Correlation between aggregated molecular cancer subtypes and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1D50K7P
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1D50K7P
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 241 patients, 21 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.

  • 4 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',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

  • 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 3 subtypes that correlate to 'Time to Death' 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'.

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 6 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 21 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 0.247
(1.00) 		0.326
(1.00) 		0.101
(1.00) 		0.441
(1.00) 		0.0226
(1.00) 		0.764
(1.00)
mRNA cHierClus subtypes 0.0482
(1.00) 		0.35
(1.00) 		0.122
(1.00) 		0.205
(1.00) 		0.0109
(0.558) 		0.471
(1.00)
Copy Number Ratio CNMF subtypes 7.03e-05
(0.00415) 		0.000126
(0.00729) 		0.0153
(0.763) 		0.433
(1.00) 		4.72e-09
(3.17e-07) 		0.0572
(1.00)
METHLYATION CNMF 6.86e-08
(4.39e-06) 		3.93e-10
(2.79e-08) 		0.133
(1.00) 		0.42
(1.00) 		6.7e-16
(4.83e-14) 		0.00149
(0.079)
RPPA CNMF subtypes 0.000238
(0.0134) 		0.0759
(1.00) 		0.0153
(0.763) 		0.335
(1.00) 		4.5e-05
(0.0027) 		0.408
(1.00)
RPPA cHierClus subtypes 0.386
(1.00) 		0.177
(1.00) 		0.657
(1.00) 		0.441
(1.00) 		0.0903
(1.00) 		0.506
(1.00)
RNAseq CNMF subtypes 1.63e-06
(0.000101) 		0.0987
(1.00) 		0.667
(1.00) 		0.0614
(1.00) 		5.51e-08
(3.58e-06) 		0.0789
(1.00)
RNAseq cHierClus subtypes 0.004
(0.208) 		0.0243
(1.00) 		0.698
(1.00) 		0.28
(1.00) 		4.83e-10
(3.38e-08) 		0.0924
(1.00)
MIRSEQ CNMF 0.0658
(1.00) 		0.991
(1.00) 		0.672
(1.00) 		0.399
(1.00) 		1.49e-06
(9.4e-05) 		0.12
(1.00)
MIRSEQ CHIERARCHICAL 0.0158
(0.763) 		0.339
(1.00) 		0.0663
(1.00) 		0.233
(1.00) 		0.000815
(0.0448) 		0.461
(1.00)
MIRseq Mature CNMF subtypes 3.42e-09
(2.32e-07) 		2.7e-06
(0.000165) 		0.156
(1.00) 		0.21
(1.00) 		1.55e-09
(1.07e-07) 		0.493
(1.00)
MIRseq Mature cHierClus subtypes 0.000213
(0.0121) 		0.000947
(0.0511) 		0.373
(1.00) 		0.299
(1.00) 		2.86e-08
(1.89e-06) 		0.229
(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 = 0.247 (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 9 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 2 14.4 - 134.3 (51.3)

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 = 1

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 = 0.764 (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 19 8
subtype1 6 3
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 = 0.0482 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 27 9 0.1 - 134.3 (46.6)
subtype1 7 1 0.1 - 78.2 (31.8)
subtype2 6 1 14.4 - 134.3 (52.7)
subtype3 10 5 10.6 - 130.8 (37.1)
subtype4 4 2 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 = 0.56

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 = 0.471 (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 19 8
subtype1 5 2
subtype2 3 3
subtype3 7 3
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 4
Number of samples 44 48 45 92
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 7.03e-05 (logrank test), Q value = 0.0042

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

nPatients nDeath Duration Range (Median), Month
ALL 229 55 0.0 - 211.2 (14.5)
subtype1 44 8 0.1 - 156.2 (17.7)
subtype2 48 22 0.2 - 211.2 (12.4)
subtype3 45 15 0.1 - 130.8 (17.3)
subtype4 92 10 0.0 - 182.3 (13.1)

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 = 0.000126 (ANOVA), Q value = 0.0073

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

nPatients Mean (Std.Dev)
ALL 229 42.8 (13.4)
subtype1 44 36.8 (10.6)
subtype2 48 49.2 (13.4)
subtype3 45 42.8 (13.6)
subtype4 92 42.2 (13.2)

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 = 0.0153 (Fisher's exact test), Q value = 0.76

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

nPatients FEMALE MALE
ALL 102 127
subtype1 15 29
subtype2 31 17
subtype3 18 27
subtype4 38 54

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 = 0.433 (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 101 88.1 (10.6)
subtype1 17 90.6 (8.3)
subtype2 24 88.8 (8.0)
subtype3 23 85.2 (12.4)
subtype4 37 88.4 (11.7)

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 = 4.72e-09 (Chi-square test), Q value = 3.2e-07

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 71 64 93
subtype1 20 16 7
subtype2 22 18 8
subtype3 18 9 18
subtype4 11 21 60

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 = 0.0572 (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 90 139
subtype1 16 28
subtype2 20 28
subtype3 25 20
subtype4 29 63

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 100 38 68 30
'METHLYATION CNMF' versus 'Time to Death'

P value = 6.86e-08 (logrank test), Q value = 4.4e-06

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

nPatients nDeath Duration Range (Median), Month
ALL 236 51 0.0 - 211.2 (14.3)
subtype1 100 22 0.0 - 156.2 (17.7)
subtype2 38 19 0.1 - 211.2 (10.9)
subtype3 68 8 0.1 - 182.3 (13.0)
subtype4 30 2 0.1 - 97.9 (8.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 3.93e-10 (ANOVA), Q value = 2.8e-08

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

nPatients Mean (Std.Dev)
ALL 236 42.9 (13.4)
subtype1 100 38.3 (11.9)
subtype2 38 53.2 (11.9)
subtype3 68 46.2 (12.7)
subtype4 30 37.6 (12.8)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.133 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 108 128
subtype1 39 61
subtype2 22 16
subtype3 30 38
subtype4 17 13

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

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

P value = 0.42 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 88.2 (10.4)
subtype1 53 89.6 (10.0)
subtype2 18 86.7 (7.7)
subtype3 28 87.9 (12.6)
subtype4 8 83.8 (10.6)

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 = 6.7e-16 (Chi-square test), Q value = 4.8e-14

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 71 68 96
subtype1 39 38 22
subtype2 24 8 6
subtype3 1 12 55
subtype4 7 10 13

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 = 0.00149 (Fisher's exact test), Q value = 0.079

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

nPatients NO YES
ALL 86 150
subtype1 49 51
subtype2 15 23
subtype3 16 52
subtype4 6 24

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 39 54 63 66
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000238 (logrank test), Q value = 0.013

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

nPatients nDeath Duration Range (Median), Month
ALL 222 52 0.0 - 211.2 (14.7)
subtype1 39 5 0.1 - 60.0 (12.4)
subtype2 54 28 0.2 - 156.2 (15.6)
subtype3 63 10 0.0 - 211.2 (16.5)
subtype4 66 9 0.1 - 138.2 (14.7)

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 = 0.0759 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 222 42.7 (13.3)
subtype1 39 38.3 (11.5)
subtype2 54 45.5 (13.5)
subtype3 63 43.4 (13.2)
subtype4 66 42.5 (13.9)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0153 (Fisher's exact test), Q value = 0.76

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

nPatients FEMALE MALE
ALL 100 122
subtype1 16 23
subtype2 31 23
subtype3 19 44
subtype4 34 32

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 = 0.335 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 96 88.0 (10.8)
subtype1 21 90.0 (8.4)
subtype2 27 85.9 (13.1)
subtype3 23 90.4 (11.1)
subtype4 25 86.4 (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 = 4.5e-05 (Chi-square test), Q value = 0.0027

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 66 64 91
subtype1 15 17 7
subtype2 26 10 17
subtype3 15 20 28
subtype4 10 17 39

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 = 0.408 (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 86 136
subtype1 16 23
subtype2 20 34
subtype3 29 34
subtype4 21 45

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 7 112 103
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.386 (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 222 52 0.0 - 211.2 (14.7)
subtype1 7 1 0.1 - 75.2 (6.4)
subtype2 112 37 0.0 - 211.2 (17.8)
subtype3 103 14 0.1 - 138.2 (12.7)

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 = 0.177 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 222 42.7 (13.3)
subtype1 7 39.6 (6.8)
subtype2 112 44.4 (13.4)
subtype3 103 41.2 (13.4)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.657 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 100 122
subtype1 2 5
subtype2 50 62
subtype3 48 55

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 = 0.441 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 96 88.0 (10.8)
subtype1 1 80.0 (NA)
subtype2 48 89.0 (11.5)
subtype3 47 87.2 (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 = 0.0903 (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 66 64 91
subtype1 2 1 4
subtype2 42 28 41
subtype3 22 35 46

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 = 0.506 (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 86 136
subtype1 4 3
subtype2 45 67
subtype3 37 66

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
Number of samples 76 69 86
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.63e-06 (logrank test), Q value = 1e-04

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

nPatients nDeath Duration Range (Median), Month
ALL 231 55 0.0 - 211.2 (14.5)
subtype1 76 30 0.1 - 211.2 (11.9)
subtype2 69 13 0.0 - 182.3 (16.2)
subtype3 86 12 0.1 - 156.2 (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 = 0.0987 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 231 42.7 (13.4)
subtype1 76 45.1 (14.2)
subtype2 69 40.4 (11.3)
subtype3 86 42.5 (13.9)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.667 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 103 128
subtype1 37 39
subtype2 30 39
subtype3 36 50

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 = 0.0614 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 101 88.1 (10.6)
subtype1 34 90.0 (7.0)
subtype2 37 89.5 (11.5)
subtype3 30 84.3 (11.9)

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 = 5.51e-08 (Chi-square test), Q value = 3.6e-06

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 71 65 94
subtype1 43 19 14
subtype2 15 20 34
subtype3 13 26 46

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 = 0.0789 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 90 141
subtype1 36 40
subtype2 28 41
subtype3 26 60

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 34 115 82
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.004 (logrank test), Q value = 0.21

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

nPatients nDeath Duration Range (Median), Month
ALL 231 55 0.0 - 211.2 (14.5)
subtype1 34 8 0.1 - 182.3 (11.3)
subtype2 115 36 0.0 - 211.2 (14.3)
subtype3 82 11 0.1 - 156.2 (14.7)

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 = 0.0243 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 231 42.7 (13.4)
subtype1 34 48.5 (12.7)
subtype2 115 41.6 (13.2)
subtype3 82 41.9 (13.4)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.698 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 103 128
subtype1 17 17
subtype2 52 63
subtype3 34 48

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 = 0.28 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 101 88.1 (10.6)
subtype1 16 86.9 (15.4)
subtype2 55 89.6 (9.4)
subtype3 30 86.0 (9.3)

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 = 4.83e-10 (Chi-square test), Q value = 3.4e-08

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 71 65 94
subtype1 1 5 28
subtype2 54 34 26
subtype3 16 26 40

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 = 0.0924 (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 90 141
subtype1 11 23
subtype2 53 62
subtype3 26 56

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
Number of samples 55 38 79 18 50
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0658 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 240 54 0.0 - 211.2 (14.5)
subtype1 55 15 0.0 - 117.4 (12.6)
subtype2 38 13 0.1 - 211.2 (15.6)
subtype3 79 10 0.1 - 156.2 (14.4)
subtype4 18 2 0.1 - 90.8 (11.4)
subtype5 50 14 0.1 - 182.3 (15.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.991 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 240 43.0 (13.4)
subtype1 55 43.3 (14.3)
subtype2 38 42.1 (12.6)
subtype3 79 43.0 (13.3)
subtype4 18 43.3 (12.1)
subtype5 50 43.5 (14.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.672 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 108 132
subtype1 23 32
subtype2 14 24
subtype3 37 42
subtype4 10 8
subtype5 24 26

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

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

P value = 0.399 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 88.2 (10.4)
subtype1 26 91.5 (9.7)
subtype2 19 87.4 (10.5)
subtype3 29 86.2 (10.1)
subtype4 11 89.1 (7.0)
subtype5 22 87.3 (12.8)

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 = 1.49e-06 (Chi-square test), Q value = 9.4e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 72 69 98
subtype1 24 14 16
subtype2 24 9 5
subtype3 14 25 40
subtype4 2 5 11
subtype5 8 16 26

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 = 0.12 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 89 151
subtype1 17 38
subtype2 21 17
subtype3 25 54
subtype4 7 11
subtype5 19 31

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 12 130 98
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0158 (logrank test), Q value = 0.76

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

nPatients nDeath Duration Range (Median), Month
ALL 240 54 0.0 - 211.2 (14.5)
subtype1 12 4 3.2 - 211.2 (14.1)
subtype2 130 38 0.0 - 182.3 (15.0)
subtype3 98 12 0.1 - 156.2 (14.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.339 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 240 43.0 (13.4)
subtype1 12 48.3 (14.7)
subtype2 130 42.4 (13.0)
subtype3 98 43.2 (13.8)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0663 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 108 132
subtype1 9 3
subtype2 53 77
subtype3 46 52

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

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

P value = 0.233 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 88.2 (10.4)
subtype1 10 89.0 (7.4)
subtype2 57 89.6 (9.8)
subtype3 40 86.0 (11.7)

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 = 0.000815 (Chi-square test), Q value = 0.045

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 72 69 98
subtype1 7 3 2
subtype2 49 36 44
subtype3 16 30 52

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 = 0.461 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 89 151
subtype1 6 6
subtype2 50 80
subtype3 33 65

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 8
Number of samples 41 38 28 67 27 30 2 7
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 3.42e-09 (logrank test), Q value = 2.3e-07

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

nPatients nDeath Duration Range (Median), Month
ALL 238 53 0.0 - 211.2 (14.5)
subtype1 41 5 0.0 - 117.4 (7.7)
subtype2 38 17 0.1 - 75.2 (10.0)
subtype3 28 5 6.6 - 130.8 (19.4)
subtype4 67 10 0.1 - 156.2 (12.6)
subtype5 27 3 0.1 - 154.1 (10.0)
subtype6 30 13 1.2 - 211.2 (18.8)
subtype8 7 0 7.1 - 52.4 (20.0)

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 = 2.7e-06 (ANOVA), Q value = 0.00016

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

nPatients Mean (Std.Dev)
ALL 238 43.0 (13.5)
subtype1 41 36.2 (11.4)
subtype2 38 51.2 (13.3)
subtype3 28 38.1 (8.0)
subtype4 67 43.7 (13.4)
subtype5 27 43.6 (14.0)
subtype6 30 46.5 (14.4)
subtype8 7 33.6 (7.5)

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 = 0.156 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 107 131
subtype1 11 30
subtype2 17 21
subtype3 13 15
subtype4 32 35
subtype5 15 12
subtype6 17 13
subtype8 2 5

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 = 0.21 (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 105 88.2 (10.5)
subtype1 19 91.1 (9.4)
subtype2 13 87.7 (7.3)
subtype3 16 87.5 (11.8)
subtype4 26 84.2 (10.3)
subtype5 13 92.3 (9.3)
subtype6 17 88.8 (13.2)
subtype8 1 90.0 (NA)

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 = 1.55e-09 (Chi-square test), Q value = 1.1e-07

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 70 69 98
subtype1 18 11 11
subtype2 25 7 6
subtype3 8 15 5
subtype4 12 19 36
subtype5 2 4 21
subtype6 5 9 16
subtype8 0 4 3

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 = 0.493 (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 87 151
subtype1 15 26
subtype2 15 23
subtype3 10 18
subtype4 20 47
subtype5 9 18
subtype6 16 14
subtype8 2 5

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 38 94 108
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.000213 (logrank test), Q value = 0.012

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

nPatients nDeath Duration Range (Median), Month
ALL 240 54 0.0 - 211.2 (14.5)
subtype1 38 16 0.1 - 211.2 (12.2)
subtype2 94 26 0.0 - 182.3 (17.6)
subtype3 108 12 0.1 - 156.2 (12.7)

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 = 0.000947 (ANOVA), Q value = 0.051

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

nPatients Mean (Std.Dev)
ALL 240 43.0 (13.4)
subtype1 38 49.7 (12.9)
subtype2 94 40.2 (12.1)
subtype3 108 43.2 (13.9)

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 = 0.373 (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 108 132
subtype1 19 19
subtype2 37 57
subtype3 52 56

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 = 0.299 (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 107 88.2 (10.4)
subtype1 17 87.1 (7.7)
subtype2 47 90.0 (10.2)
subtype3 43 86.7 (11.5)

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 = 2.86e-08 (Chi-square test), Q value = 1.9e-06

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 72 69 98
subtype1 25 7 6
subtype2 33 27 33
subtype3 14 35 59

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 = 0.229 (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 89 151
subtype1 17 21
subtype2 38 56
subtype3 34 74

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.clin.merged.picked.txt

  • Number of patients = 241

  • 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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