Correlation between aggregated molecular cancer subtypes and selected clinical features

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 14 different clustering approaches and 9 clinical features across 1107 patients, 61 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', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'RACE', and 'ETHNICITY'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'GENDER', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'TUMOR_TISSUE_SITE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 14 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 61 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	TUMOR TISSUE SITE	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	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0522 (0.106)	0.0788 (0.153)		0.397 (0.511)	0.519 (0.629)	0.474 (0.586)	0.00448 (0.0111)	0.833 (0.938)	0.123 (0.222)
mRNA cHierClus subtypes	0.00017 (0.000562)	0.00252 (0.00675)		0.092 (0.175)	0.227 (0.349)	0.515 (0.629)	0.0295 (0.0677)	0.589 (0.7)	0.684 (0.788)
miR CNMF subtypes	0.00335 (0.00845)	0.38 (0.494)		0.651 (0.767)	0.351 (0.481)	0.861 (0.948)	0.363 (0.482)	0.203 (0.324)	0.855 (0.948)
miR cHierClus subtypes	0.194 (0.317)	0.127 (0.224)		0.154 (0.261)	0.463 (0.578)	0.278 (0.398)	0.284 (0.398)	0.193 (0.317)	0.676 (0.788)
Copy Number Ratio CNMF subtypes	0 (0)	4.25e-77 (8.93e-76)	1e-05 (3.94e-05)	0.222 (0.345)	1e-05 (3.94e-05)	3.28e-20 (4.13e-19)	1e-05 (3.94e-05)	0.0348 (0.0769)	0.00083 (0.00238)
METHLYATION CNMF	0 (0)	1.5e-46 (2.69e-45)	1e-05 (3.94e-05)	0.898 (0.975)	1e-05 (3.94e-05)	5.93e-11 (4.98e-10)	1e-05 (3.94e-05)	0.0002 (0.000646)	0.128 (0.224)
RPPA CNMF subtypes	0.151 (0.261)	0.041 (0.0861)	0.00239 (0.00655)	0.0357 (0.0777)	0.0081 (0.0196)	0.278 (0.398)	0.00308 (0.00792)	0.284 (0.398)	0.745 (0.846)
RPPA cHierClus subtypes	0.0528 (0.106)	0.00266 (0.00699)	0.0008 (0.00234)	0.202 (0.324)	0.0114 (0.027)	0.104 (0.19)	3e-05 (0.000111)	0.357 (0.482)	0.942 (1.00)
RNAseq CNMF subtypes	0 (0)	3.33e-33 (4.67e-32)	1e-05 (3.94e-05)	0.294 (0.408)	1e-05 (3.94e-05)	1.49e-16 (1.7e-15)	1e-05 (3.94e-05)	0.38 (0.494)	0.0431 (0.089)
RNAseq cHierClus subtypes	0 (0)	4.19e-44 (6.6e-43)	1e-05 (3.94e-05)	0.362 (0.482)	1e-05 (3.94e-05)	5.38e-15 (5.65e-14)	1e-05 (3.94e-05)	0.176 (0.295)	0.242 (0.362)
MIRSEQ CNMF	0.0123 (0.0287)	0.917 (0.987)		0.688 (0.788)	0.00231 (0.00647)	0.0929 (0.175)	1e-05 (3.94e-05)	0.273 (0.398)	0.437 (0.55)
MIRSEQ CHIERARCHICAL	6.06e-14 (5.88e-13)	1.78e-08 (1.4e-07)		0.283 (0.398)	1e-05 (3.94e-05)	0.000222 (7e-04)	1e-05 (3.94e-05)	0.561 (0.674)	0.231 (0.351)
MIRseq Mature CNMF subtypes	0.000268 (0.000824)	0.0547 (0.108)		0.0323 (0.0726)	5e-05 (0.00018)	0.0393 (0.0838)	3e-05 (0.000111)	0.865 (0.948)	0.00072 (0.00216)
MIRseq Mature cHierClus subtypes	0 (0)	1.6e-12 (1.44e-11)		0.097 (0.18)	0.00015 (0.000511)	5.43e-05 (0.00019)	1e-05 (3.94e-05)	0.205 (0.324)	0.432 (0.55)

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	154	97	156	118

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0522 (logrank test), Q value = 0.11

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	525	447	0.1 - 127.6 (12.2)
subtype1	154	138	0.1 - 127.6 (10.6)
subtype2	97	81	0.2 - 115.9 (14.5)
subtype3	156	128	0.1 - 94.8 (13.8)
subtype4	118	100	0.2 - 91.8 (12.6)

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 = 0.0788 (Kruskal-Wallis (anova)), Q value = 0.15

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	154	58.4 (12.4)
subtype2	97	54.2 (17.4)
subtype3	156	60.0 (13.3)
subtype4	118	56.5 (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 = 0.397 (Fisher's exact test), Q value = 0.51

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	205	320
subtype1	59	95
subtype2	41	56
subtype3	66	90
subtype4	39	79

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.519 (Fisher's exact test), Q value = 0.63

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	70	435
subtype1	21	127
subtype2	14	79
subtype3	16	134
subtype4	19	95

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.474 (Kruskal-Wallis (anova)), Q value = 0.59

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	396	77.1 (14.6)
subtype1	115	77.6 (15.8)
subtype2	76	76.6 (11.0)
subtype3	120	76.1 (15.4)
subtype4	85	78.6 (14.7)

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00448 (Fisher's exact test), Q value = 0.011

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	6	20	499
subtype1	1	8	145
subtype2	0	7	90
subtype3	0	4	152
subtype4	5	1	112

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.833 (Fisher's exact test), Q value = 0.94

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	31	462
subtype1	4	12	135
subtype2	2	7	84
subtype3	5	7	139
subtype4	2	5	104

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.123 (Fisher's exact test), Q value = 0.22

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	438
subtype1	3	131
subtype2	5	68
subtype3	3	135
subtype4	1	104

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: '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 = 0.00017 (logrank test), Q value = 0.00056

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	525	447	0.1 - 127.6 (12.2)
subtype1	153	138	0.1 - 91.0 (12.2)
subtype2	107	80	0.2 - 115.9 (14.7)
subtype3	103	87	0.1 - 94.8 (13.8)
subtype4	162	142	0.1 - 127.6 (10.4)

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 = 0.0068

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 = 0.092 (Fisher's exact test), Q value = 0.17

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: '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 #4: 'GENDER'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.227 (Fisher's exact test), Q value = 0.35

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: '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 #5: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.515 (Kruskal-Wallis (anova)), Q value = 0.63

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: '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 #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0295 (Fisher's exact test), Q value = 0.068

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: '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 #7: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.589 (Fisher's exact test), Q value = 0.7

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: '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 #8: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.684 (Fisher's exact test), Q value = 0.79

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: '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 #9: '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	165	182	90	124

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.00335 (logrank test), Q value = 0.0084

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	466	0.1 - 127.6 (12.2)
subtype1	165	141	0.1 - 91.0 (11.3)
subtype2	182	147	0.1 - 127.6 (13.7)
subtype3	90	74	0.4 - 65.3 (9.3)
subtype4	124	104	0.1 - 120.6 (12.8)

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 = 0.38 (Kruskal-Wallis (anova)), Q value = 0.49

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

	nPatients	Mean (Std.Dev)
ALL	561	57.9 (14.3)
subtype1	165	59.8 (11.6)
subtype2	182	55.7 (16.6)
subtype3	90	58.5 (14.6)
subtype4	124	58.1 (13.6)

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 = 0.651 (Fisher's exact test), Q value = 0.77

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	218	343
subtype1	63	102
subtype2	76	106
subtype3	36	54
subtype4	43	81

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'miR CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.351 (Fisher's exact test), Q value = 0.48

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	465
subtype1	19	141
subtype2	21	153
subtype3	17	70
subtype4	16	101

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'miR CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.861 (Kruskal-Wallis (anova)), Q value = 0.95

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	428	77.4 (14.7)
subtype1	129	76.1 (15.5)
subtype2	132	77.4 (14.8)
subtype3	74	78.2 (13.5)
subtype4	93	78.3 (14.6)

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'miR CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.363 (Fisher's exact test), Q value = 0.48

Table S25. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	9	20	532
subtype1	2	4	159
subtype2	1	8	173
subtype3	3	5	82
subtype4	3	3	118

Figure S22. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'miR CNMF subtypes' versus 'RACE'

P value = 0.203 (Fisher's exact test), Q value = 0.32

Table S26. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	493
subtype1	3	13	140
subtype2	7	9	157
subtype3	1	1	85
subtype4	2	7	111

Figure S23. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'RACE'

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 0.855 (Fisher's exact test), Q value = 0.95

Table S27. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	459
subtype1	5	134
subtype2	4	153
subtype3	1	71
subtype4	2	101

Figure S24. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #9: '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	302	129	130

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.194 (logrank test), Q value = 0.32

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	561	466	0.1 - 127.6 (12.2)
subtype1	302	252	0.1 - 120.6 (12.6)
subtype2	129	107	0.1 - 92.7 (10.8)
subtype3	130	107	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 = 0.127 (Kruskal-Wallis (anova)), Q value = 0.22

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

	nPatients	Mean (Std.Dev)
ALL	561	57.9 (14.3)
subtype1	302	56.5 (15.9)
subtype2	129	59.0 (11.5)
subtype3	130	60.0 (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 = 0.154 (Fisher's exact test), Q value = 0.26

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

nPatients	FEMALE	MALE
ALL	218	343
subtype1	110	192
subtype2	48	81
subtype3	60	70

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

'miR cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.463 (Fisher's exact test), Q value = 0.58

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	73	465
subtype1	44	245
subtype2	13	109
subtype3	16	111

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'miR cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4

Table S33. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	428	77.4 (14.7)
subtype1	227	78.5 (14.0)
subtype2	101	76.5 (15.6)
subtype3	100	75.5 (15.4)

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

'miR cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.284 (Fisher's exact test), Q value = 0.4

Table S34. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	GLIOBLASTOMA MULTIFORME (GBM)	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	9	20	532
subtype1	6	13	283
subtype2	3	2	124
subtype3	0	5	125

Figure S30. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.193 (Fisher's exact test), Q value = 0.32

Table S35. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	13	30	493
subtype1	7	10	272
subtype2	3	11	113
subtype3	3	9	108

Figure S31. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.676 (Fisher's exact test), Q value = 0.79

Table S36. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	12	459
subtype1	6	249
subtype2	2	109
subtype3	4	101

Figure S32. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #9: '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	529	539	15

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

P value = 0 (logrank test), Q value = 0

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	1079	591	0.0 - 211.2 (15.7)
subtype1	528	432	0.1 - 127.6 (12.1)
subtype2	536	148	0.0 - 211.2 (21.4)
subtype3	15	11	1.8 - 92.7 (14.9)

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 = 4.25e-77 (Kruskal-Wallis (anova)), Q value = 8.9e-76

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

	nPatients	Mean (Std.Dev)
ALL	1082	50.8 (15.8)
subtype1	529	59.7 (11.9)
subtype2	538	41.9 (14.0)
subtype3	15	54.1 (15.7)

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 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

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

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	571	512
subtype1	466	63
subtype2	92	447
subtype3	13	2

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test), Q value = 0.35

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

nPatients	FEMALE	MALE
ALL	452	631
subtype1	207	322
subtype2	239	300
subtype3	6	9

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

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

nPatients	NO	YES
ALL	258	767
subtype1	70	433
subtype2	186	321
subtype3	2	13

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 3.28e-20 (Kruskal-Wallis (anova)), Q value = 4.1e-19

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

	nPatients	Mean (Std.Dev)
ALL	730	81.3 (14.7)
subtype1	385	76.7 (15.4)
subtype2	334	86.5 (11.9)
subtype3	11	82.7 (14.2)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

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

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	28	129	189	18	525
subtype1	39	19	12	12	10	437
subtype2	153	9	117	177	7	76
subtype3	2	0	0	0	1	12

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0348 (Fisher's exact test), Q value = 0.077

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	21	71	957
subtype1	1	8	47	451
subtype2	0	13	24	491
subtype3	0	0	0	15

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.00083 (Fisher's exact test), Q value = 0.0024

Table S46. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	44	919
subtype1	9	440
subtype2	35	466
subtype3	0	13

Figure S41. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S47. Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	184	237	64	166

'METHLYATION CNMF' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S48. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	647	211	0.0 - 211.2 (18.5)
subtype1	183	119	0.2 - 211.2 (11.6)
subtype2	236	48	0.0 - 172.8 (24.6)
subtype3	64	24	0.1 - 146.1 (18.7)
subtype4	164	20	0.1 - 182.3 (21.8)

Figure S42. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.5e-46 (Kruskal-Wallis (anova)), Q value = 2.7e-45

Table S49. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	650	46.4 (14.9)
subtype1	184	59.3 (11.5)
subtype2	237	37.9 (11.0)
subtype3	64	44.2 (16.8)
subtype4	165	45.2 (12.4)

Figure S43. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S50. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	136	515
subtype1	118	66
subtype2	8	229
subtype3	10	54
subtype4	0	166

Figure S44. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.898 (Fisher's exact test), Q value = 0.97

Table S51. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	287	364
subtype1	79	105
subtype2	104	133
subtype3	31	33
subtype4	73	93

Figure S45. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S52. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	204	401
subtype1	25	143
subtype2	66	160
subtype3	25	32
subtype4	88	66

Figure S46. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 5.93e-11 (Kruskal-Wallis (anova)), Q value = 5e-10

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

	nPatients	Mean (Std.Dev)
ALL	402	84.5 (13.8)
subtype1	123	77.9 (15.1)
subtype2	151	87.7 (11.8)
subtype3	33	82.4 (13.5)
subtype4	95	88.6 (11.8)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S54. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	19	130	191	1	116
subtype1	42	14	10	14	1	103
subtype2	125	3	70	34	0	5
subtype3	21	2	15	18	0	8
subtype4	6	0	35	125	0	0

Figure S48. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 2e-04 (Fisher's exact test), Q value = 0.00065

Table S55. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	45	580
subtype1	0	2	25	150
subtype2	0	2	8	225
subtype3	1	0	6	55
subtype4	0	4	6	150

Figure S49. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.128 (Fisher's exact test), Q value = 0.22

Table S56. Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	33	542
subtype1	3	138
subtype2	14	207
subtype3	4	53
subtype4	12	144

Figure S50. Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S57. Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	229	207	222

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.151 (logrank test), Q value = 0.26

Table S58. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	654	265	0.0 - 211.2 (16.4)
subtype1	229	91	0.0 - 211.2 (15.5)
subtype2	204	66	0.1 - 154.4 (15.2)
subtype3	221	108	0.1 - 182.3 (17.8)

Figure S51. 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 = 0.041 (Kruskal-Wallis (anova)), Q value = 0.086

Table S59. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	657	48.6 (15.8)
subtype1	229	49.2 (15.5)
subtype2	207	46.4 (16.0)
subtype3	221	49.9 (15.8)

Figure S52. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 0.00239 (Fisher's exact test), Q value = 0.0065

Table S60. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	230	428
subtype1	91	138
subtype2	53	154
subtype3	86	136

Figure S53. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0357 (Fisher's exact test), Q value = 0.078

Table S61. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	282	376
subtype1	84	145
subtype2	90	117
subtype3	108	114

Figure S54. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0081 (Fisher's exact test), Q value = 0.02

Table S62. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	181	428
subtype1	59	149
subtype2	73	121
subtype3	49	158

Figure S55. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.278 (Kruskal-Wallis (anova)), Q value = 0.4

Table S63. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	415	81.3 (15.0)
subtype1	144	81.5 (15.6)
subtype2	123	82.5 (14.8)
subtype3	148	80.2 (14.6)

Figure S56. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00308 (Fisher's exact test), Q value = 0.0079

Table S64. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	147	17	114	167	3	210
subtype1	51	5	36	51	2	84
subtype2	38	4	47	69	0	49
subtype3	58	8	31	47	1	77

Figure S57. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.284 (Fisher's exact test), Q value = 0.4

Table S65. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	45	576
subtype1	0	3	18	202
subtype2	1	3	9	189
subtype3	0	6	18	185

Figure S58. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.745 (Fisher's exact test), Q value = 0.85

Table S66. Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	571
subtype1	12	198
subtype2	8	187
subtype3	9	186

Figure S59. Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S67. Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	239	256	163

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0528 (logrank test), Q value = 0.11

Table S68. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	654	265	0.0 - 211.2 (16.4)
subtype1	238	116	0.1 - 211.2 (15.9)
subtype2	256	98	0.0 - 182.3 (17.0)
subtype3	160	51	0.1 - 154.4 (16.1)

Figure S60. 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 = 0.00266 (Kruskal-Wallis (anova)), Q value = 0.007

Table S69. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	657	48.6 (15.8)
subtype1	239	49.9 (15.9)
subtype2	255	49.7 (15.7)
subtype3	163	44.8 (15.3)

Figure S61. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 8e-04 (Fisher's exact test), Q value = 0.0023

Table S70. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	230	428
subtype1	88	151
subtype2	104	152
subtype3	38	125

Figure S62. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.202 (Fisher's exact test), Q value = 0.32

Table S71. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	282	376
subtype1	104	135
subtype2	100	156
subtype3	78	85

Figure S63. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0114 (Fisher's exact test), Q value = 0.027

Table S72. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	181	428
subtype1	56	167
subtype2	66	170
subtype3	59	91

Figure S64. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.104 (Kruskal-Wallis (anova)), Q value = 0.19

Table S73. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	415	81.3 (15.0)
subtype1	146	79.2 (16.7)
subtype2	171	81.9 (13.8)
subtype3	98	83.7 (14.0)

Figure S65. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00011

Table S74. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	147	17	114	167	3	210
subtype1	61	6	33	57	2	80
subtype2	61	10	43	48	1	93
subtype3	25	1	38	62	0	37

Figure S66. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.357 (Fisher's exact test), Q value = 0.48

Table S75. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	45	576
subtype1	0	4	19	206
subtype2	0	5	20	222
subtype3	1	3	6	148

Figure S67. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.942 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	29	571
subtype1	10	208
subtype2	11	220
subtype3	8	143

Figure S68. Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S77. Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	239	235	193

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S78. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	664	239	0.0 - 211.2 (18.0)
subtype1	238	161	0.1 - 211.2 (12.6)
subtype2	234	39	0.0 - 182.3 (24.3)
subtype3	192	39	0.1 - 172.8 (21.8)

Figure S69. 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 = 3.33e-33 (Kruskal-Wallis (anova)), Q value = 4.7e-32

Table S79. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	666	46.8 (15.1)
subtype1	239	56.2 (14.3)
subtype2	235	39.6 (11.5)
subtype3	192	43.8 (14.1)

Figure S70. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S80. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	152	515
subtype1	147	92
subtype2	2	233
subtype3	3	190

Figure S71. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.294 (Fisher's exact test), Q value = 0.41

Table S81. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	384
subtype1	97	142
subtype2	95	140
subtype3	91	102

Figure S72. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S82. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	209	415
subtype1	37	185
subtype2	93	130
subtype3	79	100

Figure S73. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 1.49e-16 (Kruskal-Wallis (anova)), Q value = 1.7e-15

Table S83. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	424	83.6 (14.1)
subtype1	172	77.2 (14.7)
subtype2	143	89.3 (11.0)
subtype3	109	86.3 (12.5)

Figure S74. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S84. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	1	130	191	1	150
subtype1	64	0	14	14	1	146
subtype2	83	1	71	79	0	1
subtype3	47	0	45	98	0	3

Figure S75. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.38 (Fisher's exact test), Q value = 0.49

Table S85. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	13	31	611
subtype1	1	6	16	214
subtype2	0	4	7	220
subtype3	0	3	8	177

Figure S76. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.0431 (Fisher's exact test), Q value = 0.089

Table S86. Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	574
subtype1	6	205
subtype2	18	198
subtype3	11	171

Figure S77. Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S87. Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	208	190	269

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S88. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	664	239	0.0 - 211.2 (18.0)
subtype1	208	151	0.1 - 133.7 (11.5)
subtype2	189	39	0.0 - 211.2 (25.0)
subtype3	267	49	0.1 - 182.3 (21.4)

Figure S78. 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 = 4.19e-44 (Kruskal-Wallis (anova)), Q value = 6.6e-43

Table S89. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	666	46.8 (15.1)
subtype1	208	58.7 (12.9)
subtype2	190	38.3 (11.1)
subtype3	268	43.5 (13.5)

Figure S79. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'TUMOR_TISSUE_SITE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S90. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

nPatients	BRAIN	CENTRAL NERVOUS SYSTEM
ALL	152	515
subtype1	139	69
subtype2	9	181
subtype3	4	265

Figure S80. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'TUMOR_TISSUE_SITE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.362 (Fisher's exact test), Q value = 0.48

Table S91. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	283	384
subtype1	84	124
subtype2	76	114
subtype3	123	146

Figure S81. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S92. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	209	415
subtype1	34	158
subtype2	49	130
subtype3	126	127

Figure S82. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 5.38e-15 (Kruskal-Wallis (anova)), Q value = 5.6e-14

Table S93. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	424	83.6 (14.1)
subtype1	147	76.2 (15.1)
subtype2	125	87.7 (11.5)
subtype3	152	87.4 (11.9)

Figure S83. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S94. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	GLIOBLASTOMA MULTIFORME (GBM)	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA	TREATED PRIMARY GBM	UNTREATED PRIMARY (DE NOVO) GBM
ALL	194	1	130	191	1	150
subtype1	44	0	13	12	1	138
subtype2	100	1	52	29	0	8
subtype3	50	0	65	150	0	4

Figure S84. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.176 (Fisher's exact test), Q value = 0.3

Table S95. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	13	31	611
subtype1	1	4	16	185
subtype2	0	3	7	178
subtype3	0	6	8	248

Figure S85. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.242 (Fisher's exact test), Q value = 0.36

Table S96. Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	35	574
subtype1	6	174
subtype2	12	165
subtype3	17	235

Figure S86. Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S97. Description of clustering approach #11: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	137	106	185	83

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0123 (logrank test), Q value = 0.029

Table S98. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	508	120	0.0 - 211.2 (20.9)
subtype1	136	30	0.0 - 145.1 (20.0)
subtype2	106	38	0.1 - 211.2 (20.8)
subtype3	184	37	0.1 - 156.2 (21.0)
subtype4	82	15	0.1 - 182.3 (22.8)

Figure S87. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.917 (Kruskal-Wallis (anova)), Q value = 0.99

Table S99. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	137	42.4 (13.4)
subtype2	106	42.8 (13.1)
subtype3	184	43.2 (13.8)
subtype4	83	43.7 (13.1)

Figure S88. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.688 (Fisher's exact test), Q value = 0.79

Table S100. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	59	78
subtype2	44	62
subtype3	89	96
subtype4	38	45

Figure S89. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00231 (Fisher's exact test), Q value = 0.0065

Table S101. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	292
subtype1	44	81
subtype2	27	76
subtype3	73	99
subtype4	40	36

Figure S90. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0929 (Kruskal-Wallis (anova)), Q value = 0.17

Table S102. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.6 (12.6)
subtype1	83	86.4 (12.6)
subtype2	71	84.4 (12.7)
subtype3	104	87.1 (12.7)
subtype4	47	89.4 (11.9)

Figure S91. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S103. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	69	36	32
subtype2	65	22	19
subtype3	54	50	81
subtype4	5	19	59

Figure S92. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.273 (Fisher's exact test), Q value = 0.4

Table S104. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	1	1	10	122
subtype2	0	2	3	100
subtype3	0	2	6	174
subtype4	0	3	2	75

Figure S93. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.437 (Fisher's exact test), Q value = 0.55

Table S105. Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	8	119
subtype2	4	93
subtype3	12	165
subtype4	8	68

Figure S94. Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S106. Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	222	186	103

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 6.06e-14 (logrank test), Q value = 5.9e-13

Table S107. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	508	120	0.0 - 211.2 (20.9)
subtype1	220	40	0.0 - 182.3 (25.0)
subtype2	185	35	0.1 - 172.8 (22.6)
subtype3	103	45	0.1 - 211.2 (16.8)

Figure S95. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.78e-08 (Kruskal-Wallis (anova)), Q value = 1.4e-07

Table S108. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	222	40.0 (11.5)
subtype2	185	42.6 (13.8)
subtype3	103	50.0 (13.8)

Figure S96. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 0.4

Table S109. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	91	131
subtype2	90	96
subtype3	49	54

Figure S97. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S110. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	292
subtype1	91	118
subtype2	76	96
subtype3	17	78

Figure S98. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000222 (Kruskal-Wallis (anova)), Q value = 7e-04

Table S111. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.6 (12.6)
subtype1	139	88.8 (11.1)
subtype2	105	87.0 (12.5)
subtype3	61	80.8 (14.3)

Figure S99. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S112. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	76	67	79
subtype2	48	44	94
subtype3	69	16	18

Figure S100. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.561 (Fisher's exact test), Q value = 0.67

Table S113. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	0	3	7	208
subtype2	0	3	8	170
subtype3	1	2	6	93

Figure S101. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.231 (Fisher's exact test), Q value = 0.35

Table S114. Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	17	188
subtype2	12	164
subtype3	3	93

Figure S102. Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S115. Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	185	161	161

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

P value = 0.000268 (logrank test), Q value = 0.00082

Table S116. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	504	120	0.0 - 211.2 (20.9)
subtype1	185	61	0.0 - 182.3 (19.1)
subtype2	159	24	0.1 - 169.8 (25.0)
subtype3	160	35	0.1 - 211.2 (20.2)

Figure S103. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0547 (Kruskal-Wallis (anova)), Q value = 0.11

Table S117. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	185	44.4 (14.3)
subtype2	161	40.7 (11.7)
subtype3	160	43.8 (13.7)

Figure S104. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0323 (Fisher's exact test), Q value = 0.073

Table S118. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	70	115
subtype2	76	85
subtype3	83	78

Figure S105. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 5e-05 (Fisher's exact test), Q value = 0.00018

Table S119. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	290
subtype1	45	126
subtype2	72	78
subtype3	66	86

Figure S106. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.084

Table S120. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	104	85.0 (13.2)
subtype2	106	89.2 (10.8)
subtype3	94	85.6 (13.3)

Figure S107. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00011

Table S121. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	95	43	47
subtype2	50	41	70
subtype3	46	42	73

Figure S108. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.865 (Fisher's exact test), Q value = 0.95

Table S122. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	1	2	9	169
subtype2	0	4	6	147
subtype3	0	2	6	151

Figure S109. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.00072 (Fisher's exact test), Q value = 0.0022

Table S123. Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	6	165
subtype2	20	128
subtype3	6	148

Figure S110. Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S124. Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	90	138	190	89

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

P value = 0 (logrank test), Q value = 0

Table S125. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	504	120	0.0 - 211.2 (20.9)
subtype1	89	14	0.0 - 145.1 (23.7)
subtype2	137	22	0.1 - 172.8 (26.0)
subtype3	189	37	0.1 - 211.2 (22.3)
subtype4	89	47	0.1 - 133.7 (15.7)

Figure S111. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.6e-12 (Kruskal-Wallis (anova)), Q value = 1.4e-11

Table S126. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	90	36.9 (11.3)
subtype2	138	42.4 (12.0)
subtype3	189	42.0 (13.0)
subtype4	89	52.3 (13.8)

Figure S112. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.097 (Fisher's exact test), Q value = 0.18

Table S127. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	35	55
subtype2	54	84
subtype3	96	94
subtype4	44	45

Figure S113. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.00015 (Fisher's exact test), Q value = 0.00051

Table S128. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	290
subtype1	32	51
subtype2	60	72
subtype3	77	101
subtype4	14	66

Figure S114. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 5.43e-05 (Kruskal-Wallis (anova)), Q value = 0.00019

Table S129. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	56	87.3 (11.7)
subtype2	91	90.1 (10.5)
subtype3	104	86.8 (12.6)
subtype4	53	79.8 (14.3)

Figure S115. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.9e-05

Table S130. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	49	26	15
subtype2	36	37	65
subtype3	45	51	94
subtype4	61	12	16

Figure S116. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.205 (Fisher's exact test), Q value = 0.32

Table S131. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	0	0	2	86
subtype2	0	4	5	127
subtype3	0	2	7	177
subtype4	1	2	7	77

Figure S117. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.432 (Fisher's exact test), Q value = 0.55

Table S132. Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	7	73
subtype2	11	116
subtype3	11	172
subtype4	3	80

Figure S118. Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/GBMLGG-TP/20148947/GBMLGG-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBMLGG-TP/19775190/GBMLGG-TP.merged_data.txt
Number of patients = 1107
Number of clustering approaches = 14
Number of selected clinical features = 9
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.

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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