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 373 patients, 26 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 '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 4 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'NEOADJUVANT.THERAPY'.
CNMF clustering analysis on sequencing-based miR 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 miR expression data identified 3 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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, 26 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	4.81e-06	0.0317	0.0124
mRNA cHierClus subtypes	0.73	0.0598	1.45e-05	0.0666	0.0114
METHLYATION CNMF	0.0807	0.518	1.69e-10	0.611	0.0116
RNAseq CNMF subtypes	0.00713	0.00315	2.04e-23	0.0173	0.000161
RNAseq cHierClus subtypes	0.00623	0.000371	4.2e-24	0.0697	0.000172
MIRseq CNMF subtypes	0.012	0.00141	6.65e-23	0.0257	0.00453
MIRseq cHierClus subtypes	0.0138	0.0022	2.83e-19	0.00213	0.0567

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 = 4.81e-06 (Chi-square test)

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	25	16	1	12
subtype1	4	8	0	1
subtype2	2	6	0	11
subtype3	12	1	1	0
subtype4	7	1	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	36	18
subtype1	7	6
subtype2	9	10
subtype3	12	2
subtype4	8	0

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 = 1.45e-05 (Chi-square test)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1 OR 2)	ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3)	MIXED SEROUS AND ENDOMETRIOID	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	25	16	1	12
subtype1	17	2	1	0
subtype2	4	9	0	2
subtype3	4	5	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	36	18
subtype1	18	2
subtype2	9	6
subtype3	9	10

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	38	50	29

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0807 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	117	10	0.9 - 133.2 (28.5)
subtype1	38	4	0.9 - 133.2 (26.5)
subtype2	50	6	1.3 - 98.2 (34.2)
subtype3	29	0	4.2 - 89.3 (24.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.518 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	117	63.1 (10.9)
subtype1	38	63.1 (12.3)
subtype2	50	64.2 (10.7)
subtype3	29	61.2 (9.2)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.69e-10 (Chi-square test)

Table S16. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	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	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	62	3	2	32	1	17
subtype1	8	1	1	10	1	17
subtype2	26	2	1	21	0	0
subtype3	28	0	0	1	0	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 = 0.611 (Fisher's exact test)

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

nPatients	NO	YES
ALL	54	63
subtype1	19	19
subtype2	24	26
subtype3	11	18

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 = 0.0116 (Fisher's exact test)

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

nPatients	NO	YES
ALL	81	36
subtype1	20	18
subtype2	36	14
subtype3	25	4

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	95	83	88

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00713 (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	95	13	0.5 - 133.2 (17.8)
subtype2	83	2	0.6 - 101.1 (21.4)
subtype3	88	5	0.0 - 173.6 (22.7)

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 = 0.00315 (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	95	66.0 (10.6)
subtype2	83	62.4 (10.6)
subtype3	88	60.7 (10.6)

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 = 2.04e-23 (Chi-square test)

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

nPatients	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	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	95	19	37	67	7	41
subtype1	8	2	6	32	6	41
subtype2	44	10	16	12	1	0
subtype3	43	7	15	23	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 = 0.0173 (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	43	52
subtype2	21	62
subtype3	35	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 = 0.000161 (Fisher's exact test)

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

nPatients	NO	YES
ALL	183	83
subtype1	50	45
subtype2	64	19
subtype3	69	19

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	4
Number of samples	92	53	41	80

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00623 (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	92	12	0.6 - 133.2 (17.7)
subtype2	53	4	0.0 - 173.6 (16.8)
subtype3	41	3	0.3 - 98.2 (27.4)
subtype4	80	1	0.6 - 101.1 (22.7)

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 = 0.000371 (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	92	66.3 (10.4)
subtype2	53	58.5 (10.3)
subtype3	41	63.0 (11.0)
subtype4	80	62.6 (10.5)

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 = 4.2e-24 (Chi-square test)

Table S28. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	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	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	95	19	37	67	7	41
subtype1	8	1	5	31	6	41
subtype2	31	4	10	8	0	0
subtype3	13	4	7	17	0	0
subtype4	43	10	15	11	1	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 = 0.0697 (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	43	49
subtype2	18	35
subtype3	16	25
subtype4	22	58

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 = 0.000172 (Fisher's exact test)

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

nPatients	NO	YES
ALL	183	83
subtype1	48	44
subtype2	43	10
subtype3	28	13
subtype4	64	16

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	107	131	121

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.012 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	357	27	0.0 - 187.1 (16.9)
subtype1	106	12	0.0 - 187.1 (15.3)
subtype2	130	3	0.1 - 101.1 (16.6)
subtype3	121	12	0.5 - 173.6 (22.6)

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 = 0.00141 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	359	63.0 (11.1)
subtype1	107	66.2 (10.1)
subtype2	131	61.5 (11.5)
subtype3	121	61.8 (10.9)

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 = 6.65e-23 (Chi-square test)

Table S34. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'HISTOLOGICAL.TYPE'

nPatients	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	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	99	34	57	108	13	48
subtype1	5	5	10	40	8	39
subtype2	57	20	30	23	1	0
subtype3	37	9	17	45	4	9

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 = 0.0257 (Fisher's exact test)

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

nPatients	NO	YES
ALL	121	238
subtype1	44	63
subtype2	33	98
subtype3	44	77

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 = 0.00453 (Fisher's exact test)

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

nPatients	NO	YES
ALL	265	94
subtype1	68	39
subtype2	108	23
subtype3	89	32

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	112	169	78

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0138 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	357	27	0.0 - 187.1 (16.9)
subtype1	112	2	0.1 - 89.3 (17.0)
subtype2	167	17	0.0 - 187.1 (16.8)
subtype3	78	8	0.6 - 173.6 (18.9)

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 = 0.0022 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	359	63.0 (11.1)
subtype1	112	62.9 (10.3)
subtype2	169	64.7 (10.9)
subtype3	78	59.4 (11.6)

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 = 2.83e-19 (Chi-square test)

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

nPatients	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	UTERINE SEROUS ENDOMETRIAL ADENOCARCINOMA
ALL	99	34	57	108	13	48
subtype1	43	18	26	21	2	2
subtype2	18	9	20	65	11	46
subtype3	38	7	11	22	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 = 0.00213 (Fisher's exact test)

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

nPatients	NO	YES
ALL	121	238
subtype1	32	80
subtype2	72	97
subtype3	17	61

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 = 0.0567 (Fisher's exact test)

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

nPatients	NO	YES
ALL	265	94
subtype1	90	22
subtype2	115	54
subtype3	60	18

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 = 373
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. Location of data archives could not be determined.

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)