Correlation between molecular cancer subtypes and selected clinical features

Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Uterine Corpus Endometrioid Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1833Q1V

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 5 clinical features across 462 patients, 21 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 'HISTOLOGICAL.TYPE'.
5 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' 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 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 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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 'AGE' and 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 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, 21 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	0.942 (1.00)	0.0107 (0.311)	0.000456 (0.0178)	0.0317 (0.856)	0.847 (1.00)
mRNA cHierClus subtypes	0.942 (1.00)	0.0598 (1.00)	0.00134 (0.047)	0.0666 (1.00)	0.673 (1.00)
Copy Number Ratio CNMF subtypes	0.00545 (0.169)	8.2e-11 (3.61e-09)	1.19e-44 (5.93e-43)	0.0819 (1.00)	0.134 (1.00)
METHLYATION CNMF	0.0434 (1.00)	0.0348 (0.904)	2.39e-22 (1.1e-20)	0.171 (1.00)	0.00477 (0.153)
RPPA CNMF subtypes	0.586 (1.00)	0.203 (1.00)	0.00103 (0.0371)	0.461 (1.00)	0.102 (1.00)
RPPA cHierClus subtypes	0.263 (1.00)	0.236 (1.00)	0.000814 (0.0301)	0.46 (1.00)	0.00347 (0.115)
RNAseq CNMF subtypes	0.00311 (0.106)	1.1e-06 (4.52e-05)	4.3e-26 (2.02e-24)	0.0553 (1.00)	0.176 (1.00)
RNAseq cHierClus subtypes	0.0994 (1.00)	8.03e-07 (3.37e-05)	1.47e-19 (6.62e-18)	0.00715 (0.214)	0.231 (1.00)
MIRSEQ CNMF	0.000475 (0.0181)	2.64e-09 (1.13e-07)	1.51e-33 (7.26e-32)	0.75 (1.00)	0.1 (1.00)
MIRSEQ CHIERARCHICAL	0.0151 (0.422)	2.65e-06 (0.000106)	5.07e-38 (2.48e-36)	0.518 (1.00)	0.69 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	13	19	14	8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.942 (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	7	6.0 - 133.2 (35.4)
subtype1	13	2	9.0 - 133.2 (39.0)
subtype2	19	3	6.0 - 113.2 (37.7)
subtype3	14	1	8.6 - 89.3 (30.9)
subtype4	8	1	6.4 - 65.5 (22.9)

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 = 0.0107 (ANOVA), Q value = 0.31

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

	nPatients	Mean (Std.Dev)
ALL	54	62.9 (11.8)
subtype1	13	65.1 (12.0)
subtype2	19	68.4 (9.1)
subtype3	14	58.2 (11.0)
subtype4	8	54.8 (12.9)

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 = 0.000456 (Chi-square test), Q value = 0.018

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	1
subtype2	8	0	11
subtype3	13	1	0
subtype4	8	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 = 0.0317 (Fisher's exact test), Q value = 0.86

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

nPatients	NO	YES
ALL	25	29
subtype1	7	6
subtype2	13	6
subtype3	3	11
subtype4	2	6

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 = 0.847 (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	7	2	1	0
subtype2	15	1	1	1
subtype3	11	2	0	0
subtype4	6	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. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	20	15	19

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.942 (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	7	6.0 - 133.2 (35.4)
subtype1	20	2	6.4 - 89.3 (29.8)
subtype2	15	2	9.0 - 133.2 (39.0)
subtype3	19	3	6.0 - 113.2 (37.7)

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 = 0.0598 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	54	62.9 (11.8)
subtype1	20	58.2 (13.3)
subtype2	15	64.0 (10.6)
subtype3	19	67.1 (9.9)

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 = 0.00134 (Chi-square test), Q value = 0.047

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	19	1	0
subtype2	13	0	2
subtype3	9	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 = 0.0666 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	25	29
subtype1	5	15
subtype2	9	6
subtype3	11	8

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 = 0.673 (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	14	3	0	0
subtype2	10	2	1	0
subtype3	15	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. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	276	38	118	16	6

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

P value = 0.00545 (logrank test), Q value = 0.17

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	42	0.0 - 187.1 (15.5)
subtype1	273	18	0.0 - 187.1 (17.2)
subtype2	38	4	0.2 - 133.2 (8.3)
subtype3	117	16	0.0 - 113.2 (12.4)
subtype4	16	4	1.7 - 33.5 (13.9)
subtype5	6	0	0.3 - 31.3 (18.6)

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 = 8.2e-11 (ANOVA), Q value = 3.6e-09

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

	nPatients	Mean (Std.Dev)
ALL	453	63.6 (11.2)
subtype1	275	61.2 (11.1)
subtype2	38	63.1 (11.8)
subtype3	118	69.6 (8.4)
subtype4	16	60.2 (12.9)
subtype5	6	71.0 (14.7)

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 = 1.19e-44 (Chi-square test), Q value = 5.9e-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	351	18	85
subtype1	263	8	5
subtype2	34	0	4
subtype3	35	8	75
subtype4	14	1	1
subtype5	5	1	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 = 0.0819 (Chi-square test), Q value = 1

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

nPatients	NO	YES
ALL	134	320
subtype1	94	182
subtype2	6	32
subtype3	28	90
subtype4	4	12
subtype5	2	4

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 = 0.134 (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	311	24	15	25
subtype1	195	15	4	14
subtype2	26	1	1	3
subtype3	74	8	8	8
subtype4	10	0	2	0
subtype5	6	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. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	124	76	145

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0434 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	341	32	0.0 - 187.1 (12.2)
subtype1	122	18	0.0 - 187.1 (11.1)
subtype2	76	5	0.0 - 102.0 (16.8)
subtype3	143	9	0.1 - 173.6 (12.2)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0348 (ANOVA), Q value = 0.9

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

	nPatients	Mean (Std.Dev)
ALL	344	63.8 (11.3)
subtype1	123	65.8 (10.7)
subtype2	76	63.2 (12.9)
subtype3	145	62.3 (10.7)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.39e-22 (Chi-square test), Q value = 1.1e-20

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	259	17	69
subtype1	55	12	57
subtype2	61	3	12
subtype3	143	2	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 = 0.171 (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	82	263
subtype1	33	91
subtype2	12	64
subtype3	37	108

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 = 0.00477 (Chi-square test), Q value = 0.15

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

nPatients	R0	R1	R2	RX
ALL	227	18	11	23
subtype1	79	8	7	7
subtype2	47	4	3	12
subtype3	101	6	1	4

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. Get Full Table 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 = 0.586 (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	13	0.0 - 173.6 (21.7)
subtype1	42	4	0.6 - 106.9 (21.7)
subtype2	39	3	1.3 - 133.2 (23.3)
subtype3	40	1	1.8 - 173.6 (22.8)
subtype4	12	2	1.4 - 82.7 (26.6)
subtype5	41	1	0.0 - 101.1 (13.3)
subtype6	26	2	0.7 - 66.9 (22.7)

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 = 0.037

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. Get Full Table 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 = 0.263 (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	13	0.0 - 173.6 (21.7)
subtype1	124	6	0.7 - 173.6 (21.8)
subtype2	76	7	0.0 - 133.2 (21.5)

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 = 0.03

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 = 0.11

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

Cluster Labels	1	2	3
Number of samples	142	113	114

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00311 (logrank test), Q value = 0.11

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	367	32	0.0 - 187.1 (17.9)
subtype1	141	21	0.1 - 187.1 (15.3)
subtype2	112	3	0.3 - 101.1 (17.8)
subtype3	114	8	0.0 - 173.6 (22.7)

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 = 1.1e-06 (ANOVA), Q value = 4.5e-05

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

	nPatients	Mean (Std.Dev)
ALL	369	63.2 (11.0)
subtype1	142	66.3 (10.0)
subtype2	113	63.4 (10.8)
subtype3	114	59.2 (11.2)

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 = 4.3e-26 (Chi-square test), Q value = 2e-24

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	302	10	57
subtype1	76	9	57
subtype2	112	1	0
subtype3	114	0	0

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 = 0.0553 (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	123	246
subtype1	55	87
subtype2	28	85
subtype3	40	74

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 = 0.176 (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	261	21	12	22
subtype1	96	12	8	10
subtype2	80	4	2	8
subtype3	85	5	2	4

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

Cluster Labels	1	2	3	4
Number of samples	65	98	65	141

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0994 (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	367	32	0.0 - 187.1 (17.9)
subtype1	64	6	0.0 - 173.6 (17.5)
subtype2	98	2	0.3 - 101.1 (17.0)
subtype3	65	8	0.3 - 106.9 (25.0)
subtype4	140	16	0.1 - 187.1 (16.5)

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 = 8.03e-07 (ANOVA), Q value = 3.4e-05

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

	nPatients	Mean (Std.Dev)
ALL	369	63.2 (11.0)
subtype1	65	58.3 (10.4)
subtype2	98	62.8 (10.9)
subtype3	65	61.1 (11.5)
subtype4	141	66.8 (10.0)

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 = 1.47e-19 (Chi-square test), Q value = 6.6e-18

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	302	10	57
subtype1	65	0	0
subtype2	97	1	0
subtype3	60	2	3
subtype4	80	7	54

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 = 0.00715 (Fisher's exact test), Q value = 0.21

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

nPatients	NO	YES
ALL	123	246
subtype1	18	47
subtype2	25	73
subtype3	33	32
subtype4	47	94

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 = 0.231 (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	261	21	12	22
subtype1	48	4	1	4
subtype2	72	1	1	7
subtype3	47	5	2	2
subtype4	94	11	8	9

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

Cluster Labels	1	2	3
Number of samples	178	144	131

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000475 (logrank test), Q value = 0.018

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	449	41	0.0 - 187.1 (15.3)
subtype1	175	27	0.0 - 187.1 (12.4)
subtype2	143	3	0.1 - 101.1 (15.1)
subtype3	131	11	0.2 - 173.6 (19.4)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 2.64e-09 (ANOVA), Q value = 1.1e-07

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

	nPatients	Mean (Std.Dev)
ALL	452	63.5 (11.3)
subtype1	177	67.2 (10.1)
subtype2	144	62.8 (11.1)
subtype3	131	59.3 (11.3)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.51e-33 (Chi-square test), Q value = 7.3e-32

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	354	18	81
subtype1	85	15	78
subtype2	140	1	3
subtype3	129	2	0

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 = 0.75 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	130	323
subtype1	49	129
subtype2	40	104
subtype3	41	90

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 = 0.1 (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	309	24	14	25
subtype1	114	11	10	12
subtype2	104	4	2	8
subtype3	91	9	2	5

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

Cluster Labels	1	2	3	4
Number of samples	191	79	26	157

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0151 (logrank test), Q value = 0.42

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	449	41	0.0 - 187.1 (15.3)
subtype1	191	7	0.1 - 173.6 (16.3)
subtype2	78	9	0.0 - 98.2 (19.5)
subtype3	26	4	1.4 - 67.5 (15.6)
subtype4	154	21	0.0 - 187.1 (12.4)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 2.65e-06 (ANOVA), Q value = 0.00011

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

	nPatients	Mean (Std.Dev)
ALL	452	63.5 (11.3)
subtype1	191	62.0 (11.4)
subtype2	79	60.8 (12.0)
subtype3	26	60.4 (11.5)
subtype4	156	67.3 (9.6)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 5.07e-38 (Chi-square test), Q value = 2.5e-36

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	354	18	81
subtype1	186	2	3
subtype2	76	3	0
subtype3	26	0	0
subtype4	66	13	78

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 = 0.518 (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	130	323
subtype1	53	138
subtype2	27	52
subtype3	9	17
subtype4	41	116

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 = 0.69 (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	309	24	14	25
subtype1	130	9	3	11
subtype2	60	4	2	2
subtype3	16	2	1	2
subtype4	103	9	8	10

Figure S50. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' 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 = 462
Number of clustering approaches = 10
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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)

Made with Nozzle