Uterine Corpus Endometrioid Carcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Uterine Corpus Endometrioid Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 4 clinical features across 451 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 'AGE' and '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 'CN CNMF'. 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 6 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', '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 4 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
Statistical Tests	logrank test	ANOVA	Chi-square test	Fisher's exact test
mRNA CNMF subtypes	0.942 (1.00)	0.0107 (0.215)	0.000456 (0.0132)	0.0317 (0.57)
mRNA cHierClus subtypes	0.942 (1.00)	0.0598 (0.897)	0.00134 (0.0349)	0.0666 (0.932)
CN CNMF	0.00599 (0.132)	5.7e-10 (1.94e-08)	2.07e-35 (8.29e-34)	0.0983 (1.00)
METHLYATION CNMF	0.024 (0.456)	0.0445 (0.757)	2.17e-18 (8.03e-17)	0.206 (1.00)
RPPA CNMF subtypes	0.778 (1.00)	0.343 (1.00)	3.91e-06 (0.000117)	0.327 (1.00)
RPPA cHierClus subtypes	0.954 (1.00)	0.463 (1.00)	0.00421 (0.0968)	0.584 (1.00)
RNAseq CNMF subtypes	0.00308 (0.0739)	1.1e-06 (3.53e-05)	2.35e-23 (8.92e-22)	0.0553 (0.885)
RNAseq cHierClus subtypes	0.0985 (1.00)	8.03e-07 (2.65e-05)	2.96e-17 (1.03e-15)	0.00715 (0.15)
MIRseq CNMF subtypes	0.00052 (0.0146)	1.13e-06 (3.53e-05)	5.84e-26 (2.28e-24)	0.406 (1.00)
MIRseq cHierClus subtypes	0.00112 (0.0302)	0.00244 (0.0611)	2.28e-18 (8.21e-17)	0.207 (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 = 0.21

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 = 0.013

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 = 0.57

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'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S6. 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 S7. 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 S5. 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 = 0.9

Table S8. 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 S6. 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 = 0.035

Table S9. 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 S7. 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 = 0.93

Table S10. 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 S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'CN CNMF'

Table S11. Get Full Table Description of clustering approach #3: 'CN CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	273	37	111	16	6

'CN CNMF' versus 'Time to Death'

P value = 0.00599 (logrank test), Q value = 0.13

Table S12. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	438	41	0.0 - 187.1 (15.8)
subtype1	270	18	0.0 - 187.1 (17.3)
subtype2	37	4	0.2 - 133.2 (8.0)
subtype3	109	15	0.0 - 113.2 (13.1)
subtype4	16	4	1.7 - 33.5 (13.9)
subtype5	6	0	0.3 - 31.3 (18.6)

Figure S9. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 5.7e-10 (ANOVA), Q value = 1.9e-08

Table S13. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	442	63.5 (11.2)
subtype1	272	61.2 (11.2)
subtype2	37	63.2 (11.9)
subtype3	111	69.5 (8.2)
subtype4	16	60.2 (12.9)
subtype5	6	71.0 (14.7)

Figure S10. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.07e-35 (Chi-square test), Q value = 8.3e-34

Table S14. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	327	3	8	2	7	18	78
subtype1	244	3	8	1	4	8	5
subtype2	32	0	0	1	1	0	3
subtype3	33	0	0	0	1	8	69
subtype4	13	0	0	0	1	1	1
subtype5	5	0	0	0	0	1	0

Figure S11. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0983 (Chi-square test), Q value = 1

Table S15. Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	133	310
subtype1	94	179
subtype2	6	31
subtype3	27	84
subtype4	4	12
subtype5	2	4

Figure S12. Get High-res Image Clustering Approach #3: 'CN CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S16. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	119	73	142

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.024 (logrank test), Q value = 0.46

Table S17. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	329	31	0.0 - 187.1 (12.3)
subtype1	116	18	0.0 - 187.1 (11.4)
subtype2	73	4	0.0 - 92.0 (16.1)
subtype3	140	9	0.1 - 173.6 (11.8)

Figure S13. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0445 (ANOVA), Q value = 0.76

Table S18. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	333	63.6 (11.3)
subtype1	118	65.7 (10.6)
subtype2	73	62.9 (12.9)
subtype3	142	62.3 (10.8)

Figure S14. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.17e-18 (Chi-square test), Q value = 8e-17

Table S19. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	245	6	1	3	17	62
subtype1	54	0	0	1	12	52
subtype2	57	2	1	0	3	10
subtype3	134	4	0	2	2	0

Figure S15. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.206 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	81	253
subtype1	32	87
subtype2	12	61
subtype3	37	105

Figure S16. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	41	38	41	16	38	26

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.778 (logrank test), Q value = 1

Table S22. 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	41	4	0.6 - 106.9 (21.0)
subtype2	38	2	1.3 - 133.2 (24.7)
subtype3	41	2	1.8 - 173.6 (22.6)
subtype4	16	2	1.4 - 82.7 (26.6)
subtype5	38	1	0.0 - 101.1 (12.4)
subtype6	26	2	0.7 - 66.9 (20.8)

Figure S17. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.343 (ANOVA), Q value = 1

Table S23. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	62.7 (11.1)
subtype1	41	62.9 (12.2)
subtype2	38	63.4 (10.6)
subtype3	41	61.9 (10.8)
subtype4	16	68.1 (8.2)
subtype5	38	60.6 (9.6)
subtype6	26	62.6 (13.3)

Figure S18. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3.91e-06 (Chi-square test), Q value = 0.00012

Table S24. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	164	3	3	4	3	23
subtype1	33	0	0	1	1	6
subtype2	26	0	0	1	0	11
subtype3	40	0	1	0	0	0
subtype4	12	2	0	2	0	0
subtype5	36	0	2	0	0	0
subtype6	17	1	0	0	2	6

Figure S19. 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 = 0.327 (Chi-square test), Q value = 1

Table S25. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	120
subtype1	20	21
subtype2	18	20
subtype3	13	28
subtype4	4	12
subtype5	13	25
subtype6	12	14

Figure S20. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	7	33	39	39	56	26

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.954 (logrank test), Q value = 1

Table S27. 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	7	0	9.2 - 70.4 (15.1)
subtype2	33	2	0.0 - 89.3 (12.2)
subtype3	39	2	0.7 - 98.2 (16.3)
subtype4	39	4	1.3 - 133.2 (23.3)
subtype5	56	3	1.4 - 173.6 (23.7)
subtype6	26	2	0.7 - 101.1 (22.7)

Figure S21. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.463 (ANOVA), Q value = 1

Table S28. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	200	62.7 (11.1)
subtype1	7	65.0 (17.0)
subtype2	33	62.5 (11.5)
subtype3	39	59.8 (9.3)
subtype4	39	65.0 (11.3)
subtype5	56	63.1 (9.8)
subtype6	26	62.5 (13.2)

Figure S22. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00421 (Chi-square test), Q value = 0.097

Table S29. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	164	3	3	4	3	23
subtype1	7	0	0	0	0	0
subtype2	29	0	0	0	1	3
subtype3	35	0	1	0	1	2
subtype4	25	0	0	1	0	13
subtype5	50	1	1	3	1	0
subtype6	18	2	1	0	0	5

Figure S23. 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 = 0.584 (Chi-square test), Q value = 1

Table S30. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	120
subtype1	2	5
subtype2	10	23
subtype3	15	24
subtype4	19	20
subtype5	25	31
subtype6	9	17

Figure S24. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S31. 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 = 0.00308 (logrank test), Q value = 0.074

Table S32. 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 (17.8)
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 S25. 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 = 3.5e-05

Table S33. 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 S26. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.35e-23 (Chi-square test), Q value = 8.9e-22

Table S34. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	283	3	7	2	7	10	57
subtype1	73	0	0	0	3	9	57
subtype2	106	2	4	0	0	1	0
subtype3	104	1	3	2	4	0	0

Figure S27. 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 = 0.88

Table S35. 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 S28. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S36. 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 = 0.0985 (logrank test), Q value = 1

Table S37. 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 (17.8)
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 S29. 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 = 2.7e-05

Table S38. 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 S30. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.96e-17 (Chi-square test), Q value = 1e-15

Table S39. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	283	3	7	2	7	10	57
subtype1	59	1	1	2	2	0	0
subtype2	91	2	4	0	0	1	0
subtype3	57	0	2	0	1	2	3
subtype4	76	0	0	0	4	7	54

Figure S31. 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 = 0.15

Table S40. 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 S32. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq CNMF subtypes'

Table S41. Get Full Table Description of clustering approach #9: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	159	146	131

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00052 (logrank test), Q value = 0.015

Table S42. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	431	39	0.0 - 187.1 (15.7)
subtype1	156	24	0.0 - 187.1 (12.4)
subtype2	145	3	0.1 - 101.1 (15.2)
subtype3	130	12	0.2 - 173.6 (19.4)

Figure S33. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 1.13e-06 (ANOVA), Q value = 3.5e-05

Table S43. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	435	63.4 (11.2)
subtype1	158	66.8 (10.5)
subtype2	146	62.5 (11.2)
subtype3	131	60.2 (11.0)

Figure S34. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.84e-26 (Chi-square test), Q value = 2.3e-24

Table S44. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	324	3	9	2	7	18	73
subtype1	74	0	0	0	4	14	67
subtype2	135	2	5	0	1	1	2
subtype3	115	1	4	2	2	3	4

Figure S35. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

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

Table S45. Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	127	309
subtype1	44	115
subtype2	39	107
subtype3	44	87

Figure S36. Get High-res Image Clustering Approach #9: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRseq cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #10: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	24	207	205

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00112 (logrank test), Q value = 0.03

Table S47. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	431	39	0.0 - 187.1 (15.7)
subtype1	24	4	0.5 - 68.7 (13.4)
subtype2	203	28	0.0 - 187.1 (14.0)
subtype3	204	7	0.0 - 173.6 (16.6)

Figure S37. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.00244 (ANOVA), Q value = 0.061

Table S48. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	435	63.4 (11.2)
subtype1	24	59.6 (11.4)
subtype2	206	65.3 (10.7)
subtype3	205	61.9 (11.4)

Figure S38. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.28e-18 (Chi-square test), Q value = 8.2e-17

Table S49. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	324	3	9	2	7	18	73
subtype1	21	0	1	0	2	0	0
subtype2	118	0	0	1	3	16	69
subtype3	185	3	8	1	2	2	4

Figure S39. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.207 (Fisher's exact test), Q value = 1

Table S50. Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	127	309
subtype1	10	14
subtype2	64	143
subtype3	53	152

Figure S40. Get High-res Image Clustering Approach #10: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = UCEC-TP.mergedcluster.txt
Clinical data file = UCEC-TP.clin.merged.picked.txt
Number of patients = 451
Number of clustering approaches = 10
Number of selected clinical features = 4
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

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] 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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