Glioblastoma Multiforme: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 5 clinical features across 564 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on array-based miR expression data identified 4 subtypes that do not correlate to any clinical features.

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

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

  • 4 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 6 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 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 5 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.

Clinical
Features
Time
to
Death
AGE GENDER KARNOFSKY
PERFORMANCE
SCORE
RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test ANOVA Chi-square test ANOVA Chi-square test
mRNA CNMF subtypes 0.367
(1.00)
0.0257
(1.00)
0.497
(1.00)
0.839
(1.00)
0.259
(1.00)
mRNA cHierClus subtypes 0.161
(1.00)
0.0276
(1.00)
0.563
(1.00)
0.487
(1.00)
0.122
(1.00)
miR CNMF subtypes 0.00835
(0.401)
0.101
(1.00)
0.508
(1.00)
0.943
(1.00)
0.638
(1.00)
miR cHierClus subtypes 0.0178
(0.839)
0.000872
(0.0427)
0.119
(1.00)
0.785
(1.00)
0.758
(1.00)
CN CNMF 0.0332
(1.00)
1.16e-06
(5.8e-05)
0.488
(1.00)
0.871
(1.00)
0.1
(1.00)
METHLYATION CNMF 0.619
(1.00)
0.148
(1.00)
0.125
(1.00)
0.945
(1.00)
0.549
(1.00)
RPPA CNMF subtypes 0.856
(1.00)
0.285
(1.00)
0.188
(1.00)
0.418
(1.00)
0.614
(1.00)
RPPA cHierClus subtypes 0.964
(1.00)
0.457
(1.00)
0.528
(1.00)
0.438
(1.00)
0.303
(1.00)
RNAseq CNMF subtypes 0.371
(1.00)
0.15
(1.00)
0.307
(1.00)
0.755
(1.00)
0.19
(1.00)
RNAseq cHierClus subtypes 0.35
(1.00)
0.349
(1.00)
0.0251
(1.00)
0.469
(1.00)
0.957
(1.00)
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 = 0.367 (logrank test), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S7.  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 = 0.161 (logrank test), Q value = 1

Table S8.  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 S6.  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), Q value = 1

Table S9.  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 S7.  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), Q value = 1

Table S10.  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 S8.  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), Q value = 1

Table S11.  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 S9.  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), Q value = 1

Table S12.  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 S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'miR CNMF subtypes'

Table S13.  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 = 0.00835 (logrank test), Q value = 0.4

Table S14.  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 S11.  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), Q value = 1

Table S15.  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 S12.  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), Q value = 1

Table S16.  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 S13.  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), Q value = 1

Table S17.  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 S14.  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), Q value = 1

Table S18.  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 S15.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S19.  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 = 0.0178 (logrank test), Q value = 0.84

Table S20.  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 S16.  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), Q value = 0.043

Table S21.  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 S17.  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), Q value = 1

Table S22.  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 S18.  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), Q value = 1

Table S23.  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 S19.  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), Q value = 1

Table S24.  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 S20.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'CN CNMF'

Table S25.  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 = 0.0332 (logrank test), Q value = 1

Table S26.  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 S21.  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), Q value = 5.8e-05

Table S27.  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 S22.  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), Q value = 1

Table S28.  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 S23.  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), Q value = 1

Table S29.  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 S24.  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), Q value = 1

Table S30.  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 S25.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'METHLYATION CNMF'

Table S31.  Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 34 19 26 27
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.619 (logrank test), Q value = 1

Table S32.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 106 53 0.1 - 58.8 (6.9)
subtype1 34 17 0.8 - 58.8 (5.4)
subtype2 19 8 1.6 - 20.9 (8.3)
subtype3 26 17 0.1 - 47.9 (7.9)
subtype4 27 11 1.4 - 31.3 (7.5)

Figure S26.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.148 (ANOVA), Q value = 1

Table S33.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 106 61.2 (11.5)
subtype1 34 61.3 (14.1)
subtype2 19 64.8 (8.0)
subtype3 26 62.5 (8.5)
subtype4 27 57.3 (11.8)

Figure S27.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.125 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 47 59
subtype1 13 21
subtype2 5 14
subtype3 13 13
subtype4 16 11

Figure S28.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.945 (ANOVA), Q value = 1

Table S35.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 80 76.5 (15.2)
subtype1 23 77.0 (17.9)
subtype2 13 76.2 (16.6)
subtype3 22 75.0 (15.0)
subtype4 22 77.7 (11.9)

Figure S29.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.549 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 74 32
subtype1 26 8
subtype2 12 7
subtype3 16 10
subtype4 20 7

Figure S30.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S37.  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 = 0.856 (logrank test), Q value = 1

Table S38.  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 S31.  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), Q value = 1

Table S39.  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 S32.  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), Q value = 1

Table S40.  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 S33.  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), Q value = 1

Table S41.  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 S34.  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), Q value = 1

Table S42.  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 S35.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S43.  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 = 0.964 (logrank test), Q value = 1

Table S44.  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 S36.  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), Q value = 1

Table S45.  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 S37.  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), Q value = 1

Table S46.  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 S38.  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), Q value = 1

Table S47.  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 S39.  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), Q value = 1

Table S48.  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 S40.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S49.  Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 37 32 28 24 6 25
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.371 (logrank test), Q value = 1

Table S50.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 152 99 0.2 - 54.0 (8.7)
subtype1 37 23 0.2 - 54.0 (8.8)
subtype2 32 22 0.4 - 29.0 (7.2)
subtype3 28 19 2.5 - 47.9 (11.3)
subtype4 24 15 0.2 - 31.3 (5.3)
subtype5 6 3 1.1 - 40.4 (5.8)
subtype6 25 17 1.2 - 33.1 (9.9)

Figure S41.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.15 (ANOVA), Q value = 1

Table S51.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 152 60.1 (13.2)
subtype1 37 56.0 (16.1)
subtype2 32 60.5 (13.0)
subtype3 28 59.2 (10.4)
subtype4 24 60.8 (13.3)
subtype5 6 63.0 (10.7)
subtype6 25 65.4 (10.8)

Figure S42.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.307 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 54 98
subtype1 11 26
subtype2 15 17
subtype3 8 20
subtype4 7 17
subtype5 1 5
subtype6 12 13

Figure S43.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.755 (ANOVA), Q value = 1

Table S53.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 114 75.6 (14.4)
subtype1 27 77.4 (12.3)
subtype2 24 74.2 (11.0)
subtype3 24 77.1 (16.0)
subtype4 16 76.9 (12.5)
subtype5 5 68.0 (26.8)
subtype6 18 73.9 (17.2)

Figure S44.  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 = 0.19 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 97 55
subtype1 26 11
subtype2 19 13
subtype3 22 6
subtype4 11 13
subtype5 3 3
subtype6 16 9

Figure S45.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S55.  Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 31 3 32 23 19 44
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.35 (logrank test), Q value = 1

Table S56.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 152 99 0.2 - 54.0 (8.7)
subtype1 31 19 0.2 - 54.0 (9.4)
subtype2 3 2 0.4 - 28.9 (4.2)
subtype3 32 21 0.2 - 31.3 (6.7)
subtype4 23 15 0.9 - 33.1 (7.3)
subtype5 19 12 1.1 - 47.9 (13.3)
subtype6 44 30 0.9 - 40.4 (8.3)

Figure S46.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.349 (ANOVA), Q value = 1

Table S57.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 152 60.1 (13.2)
subtype1 31 56.0 (17.2)
subtype2 3 53.7 (26.3)
subtype3 32 59.6 (12.8)
subtype4 23 62.0 (12.0)
subtype5 19 62.9 (7.8)
subtype6 44 61.6 (11.6)

Figure S47.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0251 (Chi-square test), Q value = 1

Table S58.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 54 98
subtype1 9 22
subtype2 2 1
subtype3 5 27
subtype4 7 16
subtype5 9 10
subtype6 22 22

Figure S48.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.469 (ANOVA), Q value = 1

Table S59.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 114 75.6 (14.4)
subtype1 23 76.1 (12.3)
subtype2 2 70.0 (14.1)
subtype3 23 78.7 (14.2)
subtype4 16 78.1 (14.7)
subtype5 16 70.6 (19.1)
subtype6 34 74.7 (13.3)

Figure S49.  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 = 0.957 (Chi-square test), Q value = 1

Table S60.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 97 55
subtype1 22 9
subtype2 2 1
subtype3 19 13
subtype4 14 9
subtype5 12 7
subtype6 28 16

Figure S50.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input
  • Cluster data file = GBM-TP.mergedcluster.txt

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

  • Number of patients = 564

  • Number of clustering approaches = 10

  • Number of selected clinical features = 5

  • 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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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

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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] 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)