Correlation between aggregated molecular cancer subtypes and selected clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C189147T

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 5 clinical features across 478 patients, 22 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 3 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.
6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 2 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'COMPLETENESS.OF.RESECTION'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
3 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'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death' 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 5 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 22 significant findings detected.


Clinical Features	Time to Death	AGE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION
Statistical Tests	logrank test	ANOVA	Chi-square test	Fisher's exact test	Chi-square test
mRNA CNMF subtypes	0.858 (1.00)	0.0116 (0.429)	0.00151 (0.065)	0.0371 (1.00)	0.855 (1.00)
mRNA cHierClus subtypes	0.727 (1.00)	0.00301 (0.12)	9.4e-05 (0.00489)	0.0234 (0.82)	0.677 (1.00)
Copy Number Ratio CNMF subtypes	0.00156 (0.0654)	1.37e-09 (7.42e-08)	7.4e-45 (4.44e-43)	0.0213 (0.768)	0.398 (1.00)
METHLYATION CNMF	0.00762 (0.29)	0.0332 (1.00)	1.26e-23 (7.17e-22)	0.121 (1.00)	0.0713 (1.00)
RPPA CNMF subtypes	0.0702 (1.00)	0.203 (1.00)	0.00103 (0.0464)	0.461 (1.00)	0.102 (1.00)
RPPA cHierClus subtypes	0.089 (1.00)	0.236 (1.00)	0.000814 (0.0383)	0.46 (1.00)	0.00347 (0.135)
RNAseq CNMF subtypes	0.61 (1.00)	0.0342 (1.00)	0.000159 (0.00797)	0.104 (1.00)	0.58 (1.00)
RNAseq cHierClus subtypes	0.914 (1.00)	0.0752 (1.00)	0.000338 (0.0162)	0.23 (1.00)	0.265 (1.00)
MIRSEQ CNMF	0.00112 (0.0492)	7.57e-08 (4.01e-06)	7.25e-27 (4.28e-25)	0.000231 (0.0113)	0.0804 (1.00)
MIRSEQ CHIERARCHICAL	0.000953 (0.0438)	0.000145 (0.0074)	3.89e-24 (2.26e-22)	0.309 (1.00)	0.633 (1.00)
MIRseq Mature CNMF subtypes	0.0635 (1.00)	0.0687 (1.00)	1.79e-11 (9.85e-10)	0.547 (1.00)	0.0452 (1.00)
MIRseq Mature cHierClus subtypes	0.00264 (0.108)	0.0589 (1.00)	3.8e-19 (2.13e-17)	0.436 (1.00)	0.256 (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 = 0.858 (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 - 149.5 (37.4)
subtype1	14	2	13.6 - 149.5 (41.7)
subtype2	18	4	6.0 - 125.4 (46.2)
subtype3	12	1	12.7 - 105.4 (31.1)
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 = 0.0116 (ANOVA), Q value = 0.43

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 = 0.00151 (Chi-square test), Q value = 0.065

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 = 0.0371 (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 = 0.855 (Chi-square 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	39	6	2	1
subtype1	8	2	1	0
subtype2	14	1	1	1
subtype3	9	2	0	0
subtype4	8	1	0	0

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	19	19	16

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.727 (logrank test), Q value = 1

Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	54	9	6.0 - 149.5 (37.4)
subtype1	19	3	13.6 - 149.5 (46.5)
subtype2	19	2	7.9 - 105.4 (28.3)
subtype3	16	4	6.0 - 125.4 (46.2)

Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00301 (ANOVA), Q value = 0.12

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	54	62.9 (11.8)
subtype1	19	63.9 (10.5)
subtype2	19	56.4 (12.5)
subtype3	16	69.5 (8.7)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 9.4e-05 (Chi-square test), Q value = 0.0049

Table S10. 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	16	1	2
subtype2	19	0	0
subtype3	6	0	10

Figure S8. 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 = 0.0234 (Fisher's exact test), Q value = 0.82

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	25	29
subtype1	11	8
subtype2	4	15
subtype3	10	6

Figure S9. 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 = 0.677 (Chi-square test), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	39	6	2	1
subtype1	13	2	1	0
subtype2	13	3	0	0
subtype3	13	1	1	1

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

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

Table S13. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	280	8	138	12	20	12

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

P value = 0.00156 (logrank test), Q value = 0.065

Table S14. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	467	56	0.0 - 191.8 (20.9)
subtype1	278	22	0.1 - 191.8 (24.3)
subtype2	8	3	0.5 - 149.5 (14.7)
subtype3	137	26	0.0 - 125.4 (15.1)
subtype4	12	1	8.0 - 68.3 (22.1)
subtype5	20	2	0.2 - 72.7 (16.5)
subtype6	12	2	8.5 - 73.2 (30.4)

Figure S11. 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 = 1.37e-09 (ANOVA), Q value = 7.4e-08

Table S15. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	469	63.8 (11.3)
subtype1	279	61.4 (11.2)
subtype2	8	64.1 (11.8)
subtype3	138	69.3 (8.9)
subtype4	12	62.7 (11.0)
subtype5	20	60.6 (14.1)
subtype6	12	61.2 (11.8)

Figure S12. 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 = 7.4e-45 (Chi-square test), Q value = 4.4e-43

Table S16. 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	359	18	93
subtype1	267	8	5
subtype2	5	0	3
subtype3	44	9	85
subtype4	11	1	0
subtype5	20	0	0
subtype6	12	0	0

Figure S13. 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 = 0.0213 (Chi-square test), Q value = 0.77

Table S17. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	135	335
subtype1	95	185
subtype2	1	7
subtype3	29	109
subtype4	5	7
subtype5	2	18
subtype6	3	9

Figure S14. 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 = 0.398 (Chi-square test), Q value = 1

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	324	24	16	26
subtype1	199	14	4	15
subtype2	5	0	0	1
subtype3	90	8	10	8
subtype4	9	1	1	0
subtype5	12	1	1	2
subtype6	9	0	0	0

Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S19. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	131	81	150

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00762 (logrank test), Q value = 0.29

Table S20. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	359	41	0.0 - 191.8 (16.8)
subtype1	129	23	0.0 - 191.8 (13.8)
subtype2	81	5	0.1 - 102.0 (22.5)
subtype3	149	13	0.1 - 185.8 (17.0)

Figure S16. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0332 (ANOVA), Q value = 1

Table S21. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	361	63.9 (11.3)
subtype1	130	65.8 (10.6)
subtype2	81	63.9 (12.8)
subtype3	150	62.3 (10.9)

Figure S17. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.26e-23 (Chi-square test), Q value = 7.2e-22

Table S22. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	267	17	78
subtype1	56	12	63
subtype2	64	2	15
subtype3	147	3	0

Figure S18. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.121 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	84	278
subtype1	34	97
subtype2	12	69
subtype3	38	112

Figure S19. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0713 (Chi-square test), Q value = 1

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	240	18	12	25
subtype1	84	8	7	9
subtype2	51	4	4	10
subtype3	105	6	1	6

Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S25. 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 = 0.0702 (logrank test), Q value = 1

Table S26. 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 (28.9)
subtype1	42	4	0.6 - 106.9 (29.5)
subtype2	39	6	6.0 - 149.5 (28.1)
subtype3	40	1	1.8 - 185.8 (29.9)
subtype4	12	4	1.4 - 87.3 (35.2)
subtype5	41	2	0.7 - 113.3 (24.6)
subtype6	26	3	0.7 - 78.2 (29.6)

Figure S21. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.203 (ANOVA), Q value = 1

Table S27. 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 S22. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00103 (Chi-square test), Q value = 0.046

Table S28. 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 S23. 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 = 0.461 (Chi-square test), Q value = 1

Table S29. 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 S24. 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 = 0.102 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	141	9	5	16
subtype1	24	3	3	6
subtype2	29	1	1	2
subtype3	26	0	0	5
subtype4	10	1	1	0
subtype5	30	4	0	1
subtype6	22	0	0	2

Figure S25. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S31. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2
Number of samples	124	76

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.089 (logrank test), Q value = 1

Table S32. 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 (28.9)
subtype1	124	9	0.7 - 185.8 (29.7)
subtype2	76	11	0.6 - 149.5 (23.9)

Figure S26. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.236 (t-test), Q value = 1

Table S33. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	62.7 (11.1)
subtype1	124	62.0 (10.9)
subtype2	76	63.9 (11.3)

Figure S27. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000814 (Chi-square test), Q value = 0.038

Table S34. 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	116	2	6
subtype2	58	1	17

Figure S28. 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 = 0.46 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	120
subtype1	47	77
subtype2	33	43

Figure S29. 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 = 0.00347 (Chi-square test), Q value = 0.14

Table S36. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	141	9	5	16
subtype1	94	6	0	6
subtype2	47	3	5	10

Figure S30. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S37. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	39	25	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.61 (logrank test), Q value = 1

Table S38. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	83	12	0.0 - 102.0 (5.3)
subtype1	38	6	0.0 - 102.0 (4.2)
subtype2	24	3	0.1 - 91.0 (11.9)
subtype3	21	3	0.1 - 43.3 (4.7)

Figure S31. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0342 (ANOVA), Q value = 1

Table S39. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	84	65.7 (11.7)
subtype1	39	67.0 (12.2)
subtype2	24	60.6 (11.5)
subtype3	21	69.0 (9.3)

Figure S32. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000159 (Chi-square test), Q value = 0.008

Table S40. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	46	5	34
subtype1	12	1	26
subtype2	18	3	4
subtype3	16	1	4

Figure S33. 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 = 0.104 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	79
subtype1	1	38
subtype2	4	21
subtype3	1	20

Figure S34. 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 = 0.58 (Chi-square test), Q value = 1

Table S42. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	52	2	4	4
subtype1	22	1	2	3
subtype2	15	1	2	0
subtype3	15	0	0	1

Figure S35. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S43. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	35	27	23

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.914 (logrank test), Q value = 1

Table S44. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	83	12	0.0 - 102.0 (5.3)
subtype1	34	6	0.0 - 102.0 (4.2)
subtype2	26	4	0.1 - 91.0 (9.8)
subtype3	23	2	0.1 - 62.1 (5.3)

Figure S36. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0752 (ANOVA), Q value = 1

Table S45. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	84	65.7 (11.7)
subtype1	35	68.1 (12.6)
subtype2	26	61.4 (8.9)
subtype3	23	66.7 (12.2)

Figure S37. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000338 (Chi-square test), Q value = 0.016

Table S46. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	46	5	34
subtype1	10	2	23
subtype2	16	2	9
subtype3	20	1	2

Figure S38. 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 = 0.23 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	6	79
subtype1	1	34
subtype2	4	23
subtype3	1	22

Figure S39. 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 = 0.265 (Chi-square test), Q value = 1

Table S48. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	52	2	4	4
subtype1	19	0	1	2
subtype2	16	2	3	1
subtype3	17	0	0	1

Figure S40. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S49. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	131	128	45	53	112

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.00112 (logrank test), Q value = 0.049

Table S50. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	466	53	0.0 - 191.8 (20.9)
subtype1	129	22	0.0 - 125.4 (15.0)
subtype2	128	5	0.1 - 113.3 (24.3)
subtype3	44	4	0.9 - 88.3 (23.5)
subtype4	53	9	0.2 - 96.5 (17.4)
subtype5	112	13	1.0 - 191.8 (31.1)

Figure S41. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 7.57e-08 (ANOVA), Q value = 4e-06

Table S51. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	468	63.7 (11.3)
subtype1	131	68.1 (9.9)
subtype2	128	63.4 (10.8)
subtype3	44	64.6 (11.5)
subtype4	53	59.3 (12.3)
subtype5	112	60.4 (11.2)

Figure S42. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 7.25e-27 (Chi-square test), Q value = 4.3e-25

Table S52. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	362	18	89
subtype1	56	9	66
subtype2	124	2	2
subtype3	32	1	12
subtype4	50	2	1
subtype5	100	4	8

Figure S43. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.000231 (Chi-square test), Q value = 0.011

Table S53. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	131	338
subtype1	27	104
subtype2	30	98
subtype3	13	32
subtype4	11	42
subtype5	50	62

Figure S44. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0804 (Chi-square test), Q value = 1

Table S54. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	322	24	15	26
subtype1	76	10	9	9
subtype2	94	3	1	8
subtype3	36	3	0	2
subtype4	37	2	1	4
subtype5	79	6	4	3

Figure S45. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S55. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	24	220	225

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.000953 (logrank test), Q value = 0.044

Table S56. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	466	53	0.0 - 191.8 (20.9)
subtype1	24	2	1.4 - 73.2 (21.7)
subtype2	220	14	0.1 - 185.8 (24.3)
subtype3	222	37	0.0 - 191.8 (17.6)

Figure S46. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000145 (ANOVA), Q value = 0.0074

Table S57. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	468	63.7 (11.3)
subtype1	24	60.8 (10.8)
subtype2	220	61.7 (11.5)
subtype3	224	65.9 (10.8)

Figure S47. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 3.89e-24 (Chi-square test), Q value = 2.3e-22

Table S58. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	362	18	89
subtype1	24	0	0
subtype2	213	3	4
subtype3	125	15	85

Figure S48. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.309 (Fisher's exact test), Q value = 1

Table S59. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	131	338
subtype1	10	14
subtype2	59	161
subtype3	62	163

Figure S49. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.633 (Chi-square test), Q value = 1

Table S60. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	322	24	15	26
subtype1	16	2	0	1
subtype2	150	10	5	15
subtype3	156	12	10	10

Figure S50. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S61. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	148	112	82

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

P value = 0.0635 (logrank test), Q value = 1

Table S62. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	339	42	0.0 - 191.8 (16.6)
subtype1	145	22	0.0 - 191.8 (14.0)
subtype2	112	7	0.1 - 113.3 (19.4)
subtype3	82	13	0.1 - 92.0 (21.8)

Figure S51. 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 = 0.0687 (ANOVA), Q value = 1

Table S63. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	341	64.3 (11.3)
subtype1	147	65.9 (11.2)
subtype2	112	63.5 (10.1)
subtype3	82	62.5 (12.7)

Figure S52. 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 = 1.79e-11 (Chi-square test), Q value = 9.9e-10

Table S64. 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	249	16	77
subtype1	85	8	55
subtype2	109	1	2
subtype3	55	7	20

Figure S53. 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 = 0.547 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	82	260
subtype1	32	116
subtype2	27	85
subtype3	23	59

Figure S54. 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 = 0.0452 (Chi-square test), Q value = 1

Table S66. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	231	17	12	20
subtype1	95	9	10	6
subtype2	73	4	2	10
subtype3	63	4	0	4

Figure S55. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S67. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	89	132	96	25

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

P value = 0.00264 (logrank test), Q value = 0.11

Table S68. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	339	42	0.0 - 191.8 (16.6)
subtype1	88	11	0.1 - 92.0 (16.8)
subtype2	132	7	0.1 - 88.3 (18.6)
subtype3	94	17	0.0 - 191.8 (14.6)
subtype4	25	7	0.2 - 60.5 (12.3)

Figure S56. 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 = 0.0589 (ANOVA), Q value = 1

Table S69. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	341	64.3 (11.3)
subtype1	88	64.1 (11.4)
subtype2	132	62.7 (11.3)
subtype3	96	66.8 (10.1)
subtype4	25	64.2 (13.8)

Figure S57. 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 = 3.8e-19 (Chi-square test), Q value = 2.1e-17

Table S70. 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	249	16	77
subtype1	58	5	26
subtype2	128	2	2
subtype3	39	9	48
subtype4	24	0	1

Figure S58. 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 = 0.436 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	82	260
subtype1	25	64
subtype2	34	98
subtype3	19	77
subtype4	4	21

Figure S59. 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 = 0.256 (Chi-square test), Q value = 1

Table S72. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	231	17	12	20
subtype1	66	4	2	4
subtype2	91	4	2	10
subtype3	58	7	7	4
subtype4	16	2	1	2

Figure S60. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = UCEC-TP.mergedcluster.txt
Clinical data file = UCEC-TP.clin.merged.picked.txt
Number of patients = 478
Number of clustering approaches = 12
Number of selected clinical features = 5
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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[6] 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)

[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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