Correlation between aggregated molecular cancer subtypes and selected clinical features
Glioma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1M044FN
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/C1M044FN
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 12 different clustering approaches and 9 clinical features across 1066 patients, 48 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',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • 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',  'PRIMARY_SITE_OF_DISEASE',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION',  'RACE', and 'ETHNICITY'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PRIMARY_SITE_OF_DISEASE',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PRIMARY_SITE_OF_DISEASE',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'PRIMARY_SITE_OF_DISEASE',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  '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',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 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, 48 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		PRIMARY
SITE
OF
DISEASE 		GENDER KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.0641
(0.136) 		0.0788
(0.161) 		0.397
(0.579) 		0.6
(0.771) 		0.00404
(0.0112) 		0.0682
(0.142) 		0.833
(0.947) 		0.125
(0.237)
mRNA cHierClus subtypes 0.000134
(0.000438) 		0.00252
(0.00716) 		0.0898
(0.18) 		0.509
(0.696) 		0.0296
(0.0712) 		0.0134
(0.0338) 		0.588
(0.765) 		0.683
(0.828)
miR CNMF subtypes 0.0042
(0.0113) 		0.38
(0.562) 		0.649
(0.819) 		0.882
(0.982) 		0.362
(0.543) 		0.418
(0.586) 		0.205
(0.349) 		0.855
(0.962)
miR cHierClus subtypes 0.165
(0.293) 		0.127
(0.237) 		0.151
(0.276) 		0.571
(0.752) 		0.284
(0.457) 		0.977
(1.00) 		0.193
(0.336) 		0.675
(0.828)
Copy Number Ratio CNMF subtypes 0
(0) 		1.62e-72
(2.91e-71) 		1e-05
(3.48e-05) 		0.336
(0.518) 		3.59e-19
(3.87e-18) 		1e-05
(3.48e-05) 		1e-05
(3.48e-05) 		0.013
(0.0334) 		0.0419
(0.0944)
METHLYATION CNMF 0
(0) 		2.12e-43
(3.27e-42) 		1e-05
(3.48e-05) 		0.917
(1.00) 		1.14e-10
(8.21e-10) 		1e-05
(3.48e-05) 		1e-05
(3.48e-05) 		0.00023
(0.00071) 		0.663
(0.823)
RNAseq CNMF subtypes 0
(0) 		1.2e-31
(1.44e-30) 		1e-05
(3.48e-05) 		0.207
(0.349) 		7.94e-19
(7.8e-18) 		1e-05
(3.48e-05) 		1e-05
(3.48e-05) 		0.347
(0.528) 		0.505
(0.696)
RNAseq cHierClus subtypes 0
(0) 		1.69e-41
(2.29e-40) 		1e-05
(3.48e-05) 		0.256
(0.418) 		1.05e-15
(9.44e-15) 		1e-05
(3.48e-05) 		1e-05
(3.48e-05) 		0.126
(0.237) 		0.818
(0.947)
MIRSEQ CNMF 0.042
(0.0944) 		0.652
(0.819) 		0.707
(0.839) 		0.0232
(0.057) 		1e-05
(3.48e-05) 		0.316
(0.494) 		0.106
(0.209) 		0.825
(0.947)
MIRSEQ CHIERARCHICAL 1.14e-11
(9.47e-11) 		8.83e-08
(5.96e-07) 		0.231
(0.384) 		0.000327
(0.000982) 		1e-05
(3.48e-05) 		0.411
(0.585) 		0.559
(0.745) 		1
(1.00)
MIRseq Mature CNMF subtypes 0.000731
(0.00213) 		0.163
(0.293) 		0.0321
(0.0755) 		0.0535
(0.118) 		8e-05
(0.00027) 		0.00651
(0.0171) 		0.739
(0.867) 		0.408
(0.585)
MIRseq Mature cHierClus subtypes 0
(0) 		1.51e-11
(1.17e-10) 		0.0626
(0.135) 		0.000172
(0.000546) 		1e-05
(3.48e-05) 		0.702
(0.839) 		0.3
(0.477) 		0.526
(0.71)
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 = 0.0641 (logrank test), Q value = 0.14

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

nPatients nDeath Duration Range (Median), Month
ALL 525 446 0.1 - 127.6 (12.2)
subtype1 154 138 0.1 - 127.6 (10.7)
subtype2 97 80 0.2 - 115.9 (14.2)
subtype3 156 129 0.1 - 94.8 (13.8)
subtype4 118 99 0.2 - 91.8 (12.5)

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 = 0.16

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 = 0.58

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.6 (Kruskal-Wallis (anova)), Q value = 0.77

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

nPatients Mean (Std.Dev)
ALL 392 77.2 (14.4)
subtype1 114 78.0 (15.5)
subtype2 75 76.5 (11.0)
subtype3 119 76.5 (15.2)
subtype4 84 77.9 (14.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00404 (Fisher's exact test), Q value = 0.011

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

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

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

'mRNA CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0682 (Fisher's exact test), Q value = 0.14

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

nPatients NO YES
ALL 360 165
subtype1 106 48
subtype2 68 29
subtype3 116 40
subtype4 70 48

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.833 (Fisher's exact test), Q value = 0.95

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 = 0.125 (Fisher's exact test), Q value = 0.24

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 = 0.000134 (logrank test), Q value = 0.00044

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

nPatients nDeath Duration Range (Median), Month
ALL 525 446 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 162 141 0.1 - 127.6 (10.3)

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 = 0.0072

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 = 0.0898 (Fisher's exact test), Q value = 0.18

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.509 (Kruskal-Wallis (anova)), Q value = 0.7

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

nPatients Mean (Std.Dev)
ALL 392 77.2 (14.4)
subtype1 117 78.5 (14.9)
subtype2 83 77.6 (11.4)
subtype3 81 75.4 (15.5)
subtype4 111 76.9 (14.9)

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0296 (Fisher's exact test), Q value = 0.071

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

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

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.0134 (Fisher's exact test), Q value = 0.034

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

nPatients NO YES
ALL 360 165
subtype1 114 39
subtype2 78 29
subtype3 73 30
subtype4 95 67

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.588 (Fisher's exact test), Q value = 0.77

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 = 0.683 (Fisher's exact test), Q value = 0.83

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 = 0.0042 (logrank test), Q value = 0.011

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

nPatients nDeath Duration Range (Median), Month
ALL 561 462 0.1 - 127.6 (11.9)
subtype1 165 140 0.1 - 91.0 (11.3)
subtype2 182 147 0.1 - 127.6 (13.3)
subtype3 90 72 0.4 - 65.3 (9.1)
subtype4 124 103 0.1 - 120.6 (12.6)

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 = 0.56

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 = 0.649 (Fisher's exact test), Q value = 0.82

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.882 (Kruskal-Wallis (anova)), Q value = 0.98

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

nPatients Mean (Std.Dev)
ALL 424 77.5 (14.5)
subtype1 127 76.6 (15.3)
subtype2 131 77.3 (14.6)
subtype3 73 78.2 (13.6)
subtype4 93 78.4 (14.2)

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

'miR CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.362 (Fisher's exact test), Q value = 0.54

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

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

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

'miR CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.418 (Fisher's exact test), Q value = 0.59

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

nPatients NO YES
ALL 389 172
subtype1 118 47
subtype2 131 51
subtype3 57 33
subtype4 83 41

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

'miR CNMF subtypes' versus 'RACE'

P value = 0.205 (Fisher's exact test), Q value = 0.35

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 = 0.96

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 = 0.165 (logrank test), Q value = 0.29

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

nPatients nDeath Duration Range (Median), Month
ALL 561 462 0.1 - 127.6 (11.9)
subtype1 302 249 0.1 - 120.6 (12.5)
subtype2 129 106 0.1 - 92.7 (10.6)
subtype3 130 107 0.1 - 127.6 (12.0)

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 = 0.24

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 = 0.151 (Fisher's exact test), Q value = 0.28

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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.571 (Kruskal-Wallis (anova)), Q value = 0.75

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

nPatients Mean (Std.Dev)
ALL 424 77.5 (14.5)
subtype1 225 78.4 (13.6)
subtype2 100 76.9 (15.4)
subtype3 99 76.0 (15.6)

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

'miR cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.284 (Fisher's exact test), Q value = 0.46

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

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

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

'miR cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.977 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 389 172
subtype1 208 94
subtype2 90 39
subtype3 91 39

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

'miR cHierClus subtypes' versus 'RACE'

P value = 0.193 (Fisher's exact test), Q value = 0.34

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 = 0.675 (Fisher's exact test), Q value = 0.83

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 520 507 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 1039 559 0.0 - 211.2 (14.8)
subtype1 519 416 0.1 - 127.6 (11.7)
subtype2 505 132 0.0 - 211.2 (19.0)
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 = 1.62e-72 (Kruskal-Wallis (anova)), Q value = 2.9e-71

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

nPatients Mean (Std.Dev)
ALL 1041 51.1 (15.8)
subtype1 520 59.8 (12.0)
subtype2 506 42.1 (14.2)
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 'PRIMARY_SITE_OF_DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients BRAIN CENTRAL NERVOUS SYSTEM
ALL 570 472
subtype1 465 55
subtype2 92 415
subtype3 13 2

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.336 (Fisher's exact test), Q value = 0.52

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

nPatients FEMALE MALE
ALL 430 612
subtype1 203 317
subtype2 221 286
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 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 3.59e-19 (Kruskal-Wallis (anova)), Q value = 3.9e-18

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

nPatients Mean (Std.Dev)
ALL 689 81.5 (14.6)
subtype1 375 77.2 (15.2)
subtype2 303 86.8 (11.8)
subtype3 11 82.7 (14.2)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients ASTROCYTOMA GLIOBLASTOMA MULTIFORME (GBM) OLIGOASTROCYTOMA OLIGODENDROGLIOMA TREATED PRIMARY GBM UNTREATED PRIMARY (DE NOVO) GBM
ALL 176 27 118 178 18 525
subtype1 34 18 11 10 10 437
subtype2 140 9 107 168 7 76
subtype3 2 0 0 0 1 12

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients NO YES
ALL 474 568
subtype1 319 201
subtype2 146 361
subtype3 9 6

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.013 (Fisher's exact test), Q value = 0.033

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 64 922
subtype1 1 8 45 444
subtype2 0 13 19 463
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 = 0.0419 (Fisher's exact test), Q value = 0.094

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 30 891
subtype1 8 432
subtype2 22 446
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 176 219 61 154
'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 607 180 0.0 - 211.2 (16.3)
subtype1 175 104 0.2 - 211.2 (10.7)
subtype2 218 37 0.0 - 172.8 (20.4)
subtype3 61 21 0.1 - 133.7 (15.3)
subtype4 153 18 0.1 - 182.3 (19.9)

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 = 2.12e-43 (Kruskal-Wallis (anova)), Q value = 3.3e-42

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

nPatients Mean (Std.Dev)
ALL 609 46.7 (15.0)
subtype1 176 59.4 (11.7)
subtype2 219 38.0 (11.3)
subtype3 61 44.4 (16.8)
subtype4 153 45.5 (12.3)

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

'METHLYATION CNMF' versus 'PRIMARY_SITE_OF_DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients BRAIN CENTRAL NERVOUS SYSTEM
ALL 135 475
subtype1 117 59
subtype2 8 211
subtype3 10 51
subtype4 0 154

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.917 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 265 345
subtype1 76 100
subtype2 93 126
subtype3 29 32
subtype4 67 87

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 1.14e-10 (Kruskal-Wallis (anova)), Q value = 8.2e-10

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

nPatients Mean (Std.Dev)
ALL 365 85.2 (13.6)
subtype1 117 78.8 (15.1)
subtype2 134 89.0 (11.4)
subtype3 29 82.4 (12.4)
subtype4 85 88.7 (11.7)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients ASTROCYTOMA GLIOBLASTOMA MULTIFORME (GBM) OLIGOASTROCYTOMA OLIGODENDROGLIOMA TREATED PRIMARY GBM UNTREATED PRIMARY (DE NOVO) GBM
ALL 176 18 119 180 1 116
subtype1 37 13 10 12 1 103
subtype2 114 3 65 32 0 5
subtype3 20 2 14 17 0 8
subtype4 5 0 30 119 0 0

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

'METHLYATION CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

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

nPatients NO YES
ALL 177 433
subtype1 89 87
subtype2 56 163
subtype3 12 49
subtype4 20 134

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00023 (Fisher's exact test), Q value = 0.00071

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 38 545
subtype1 0 2 23 144
subtype2 0 2 7 207
subtype3 1 0 4 54
subtype4 0 4 4 140

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.663 (Fisher's exact test), Q value = 0.82

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 19 514
subtype1 3 130
subtype2 8 196
subtype3 3 51
subtype4 5 137

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 230 216 181
'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

nPatients nDeath Duration Range (Median), Month
ALL 625 212 0.0 - 211.2 (16.2)
subtype1 229 150 0.1 - 211.2 (12.2)
subtype2 216 33 0.0 - 182.3 (20.8)
subtype3 180 29 0.1 - 172.8 (20.0)

Figure S51.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.2e-31 (Kruskal-Wallis (anova)), Q value = 1.4e-30

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

nPatients Mean (Std.Dev)
ALL 626 47.1 (15.3)
subtype1 230 56.4 (14.4)
subtype2 216 39.6 (11.6)
subtype3 180 44.1 (14.2)

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

'RNAseq CNMF subtypes' versus 'PRIMARY_SITE_OF_DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S60.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY_SITE_OF_DISEASE'

nPatients BRAIN CENTRAL NERVOUS SYSTEM
ALL 152 475
subtype1 147 83
subtype2 2 214
subtype3 3 178

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.207 (Fisher's exact test), Q value = 0.35

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

nPatients FEMALE MALE
ALL 261 366
subtype1 93 137
subtype2 83 133
subtype3 85 96

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 7.94e-19 (Kruskal-Wallis (anova)), Q value = 7.8e-18

Table S62.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 383 84.2 (13.8)
subtype1 160 77.6 (14.2)
subtype2 126 90.9 (10.0)
subtype3 97 86.3 (12.8)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S63.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA GLIOBLASTOMA MULTIFORME (GBM) OLIGOASTROCYTOMA OLIGODENDROGLIOMA TREATED PRIMARY GBM UNTREATED PRIMARY (DE NOVO) GBM
ALL 176 1 119 180 1 150
subtype1 57 0 13 13 1 146
subtype2 77 1 65 72 0 1
subtype3 42 0 41 95 0 3

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

'RNAseq CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S64.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 194 433
subtype1 113 117
subtype2 51 165
subtype3 30 151

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.347 (Fisher's exact test), Q value = 0.53

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 13 25 576
subtype1 1 6 14 207
subtype2 0 4 5 202
subtype3 0 3 6 167

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.505 (Fisher's exact test), Q value = 0.7

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 21 547
subtype1 5 197
subtype2 9 188
subtype3 7 162

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 202 176 249
'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

nPatients nDeath Duration Range (Median), Month
ALL 625 212 0.0 - 211.2 (16.2)
subtype1 202 143 0.1 - 133.7 (11.1)
subtype2 175 31 0.0 - 211.2 (20.8)
subtype3 248 38 0.1 - 182.3 (19.7)

Figure S60.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.69e-41 (Kruskal-Wallis (anova)), Q value = 2.3e-40

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

nPatients Mean (Std.Dev)
ALL 626 47.1 (15.3)
subtype1 202 58.8 (13.0)
subtype2 176 38.3 (11.3)
subtype3 248 43.8 (13.5)

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

'RNAseq cHierClus subtypes' versus 'PRIMARY_SITE_OF_DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S70.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY_SITE_OF_DISEASE'

nPatients BRAIN CENTRAL NERVOUS SYSTEM
ALL 152 475
subtype1 139 63
subtype2 9 167
subtype3 4 245

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.256 (Fisher's exact test), Q value = 0.42

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

nPatients FEMALE MALE
ALL 261 366
subtype1 82 120
subtype2 66 110
subtype3 113 136

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

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 1.05e-15 (Kruskal-Wallis (anova)), Q value = 9.4e-15

Table S72.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 383 84.2 (13.8)
subtype1 139 77.0 (14.6)
subtype2 108 89.3 (10.8)
subtype3 136 87.5 (12.1)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S73.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA GLIOBLASTOMA MULTIFORME (GBM) OLIGOASTROCYTOMA OLIGODENDROGLIOMA TREATED PRIMARY GBM UNTREATED PRIMARY (DE NOVO) GBM
ALL 176 1 119 180 1 150
subtype1 40 0 12 11 1 138
subtype2 92 1 48 27 0 8
subtype3 44 0 59 142 0 4

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

'RNAseq cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S74.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 194 433
subtype1 103 99
subtype2 51 125
subtype3 40 209

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.126 (Fisher's exact test), Q value = 0.24

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 13 25 576
subtype1 1 4 14 181
subtype2 0 3 6 164
subtype3 0 6 5 231

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.818 (Fisher's exact test), Q value = 0.95

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 21 547
subtype1 5 169
subtype2 7 157
subtype3 9 221

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 124 103 172 72
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.042 (logrank test), Q value = 0.094

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

nPatients nDeath Duration Range (Median), Month
ALL 469 93 0.0 - 211.2 (18.7)
subtype1 123 23 0.0 - 122.5 (18.6)
subtype2 103 32 0.1 - 211.2 (19.6)
subtype3 171 26 0.1 - 156.2 (17.4)
subtype4 72 12 0.1 - 182.3 (20.8)

Figure S69.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.652 (Kruskal-Wallis (anova)), Q value = 0.82

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

nPatients Mean (Std.Dev)
ALL 470 43.0 (13.5)
subtype1 124 42.0 (13.5)
subtype2 103 42.8 (13.2)
subtype3 171 43.4 (13.8)
subtype4 72 44.2 (13.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.707 (Fisher's exact test), Q value = 0.84

Table S80.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 208 263
subtype1 51 73
subtype2 43 60
subtype3 81 91
subtype4 33 39

Figure S71.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0232 (Kruskal-Wallis (anova)), Q value = 0.057

Table S81.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 268 87.7 (12.0)
subtype1 70 89.9 (10.4)
subtype2 66 84.7 (12.6)
subtype3 93 87.5 (12.4)
subtype4 39 89.5 (12.1)

Figure S72.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S82.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 175 116 180
subtype1 61 33 30
subtype2 62 22 19
subtype3 48 46 78
subtype4 4 15 53

Figure S73.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.316 (Fisher's exact test), Q value = 0.49

Table S83.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 91 380
subtype1 26 98
subtype2 25 78
subtype3 30 142
subtype4 10 62

Figure S74.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.106 (Fisher's exact test), Q value = 0.21

Table S84.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 436
subtype1 1 1 8 111
subtype2 0 2 2 97
subtype3 0 2 5 162
subtype4 0 3 0 66

Figure S75.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.825 (Fisher's exact test), Q value = 0.95

Table S85.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 418
subtype1 5 110
subtype2 4 90
subtype3 8 156
subtype4 1 62

Figure S76.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S86.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 202 172 97
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 1.14e-11 (logrank test), Q value = 9.5e-11

Table S87.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 469 93 0.0 - 211.2 (18.7)
subtype1 201 34 0.0 - 182.3 (23.2)
subtype2 171 23 0.1 - 172.8 (18.9)
subtype3 97 36 0.1 - 211.2 (14.5)

Figure S77.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 8.83e-08 (Kruskal-Wallis (anova)), Q value = 6e-07

Table S88.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 470 43.0 (13.5)
subtype1 202 40.0 (11.7)
subtype2 171 42.7 (13.8)
subtype3 97 49.9 (14.0)

Figure S78.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.231 (Fisher's exact test), Q value = 0.38

Table S89.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 208 263
subtype1 80 122
subtype2 82 90
subtype3 46 51

Figure S79.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000327 (Kruskal-Wallis (anova)), Q value = 0.00098

Table S90.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 268 87.7 (12.0)
subtype1 122 90.2 (10.3)
subtype2 92 87.4 (12.7)
subtype3 54 82.8 (12.9)

Figure S80.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S91.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 175 116 180
subtype1 68 60 74
subtype2 43 40 89
subtype3 64 16 17

Figure S81.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.411 (Fisher's exact test), Q value = 0.58

Table S92.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 91 380
subtype1 44 158
subtype2 28 144
subtype3 19 78

Figure S82.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.559 (Fisher's exact test), Q value = 0.74

Table S93.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 436
subtype1 0 3 5 189
subtype2 0 3 5 159
subtype3 1 2 5 88

Figure S83.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S94.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 418
subtype1 8 177
subtype2 7 154
subtype3 3 87

Figure S84.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S95.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 173 143 152
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.000731 (logrank test), Q value = 0.0021

Table S96.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 466 93 0.0 - 211.2 (18.7)
subtype1 173 50 0.0 - 182.3 (17.6)
subtype2 142 18 0.1 - 169.8 (21.8)
subtype3 151 25 0.1 - 211.2 (17.9)

Figure S85.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.163 (Kruskal-Wallis (anova)), Q value = 0.29

Table S97.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 467 43.1 (13.5)
subtype1 173 44.1 (14.4)
subtype2 143 41.0 (11.8)
subtype3 151 43.9 (13.8)

Figure S86.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0321 (Fisher's exact test), Q value = 0.075

Table S98.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 208 260
subtype1 64 109
subtype2 66 77
subtype3 78 74

Figure S87.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0535 (Kruskal-Wallis (anova)), Q value = 0.12

Table S99.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 267 87.8 (12.0)
subtype1 89 87.9 (11.2)
subtype2 93 89.9 (10.9)
subtype3 85 85.4 (13.5)

Figure S88.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 8e-05 (Fisher's exact test), Q value = 0.00027

Table S100.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 174 115 179
subtype1 86 42 45
subtype2 46 34 63
subtype3 42 39 71

Figure S89.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.00651 (Fisher's exact test), Q value = 0.017

Table S101.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 91 377
subtype1 46 127
subtype2 18 125
subtype3 27 125

Figure S90.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.739 (Fisher's exact test), Q value = 0.87

Table S102.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 433
subtype1 1 2 7 159
subtype2 0 4 3 131
subtype3 0 2 5 143

Figure S91.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.408 (Fisher's exact test), Q value = 0.58

Table S103.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 415
subtype1 5 155
subtype2 8 120
subtype3 5 140

Figure S92.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S104.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 83 125 178 82
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

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

Table S105.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 466 93 0.0 - 211.2 (18.7)
subtype1 82 12 0.0 - 122.5 (22.1)
subtype2 125 16 0.1 - 172.8 (21.4)
subtype3 177 27 0.1 - 211.2 (19.6)
subtype4 82 38 0.1 - 133.7 (13.2)

Figure S93.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.51e-11 (Kruskal-Wallis (anova)), Q value = 1.2e-10

Table S106.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 467 43.1 (13.5)
subtype1 83 36.6 (11.4)
subtype2 125 42.7 (12.0)
subtype3 177 42.1 (13.0)
subtype4 82 52.2 (14.1)

Figure S94.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0626 (Fisher's exact test), Q value = 0.14

Table S107.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

nPatients FEMALE MALE
ALL 208 260
subtype1 31 52
subtype2 47 78
subtype3 89 89
subtype4 41 41

Figure S95.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000172 (Kruskal-Wallis (anova)), Q value = 0.00055

Table S108.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

nPatients Mean (Std.Dev)
ALL 267 87.8 (12.0)
subtype1 48 90.4 (9.9)
subtype2 81 90.5 (10.2)
subtype3 93 87.0 (12.8)
subtype4 45 81.8 (13.0)

Figure S96.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.5e-05

Table S109.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 174 115 179
subtype1 45 24 14
subtype2 33 33 59
subtype3 40 47 91
subtype4 56 11 15

Figure S97.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS_RADIATION_REGIMENINDICATION'

P value = 0.702 (Fisher's exact test), Q value = 0.84

Table S110.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

nPatients NO YES
ALL 91 377
subtype1 18 65
subtype2 20 105
subtype3 37 141
subtype4 16 66

Figure S98.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS_RADIATION_REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.3 (Fisher's exact test), Q value = 0.48

Table S111.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 433
subtype1 0 0 2 79
subtype2 0 4 3 116
subtype3 0 2 5 166
subtype4 1 2 5 72

Figure S99.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.526 (Fisher's exact test), Q value = 0.71

Table S112.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 415
subtype1 5 69
subtype2 3 109
subtype3 8 163
subtype4 2 74

Figure S100.  Get High-res Image Clustering Approach #12: '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/15249296/GBMLGG-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/GBMLGG-TP/15078950/GBMLGG-TP.merged_data.txt

  • Number of patients = 1066

  • Number of clustering approaches = 12

  • 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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle