Correlation between aggregated molecular cancer subtypes and selected clinical features

Sarcoma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

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 (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1K936M4

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 250 patients, 19 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'GENDER'.
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'GENDER'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
Consensus hierarchical clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH' and 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'GENDER', and 'ETHNICITY'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
8 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'GENDER'.

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, 19 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.362 (0.441)	0.000782 (0.00652)	0.0301 (0.0836)	0.653 (0.726)	0.924 (0.943)
METHLYATION CNMF	0.0221 (0.0691)	0.000268 (0.00334)	0.00021 (0.00334)	0.193 (0.33)	0.313 (0.4)
RPPA CNMF subtypes	0.00192 (0.012)	0.00799 (0.0333)	0.0533 (0.124)	0.991 (0.991)	0.234 (0.334)
RPPA cHierClus subtypes	0.00742 (0.0333)	0.00038 (0.0038)	0.0502 (0.124)	0.726 (0.772)	0.199 (0.33)
RNAseq CNMF subtypes	0.0559 (0.124)	0.000968 (0.00691)	0.00258 (0.0129)	0.824 (0.858)	0.248 (0.34)
RNAseq cHierClus subtypes	0.0443 (0.117)	8.76e-05 (0.00334)	0.0183 (0.0609)	0.163 (0.318)	0.391 (0.466)
MIRSEQ CNMF	0.233 (0.334)	0.178 (0.318)	0.0572 (0.124)	0.724 (0.772)	0.135 (0.282)
MIRSEQ CHIERARCHICAL	0.32 (0.4)	0.00018 (0.00334)	0.0284 (0.0836)	0.645 (0.726)	0.0136 (0.0486)
MIRseq Mature CNMF subtypes	0.217 (0.334)	0.178 (0.318)	0.205 (0.33)	0.499 (0.58)	0.31 (0.4)
MIRseq Mature cHierClus subtypes	0.173 (0.318)	0.00943 (0.0363)	0.00254 (0.0129)	0.229 (0.334)	0.251 (0.34)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	66	83	72	25

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

P value = 0.362 (logrank test), Q value = 0.44

Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	244	82	0.1 - 188.2 (23.3)
subtype1	66	23	0.7 - 124.4 (20.6)
subtype2	82	24	0.1 - 143.4 (30.2)
subtype3	72	28	0.1 - 188.2 (25.7)
subtype4	24	7	0.5 - 108.1 (18.2)

Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000782 (Kruskal-Wallis (anova)), Q value = 0.0065

Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	245	61.3 (14.5)
subtype1	65	61.8 (14.3)
subtype2	83	56.8 (15.8)
subtype3	72	66.2 (11.7)
subtype4	25	61.0 (13.5)

Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0301 (Fisher's exact test), Q value = 0.084

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	132	114
subtype1	43	23
subtype2	41	42
subtype3	40	32
subtype4	8	17

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.653 (Fisher's exact test), Q value = 0.73

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	194
subtype1	2	8	51
subtype2	2	6	65
subtype3	2	4	58
subtype4	0	0	20

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.924 (Fisher's exact test), Q value = 0.94

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	191
subtype1	1	58
subtype2	2	58
subtype3	2	55
subtype4	0	20

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S7. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	48	46	60	32	44	15	5

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0221 (logrank test), Q value = 0.069

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	248	83	0.1 - 188.2 (23.3)
subtype1	48	16	4.3 - 124.4 (32.4)
subtype2	45	10	0.7 - 102.0 (17.9)
subtype3	60	19	0.1 - 120.2 (27.3)
subtype4	32	16	0.1 - 135.5 (17.3)
subtype5	44	19	0.6 - 143.4 (18.4)
subtype6	14	2	0.1 - 123.8 (29.4)
subtype7	5	1	0.5 - 188.2 (15.9)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000268 (Kruskal-Wallis (anova)), Q value = 0.0033

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	249	61.4 (14.4)
subtype1	48	64.1 (13.0)
subtype2	46	63.7 (14.5)
subtype3	59	59.9 (11.9)
subtype4	32	52.2 (19.4)
subtype5	44	67.0 (12.9)
subtype6	15	52.9 (8.6)
subtype7	5	63.6 (6.7)

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00021 (Fisher's exact test), Q value = 0.0033

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	135	115
subtype1	18	30
subtype2	19	27
subtype3	38	22
subtype4	25	7
subtype5	19	25
subtype6	11	4
subtype7	5	0

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.193 (Fisher's exact test), Q value = 0.33

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	198
subtype1	0	2	39
subtype2	2	2	35
subtype3	0	7	51
subtype4	1	1	26
subtype5	1	4	34
subtype6	2	2	9
subtype7	0	0	4

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.313 (Fisher's exact test), Q value = 0.4

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	194
subtype1	1	38
subtype2	1	36
subtype3	0	54
subtype4	2	19
subtype5	1	35
subtype6	0	9
subtype7	0	3

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S13. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	57	100	59

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.00192 (logrank test), Q value = 0.012

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	216	72	0.1 - 188.2 (21.8)
subtype1	57	16	4.3 - 108.1 (27.8)
subtype2	100	41	0.1 - 143.4 (17.7)
subtype3	59	15	0.1 - 188.2 (26.6)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00799 (Kruskal-Wallis (anova)), Q value = 0.033

Table S15. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	215	62.2 (14.2)
subtype1	57	59.9 (14.7)
subtype2	100	65.0 (14.8)
subtype3	58	59.7 (11.6)

Figure S12. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0533 (Fisher's exact test), Q value = 0.12

Table S16. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	114	102
subtype1	26	31
subtype2	49	51
subtype3	39	20

Figure S13. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.991 (Fisher's exact test), Q value = 0.99

Table S17. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	13	169
subtype1	1	3	40
subtype2	3	7	77
subtype3	2	3	52

Figure S14. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.234 (Fisher's exact test), Q value = 0.33

Table S18. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	165
subtype1	2	39
subtype2	1	77
subtype3	0	49

Figure S15. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S19. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	23	28	38	66	39	22

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00742 (logrank test), Q value = 0.033

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	216	72	0.1 - 188.2 (21.8)
subtype1	23	6	4.3 - 108.1 (23.5)
subtype2	28	7	5.3 - 124.4 (31.8)
subtype3	38	17	0.6 - 188.2 (17.4)
subtype4	66	18	0.1 - 123.8 (26.5)
subtype5	39	14	0.7 - 74.7 (19.7)
subtype6	22	10	0.1 - 71.3 (13.3)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00038 (Kruskal-Wallis (anova)), Q value = 0.0038

Table S21. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	215	62.2 (14.2)
subtype1	23	63.2 (14.5)
subtype2	28	62.5 (12.9)
subtype3	38	69.5 (13.3)
subtype4	65	58.6 (11.3)
subtype5	39	64.8 (13.9)
subtype6	22	54.7 (18.8)

Figure S17. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0502 (Fisher's exact test), Q value = 0.12

Table S22. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	114	102
subtype1	10	13
subtype2	11	17
subtype3	16	22
subtype4	44	22
subtype5	19	20
subtype6	14	8

Figure S18. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.726 (Fisher's exact test), Q value = 0.77

Table S23. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	13	169
subtype1	0	1	20
subtype2	1	0	18
subtype3	0	4	31
subtype4	3	5	54
subtype5	2	1	27
subtype6	0	2	19

Figure S19. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.199 (Fisher's exact test), Q value = 0.33

Table S24. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	165
subtype1	1	19
subtype2	0	18
subtype3	2	30
subtype4	0	56
subtype5	0	26
subtype6	0	16

Figure S20. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S25. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	113	74	61

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0559 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	82	0.1 - 188.2 (23.3)
subtype1	112	36	0.6 - 143.4 (22.1)
subtype2	74	21	0.1 - 123.8 (27.8)
subtype3	60	25	0.1 - 188.2 (16.5)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000968 (Kruskal-Wallis (anova)), Q value = 0.0069

Table S27. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	247	61.2 (14.4)
subtype1	113	65.0 (13.3)
subtype2	73	58.2 (11.5)
subtype3	61	57.7 (17.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00258 (Fisher's exact test), Q value = 0.013

Table S28. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	114
subtype1	49	64
subtype2	51	23
subtype3	34	27

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.824 (Fisher's exact test), Q value = 0.86

Table S29. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	196
subtype1	3	6	85
subtype2	2	8	61
subtype3	1	4	50

Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.248 (Fisher's exact test), Q value = 0.34

Table S30. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	193
subtype1	3	86
subtype2	0	63
subtype3	2	44

Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	45	22	50	46	27	26	32

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0443 (logrank test), Q value = 0.12

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	82	0.1 - 188.2 (23.3)
subtype1	45	16	0.7 - 124.4 (33.5)
subtype2	21	2	4.3 - 102.0 (23.1)
subtype3	50	15	0.1 - 120.2 (30.5)
subtype4	45	18	0.1 - 188.2 (19.8)
subtype5	27	11	0.6 - 106.5 (16.9)
subtype6	26	6	0.1 - 123.8 (22.0)
subtype7	32	14	1.1 - 143.4 (16.9)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 8.76e-05 (Kruskal-Wallis (anova)), Q value = 0.0033

Table S33. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	247	61.2 (14.4)
subtype1	45	66.8 (12.6)
subtype2	22	61.7 (11.3)
subtype3	50	60.0 (12.0)
subtype4	46	54.9 (18.3)
subtype5	27	69.0 (13.3)
subtype6	25	54.3 (9.6)
subtype7	32	62.7 (13.7)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0183 (Fisher's exact test), Q value = 0.061

Table S34. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	134	114
subtype1	21	24
subtype2	9	13
subtype3	30	20
subtype4	25	21
subtype5	12	15
subtype6	22	4
subtype7	15	17

Figure S28. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.163 (Fisher's exact test), Q value = 0.32

Table S35. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	196
subtype1	1	1	33
subtype2	1	2	15
subtype3	0	5	44
subtype4	1	2	39
subtype5	1	0	22
subtype6	2	3	19
subtype7	0	5	24

Figure S29. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.391 (Fisher's exact test), Q value = 0.47

Table S36. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	193
subtype1	1	32
subtype2	1	16
subtype3	0	46
subtype4	2	33
subtype5	1	22
subtype6	0	19
subtype7	0	25

Figure S30. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	104	49	95

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.233 (logrank test), Q value = 0.33

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	83	0.1 - 188.2 (23.3)
subtype1	103	40	0.5 - 188.2 (20.1)
subtype2	48	13	0.7 - 124.4 (23.1)
subtype3	95	30	0.1 - 123.8 (25.2)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.178 (Kruskal-Wallis (anova)), Q value = 0.32

Table S39. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	247	61.3 (14.5)
subtype1	104	61.4 (16.5)
subtype2	49	64.0 (13.3)
subtype3	94	59.8 (12.4)

Figure S32. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0572 (Fisher's exact test), Q value = 0.12

Table S40. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	133	115
subtype1	49	55
subtype2	24	25
subtype3	60	35

Figure S33. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.724 (Fisher's exact test), Q value = 0.77

Table S41. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	197
subtype1	1	6	80
subtype2	1	4	39
subtype3	4	8	78

Figure S34. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.135 (Fisher's exact test), Q value = 0.28

Table S42. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	193
subtype1	3	73
subtype2	2	42
subtype3	0	78

Figure S35. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S43. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	76	51	98	23

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.32 (logrank test), Q value = 0.4

Table S44. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	246	83	0.1 - 188.2 (23.3)
subtype1	75	29	0.5 - 143.4 (20.3)
subtype2	50	14	0.1 - 124.4 (24.2)
subtype3	98	30	0.1 - 123.8 (25.1)
subtype4	23	10	1.1 - 188.2 (17.7)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.00018 (Kruskal-Wallis (anova)), Q value = 0.0033

Table S45. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	247	61.3 (14.5)
subtype1	76	65.2 (13.1)
subtype2	51	64.8 (13.1)
subtype3	97	59.7 (12.6)
subtype4	23	47.8 (20.0)

Figure S37. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0284 (Fisher's exact test), Q value = 0.084

Table S46. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	133	115
subtype1	32	44
subtype2	25	26
subtype3	60	38
subtype4	16	7

Figure S38. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.645 (Fisher's exact test), Q value = 0.73

Table S47. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	6	18	197
subtype1	1	2	57
subtype2	1	5	40
subtype3	4	9	80
subtype4	0	2	20

Figure S39. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0136 (Fisher's exact test), Q value = 0.049

Table S48. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	5	193
subtype1	1	56
subtype2	2	43
subtype3	0	81
subtype4	2	13

Figure S40. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S49. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	80	62	47

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

P value = 0.217 (logrank test), Q value = 0.33

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	187	65	0.1 - 188.2 (21.4)
subtype1	79	32	0.1 - 188.2 (19.4)
subtype2	62	22	0.1 - 120.2 (23.5)
subtype3	46	11	0.7 - 124.4 (21.8)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.178 (Kruskal-Wallis (anova)), Q value = 0.32

Table S51. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	188	61.9 (14.5)
subtype1	80	61.0 (16.6)
subtype2	61	60.2 (12.3)
subtype3	47	65.5 (12.9)

Figure S42. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.205 (Fisher's exact test), Q value = 0.33

Table S52. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	105	84
subtype1	40	40
subtype2	40	22
subtype3	25	22

Figure S43. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.499 (Fisher's exact test), Q value = 0.58

Table S53. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	13	152
subtype1	1	3	64
subtype2	1	6	51
subtype3	2	4	37

Figure S44. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.31 (Fisher's exact test), Q value = 0.4

Table S54. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	152
subtype1	2	57
subtype2	0	55
subtype3	2	40

Figure S45. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S55. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	21	33	37	24	10	20	22	22

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

P value = 0.173 (logrank test), Q value = 0.32

Table S56. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	187	65	0.1 - 188.2 (21.4)
subtype1	21	4	0.1 - 124.4 (33.1)
subtype2	32	12	0.5 - 108.1 (32.4)
subtype3	37	14	0.1 - 120.2 (29.9)
subtype4	24	10	0.1 - 102.0 (20.0)
subtype5	10	1	9.0 - 36.4 (22.0)
subtype6	20	10	0.6 - 143.4 (19.1)
subtype7	21	4	0.7 - 106.5 (13.3)
subtype8	22	10	1.1 - 188.2 (16.3)

Figure S46. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00943 (Kruskal-Wallis (anova)), Q value = 0.036

Table S57. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	188	61.9 (14.5)
subtype1	21	60.3 (11.1)
subtype2	33	63.3 (13.1)
subtype3	37	60.6 (11.3)
subtype4	24	66.5 (13.0)
subtype5	9	54.6 (11.5)
subtype6	20	69.0 (12.7)
subtype7	22	66.6 (14.1)
subtype8	22	50.4 (20.7)

Figure S47. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00254 (Fisher's exact test), Q value = 0.013

Table S58. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	105	84
subtype1	10	11
subtype2	11	22
subtype3	27	10
subtype4	14	10
subtype5	6	4
subtype6	7	13
subtype7	12	10
subtype8	18	4

Figure S48. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.229 (Fisher's exact test), Q value = 0.33

Table S59. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	13	152
subtype1	0	2	18
subtype2	0	0	24
subtype3	0	4	31
subtype4	1	2	18
subtype5	1	2	7
subtype6	0	0	19
subtype7	1	1	17
subtype8	1	2	18

Figure S49. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.251 (Fisher's exact test), Q value = 0.34

Table S60. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	4	152
subtype1	0	19
subtype2	1	22
subtype3	0	33
subtype4	0	19
subtype5	0	10
subtype6	0	17
subtype7	2	18
subtype8	1	14

Figure S50. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/SARC-TP/15111074/SARC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/SARC-TP/15087563/SARC-TP.merged_data.txt
Number of patients = 250
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] 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)

[5] 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