Correlation between aggregated molecular cancer subtypes and selected clinical features

Glioblastoma Multiforme (Primary solid tumor)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C10G3JJT

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 8 clinical features across 595 patients, 15 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on array-based miR expression data identified 4 subtypes that correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'RADIATION_THERAPY'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', and 'KARNOFSKY_PERFORMANCE_SCORE'.
CNMF clustering analysis on RPPA data identified 5 subtypes that correlate to 'GENDER'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'KARNOFSKY_PERFORMANCE_SCORE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 15 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0968 (0.31)	0.123 (0.339)	0.111 (0.33)	0.413 (0.751)	0.606 (0.865)	0.00256 (0.0293)	0.943 (0.979)	0.163 (0.399)
mRNA cHierClus subtypes	0.000163 (0.00653)	0.00252 (0.0293)	0.0935 (0.31)	0.223 (0.469)	0.515 (0.842)	0.0308 (0.205)	0.588 (0.865)	0.685 (0.87)
miR CNMF subtypes	0.00783 (0.0696)	0.71 (0.888)	0.507 (0.842)	0.291 (0.553)	0.839 (0.924)	0.55 (0.862)	0.134 (0.358)	1 (1.00)
miR cHierClus subtypes	0.245 (0.502)	0.12 (0.339)	0.182 (0.429)	0.476 (0.815)	0.261 (0.515)	0.542 (0.862)	0.193 (0.429)	0.676 (0.87)
Copy Number Ratio CNMF subtypes	0.00134 (0.0268)	1.43e-06 (0.000114)	0.0548 (0.236)	0.00737 (0.0696)	0.0733 (0.285)	0.0547 (0.236)	0.111 (0.33)	0.165 (0.399)
METHLYATION CNMF	0.00212 (0.0293)	0.000553 (0.0147)	0.479 (0.815)	0.213 (0.461)	0.0148 (0.118)	0.322 (0.599)	0.879 (0.95)	0.661 (0.867)
RPPA CNMF subtypes	0.655 (0.867)	0.844 (0.924)	0.048 (0.236)	0.785 (0.894)	0.954 (0.979)	0.935 (0.979)	0.989 (1.00)	0.627 (0.867)
RPPA cHierClus subtypes	0.0196 (0.143)	0.0518 (0.236)	0.192 (0.429)	0.646 (0.867)	0.0779 (0.285)	0.638 (0.867)	0.424 (0.754)	0.78 (0.894)
RNAseq CNMF subtypes	0.595 (0.865)	0.056 (0.236)	0.0379 (0.217)	0.763 (0.894)	0.264 (0.515)	0.783 (0.894)	0.764 (0.894)	0.793 (0.894)
RNAseq cHierClus subtypes	0.086 (0.299)	0.0784 (0.285)	0.143 (0.369)	0.898 (0.958)	0.0366 (0.217)	0.601 (0.865)	0.577 (0.865)	0.737 (0.894)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	167	97	139	122

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0968 (logrank test), Q value = 0.31

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	448	0.1 - 127.6 (12.4)
subtype1	167	149	0.1 - 127.6 (11.3)
subtype2	97	83	0.2 - 115.9 (13.6)
subtype3	138	114	0.1 - 94.8 (13.8)
subtype4	121	102	0.2 - 91.8 (12.7)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.123 (Kruskal-Wallis (anova)), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	167	58.4 (12.1)
subtype2	97	54.3 (17.3)
subtype3	139	60.0 (13.8)
subtype4	122	56.7 (15.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.111 (Fisher's exact test), Q value = 0.33

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

nPatients	FEMALE	MALE
ALL	205	320
subtype1	63	104
subtype2	44	53
subtype3	60	79
subtype4	38	84

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.413 (Fisher's exact test), Q value = 0.75

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	70	435
subtype1	21	138
subtype2	16	77
subtype3	14	121
subtype4	19	99

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.606 (Kruskal-Wallis (anova)), Q value = 0.87

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

	nPatients	Mean (Std.Dev)
ALL	396	77.1 (14.6)
subtype1	125	77.6 (15.4)
subtype2	75	76.3 (11.4)
subtype3	107	76.4 (15.7)
subtype4	89	78.2 (14.6)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00256 (Fisher's exact test), Q value = 0.029

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	499
subtype1	1	8	158
subtype2	0	8	89
subtype3	0	3	136
subtype4	5	1	116

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.943 (Fisher's exact test), Q value = 0.98

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	31	462
subtype1	4	12	146
subtype2	2	7	84
subtype3	4	7	125
subtype4	3	5	107

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.163 (Fisher's exact test), Q value = 0.4

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	438
subtype1	3	139
subtype2	5	72
subtype3	3	121
subtype4	1	106

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	153	107	103	162

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.000163 (logrank test), Q value = 0.0065

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	523	448	0.1 - 127.6 (12.4)
subtype1	153	138	0.1 - 91.0 (12.2)
subtype2	107	81	0.2 - 115.9 (14.9)
subtype3	102	86	0.1 - 94.8 (13.8)
subtype4	161	143	0.1 - 127.6 (10.6)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00252 (Kruskal-Wallis (anova)), Q value = 0.029

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	525	57.7 (14.6)
subtype1	153	56.9 (13.6)
subtype2	107	52.9 (17.9)
subtype3	103	60.6 (12.1)
subtype4	162	59.8 (13.7)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.0935 (Fisher's exact test), Q value = 0.31

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	320
subtype1	51	102
subtype2	44	63
subtype3	50	53
subtype4	60	102

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.223 (Fisher's exact test), Q value = 0.47

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	70	435
subtype1	17	131
subtype2	13	91
subtype3	11	88
subtype4	29	125

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.515 (Kruskal-Wallis (anova)), Q value = 0.84

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	396	77.1 (14.6)
subtype1	118	78.1 (15.4)
subtype2	84	77.6 (11.6)
subtype3	81	75.2 (15.6)
subtype4	113	77.3 (15.1)

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0308 (Fisher's exact test), Q value = 0.21

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	499
subtype1	0	11	142
subtype2	1	3	103
subtype3	0	3	100
subtype4	5	3	154

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.588 (Fisher's exact test), Q value = 0.87

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	31	462
subtype1	4	13	130
subtype2	4	6	95
subtype3	3	4	93
subtype4	2	8	144

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.685 (Fisher's exact test), Q value = 0.87

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	438
subtype1	5	124
subtype2	1	91
subtype3	2	90
subtype4	4	133

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S19. Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	151	189	93	130

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00783 (logrank test), Q value = 0.07

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	469	0.1 - 127.6 (12.2)
subtype1	151	130	0.1 - 91.0 (12.1)
subtype2	189	155	0.1 - 127.6 (13.0)
subtype3	92	78	0.4 - 65.3 (9.6)
subtype4	129	106	0.1 - 120.6 (12.5)

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.71 (Kruskal-Wallis (anova)), Q value = 0.89

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	563	57.9 (14.3)
subtype1	151	59.7 (11.8)
subtype2	189	56.5 (16.0)
subtype3	93	57.8 (15.3)
subtype4	130	58.1 (13.7)

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.507 (Fisher's exact test), Q value = 0.84

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	219	344
subtype1	59	92
subtype2	80	109
subtype3	36	57
subtype4	44	86

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.291 (Fisher's exact test), Q value = 0.55

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	466
subtype1	17	129
subtype2	22	157
subtype3	18	72
subtype4	16	108

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.839 (Kruskal-Wallis (anova)), Q value = 0.92

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	430	77.0 (15.6)
subtype1	119	76.0 (16.1)
subtype2	138	76.4 (17.2)
subtype3	75	78.0 (13.5)
subtype4	98	78.4 (14.3)

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.55 (Fisher's exact test), Q value = 0.86

Table S25. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	11	20	532
subtype1	2	3	146
subtype2	3	9	177
subtype3	3	5	85
subtype4	3	3	124

Figure S22. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'miR CNMF subtypes' versus 'RACE'

P value = 0.134 (Fisher's exact test), Q value = 0.36

Table S26. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	495
subtype1	3	13	127
subtype2	7	9	162
subtype3	1	1	89
subtype4	2	7	117

Figure S23. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

'miR CNMF subtypes' versus 'ETHNICITY'

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

Table S27. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	461
subtype1	3	124
subtype2	4	158
subtype3	2	74
subtype4	3	105

Figure S24. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S28. Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	303	129	131

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.245 (logrank test), Q value = 0.5

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	469	0.1 - 127.6 (12.2)
subtype1	302	255	0.1 - 120.6 (12.7)
subtype2	128	106	0.1 - 92.7 (11.0)
subtype3	131	108	0.1 - 127.6 (11.8)

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 0.34

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	563	57.9 (14.3)
subtype1	303	56.6 (15.9)
subtype2	129	59.0 (11.5)
subtype3	131	60.1 (12.5)

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.182 (Fisher's exact test), Q value = 0.43

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	219	344
subtype1	111	192
subtype2	48	81
subtype3	60	71

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.476 (Fisher's exact test), Q value = 0.81

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	466
subtype1	44	246
subtype2	13	109
subtype3	16	111

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'miR cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.261 (Kruskal-Wallis (anova)), Q value = 0.52

Table S33. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	430	77.0 (15.6)
subtype1	228	78.2 (14.9)
subtype2	101	76.5 (15.6)
subtype3	101	74.8 (17.1)

Figure S29. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.542 (Fisher's exact test), Q value = 0.86

Table S34. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	11	20	532
subtype1	7	13	283
subtype2	3	2	124
subtype3	1	5	125

Figure S30. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.193 (Fisher's exact test), Q value = 0.43

Table S35. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	495
subtype1	7	10	273
subtype2	3	11	113
subtype3	3	9	109

Figure S31. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.676 (Fisher's exact test), Q value = 0.87

Table S36. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	461
subtype1	6	250
subtype2	2	109
subtype3	4	102

Figure S32. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S37. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	252	196	125

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

P value = 0.00134 (logrank test), Q value = 0.027

Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	570	473	0.1 - 127.6 (12.2)
subtype1	250	202	0.2 - 127.6 (12.1)
subtype2	196	170	0.1 - 87.1 (11.6)
subtype3	124	101	0.1 - 120.6 (14.4)

Figure S33. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.43e-06 (Kruskal-Wallis (anova)), Q value = 0.00011

Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	573	57.8 (14.5)
subtype1	252	54.0 (16.2)
subtype2	196	61.7 (12.9)
subtype3	125	59.2 (11.2)

Figure S34. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0548 (Fisher's exact test), Q value = 0.24

Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	225	348
subtype1	105	147
subtype2	64	132
subtype3	56	69

Figure S35. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.00737 (Fisher's exact test), Q value = 0.07

Table S41. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	74	475
subtype1	29	213
subtype2	36	150
subtype3	9	112

Figure S36. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0733 (Kruskal-Wallis (anova)), Q value = 0.29

Table S42. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	426	77.1 (15.8)
subtype1	186	78.4 (15.4)
subtype2	139	75.3 (16.4)
subtype3	101	76.9 (15.5)

Figure S37. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0547 (Fisher's exact test), Q value = 0.24

Table S43. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	30	18	525
subtype1	18	10	224
subtype2	6	2	188
subtype3	6	6	113

Figure S38. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.111 (Fisher's exact test), Q value = 0.33

Table S44. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	50	487
subtype1	8	29	206
subtype2	2	11	174
subtype3	3	10	107

Figure S39. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.165 (Fisher's exact test), Q value = 0.4

Table S45. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	475
subtype1	7	210
subtype2	1	161
subtype3	4	104

Figure S40. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S46. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	71	44	83	85

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00212 (logrank test), Q value = 0.029

Table S47. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	280	227	0.1 - 127.6 (12.5)
subtype1	71	57	0.1 - 92.7 (12.7)
subtype2	43	37	0.1 - 77.7 (9.4)
subtype3	82	68	0.1 - 49.0 (10.6)
subtype4	84	65	0.2 - 127.6 (14.3)

Figure S41. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000553 (Kruskal-Wallis (anova)), Q value = 0.015

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	283	57.8 (15.0)
subtype1	71	58.5 (12.4)
subtype2	44	58.9 (13.7)
subtype3	83	62.6 (12.8)
subtype4	85	52.0 (17.8)

Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.479 (Fisher's exact test), Q value = 0.81

Table S49. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	115	168
subtype1	32	39
subtype2	21	23
subtype3	31	52
subtype4	31	54

Figure S43. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.213 (Fisher's exact test), Q value = 0.46

Table S50. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	39	234
subtype1	7	62
subtype2	5	37
subtype3	17	62
subtype4	10	73

Figure S44. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0148 (Kruskal-Wallis (anova)), Q value = 0.12

Table S51. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	216	75.4 (15.3)
subtype1	53	78.5 (16.8)
subtype2	37	75.4 (17.4)
subtype3	61	71.1 (14.3)
subtype4	65	76.9 (13.1)

Figure S45. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.322 (Fisher's exact test), Q value = 0.6

Table S52. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	3	19	261
subtype1	3	6	62
subtype2	0	2	42
subtype3	0	5	78
subtype4	0	6	79

Figure S46. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.879 (Fisher's exact test), Q value = 0.95

Table S53. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	10	16	254
subtype1	3	5	62
subtype2	0	2	41
subtype3	4	5	74
subtype4	3	4	77

Figure S47. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.661 (Fisher's exact test), Q value = 0.87

Table S54. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	232
subtype1	3	62
subtype2	0	38
subtype3	2	66
subtype4	2	66

Figure S48. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S55. Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	46	61	46	31	48

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.655 (logrank test), Q value = 0.87

Table S56. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	230	171	0.1 - 120.6 (10.7)
subtype1	46	35	0.2 - 47.6 (9.1)
subtype2	61	49	0.1 - 53.2 (12.5)
subtype3	45	32	0.5 - 115.9 (8.3)
subtype4	31	22	0.2 - 43.5 (9.5)
subtype5	47	33	0.1 - 120.6 (12.1)

Figure S49. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.844 (Kruskal-Wallis (anova)), Q value = 0.92

Table S57. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	232	59.5 (14.3)
subtype1	46	59.4 (12.9)
subtype2	61	59.7 (13.0)
subtype3	46	56.9 (17.3)
subtype4	31	60.5 (15.1)
subtype5	48	61.4 (13.5)

Figure S50. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.048 (Fisher's exact test), Q value = 0.24

Table S58. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	140
subtype1	13	33
subtype2	19	42
subtype3	19	27
subtype4	15	16
subtype5	26	22

Figure S51. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.785 (Fisher's exact test), Q value = 0.89

Table S59. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	29	187
subtype1	6	39
subtype2	5	48
subtype3	8	36
subtype4	4	23
subtype5	6	41

Figure S52. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.954 (Kruskal-Wallis (anova)), Q value = 0.98

Table S60. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	178	74.6 (17.6)
subtype1	35	74.0 (20.3)
subtype2	47	73.8 (16.5)
subtype3	35	75.7 (17.7)
subtype4	23	72.6 (22.2)
subtype5	38	76.3 (13.4)

Figure S53. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.935 (Fisher's exact test), Q value = 0.98

Table S61. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	19	3	210
subtype1	3	0	43
subtype2	4	2	55
subtype3	4	0	42
subtype4	3	0	28
subtype5	5	1	42

Figure S54. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.989 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	27	185
subtype1	0	6	37
subtype2	1	8	48
subtype3	1	4	36
subtype4	1	3	24
subtype5	1	6	40

Figure S55. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.627 (Fisher's exact test), Q value = 0.87

Table S63. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	196
subtype1	1	40
subtype2	2	49
subtype3	0	40
subtype4	0	26
subtype5	0	41

Figure S56. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S64. Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	94	77	61

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0196 (logrank test), Q value = 0.14

Table S65. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	230	171	0.1 - 120.6 (10.7)
subtype1	94	75	0.2 - 53.2 (8.4)
subtype2	76	51	0.1 - 115.9 (9.6)
subtype3	60	45	0.1 - 120.6 (14.7)

Figure S57. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0518 (Kruskal-Wallis (anova)), Q value = 0.24

Table S66. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	232	59.5 (14.3)
subtype1	94	61.3 (13.2)
subtype2	77	55.4 (16.7)
subtype3	61	62.1 (11.2)

Figure S58. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.192 (Fisher's exact test), Q value = 0.43

Table S67. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	92	140
subtype1	34	60
subtype2	37	40
subtype3	21	40

Figure S59. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.646 (Fisher's exact test), Q value = 0.87

Table S68. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	29	187
subtype1	14	73
subtype2	9	63
subtype3	6	51

Figure S60. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0779 (Kruskal-Wallis (anova)), Q value = 0.29

Table S69. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	178	74.6 (17.6)
subtype1	70	71.3 (19.0)
subtype2	62	77.3 (18.4)
subtype3	46	76.1 (13.4)

Figure S61. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.638 (Fisher's exact test), Q value = 0.87

Table S70. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	19	3	210
subtype1	9	2	83
subtype2	7	0	70
subtype3	3	1	57

Figure S62. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.424 (Fisher's exact test), Q value = 0.75

Table S71. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	27	185
subtype1	0	13	74
subtype2	2	7	61
subtype3	2	7	50

Figure S63. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.78 (Fisher's exact test), Q value = 0.89

Table S72. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	196
subtype1	2	76
subtype2	1	66
subtype3	0	54

Figure S64. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S73. Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	48	71	33

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.595 (logrank test), Q value = 0.87

Table S74. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	120	0.2 - 88.1 (11.3)
subtype1	48	36	0.2 - 88.1 (11.4)
subtype2	70	57	0.9 - 69.9 (12.2)
subtype3	33	27	0.2 - 46.9 (10.8)

Figure S65. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.056 (Kruskal-Wallis (anova)), Q value = 0.24

Table S75. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	152	59.7 (13.5)
subtype1	48	55.2 (16.7)
subtype2	71	62.1 (10.6)
subtype3	33	61.4 (12.9)

Figure S66. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0379 (Fisher's exact test), Q value = 0.22

Table S76. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	99
subtype1	14	34
subtype2	32	39
subtype3	7	26

Figure S67. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.763 (Fisher's exact test), Q value = 0.89

Table S77. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	23	121
subtype1	6	39
subtype2	11	56
subtype3	6	26

Figure S68. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.264 (Kruskal-Wallis (anova)), Q value = 0.52

Table S78. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	75.7 (14.8)
subtype1	39	75.1 (15.0)
subtype2	54	74.3 (13.8)
subtype3	24	80.0 (16.2)

Figure S69. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.783 (Fisher's exact test), Q value = 0.89

Table S79. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	1	150
subtype1	1	0	47
subtype2	0	1	70
subtype3	0	0	33

Figure S70. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.764 (Fisher's exact test), Q value = 0.89

Table S80. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	136
subtype1	2	3	43
subtype2	1	5	64
subtype3	2	2	29

Figure S71. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.793 (Fisher's exact test), Q value = 0.89

Table S81. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	125
subtype1	1	45
subtype2	2	51
subtype3	0	29

Figure S72. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S82. Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	32	96	24

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.086 (logrank test), Q value = 0.3

Table S83. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	120	0.2 - 88.1 (11.3)
subtype1	32	23	0.2 - 88.1 (14.1)
subtype2	95	77	0.4 - 69.9 (10.4)
subtype3	24	20	0.2 - 46.9 (8.9)

Figure S73. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0784 (Kruskal-Wallis (anova)), Q value = 0.29

Table S84. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	152	59.7 (13.5)
subtype1	32	54.2 (18.3)
subtype2	96	61.6 (11.4)
subtype3	24	59.6 (12.5)

Figure S74. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.143 (Fisher's exact test), Q value = 0.37

Table S85. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	53	99
subtype1	9	23
subtype2	39	57
subtype3	5	19

Figure S75. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.898 (Fisher's exact test), Q value = 0.96

Table S86. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	23	121
subtype1	4	27
subtype2	15	74
subtype3	4	20

Figure S76. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0366 (Kruskal-Wallis (anova)), Q value = 0.22

Table S87. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	75.7 (14.8)
subtype1	26	78.1 (11.7)
subtype2	75	73.3 (14.6)
subtype3	16	83.1 (17.4)

Figure S77. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.601 (Fisher's exact test), Q value = 0.87

Table S88. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	1	1	150
subtype1	1	0	31
subtype2	0	1	95
subtype3	0	0	24

Figure S78. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.577 (Fisher's exact test), Q value = 0.87

Table S89. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	136
subtype1	2	1	29
subtype2	2	8	85
subtype3	1	1	22

Figure S79. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.737 (Fisher's exact test), Q value = 0.89

Table S90. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	125
subtype1	0	30
subtype2	3	75
subtype3	0	20

Figure S80. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/GBM-TP/22555065/GBM-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBM-TP/22506586/GBM-TP.merged_data.txt
Number of patients = 595
Number of clustering approaches = 10
Number of selected clinical features = 8
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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