Correlation between aggregated molecular cancer subtypes and selected clinical features
Glioma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C1J38RR2
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J38RR2
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.

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)
[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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