Correlation between aggregated molecular cancer subtypes and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QV3KF6

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 8 different clustering approaches and 5 clinical features across 132 patients, no significant finding 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 do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
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 do not correlate to any clinical features.
5 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 do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 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, no significant finding detected.


Clinical Features	Time to Death	AGE	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	0.885 (1.00)	0.967 (1.00)	0.974 (1.00)	0.898 (1.00)	0.306 (1.00)
METHLYATION CNMF	0.31 (1.00)	0.217 (1.00)	0.72 (1.00)	0.75 (1.00)	0.0714 (1.00)
RNAseq CNMF subtypes	0.596 (1.00)	0.952 (1.00)	0.275 (1.00)	0.794 (1.00)	0.0242 (0.969)
RNAseq cHierClus subtypes	0.685 (1.00)	0.029 (1.00)	0.588 (1.00)	0.996 (1.00)	0.0458 (1.00)
MIRSEQ CNMF	0.062 (1.00)	0.899 (1.00)	0.95 (1.00)	0.774 (1.00)	0.255 (1.00)
MIRSEQ CHIERARCHICAL	0.461 (1.00)	0.995 (1.00)	0.605 (1.00)	0.824 (1.00)	0.0675 (1.00)
MIRseq Mature CNMF subtypes	0.0961 (1.00)	0.0667 (1.00)	0.628 (1.00)	0.634 (1.00)	0.117 (1.00)
MIRseq Mature cHierClus subtypes	0.71 (1.00)	0.231 (1.00)	0.514 (1.00)	0.988 (1.00)	0.135 (1.00)

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	54	47	28

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

P value = 0.885 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	129	4	0.1 - 303.1 (15.0)
subtype1	54	2	0.6 - 303.1 (17.1)
subtype2	47	1	0.7 - 127.6 (17.5)
subtype3	28	1	0.1 - 107.5 (11.7)

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 'AGE'

P value = 0.967 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	129	48.7 (15.7)
subtype1	54	49.2 (15.7)
subtype2	47	48.7 (16.4)
subtype3	28	47.9 (14.7)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.974 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	73	56
subtype1	31	23
subtype2	26	21
subtype3	16	12

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 = 0.898 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	4	9	112
subtype1	1	3	4	45
subtype2	0	1	3	42
subtype3	0	0	2	25

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 = 0.306 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	2	99
subtype1	0	45
subtype2	2	36
subtype3	0	18

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
Number of samples	28	66	38

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.31 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	132	4	0.1 - 303.1 (16.9)
subtype1	28	2	0.9 - 303.1 (24.5)
subtype2	66	2	0.1 - 127.6 (11.9)
subtype3	38	0	0.7 - 110.5 (18.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.217 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	132	48.8 (15.7)
subtype1	28	45.6 (16.0)
subtype2	66	51.3 (15.1)
subtype3	38	46.7 (16.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.72 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	75	57
subtype1	14	14
subtype2	38	28
subtype3	23	15

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.75 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
subtype1	0	1	2	24
subtype2	1	1	5	57
subtype3	0	3	2	33

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.0714 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	101
subtype1	2	19
subtype2	0	51
subtype3	1	31

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	24	57	33	18

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.596 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	132	4	0.1 - 303.1 (16.9)
subtype1	24	1	0.9 - 303.1 (24.9)
subtype2	57	1	0.1 - 104.5 (12.5)
subtype3	33	2	0.8 - 127.6 (12.2)
subtype4	18	0	0.7 - 110.5 (17.4)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.952 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	132	48.8 (15.7)
subtype1	24	48.5 (15.8)
subtype2	57	49.7 (15.5)
subtype3	33	47.1 (16.8)
subtype4	18	49.4 (15.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.275 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	75	57
subtype1	17	7
subtype2	30	27
subtype3	16	17
subtype4	12	6

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.794 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
subtype1	0	1	3	18
subtype2	1	2	3	50
subtype3	0	1	1	31
subtype4	0	1	2	15

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0242 (Fisher's exact test), Q value = 0.97

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	101
subtype1	1	14
subtype2	0	47
subtype3	0	26
subtype4	2	14

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	28	58	28	18

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.685 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	132	4	0.1 - 303.1 (16.9)
subtype1	28	2	0.8 - 127.6 (26.2)
subtype2	58	1	0.1 - 104.5 (12.1)
subtype3	28	1	0.9 - 303.1 (14.8)
subtype4	18	0	0.7 - 110.5 (17.4)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.029 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	132	48.8 (15.7)
subtype1	28	55.0 (12.1)
subtype2	58	49.1 (15.5)
subtype3	28	42.6 (17.6)
subtype4	18	47.8 (15.9)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.588 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	75	57
subtype1	16	12
subtype2	34	24
subtype3	13	15
subtype4	12	6

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.996 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
subtype1	0	1	1	26
subtype2	1	2	5	48
subtype3	0	1	2	24
subtype4	0	1	1	16

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0458 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	101
subtype1	1	20
subtype2	0	46
subtype3	0	21
subtype4	2	14

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	50	50	32

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.062 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	132	4	0.1 - 303.1 (16.9)
subtype1	50	4	0.9 - 303.1 (21.8)
subtype2	50	0	0.1 - 104.5 (13.6)
subtype3	32	0	0.7 - 110.5 (12.5)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.899 (Kruskal-Wallis (anova)), Q value = 1

Table S27. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	132	48.8 (15.7)
subtype1	50	48.4 (16.9)
subtype2	50	48.3 (15.5)
subtype3	32	50.2 (14.7)

Figure S22. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.95 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	75	57
subtype1	28	22
subtype2	28	22
subtype3	19	13

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.774 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
subtype1	1	2	2	44
subtype2	0	2	3	43
subtype3	0	1	4	27

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.255 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	101
subtype1	1	39
subtype2	0	38
subtype3	2	24

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

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S31. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	48	49	15	20

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.461 (logrank test), Q value = 1

Table S32. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	132	4	0.1 - 303.1 (16.9)
subtype1	48	3	0.6 - 303.1 (18.4)
subtype2	49	1	0.1 - 104.5 (13.1)
subtype3	15	0	0.7 - 86.9 (19.6)
subtype4	20	0	0.8 - 110.5 (17.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.995 (Kruskal-Wallis (anova)), Q value = 1

Table S33. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	132	48.8 (15.7)
subtype1	48	48.1 (16.6)
subtype2	49	49.5 (15.9)
subtype3	15	48.9 (13.5)
subtype4	20	48.6 (16.1)

Figure S27. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.605 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	57
subtype1	24	24
subtype2	31	18
subtype3	8	7
subtype4	12	8

Figure S28. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.824 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	114
subtype1	0	2	2	43
subtype2	1	2	3	41
subtype3	0	0	1	14
subtype4	0	1	3	16

Figure S29. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.0675 (Fisher's exact test), Q value = 1

Table S36. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	101
subtype1	1	36
subtype2	0	39
subtype3	0	10
subtype4	2	16

Figure S30. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S37. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	29	52	9	22	19

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

P value = 0.0961 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	131	4	0.1 - 303.1 (17.2)
subtype1	29	0	0.7 - 127.6 (21.4)
subtype2	52	1	0.1 - 110.5 (13.6)
subtype3	9	1	0.9 - 29.0 (2.9)
subtype4	22	1	0.9 - 107.5 (17.0)
subtype5	19	1	0.9 - 303.1 (23.5)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0667 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	131	48.7 (15.8)
subtype1	29	52.9 (12.5)
subtype2	52	49.2 (15.6)
subtype3	9	55.2 (14.5)
subtype4	22	44.8 (18.8)
subtype5	19	42.1 (15.5)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.628 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	56
subtype1	19	10
subtype2	29	23
subtype3	4	5
subtype4	14	8
subtype5	9	10

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.634 (Fisher's exact test), Q value = 1

Table S41. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	113
subtype1	0	0	2	27
subtype2	0	3	4	44
subtype3	1	0	0	7
subtype4	0	1	1	19
subtype5	0	1	2	16

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.117 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	100
subtype1	1	19
subtype2	0	44
subtype3	0	5
subtype4	0	17
subtype5	2	15

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

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S43. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	63	37	15	16

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

P value = 0.71 (logrank test), Q value = 1

Table S44. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	131	4	0.1 - 303.1 (17.2)
subtype1	63	2	0.1 - 106.8 (14.2)
subtype2	37	2	0.9 - 303.1 (24.2)
subtype3	15	0	0.7 - 86.9 (19.6)
subtype4	16	0	0.8 - 110.5 (17.0)

Figure S36. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.231 (Kruskal-Wallis (anova)), Q value = 1

Table S45. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	131	48.7 (15.8)
subtype1	63	51.2 (15.1)
subtype2	37	44.2 (16.9)
subtype3	15	48.9 (13.5)
subtype4	16	49.1 (16.4)

Figure S37. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.514 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	75	56
subtype1	38	25
subtype2	18	19
subtype3	8	7
subtype4	11	5

Figure S38. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.988 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	5	9	113
subtype1	1	2	4	54
subtype2	0	2	3	31
subtype3	0	0	1	14
subtype4	0	1	1	14

Figure S39. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.135 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	100
subtype1	1	50
subtype2	0	28
subtype3	0	10
subtype4	2	12

Figure S40. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Methods & Data

Input

Cluster data file = PCPG-TP.mergedcluster.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 132
Number of clustering approaches = 8
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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