Uterine Corpus Endometrioid Carcinoma: Correlation between molecular cancer subtypes and selected clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 7 different clustering approaches and 5 clinical features across 430 patients, 28 significant findings detected with P value < 0.05.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE' and 'NEOADJUVANT.THERAPY'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.
Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'AGE', 'HISTOLOGICAL.TYPE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 28 significant findings detected.


Clinical Features	Time to Death	AGE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
Statistical Tests	logrank test	ANOVA	Chi-square test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.53	0.0107	0.000456	0.0317	0.0124
mRNA cHierClus subtypes	0.73	0.0598	0.00134	0.0666	0.0114
METHLYATION CNMF	0.00798	0.0123	4.28e-17	0.0786	0.0132
RNAseq CNMF subtypes	0.00873	0.00104	8.73e-18	0.0148	0.000242
RNAseq cHierClus subtypes	0.00985	0.00202	8.69e-18	0.0325	1.45e-05
MIRseq CNMF subtypes	0.00602	0.000193	5.4e-20	0.0958	0.00481
MIRseq cHierClus subtypes	0.0652	3.03e-05	1.14e-13	0.0303	0.0281

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 = 0.53 (logrank test)

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)

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)

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)

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 'NEOADJUVANT.THERAPY'

P value = 0.0124 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	18	36
subtype1	6	7
subtype2	10	9
subtype3	2	12
subtype4	0	8

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

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 = 0.73 (logrank test)

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)

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)

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)

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 'NEOADJUVANT.THERAPY'

P value = 0.0114 (Fisher's exact test)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	18	36
subtype1	2	18
subtype2	6	9
subtype3	10	9

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

Clustering Approach #3: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	119	71	123

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00798 (logrank test)

Table S14. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	309	30	0.0 - 187.1 (12.8)
subtype1	117	18	0.0 - 187.1 (11.5)
subtype2	71	4	0.0 - 92.0 (17.5)
subtype3	121	8	0.1 - 173.6 (13.0)

Figure S11. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0123 (ANOVA)

Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	312	63.5 (11.1)
subtype1	118	65.8 (10.0)
subtype2	71	63.0 (12.9)
subtype3	123	61.6 (10.8)

Figure S12. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 4.28e-17 (Chi-square test)

Table S16. Clustering Approach #3: '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	229	6	1	3	16	58
subtype1	54	1	0	1	13	50
subtype2	58	2	1	0	2	8
subtype3	117	3	0	2	1	0

Figure S13. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0786 (Fisher's exact test)

Table S17. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	79	234
subtype1	32	87
subtype2	11	60
subtype3	36	87

Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.0132 (Fisher's exact test)

Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	70	243
subtype1	37	82
subtype2	10	61
subtype3	23	100

Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	96	83	87

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00873 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	266	20	0.0 - 173.6 (20.2)
subtype1	96	13	0.5 - 133.2 (17.8)
subtype2	83	2	0.6 - 101.1 (21.4)
subtype3	87	5	0.0 - 173.6 (22.6)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00104 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	266	63.1 (10.8)
subtype1	96	66.2 (10.4)
subtype2	83	62.4 (10.6)
subtype3	87	60.4 (10.7)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.73e-18 (Chi-square test)

Table S22. Clustering Approach #4: '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	205	3	4	1	5	7	41
subtype1	45	0	0	0	4	6	41
subtype2	78	2	2	0	0	1	0
subtype3	82	1	2	1	1	0	0

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

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

P value = 0.0148 (Fisher's exact test)

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

nPatients	NO	YES
ALL	99	167
subtype1	44	52
subtype2	21	62
subtype3	34	53

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.000242 (Fisher's exact test)

Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	83	183
subtype1	45	51
subtype2	19	64
subtype3	19	68

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	96	82	88

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00985 (logrank test)

Table S26. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	266	20	0.0 - 173.6 (20.2)
subtype1	96	12	0.6 - 133.2 (17.7)
subtype2	82	1	0.6 - 101.1 (22.7)
subtype3	88	7	0.0 - 173.6 (22.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00202 (ANOVA)

Table S27. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	266	63.1 (10.8)
subtype1	96	66.0 (10.4)
subtype2	82	62.5 (10.4)
subtype3	88	60.5 (11.0)

Figure S22. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.69e-18 (Chi-square test)

Table S28. Clustering Approach #5: '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	205	3	4	1	5	7	41
subtype1	45	0	0	0	4	6	41
subtype2	77	2	2	0	0	1	0
subtype3	83	1	2	1	1	0	0

Figure S23. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0325 (Fisher's exact test)

Table S29. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	99	167
subtype1	44	52
subtype2	22	60
subtype3	33	55

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1.45e-05 (Fisher's exact test)

Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	83	183
subtype1	47	49
subtype2	15	67
subtype3	21	67

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	147	124	101

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00602 (logrank test)

Table S32. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	370	30	0.0 - 187.1 (17.4)
subtype1	146	18	0.0 - 187.1 (15.5)
subtype2	123	3	0.1 - 101.1 (17.3)
subtype3	101	9	0.5 - 173.6 (23.3)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.000193 (ANOVA)

Table S33. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	372	63.2 (11.2)
subtype1	147	65.9 (10.6)
subtype2	124	62.5 (11.6)
subtype3	101	60.1 (10.8)

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 5.4e-20 (Chi-square test)

Table S34. Clustering Approach #6: '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	284	3	9	2	7	13	54
subtype1	77	1	1	0	4	11	53
subtype2	117	2	3	0	1	1	0
subtype3	90	0	5	2	2	1	1

Figure S28. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0958 (Fisher's exact test)

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

nPatients	NO	YES
ALL	123	249
subtype1	58	89
subtype2	34	90
subtype3	31	70

Figure S29. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.00481 (Fisher's exact test)

Table S36. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	96	276
subtype1	51	96
subtype2	22	102
subtype3	23	78

Figure S30. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S37. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	114	169	89

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0652 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	370	30	0.0 - 187.1 (17.4)
subtype1	113	4	0.1 - 101.1 (17.3)
subtype2	168	18	0.0 - 187.1 (17.8)
subtype3	89	8	0.6 - 173.6 (16.8)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 3.03e-05 (ANOVA)

Table S39. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	372	63.2 (11.2)
subtype1	114	63.8 (10.5)
subtype2	169	65.2 (10.8)
subtype3	89	58.6 (11.7)

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.14e-13 (Chi-square test)

Table S40. Clustering Approach #7: '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	284	3	9	2	7	13	54
subtype1	103	2	4	0	1	1	3
subtype2	100	0	2	0	4	12	51
subtype3	81	1	3	2	2	0	0

Figure S33. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

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

P value = 0.0303 (Fisher's exact test)

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

nPatients	NO	YES
ALL	123	249
subtype1	31	83
subtype2	68	101
subtype3	24	65

Figure S34. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0281 (Fisher's exact test)

Table S42. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

nPatients	NO	YES
ALL	96	276
subtype1	23	91
subtype2	55	114
subtype3	18	71

Figure S35. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'

Methods & Data

Input

Cluster data file = UCEC.mergedcluster.txt
Clinical data file = UCEC.clin.merged.picked.txt
Number of patients = 430
Number of clustering approaches = 7
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

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)

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