Glioblastoma Multiforme: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 10 different clustering approaches and 6 clinical features across 564 patients, 8 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

  • 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 correlate to 'Time to Death' and 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 8 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.157 0.0257 0.497 0.839 0.259 0.821
mRNA cHierClus subtypes 0.056 0.0276 0.563 0.487 0.122 0.633
miR CNMF subtypes 0.000614 0.101 0.508 0.943 0.638 0.906
miR cHierClus subtypes 0.00455 0.000872 0.119 0.785 0.758 0.993
CN CNMF 0.00907 1.16e-06 0.488 0.871 0.1 0.393
METHLYATION CNMF 0.53 0.2 0.147 0.506 0.526 0.792
RPPA CNMF subtypes 0.379 0.285 0.188 0.418 0.614 0.638
RPPA cHierClus subtypes 0.787 0.457 0.528 0.438 0.303 0.772
RNAseq CNMF subtypes 0.295 0.205 0.0399 0.13 0.614 0.765
RNAseq cHierClus subtypes 0.0877 0.176 0.216 0.871 0.593 0.469
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 177 172 170
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 519 403 0.1 - 127.6 (9.9)
subtype1 177 145 0.2 - 127.6 (10.0)
subtype2 172 129 0.2 - 108.8 (9.2)
subtype3 170 129 0.1 - 92.6 (10.7)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.0257 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 519 57.7 (14.5)
subtype1 177 57.3 (12.8)
subtype2 172 55.8 (16.4)
subtype3 170 60.0 (13.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.497 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 204 315
subtype1 70 107
subtype2 62 110
subtype3 72 98

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

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.839 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 389 77.1 (14.4)
subtype1 137 77.5 (15.0)
subtype2 126 77.3 (13.0)
subtype3 126 76.5 (15.0)

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

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.259 (Fisher's exact test)

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

nPatients NO YES
ALL 348 171
subtype1 119 58
subtype2 108 64
subtype3 121 49

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.821 (Fisher's exact test)

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

nPatients NO YES
ALL 270 249
subtype1 94 83
subtype2 86 86
subtype3 90 80

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 223 127 169
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.056 (logrank test)

Table S9.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 519 403 0.1 - 127.6 (9.9)
subtype1 223 182 0.1 - 90.6 (10.4)
subtype2 127 94 0.1 - 92.6 (9.8)
subtype3 169 127 0.2 - 127.6 (9.4)

Figure S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0276 (ANOVA)

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 519 57.7 (14.5)
subtype1 223 57.7 (13.4)
subtype2 127 60.3 (13.8)
subtype3 169 55.7 (16.0)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.563 (Fisher's exact test)

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 204 315
subtype1 90 133
subtype2 53 74
subtype3 61 108

Figure S9.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.487 (ANOVA)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 389 77.1 (14.4)
subtype1 165 77.6 (14.4)
subtype2 98 75.6 (15.4)
subtype3 126 77.6 (13.5)

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.122 (Fisher's exact test)

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 348 171
subtype1 156 67
subtype2 89 38
subtype3 103 66

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.633 (Fisher's exact test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 270 249
subtype1 118 105
subtype2 69 58
subtype3 83 86

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15.  Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 144 159 74 105
'miR CNMF subtypes' versus 'Time to Death'

P value = 0.000614 (logrank test)

Table S16.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 482 379 0.1 - 127.6 (10.3)
subtype1 144 116 0.1 - 51.3 (10.6)
subtype2 159 124 0.1 - 127.6 (10.6)
subtype3 74 57 0.1 - 53.8 (8.4)
subtype4 105 82 0.1 - 92.6 (10.8)

Figure S13.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.101 (ANOVA)

Table S17.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 482 57.5 (14.6)
subtype1 144 59.7 (11.4)
subtype2 159 55.5 (17.0)
subtype3 74 57.9 (15.3)
subtype4 105 57.4 (13.7)

Figure S14.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.508 (Fisher's exact test)

Table S18.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 186 296
subtype1 56 88
subtype2 68 91
subtype3 26 48
subtype4 36 69

Figure S15.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.943 (ANOVA)

Table S19.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 368 77.6 (14.1)
subtype1 112 77.6 (14.4)
subtype2 114 77.5 (14.1)
subtype3 62 76.9 (14.4)
subtype4 80 78.4 (13.8)

Figure S16.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.638 (Fisher's exact test)

Table S20.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 327 155
subtype1 102 42
subtype2 108 51
subtype3 46 28
subtype4 71 34

Figure S17.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.906 (Fisher's exact test)

Table S21.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 248 234
subtype1 75 69
subtype2 78 81
subtype3 39 35
subtype4 56 49

Figure S18.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22.  Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 170 180 132
'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.00455 (logrank test)

Table S23.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 482 379 0.1 - 127.6 (10.3)
subtype1 170 137 0.1 - 92.6 (9.8)
subtype2 180 145 0.1 - 127.6 (10.0)
subtype3 132 97 0.1 - 108.8 (10.7)

Figure S19.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.000872 (ANOVA)

Table S24.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 482 57.5 (14.6)
subtype1 170 59.2 (12.6)
subtype2 180 58.9 (13.3)
subtype3 132 53.5 (17.6)

Figure S20.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.119 (Fisher's exact test)

Table S25.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 186 296
subtype1 58 112
subtype2 80 100
subtype3 48 84

Figure S21.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.785 (ANOVA)

Table S26.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 368 77.6 (14.1)
subtype1 129 78.3 (13.5)
subtype2 136 77.1 (15.5)
subtype3 103 77.4 (13.1)

Figure S22.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.758 (Fisher's exact test)

Table S27.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 327 155
subtype1 113 57
subtype2 121 59
subtype3 93 39

Figure S23.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.993 (Fisher's exact test)

Table S28.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 248 234
subtype1 88 82
subtype2 92 88
subtype3 68 64

Figure S24.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'CN CNMF'

Table S29.  Get Full Table Description of clustering approach #5: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 159 210 175
'CN CNMF' versus 'Time to Death'

P value = 0.00907 (logrank test)

Table S30.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 544 411 0.1 - 127.6 (9.6)
subtype1 159 125 0.1 - 127.6 (9.1)
subtype2 210 160 0.1 - 77.6 (9.9)
subtype3 175 126 0.1 - 108.8 (10.3)

Figure S25.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 1.16e-06 (ANOVA)

Table S31.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 544 57.8 (14.3)
subtype1 159 59.1 (14.7)
subtype2 210 60.6 (10.6)
subtype3 175 53.3 (16.6)

Figure S26.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.488 (Fisher's exact test)

Table S32.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 215 329
subtype1 68 91
subtype2 77 133
subtype3 70 105

Figure S27.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.871 (ANOVA)

Table S33.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 409 77.3 (14.7)
subtype1 113 76.8 (14.0)
subtype2 164 77.3 (15.7)
subtype3 132 77.8 (14.2)

Figure S28.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.1 (Fisher's exact test)

Table S34.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 369 175
subtype1 98 61
subtype2 144 66
subtype3 127 48

Figure S29.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.393 (Fisher's exact test)

Table S35.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 285 259
subtype1 76 83
subtype2 114 96
subtype3 95 80

Figure S30.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'METHLYATION CNMF'

Table S36.  Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 47 37 34
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.53 (logrank test)

Table S37.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 118 65 0.1 - 58.8 (7.7)
subtype1 47 28 0.1 - 47.9 (8.0)
subtype2 37 21 0.8 - 58.8 (6.6)
subtype3 34 16 1.2 - 31.3 (7.6)

Figure S31.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.2 (ANOVA)

Table S38.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 118 60.7 (11.5)
subtype1 47 62.9 (8.4)
subtype2 37 60.2 (13.2)
subtype3 34 58.3 (13.0)

Figure S32.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.147 (Fisher's exact test)

Table S39.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 52 66
subtype1 21 26
subtype2 12 25
subtype3 19 15

Figure S33.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.506 (ANOVA)

Table S40.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 89 77.3 (15.4)
subtype1 36 75.0 (15.9)
subtype2 27 79.3 (17.3)
subtype3 26 78.5 (12.6)

Figure S34.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.526 (Fisher's exact test)

Table S41.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 83 35
subtype1 30 17
subtype2 28 9
subtype3 25 9

Figure S35.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.792 (Fisher's exact test)

Table S42.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 72 46
subtype1 27 20
subtype2 24 13
subtype3 21 13

Figure S36.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S43.  Get Full Table Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 58 60 43 49
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.379 (logrank test)

Table S44.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 137 0.1 - 108.8 (7.9)
subtype1 58 42 0.1 - 53.2 (8.1)
subtype2 60 38 0.2 - 108.8 (6.5)
subtype3 43 27 0.1 - 43.2 (9.1)
subtype4 49 30 0.2 - 47.9 (6.3)

Figure S37.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA)

Table S45.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 210 59.9 (14.1)
subtype1 58 59.5 (13.6)
subtype2 60 57.4 (16.9)
subtype3 43 61.8 (12.1)
subtype4 49 62.0 (12.0)

Figure S38.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.188 (Fisher's exact test)

Table S46.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 85 125
subtype1 17 41
subtype2 26 34
subtype3 18 25
subtype4 24 25

Figure S39.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.418 (ANOVA)

Table S47.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 168 75.6 (15.3)
subtype1 47 75.1 (15.7)
subtype2 50 76.4 (16.5)
subtype3 31 78.7 (11.5)
subtype4 40 72.8 (15.8)

Figure S40.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.614 (Fisher's exact test)

Table S48.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 150 60
subtype1 40 18
subtype2 43 17
subtype3 34 9
subtype4 33 16

Figure S41.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.638 (Fisher's exact test)

Table S49.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 118 92
subtype1 29 29
subtype2 37 23
subtype3 25 18
subtype4 27 22

Figure S42.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S50.  Get Full Table Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 97 72 41
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.787 (logrank test)

Table S51.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 210 137 0.1 - 108.8 (7.9)
subtype1 97 62 0.2 - 108.8 (6.5)
subtype2 72 45 0.1 - 47.9 (8.7)
subtype3 41 30 1.2 - 53.2 (8.3)

Figure S43.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.457 (ANOVA)

Table S52.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 210 59.9 (14.1)
subtype1 97 59.9 (15.6)
subtype2 72 61.2 (12.1)
subtype3 41 57.8 (13.6)

Figure S44.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.528 (Fisher's exact test)

Table S53.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 85 125
subtype1 43 54
subtype2 28 44
subtype3 14 27

Figure S45.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.438 (ANOVA)

Table S54.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 168 75.6 (15.3)
subtype1 78 74.5 (14.9)
subtype2 57 77.7 (15.0)
subtype3 33 74.5 (16.8)

Figure S46.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.303 (Fisher's exact test)

Table S55.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 150 60
subtype1 65 32
subtype2 56 16
subtype3 29 12

Figure S47.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.772 (Fisher's exact test)

Table S56.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 118 92
subtype1 56 41
subtype2 41 31
subtype3 21 20

Figure S48.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S57.  Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 47 77 35
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.295 (logrank test)

Table S58.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 159 106 0.2 - 54.0 (8.8)
subtype1 47 32 0.2 - 54.0 (8.9)
subtype2 77 50 0.4 - 47.9 (9.0)
subtype3 35 24 0.2 - 31.3 (8.5)

Figure S49.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.205 (ANOVA)

Table S59.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 159 59.8 (13.2)
subtype1 47 57.0 (15.5)
subtype2 77 61.3 (11.2)
subtype3 35 60.1 (13.7)

Figure S50.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0399 (Fisher's exact test)

Table S60.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 56 103
subtype1 15 32
subtype2 34 43
subtype3 7 28

Figure S51.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.13 (ANOVA)

Table S61.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 119 76.3 (14.6)
subtype1 36 73.6 (15.3)
subtype2 58 75.9 (14.0)
subtype3 25 81.2 (14.2)

Figure S52.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.614 (Fisher's exact test)

Table S62.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 103 56
subtype1 33 14
subtype2 49 28
subtype3 21 14

Figure S53.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.765 (Fisher's exact test)

Table S63.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 80 79
subtype1 22 25
subtype2 41 36
subtype3 17 18

Figure S54.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S64.  Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 33 38 23 65
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0877 (logrank test)

Table S65.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 159 106 0.2 - 54.0 (8.8)
subtype1 33 21 0.2 - 54.0 (10.6)
subtype2 38 27 0.2 - 32.0 (5.7)
subtype3 23 14 1.2 - 33.1 (7.3)
subtype4 65 44 0.4 - 47.9 (9.0)

Figure S55.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.176 (ANOVA)

Table S66.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 159 59.8 (13.2)
subtype1 33 55.3 (17.1)
subtype2 38 60.3 (12.9)
subtype3 23 61.7 (10.9)
subtype4 65 61.0 (11.6)

Figure S56.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.216 (Fisher's exact test)

Table S67.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 56 103
subtype1 10 23
subtype2 9 29
subtype3 9 14
subtype4 28 37

Figure S57.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.871 (ANOVA)

Table S68.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 119 76.3 (14.6)
subtype1 25 76.4 (11.9)
subtype2 28 78.2 (16.3)
subtype3 17 75.9 (17.0)
subtype4 49 75.3 (14.3)

Figure S58.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.593 (Fisher's exact test)

Table S69.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 103 56
subtype1 24 9
subtype2 22 16
subtype3 16 7
subtype4 41 24

Figure S59.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.469 (Fisher's exact test)

Table S70.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 80 79
subtype1 16 17
subtype2 19 19
subtype3 15 8
subtype4 30 35

Figure S60.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = GBM.mergedcluster.txt

  • Clinical data file = GBM.clin.merged.picked.txt

  • Number of patients = 564

  • Number of clustering approaches = 10

  • Number of selected clinical features = 6

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] 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)
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