Correlation between aggregated molecular cancer subtypes and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1GH9GKD
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9GKD
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 6 clinical features across 362 patients, 22 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 4 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',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

  • 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 correlate to 'Time to Death',  'AGE',  'KARNOFSKY.PERFORMANCE.SCORE', and 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.

  • 7 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 7 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'AGE', and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to '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 6 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 22 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Chi-square test Fisher's exact test
mRNA CNMF subtypes 0.487
(1.00) 		0.326
(1.00) 		0.101
(1.00) 		0.441
(1.00) 		0.0226
(0.939) 		0.745
(1.00)
mRNA cHierClus subtypes 0.00939
(0.46) 		0.35
(1.00) 		0.122
(1.00) 		0.205
(1.00) 		0.0109
(0.514) 		0.421
(1.00)
Copy Number Ratio CNMF subtypes 1.27e-09
(7.89e-08) 		7.23e-10
(4.63e-08) 		0.106
(1.00) 		0.0399
(1.00) 		1.01e-15
(6.98e-14) 		0.751
(1.00)
METHLYATION CNMF 1.68e-14
(1.12e-12) 		1.28e-14
(8.67e-13) 		0.456
(1.00) 		0.0358
(1.00) 		2.17e-25
(1.56e-23) 		0.0104
(0.498)
RPPA CNMF subtypes 0.000105
(0.00578) 		0.014
(0.629) 		0.0146
(0.64) 		0.1
(1.00) 		8.74e-05
(0.00489) 		0.183
(1.00)
RPPA cHierClus subtypes 0.377
(1.00) 		0.0388
(1.00) 		0.607
(1.00) 		0.611
(1.00) 		0.0955
(1.00) 		0.232
(1.00)
RNAseq CNMF subtypes 8.14e-10
(5.13e-08) 		2.32e-09
(1.41e-07) 		0.465
(1.00) 		0.000169
(0.00913) 		2.01e-21
(1.43e-19) 		0.0211
(0.908)
RNAseq cHierClus subtypes 0.000739
(0.0377) 		0.236
(1.00) 		0.81
(1.00) 		0.11
(1.00) 		8.41e-19
(5.89e-17) 		0.0429
(1.00)
MIRSEQ CNMF 0.000363
(0.0189) 		0.000187
(0.00991) 		0.511
(1.00) 		0.15
(1.00) 		6.93e-11
(4.5e-09) 		0.0136
(0.625)
MIRSEQ CHIERARCHICAL 0.00548
(0.274) 		0.997
(1.00) 		0.125
(1.00) 		0.989
(1.00) 		1.54e-06
(9.06e-05) 		0.0224
(0.939)
MIRseq Mature CNMF subtypes 3.51e-09
(2.11e-07) 		6.09e-05
(0.00347) 		0.594
(1.00) 		0.0711
(1.00) 		6.54e-14
(4.32e-12) 		0.422
(1.00)
MIRseq Mature cHierClus subtypes 0.272
(1.00) 		0.938
(1.00) 		0.383
(1.00) 		0.947
(1.00) 		2.48e-06
(0.000144) 		0.215
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 9 10 8
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.487 (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 27 10 0.1 - 134.3 (46.6)
subtype1 9 4 10.6 - 130.8 (43.9)
subtype2 10 3 0.1 - 78.2 (36.5)
subtype3 8 3 14.4 - 134.3 (61.5)

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 = 0.326 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 27 39.3 (9.1)
subtype1 9 39.2 (6.2)
subtype2 10 42.3 (7.6)
subtype3 8 35.8 (12.6)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.101 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 9 18
subtype1 2 7
subtype2 6 4
subtype3 1 7

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 = 0.441 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 17 88.8 (12.2)
subtype1 7 84.3 (16.2)
subtype2 7 92.9 (7.6)
subtype3 3 90.0 (10.0)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0226 (Chi-square test), Q value = 0.94

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 10 9 8
subtype1 7 2 0
subtype2 2 3 5
subtype3 1 4 3

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

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

P value = 0.745 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 20 7
subtype1 7 2
subtype2 8 2
subtype3 5 3

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 7 6 10 4
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.00939 (logrank test), Q value = 0.46

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

nPatients nDeath Duration Range (Median), Month
ALL 27 10 0.1 - 134.3 (46.6)
subtype1 7 1 0.1 - 78.2 (31.8)
subtype2 6 1 14.4 - 134.3 (75.6)
subtype3 10 5 10.6 - 130.8 (40.2)
subtype4 4 3 20.0 - 47.9 (43.9)

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 = 0.35 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 27 39.3 (9.1)
subtype1 7 43.9 (8.6)
subtype2 6 34.8 (14.6)
subtype3 10 39.6 (6.0)
subtype4 4 37.5 (3.4)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.122 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 9 18
subtype1 5 2
subtype2 1 5
subtype3 2 8
subtype4 1 3

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 = 0.205 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 17 88.8 (12.2)
subtype1 5 94.0 (8.9)
subtype2 2 90.0 (14.1)
subtype3 8 85.0 (15.1)
subtype4 2 90.0 (0.0)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0109 (Chi-square test), Q value = 0.51

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 10 9 8
subtype1 1 1 5
subtype2 0 4 2
subtype3 7 2 1
subtype4 2 2 0

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

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

P value = 0.421 (Fisher's exact test), Q value = 1

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 20 7
subtype1 5 2
subtype2 3 3
subtype3 8 2
subtype4 4 0

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

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S15.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 108 82 169
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 1.27e-09 (logrank test), Q value = 7.9e-08

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

nPatients nDeath Duration Range (Median), Month
ALL 356 68 0.0 - 211.2 (14.8)
subtype1 107 20 0.1 - 156.2 (15.5)
subtype2 81 31 0.1 - 211.2 (12.2)
subtype3 168 17 0.0 - 182.3 (14.6)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 7.23e-10 (ANOVA), Q value = 4.6e-08

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

nPatients Mean (Std.Dev)
ALL 359 43.6 (13.5)
subtype1 108 39.0 (11.4)
subtype2 82 51.3 (13.4)
subtype3 169 42.8 (13.3)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.106 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 165 194
subtype1 45 63
subtype2 46 36
subtype3 74 95

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0399 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 199 87.9 (12.4)
subtype1 61 88.2 (12.7)
subtype2 48 84.2 (14.0)
subtype3 90 89.8 (11.0)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1.01e-15 (Chi-square test), Q value = 7e-14

Table S20.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 123 99 137
subtype1 61 30 17
subtype2 36 27 19
subtype3 26 42 101

Figure S17.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.751 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 89 270
subtype1 29 79
subtype2 21 61
subtype3 39 130

Figure S18.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S22.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 153 45 48 111
'METHLYATION CNMF' versus 'Time to Death'

P value = 1.68e-14 (logrank test), Q value = 1.1e-12

Table S23.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 354 64 0.0 - 211.2 (14.6)
subtype1 152 26 0.0 - 156.2 (17.4)
subtype2 45 24 0.2 - 211.2 (10.4)
subtype3 47 4 0.1 - 122.7 (11.9)
subtype4 110 10 0.1 - 182.3 (14.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 1.28e-14 (ANOVA), Q value = 8.7e-13

Table S24.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 357 43.5 (13.5)
subtype1 153 38.5 (11.5)
subtype2 45 55.7 (12.1)
subtype3 48 41.8 (14.8)
subtype4 111 46.0 (12.5)

Figure S20.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.456 (Fisher's exact test), Q value = 1

Table S25.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 167 190
subtype1 67 86
subtype2 25 20
subtype3 25 23
subtype4 50 61

Figure S21.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.0358 (ANOVA), Q value = 1

Table S26.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 199 87.9 (12.4)
subtype1 92 89.8 (12.0)
subtype2 25 82.4 (12.7)
subtype3 21 84.8 (12.9)
subtype4 61 88.5 (12.4)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.17e-25 (Chi-square test), Q value = 1.6e-23

Table S27.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 122 98 137
subtype1 77 52 24
subtype2 27 10 8
subtype3 16 13 19
subtype4 2 23 86

Figure S23.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.0104 (Fisher's exact test), Q value = 0.5

Table S28.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 85 272
subtype1 48 105
subtype2 12 33
subtype3 9 39
subtype4 16 95

Figure S24.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S29.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 48 61 68 71
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000105 (logrank test), Q value = 0.0058

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

nPatients nDeath Duration Range (Median), Month
ALL 248 55 0.0 - 211.2 (16.4)
subtype1 48 5 0.1 - 82.0 (15.0)
subtype2 61 29 0.1 - 156.2 (16.2)
subtype3 68 10 0.0 - 211.2 (19.2)
subtype4 71 11 0.1 - 138.3 (17.0)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.014 (ANOVA), Q value = 0.63

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

nPatients Mean (Std.Dev)
ALL 248 42.8 (13.3)
subtype1 48 38.3 (11.4)
subtype2 61 46.5 (13.6)
subtype3 68 43.4 (13.2)
subtype4 71 42.1 (13.7)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0146 (Fisher's exact test), Q value = 0.64

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

nPatients FEMALE MALE
ALL 107 141
subtype1 18 30
subtype2 33 28
subtype3 20 48
subtype4 36 35

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

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

P value = 0.1 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 109 87.4 (11.3)
subtype1 26 89.2 (8.9)
subtype2 29 84.5 (14.3)
subtype3 25 91.2 (10.9)
subtype4 29 85.5 (9.5)

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.74e-05 (Chi-square test), Q value = 0.0049

Table S34.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 79 70 99
subtype1 17 21 10
subtype2 30 11 20
subtype3 19 21 28
subtype4 13 17 41

Figure S29.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.183 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 84 164
subtype1 15 33
subtype2 20 41
subtype3 30 38
subtype4 19 52

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S36.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 7 127 114
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.377 (logrank test), Q value = 1

Table S37.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 248 55 0.0 - 211.2 (16.4)
subtype1 7 1 0.1 - 75.2 (8.3)
subtype2 127 38 0.0 - 211.2 (18.0)
subtype3 114 16 0.1 - 138.3 (15.2)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0388 (ANOVA), Q value = 1

Table S38.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 248 42.8 (13.3)
subtype1 7 39.6 (6.8)
subtype2 127 44.9 (13.6)
subtype3 114 40.7 (13.0)

Figure S32.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.607 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 107 141
subtype1 2 5
subtype2 53 74
subtype3 52 62

Figure S33.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.611 (ANOVA), Q value = 1

Table S40.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 109 87.4 (11.3)
subtype1 1 80.0 (NA)
subtype2 56 88.0 (12.4)
subtype3 52 86.9 (10.2)

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0955 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 79 70 99
subtype1 2 1 4
subtype2 50 33 44
subtype3 27 36 51

Figure S35.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.232 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 84 164
subtype1 4 3
subtype2 46 81
subtype3 34 80

Figure S36.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S43.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 91 65 73 104 7 19
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 8.14e-10 (logrank test), Q value = 5.1e-08

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

nPatients nDeath Duration Range (Median), Month
ALL 356 68 0.0 - 211.2 (14.9)
subtype1 90 15 0.0 - 130.8 (18.0)
subtype2 64 24 0.1 - 211.2 (10.7)
subtype3 73 9 0.1 - 182.3 (14.2)
subtype4 104 15 0.1 - 156.2 (14.7)
subtype5 7 5 7.9 - 46.6 (19.0)
subtype6 18 0 0.2 - 94.6 (14.5)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 2.32e-09 (ANOVA), Q value = 1.4e-07

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

nPatients Mean (Std.Dev)
ALL 359 43.6 (13.5)
subtype1 91 37.1 (10.5)
subtype2 65 50.7 (13.7)
subtype3 73 47.2 (12.5)
subtype4 104 42.9 (14.0)
subtype5 7 43.1 (12.5)
subtype6 19 40.0 (12.0)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.465 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 165 194
subtype1 36 55
subtype2 36 29
subtype3 35 38
subtype4 45 59
subtype5 3 4
subtype6 10 9

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

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

P value = 0.000169 (ANOVA), Q value = 0.0091

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

nPatients Mean (Std.Dev)
ALL 197 88.0 (12.5)
subtype1 55 93.1 (8.4)
subtype2 36 82.5 (13.4)
subtype3 41 90.0 (11.0)
subtype4 48 84.4 (14.6)
subtype5 4 80.0 (14.1)
subtype6 13 90.8 (11.2)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.01e-21 (Chi-square test), Q value = 1.4e-19

Table S48.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 122 100 137
subtype1 46 30 15
subtype2 43 14 8
subtype3 1 17 55
subtype4 24 30 50
subtype5 6 0 1
subtype6 2 9 8

Figure S41.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.0211 (Chi-square test), Q value = 0.91

Table S49.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 89 270
subtype1 28 63
subtype2 19 46
subtype3 10 63
subtype4 26 78
subtype5 4 3
subtype6 2 17

Figure S42.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S50.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 172 60 127
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000739 (logrank test), Q value = 0.038

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

nPatients nDeath Duration Range (Median), Month
ALL 356 68 0.0 - 211.2 (14.9)
subtype1 170 46 0.0 - 211.2 (15.0)
subtype2 60 7 0.1 - 182.3 (15.3)
subtype3 126 15 0.1 - 156.2 (14.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.236 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 359 43.6 (13.5)
subtype1 172 42.7 (13.7)
subtype2 60 46.2 (11.6)
subtype3 127 43.5 (14.0)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.81 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 165 194
subtype1 76 96
subtype2 29 31
subtype3 60 67

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

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

P value = 0.11 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 197 88.0 (12.5)
subtype1 98 88.5 (12.2)
subtype2 36 90.8 (10.5)
subtype3 63 85.6 (13.7)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.41e-19 (Chi-square test), Q value = 5.9e-17

Table S55.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 122 100 137
subtype1 92 51 29
subtype2 1 13 46
subtype3 29 36 62

Figure S47.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.0429 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 89 270
subtype1 53 119
subtype2 11 49
subtype3 25 102

Figure S48.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S57.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 46 56 25 29 107 63 22
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.000363 (logrank test), Q value = 0.019

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

nPatients nDeath Duration Range (Median), Month
ALL 347 67 0.0 - 211.2 (14.9)
subtype1 46 5 0.0 - 130.8 (14.7)
subtype2 56 20 0.1 - 117.4 (11.8)
subtype3 25 8 0.1 - 211.2 (15.4)
subtype4 29 4 0.1 - 94.0 (16.8)
subtype5 107 13 0.1 - 156.2 (13.0)
subtype6 63 15 0.1 - 182.3 (17.5)
subtype7 21 2 2.5 - 103.8 (14.3)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.000187 (ANOVA), Q value = 0.0099

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

nPatients Mean (Std.Dev)
ALL 348 43.5 (13.5)
subtype1 46 36.0 (9.9)
subtype2 56 49.2 (13.8)
subtype3 25 40.6 (14.4)
subtype4 29 44.9 (12.2)
subtype5 107 44.1 (13.8)
subtype6 63 43.6 (14.1)
subtype7 22 42.6 (10.0)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.511 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 160 188
subtype1 16 30
subtype2 24 32
subtype3 14 11
subtype4 11 18
subtype5 53 54
subtype6 31 32
subtype7 11 11

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

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 187 88.0 (12.4)
subtype1 26 91.9 (6.9)
subtype2 31 88.7 (10.2)
subtype3 19 82.1 (19.6)
subtype4 15 88.7 (11.3)
subtype5 53 87.4 (13.2)
subtype6 28 86.1 (13.1)
subtype7 15 92.0 (5.6)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 6.93e-11 (Chi-square test), Q value = 4.5e-09

Table S62.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 118 95 135
subtype1 25 16 5
subtype2 32 11 13
subtype3 17 5 3
subtype4 2 7 20
subtype5 25 26 56
subtype6 14 21 28
subtype7 3 9 10

Figure S53.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0136 (Chi-square test), Q value = 0.63

Table S63.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 88 260
subtype1 13 33
subtype2 15 41
subtype3 7 18
subtype4 7 22
subtype5 18 89
subtype6 26 37
subtype7 2 20

Figure S54.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S64.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 155 15 178
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.00548 (logrank test), Q value = 0.27

Table S65.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 347 67 0.0 - 211.2 (14.9)
subtype1 155 17 0.1 - 156.2 (13.6)
subtype2 15 1 0.2 - 88.8 (20.1)
subtype3 177 49 0.0 - 211.2 (15.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.997 (ANOVA), Q value = 1

Table S66.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 348 43.5 (13.5)
subtype1 155 43.5 (13.9)
subtype2 15 43.6 (14.4)
subtype3 178 43.4 (13.2)

Figure S56.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

Table S67.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 160 188
subtype1 79 76
subtype2 4 11
subtype3 77 101

Figure S57.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.989 (ANOVA), Q value = 1

Table S68.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 187 88.0 (12.4)
subtype1 80 88.1 (12.7)
subtype2 5 88.0 (16.4)
subtype3 102 87.8 (12.1)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1.54e-06 (Chi-square test), Q value = 9.1e-05

Table S69.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 118 95 135
subtype1 30 44 81
subtype2 5 6 4
subtype3 83 45 50

Figure S59.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0224 (Fisher's exact test), Q value = 0.94

Table S70.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 88 260
subtype1 29 126
subtype2 6 9
subtype3 53 125

Figure S60.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S71.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 70 61 39 100 39 16 23
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 3.51e-09 (logrank test), Q value = 2.1e-07

Table S72.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 347 67 0.0 - 211.2 (14.9)
subtype1 70 11 0.0 - 130.8 (16.1)
subtype2 61 25 0.1 - 114.1 (11.6)
subtype3 39 6 0.1 - 182.3 (16.8)
subtype4 100 10 0.1 - 156.2 (13.2)
subtype5 39 10 0.1 - 154.2 (17.5)
subtype6 16 4 3.6 - 211.2 (17.9)
subtype7 22 1 2.5 - 88.8 (14.5)

Figure S61.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 6.09e-05 (ANOVA), Q value = 0.0035

Table S73.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 348 43.5 (13.5)
subtype1 70 38.0 (11.3)
subtype2 61 49.8 (13.6)
subtype3 39 46.3 (11.3)
subtype4 100 43.6 (14.3)
subtype5 39 41.5 (14.5)
subtype6 16 40.4 (12.6)
subtype7 23 43.4 (11.7)

Figure S62.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.594 (Chi-square test), Q value = 1

Table S74.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 160 188
subtype1 29 41
subtype2 31 30
subtype3 15 24
subtype4 49 51
subtype5 16 23
subtype6 10 6
subtype7 10 13

Figure S63.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0711 (ANOVA), Q value = 1

Table S75.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 187 88.0 (12.4)
subtype1 42 91.9 (10.4)
subtype2 30 82.7 (13.1)
subtype3 25 87.2 (13.7)
subtype4 46 87.2 (12.4)
subtype5 16 89.4 (13.9)
subtype6 14 86.4 (14.5)
subtype7 14 91.4 (5.3)

Figure S64.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6.54e-14 (Chi-square test), Q value = 4.3e-12

Table S76.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 118 95 135
subtype1 40 20 10
subtype2 34 14 13
subtype3 3 7 29
subtype4 20 26 54
subtype5 7 14 18
subtype6 11 4 1
subtype7 3 10 10

Figure S65.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.422 (Chi-square test), Q value = 1

Table S77.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 88 260
subtype1 22 48
subtype2 14 47
subtype3 10 29
subtype4 21 79
subtype5 13 26
subtype6 5 11
subtype7 3 20

Figure S66.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S78.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 5 158 185
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.272 (logrank test), Q value = 1

Table S79.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 347 67 0.0 - 211.2 (14.9)
subtype1 5 1 5.3 - 70.5 (24.3)
subtype2 158 21 0.1 - 211.2 (13.9)
subtype3 184 45 0.0 - 182.3 (15.6)

Figure S67.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.938 (ANOVA), Q value = 1

Table S80.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 348 43.5 (13.5)
subtype1 5 42.8 (16.2)
subtype2 158 43.8 (14.1)
subtype3 185 43.3 (13.0)

Figure S68.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.383 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 160 188
subtype1 2 3
subtype2 79 79
subtype3 79 106

Figure S69.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.947 (ANOVA), Q value = 1

Table S82.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 187 88.0 (12.4)
subtype1 3 86.7 (11.5)
subtype2 83 87.7 (12.3)
subtype3 101 88.2 (12.6)

Figure S70.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.48e-06 (Chi-square test), Q value = 0.00014

Table S83.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 118 95 135
subtype1 4 1 0
subtype2 31 47 80
subtype3 83 47 55

Figure S71.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.215 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 88 260
subtype1 0 5
subtype2 35 123
subtype3 53 132

Figure S72.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

  • Cluster data file = LGG-TP.mergedcluster.txt

  • Clinical data file = LGG-TP.merged_data.txt

  • Number of patients = 362

  • Number of clustering approaches = 12

  • 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

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

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] 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)
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