Correlation between aggregated molecular cancer subtypes and selected clinical features

Thymoma (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/C1QV3KNW

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 122 patients, 13 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'YEARS_TO_BIRTH'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'YEARS_TO_BIRTH'.
2 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH' and 'ETHNICITY'.
6 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 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, 13 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	0.128 (0.325)	0.0159 (0.114)	0.718 (0.876)	0.707 (0.876)	0.156 (0.325)
METHLYATION CNMF	0.0653 (0.233)	0.00375 (0.0624)	0.679 (0.87)	0.179 (0.346)	0.303 (0.473)
RPPA CNMF subtypes	0.209 (0.373)	0.00331 (0.0624)	0.546 (0.738)	0.18 (0.346)	0.469 (0.651)
RPPA cHierClus subtypes	0.0949 (0.297)	0.0154 (0.114)	0.825 (0.937)	0.813 (0.937)	0.297 (0.473)
RNAseq CNMF subtypes	0.0492 (0.189)	0.02 (0.121)	0.346 (0.522)	0.914 (0.937)	0.123 (0.325)
RNAseq cHierClus subtypes	0.123 (0.325)	0.00675 (0.0843)	0.88 (0.937)	0.436 (0.622)	0.255 (0.426)
MIRSEQ CNMF	0.223 (0.385)	0.0218 (0.121)	0.854 (0.937)	1 (1.00)	0.048 (0.189)
MIRSEQ CHIERARCHICAL	0.00156 (0.0624)	0.013 (0.114)	0.15 (0.325)	0.841 (0.937)	0.13 (0.325)
MIRseq Mature CNMF subtypes	0.14 (0.325)	0.144 (0.325)	0.881 (0.937)	0.623 (0.819)	0.0897 (0.297)
MIRseq Mature cHierClus subtypes	0.355 (0.522)	0.031 (0.155)	0.0441 (0.189)	0.919 (0.937)	0.195 (0.361)

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
Number of samples	64	30	27

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

P value = 0.128 (logrank test), Q value = 0.32

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	6	0.1 - 150.0 (30.7)
subtype1	64	1	0.1 - 150.0 (33.4)
subtype2	30	4	0.2 - 123.7 (28.5)
subtype3	25	1	0.2 - 120.4 (30.7)

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 = 0.0159 (Kruskal-Wallis (anova)), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	120	58.7 (12.7)
subtype1	64	58.7 (11.6)
subtype2	30	53.9 (14.4)
subtype3	26	64.1 (11.4)

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 = 0.718 (Fisher's exact test), Q value = 0.88

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

nPatients	FEMALE	MALE
ALL	59	62
subtype1	29	35
subtype2	16	14
subtype3	14	13

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 = 0.707 (Fisher's exact test), Q value = 0.88

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	100
subtype1	6	4	53
subtype2	4	0	25
subtype3	3	2	22

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 = 0.156 (Fisher's exact test), Q value = 0.32

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	98
subtype1	2	53
subtype2	4	22
subtype3	3	23

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
Number of samples	37	36	25	24

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0653 (logrank test), Q value = 0.23

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	6	0.1 - 150.0 (31.1)
subtype1	37	0	1.8 - 138.9 (37.6)
subtype2	36	3	0.2 - 120.4 (27.6)
subtype3	25	3	0.1 - 133.8 (26.9)
subtype4	22	0	0.5 - 150.0 (26.2)

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 = 0.00375 (Kruskal-Wallis (anova)), Q value = 0.062

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

	nPatients	Mean (Std.Dev)
ALL	121	58.5 (12.7)
subtype1	37	55.2 (11.7)
subtype2	36	64.9 (10.5)
subtype3	25	54.5 (14.2)
subtype4	23	58.3 (12.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.679 (Fisher's exact test), Q value = 0.87

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

nPatients	FEMALE	MALE
ALL	60	62
subtype1	18	19
subtype2	15	21
subtype3	14	11
subtype4	13	11

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.179 (Fisher's exact test), Q value = 0.35

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	101
subtype1	2	5	30
subtype2	5	1	30
subtype3	2	0	22
subtype4	4	0	19

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.303 (Fisher's exact test), Q value = 0.47

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	98
subtype1	1	32
subtype2	3	30
subtype3	4	18
subtype4	2	18

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	27	24	37

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.209 (logrank test), Q value = 0.37

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	87	5	0.1 - 150.0 (27.2)
subtype1	27	3	0.1 - 120.4 (26.9)
subtype2	24	1	0.2 - 97.4 (32.2)
subtype3	36	1	2.4 - 150.0 (23.4)

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 = 0.00331 (Kruskal-Wallis (anova)), Q value = 0.062

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

	nPatients	Mean (Std.Dev)
ALL	87	58.7 (12.5)
subtype1	27	57.7 (14.3)
subtype2	24	65.5 (11.1)
subtype3	36	54.8 (10.1)

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 = 0.546 (Fisher's exact test), Q value = 0.74

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

nPatients	FEMALE	MALE
ALL	43	45
subtype1	12	15
subtype2	14	10
subtype3	17	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 = 0.18 (Fisher's exact test), Q value = 0.35

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	4	71
subtype1	6	0	20
subtype2	3	1	20
subtype3	2	3	31

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.469 (Fisher's exact test), Q value = 0.65

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	70
subtype1	4	20
subtype2	2	20
subtype3	2	30

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
Number of samples	16	23	29	20

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0949 (logrank test), Q value = 0.3

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	87	5	0.1 - 150.0 (27.2)
subtype1	16	0	7.2 - 107.4 (17.0)
subtype2	22	1	0.2 - 90.6 (32.2)
subtype3	29	1	2.4 - 150.0 (35.3)
subtype4	20	3	0.1 - 120.4 (20.3)

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 = 0.0154 (Kruskal-Wallis (anova)), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	87	58.7 (12.5)
subtype1	16	57.9 (9.8)
subtype2	22	65.3 (11.1)
subtype3	29	54.4 (10.8)
subtype4	20	58.1 (15.4)

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 = 0.825 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	43	45
subtype1	7	9
subtype2	13	10
subtype3	13	16
subtype4	10	10

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.813 (Fisher's exact test), Q value = 0.94

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	4	71
subtype1	2	1	13
subtype2	3	1	19
subtype3	2	2	24
subtype4	4	0	15

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.297 (Fisher's exact test), Q value = 0.47

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	70
subtype1	1	13
subtype2	1	20
subtype3	2	24
subtype4	4	13

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	4
Number of samples	44	31	29	14

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0492 (logrank test), Q value = 0.19

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	6	0.1 - 150.0 (30.4)
subtype1	44	3	0.1 - 133.8 (34.9)
subtype2	30	0	2.4 - 150.0 (32.3)
subtype3	28	1	0.2 - 120.4 (28.5)
subtype4	14	2	8.5 - 58.8 (24.6)

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 = 0.02 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	117	58.3 (12.8)
subtype1	44	53.9 (14.0)
subtype2	31	58.2 (10.7)
subtype3	28	63.8 (11.9)
subtype4	14	61.1 (10.6)

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 = 0.346 (Fisher's exact test), Q value = 0.52

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

nPatients	FEMALE	MALE
ALL	57	61
subtype1	20	24
subtype2	14	17
subtype3	13	16
subtype4	10	4

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.914 (Fisher's exact test), Q value = 0.94

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	12	6	98
subtype1	3	3	37
subtype2	3	2	25
subtype3	4	1	24
subtype4	2	0	12

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.123 (Fisher's exact test), Q value = 0.32

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	94
subtype1	4	33
subtype2	3	25
subtype3	0	25
subtype4	3	11

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
Number of samples	46	42	20	10

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.123 (logrank test), Q value = 0.32

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	6	0.1 - 150.0 (30.4)
subtype1	46	3	0.1 - 133.8 (34.9)
subtype2	40	1	0.5 - 150.0 (30.8)
subtype3	20	0	0.2 - 112.1 (31.5)
subtype4	10	2	8.5 - 120.4 (20.3)

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 = 0.00675 (Kruskal-Wallis (anova)), Q value = 0.084

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

	nPatients	Mean (Std.Dev)
ALL	117	58.3 (12.8)
subtype1	46	53.8 (13.9)
subtype2	41	58.6 (10.5)
subtype3	20	65.4 (11.7)
subtype4	10	63.1 (11.0)

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 = 0.88 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	57	61
subtype1	21	25
subtype2	20	22
subtype3	10	10
subtype4	6	4

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.436 (Fisher's exact test), Q value = 0.62

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	12	6	98
subtype1	3	3	39
subtype2	4	3	34
subtype3	2	0	18
subtype4	3	0	7

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.255 (Fisher's exact test), Q value = 0.43

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	94
subtype1	5	34
subtype2	3	34
subtype3	0	18
subtype4	2	8

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	4	5
Number of samples	57	61	1	2	1

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 0.38

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	116	5	0.1 - 150.0 (30.4)
subtype1	57	4	0.1 - 133.8 (28.9)
subtype2	59	1	0.2 - 150.0 (31.5)

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 = 0.0218 (Wilcoxon-test), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	117	58.2 (12.7)
subtype1	57	55.2 (13.7)
subtype2	60	61.1 (11.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.854 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	58	60
subtype1	29	28
subtype2	29	32

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

'MIRSEQ CNMF' versus 'RACE'

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

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	97
subtype1	6	3	47
subtype2	7	3	50

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.048 (Fisher's exact test), Q value = 0.19

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	95
subtype1	8	43
subtype2	2	52

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	5	6
Number of samples	26	18	28	7	16	27

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00156 (logrank test), Q value = 0.062

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	6	0.1 - 150.0 (31.1)
subtype1	26	2	0.2 - 123.7 (41.1)
subtype2	17	1	0.5 - 150.0 (32.2)
subtype3	28	1	0.1 - 133.8 (30.7)
subtype4	7	2	8.5 - 58.8 (13.5)
subtype5	16	0	0.2 - 112.1 (31.0)
subtype6	26	0	0.2 - 138.9 (29.9)

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 = 0.013 (Kruskal-Wallis (anova)), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	121	58.5 (12.7)
subtype1	26	52.2 (13.4)
subtype2	17	58.4 (10.4)
subtype3	28	58.0 (13.8)
subtype4	7	62.6 (11.4)
subtype5	16	67.8 (8.3)
subtype6	27	58.9 (11.9)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.15 (Fisher's exact test), Q value = 0.32

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

nPatients	FEMALE	MALE
ALL	60	62
subtype1	8	18
subtype2	8	10
subtype3	19	9
subtype4	4	3
subtype5	7	9
subtype6	14	13

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.841 (Fisher's exact test), Q value = 0.94

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	101
subtype1	3	1	21
subtype2	2	1	15
subtype3	1	2	25
subtype4	2	0	5
subtype5	2	0	14
subtype6	3	2	21

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.13 (Fisher's exact test), Q value = 0.32

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	98
subtype1	1	19
subtype2	1	14
subtype3	5	22
subtype4	2	5
subtype5	0	14
subtype6	1	24

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	17	59	46

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

P value = 0.14 (logrank test), Q value = 0.32

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	6	0.1 - 150.0 (31.1)
subtype1	17	0	10.0 - 107.8 (35.3)
subtype2	57	1	0.2 - 150.0 (31.5)
subtype3	46	5	0.1 - 133.8 (28.5)

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 = 0.144 (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	121	58.5 (12.7)
subtype1	17	54.8 (14.0)
subtype2	58	61.1 (11.4)
subtype3	46	56.8 (13.4)

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 = 0.881 (Fisher's exact test), Q value = 0.94

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

nPatients	FEMALE	MALE
ALL	60	62
subtype1	8	9
subtype2	28	31
subtype3	24	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 = 0.623 (Fisher's exact test), Q value = 0.82

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	101
subtype1	1	2	14
subtype2	7	3	48
subtype3	5	1	39

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 = 0.0897 (Fisher's exact test), Q value = 0.3

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	98
subtype1	1	13
subtype2	2	50
subtype3	7	35

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
Number of samples	28	33	33	28

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

P value = 0.355 (logrank test), Q value = 0.52

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	120	6	0.1 - 150.0 (31.1)
subtype1	28	2	0.2 - 123.7 (41.1)
subtype2	32	1	0.2 - 112.1 (29.3)
subtype3	33	3	0.1 - 133.8 (26.9)
subtype4	27	0	0.2 - 150.0 (32.2)

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 = 0.031 (Kruskal-Wallis (anova)), Q value = 0.16

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

	nPatients	Mean (Std.Dev)
ALL	121	58.5 (12.7)
subtype1	28	53.0 (13.3)
subtype2	32	63.3 (10.4)
subtype3	33	58.6 (13.7)
subtype4	28	58.6 (11.7)

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 = 0.0441 (Fisher's exact test), Q value = 0.19

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

nPatients	FEMALE	MALE
ALL	60	62
subtype1	9	19
subtype2	14	19
subtype3	22	11
subtype4	15	13

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 = 0.919 (Fisher's exact test), Q value = 0.94

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	6	101
subtype1	4	1	22
subtype2	4	1	28
subtype3	2	2	29
subtype4	3	2	22

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 = 0.195 (Fisher's exact test), Q value = 0.36

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	98
subtype1	2	20
subtype2	1	28
subtype3	6	26
subtype4	1	24

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/THYM-TP/15111344/THYM-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/THYM-TP/15092844/THYM-TP.merged_data.txt
Number of patients = 122
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)

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