal-Wallis (anova)), Q value = 2.7e-05

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

	nPatients	Mean (Std.Dev)
ALL	532	64.0 (11.2)
subtype1	228	66.5 (10.5)
subtype2	151	60.9 (11.6)
subtype3	153	63.1 (11.0)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	400	21	113
subtype1	102	16	112
subtype2	150	1	0
subtype3	148	4	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 = 0.00432 (Fisher's exact test), Q value = 0.013

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

nPatients	NO	YES
ALL	141	393
subtype1	54	176
subtype2	55	96
subtype3	32	121

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 = 0.108 (Fisher's exact test), Q value = 0.19

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

nPatients	R0	R1	R2	RX
ALL	366	23	17	35
subtype1	150	14	11	16
subtype2	109	6	4	6
subtype3	107	3	2	13

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 = 0.0827 (Fisher's exact test), Q value = 0.17

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	20	102	9	371
subtype1	1	5	54	6	146
subtype2	2	7	20	2	114
subtype3	1	8	28	1	111

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 1 (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	14	370
subtype1	6	163
subtype2	4	99
subtype3	4	108

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	4	5	6	7
Number of samples	122	51	46	63	107	68	77

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000338 (logrank test), Q value = 0.0013

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	532	69	0.1 - 191.8 (23.3)
subtype1	122	26	0.1 - 125.4 (20.5)
subtype2	51	4	0.7 - 185.8 (25.1)
subtype3	45	9	0.2 - 191.8 (27.4)
subtype4	63	2	0.1 - 110.4 (28.0)
subtype5	106	17	0.2 - 86.1 (18.6)
subtype6	68	8	0.4 - 116.2 (24.3)
subtype7	77	3	0.6 - 112.5 (27.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.77e-08 (Kruskal-Wallis (anova)), Q value = 7.8e-07

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

	nPatients	Mean (Std.Dev)
ALL	532	64.0 (11.2)
subtype1	121	69.3 (8.4)
subtype2	51	59.6 (10.4)
subtype3	45	63.9 (9.9)
subtype4	63	61.3 (13.0)
subtype5	107	62.7 (12.1)
subtype6	68	62.4 (11.8)
subtype7	77	63.8 (10.1)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	400	21	113
subtype1	24	9	89
subtype2	51	0	0
subtype3	20	5	21
subtype4	61	2	0
subtype5	102	3	2
subtype6	67	1	0
subtype7	75	1	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 = 0.0441 (Fisher's exact test), Q value = 0.097

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

nPatients	NO	YES
ALL	141	393
subtype1	26	96
subtype2	13	38
subtype3	16	30
subtype4	26	37
subtype5	29	78
subtype6	12	56
subtype7	19	58

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 = 0.0276 (Fisher's exact test), Q value = 0.073

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

nPatients	R0	R1	R2	RX
ALL	366	23	17	35
subtype1	79	7	3	10
subtype2	39	2	0	3
subtype3	32	1	7	3
subtype4	43	5	1	1
subtype5	72	7	4	5
subtype6	44	1	1	5
subtype7	57	0	1	8

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 = 0.0359 (Fisher's exact test), Q value = 0.084

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	20	102	9	371
subtype1	0	3	35	1	73
subtype2	0	2	8	0	39
subtype3	1	3	10	1	26
subtype4	2	1	8	3	47
subtype5	0	4	15	4	77
subtype6	1	3	15	0	48
subtype7	0	4	11	0	61

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.227 (Fisher's exact test), Q value = 0.34

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	370
subtype1	3	80
subtype2	1	36
subtype3	4	33
subtype4	2	40
subtype5	3	76
subtype6	1	45
subtype7	0	60

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	218	148	161

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000656 (logrank test), Q value = 0.0023

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	525	66	0.1 - 191.8 (23.3)
subtype1	216	39	0.1 - 191.8 (20.0)
subtype2	148	17	0.1 - 185.8 (27.1)
subtype3	161	10	0.4 - 116.2 (24.9)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 3.07e-06 (Kruskal-Wallis (anova)), Q value = 4.3e-05

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

	nPatients	Mean (Std.Dev)
ALL	525	63.9 (11.2)
subtype1	216	66.7 (10.0)
subtype2	148	61.0 (12.3)
subtype3	161	63.0 (11.0)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	398	21	108
subtype1	98	16	104
subtype2	143	3	2
subtype3	157	2	2

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 = 0.587 (Fisher's exact test), Q value = 0.66

Table S69. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	137	390
subtype1	53	165
subtype2	43	105
subtype3	41	120

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 = 0.246 (Fisher's exact test), Q value = 0.35

Table S70. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	361	23	16	35
subtype1	142	12	11	16
subtype2	102	7	3	8
subtype3	117	4	2	11

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.274 (Fisher's exact test), Q value = 0.38

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	20	102	9	364
subtype1	1	9	49	6	134
subtype2	2	4	28	2	105
subtype3	1	7	25	1	125

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.105 (Fisher's exact test), Q value = 0.19

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	369
subtype1	10	151
subtype2	2	98
subtype3	2	120

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	214	56	142	115

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 5.04e-05 (logrank test), Q value = 0.00024

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	525	66	0.1 - 191.8 (23.3)
subtype1	213	26	0.1 - 191.8 (20.6)
subtype2	56	3	0.7 - 185.8 (31.3)
subtype3	142	10	0.4 - 116.2 (24.5)
subtype4	114	27	0.3 - 149.6 (22.0)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.02e-09 (Kruskal-Wallis (anova)), Q value = 4.3e-08

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

	nPatients	Mean (Std.Dev)
ALL	525	63.9 (11.2)
subtype1	213	63.6 (12.3)
subtype2	56	56.7 (10.2)
subtype3	142	63.8 (10.2)
subtype4	114	68.2 (8.8)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	398	21	108
subtype1	185	10	19
subtype2	56	0	0
subtype3	139	1	2
subtype4	18	10	87

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 = 0.287 (Fisher's exact test), Q value = 0.38

Table S77. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients	NO	YES
ALL	137	390
subtype1	64	150
subtype2	12	44
subtype3	37	105
subtype4	24	91

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 = 0.392 (Fisher's exact test), Q value = 0.49

Table S78. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS_OF_RESECTION'

nPatients	R0	R1	R2	RX
ALL	361	23	16	35
subtype1	148	10	6	11
subtype2	36	4	1	4
subtype3	101	3	2	11
subtype4	76	6	7	9

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.139 (Fisher's exact test), Q value = 0.23

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	20	102	9	364
subtype1	2	8	38	4	146
subtype2	1	3	8	2	40
subtype3	0	6	22	2	110
subtype4	1	3	34	1	68

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.976 (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	14	369
subtype1	7	148
subtype2	1	36
subtype3	3	103
subtype4	3	82

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	165	92	134

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

P value = 0.0723 (logrank test), Q value = 0.15

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	52	0.1 - 191.8 (19.3)
subtype1	163	27	0.1 - 191.8 (17.8)
subtype2	92	14	0.3 - 87.3 (21.0)
subtype3	134	11	0.7 - 116.2 (20.2)

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 'YEARS_TO_BIRTH'

P value = 0.000821 (Kruskal-Wallis (anova)), Q value = 0.0028

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

	nPatients	Mean (Std.Dev)
ALL	389	64.3 (11.1)
subtype1	163	66.2 (10.7)
subtype2	92	64.7 (12.3)
subtype3	134	61.7 (10.1)

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	278	19	94
subtype1	89	8	68
subtype2	63	8	21
subtype3	126	3	5

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 = 0.00572 (Fisher's exact test), Q value = 0.016

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

nPatients	NO	YES
ALL	88	303
subtype1	26	139
subtype2	20	72
subtype3	42	92

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 = 0.694 (Fisher's exact test), Q value = 0.76

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

nPatients	R0	R1	R2	RX
ALL	261	17	13	29
subtype1	102	7	8	11
subtype2	70	4	1	6
subtype3	89	6	4	12

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 = 0.00044 (Fisher's exact test), Q value = 0.0016

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	7	88	5	265
subtype1	1	1	44	0	102
subtype2	1	4	24	4	54
subtype3	0	2	20	1	109

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 = 0.168 (Fisher's exact test), Q value = 0.27

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	280
subtype1	8	115
subtype2	1	64
subtype3	2	101

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	140	114	137

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

P value = 0.239 (logrank test), Q value = 0.35

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	389	52	0.1 - 191.8 (19.3)
subtype1	138	22	0.1 - 191.8 (17.8)
subtype2	114	10	0.4 - 116.2 (20.2)
subtype3	137	20	0.2 - 110.1 (20.0)

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 'YEARS_TO_BIRTH'

P value = 0.000345 (Kruskal-Wallis (anova)), Q value = 0.0013

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

	nPatients	Mean (Std.Dev)
ALL	389	64.3 (11.1)
subtype1	138	67.3 (9.0)
subtype2	114	62.5 (10.7)
subtype3	137	62.8 (12.5)

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 5.2e-05

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	278	19	94
subtype1	70	11	59
subtype2	110	2	2
subtype3	98	6	33

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 = 0.282 (Fisher's exact test), Q value = 0.38

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

nPatients	NO	YES
ALL	88	303
subtype1	27	113
subtype2	24	90
subtype3	37	100

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 = 0.181 (Fisher's exact test), Q value = 0.28

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

nPatients	R0	R1	R2	RX
ALL	261	17	13	29
subtype1	90	7	8	9
subtype2	72	4	1	13
subtype3	99	6	4	7

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 = 0.366 (Fisher's exact test), Q value = 0.47

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	7	88	5	265
subtype1	1	1	33	0	96
subtype2	1	3	23	1	83
subtype3	0	3	32	4	86

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 = 0.548 (Fisher's exact test), Q value = 0.63

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	280
subtype1	6	106
subtype2	2	84
subtype3	3	90

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

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/UCEC-TP/15120652/UCEC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCEC-TP/15094079/UCEC-TP.merged_data.txt
Number of patients = 536
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.

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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