Correlation between aggregated molecular cancer subtypes and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1Q23XSW
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/C1Q23XSW
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 281 patients, 20 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',  'GENDER', 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', and 'HISTOLOGICAL.TYPE'.

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

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

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

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

  • 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, 20 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
(0.97) 		0.745
(1.00)
mRNA cHierClus subtypes 0.00939
(0.479) 		0.35
(1.00) 		0.122
(1.00) 		0.205
(1.00) 		0.0109
(0.537) 		0.421
(1.00)
Copy Number Ratio CNMF subtypes 3.32e-06
(0.000199) 		8.43e-10
(5.65e-08) 		0.00392
(0.208) 		0.373
(1.00) 		1.27e-13
(8.75e-12) 		0.629
(1.00)
METHLYATION CNMF 4.32e-09
(2.85e-07) 		1.9e-12
(1.29e-10) 		0.068
(1.00) 		0.271
(1.00) 		4.73e-19
(3.41e-17) 		0.0207
(0.931)
RPPA CNMF subtypes 8.49e-05
(0.00492) 		0.0251
(1.00) 		0.00813
(0.423) 		0.127
(1.00) 		0.000274
(0.0153) 		0.136
(1.00)
RPPA cHierClus subtypes 0.379
(1.00) 		0.0699
(1.00) 		0.675
(1.00) 		0.613
(1.00) 		0.185
(1.00) 		0.223
(1.00)
RNAseq CNMF subtypes 2.4e-07
(1.46e-05) 		7.11e-06
(0.00042) 		0.263
(1.00) 		0.0187
(0.877) 		5.37e-14
(3.76e-12) 		0.0214
(0.94)
RNAseq cHierClus subtypes 0.0125
(0.598) 		0.0671
(1.00) 		0.637
(1.00) 		0.151
(1.00) 		3.51e-14
(2.49e-12) 		0.179
(1.00)
MIRSEQ CNMF 0.199
(1.00) 		0.305
(1.00) 		0.738
(1.00) 		0.173
(1.00) 		5.8e-08
(3.71e-06) 		0.27
(1.00)
MIRSEQ CHIERARCHICAL 0.0273
(1.00) 		0.585
(1.00) 		0.0202
(0.931) 		0.35
(1.00) 		8.79e-05
(0.00501) 		0.244
(1.00)
MIRseq Mature CNMF subtypes 3.57e-08
(2.32e-06) 		0.000496
(0.0268) 		0.192
(1.00) 		0.0758
(1.00) 		1.54e-07
(9.73e-06) 		0.000423
(0.0233)
MIRseq Mature cHierClus subtypes 0.0642
(1.00) 		0.549
(1.00) 		0.0107
(0.537) 		0.547
(1.00) 		1.57e-07
(9.73e-06) 		0.0765
(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 = 0.97

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 = 0.48

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 = 0.54

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 109 63 104
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 3.32e-06 (logrank test), Q value = 2e-04

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

nPatients nDeath Duration Range (Median), Month
ALL 275 59 0.0 - 211.2 (15.4)
subtype1 109 16 0.1 - 156.2 (15.4)
subtype2 63 28 0.1 - 211.2 (16.2)
subtype3 103 15 0.0 - 182.3 (14.7)

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 = 8.43e-10 (ANOVA), Q value = 5.6e-08

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

nPatients Mean (Std.Dev)
ALL 276 43.1 (13.3)
subtype1 109 37.8 (11.8)
subtype2 63 50.9 (12.6)
subtype3 104 43.9 (12.8)

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 = 0.00392 (Fisher's exact test), Q value = 0.21

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

nPatients FEMALE MALE
ALL 127 149
subtype1 39 70
subtype2 39 24
subtype3 49 55

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 = 0.373 (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 134 87.8 (10.8)
subtype1 52 87.7 (12.0)
subtype2 33 85.8 (10.6)
subtype3 49 89.2 (9.5)

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 = 1.27e-13 (Chi-square test), Q value = 8.8e-12

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 88 76 112
subtype1 48 37 24
subtype2 28 21 14
subtype3 12 18 74

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 = 0.629 (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 89 187
subtype1 38 71
subtype2 21 42
subtype3 30 74

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 125 43 76 32
'METHLYATION CNMF' versus 'Time to Death'

P value = 4.32e-09 (logrank test), Q value = 2.9e-07

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

nPatients nDeath Duration Range (Median), Month
ALL 275 55 0.0 - 211.2 (15.3)
subtype1 125 23 0.0 - 156.2 (17.5)
subtype2 43 20 0.1 - 211.2 (11.5)
subtype3 76 9 0.1 - 182.3 (14.4)
subtype4 31 3 0.1 - 122.7 (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 = 1.9e-12 (ANOVA), Q value = 1.3e-10

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

nPatients Mean (Std.Dev)
ALL 276 43.0 (13.3)
subtype1 125 38.6 (11.4)
subtype2 43 53.7 (12.3)
subtype3 76 46.5 (12.7)
subtype4 32 37.4 (12.5)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.068 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 128 148
subtype1 49 76
subtype2 25 18
subtype3 35 41
subtype4 19 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 = 0.271 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 136 88.1 (10.9)
subtype1 69 89.4 (11.0)
subtype2 21 85.2 (8.1)
subtype3 36 88.3 (11.8)
subtype4 10 84.0 (10.7)

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 = 4.73e-19 (Chi-square test), Q value = 3.4e-17

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 89 75 112
subtype1 55 45 25
subtype2 26 10 7
subtype3 2 12 62
subtype4 6 8 18

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 = 0.0207 (Fisher's exact test), Q value = 0.93

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

nPatients NO YES
ALL 85 191
subtype1 49 76
subtype2 14 29
subtype3 15 61
subtype4 7 25

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 47 57 65 70
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 8.49e-05 (logrank test), Q value = 0.0049

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

nPatients nDeath Duration Range (Median), Month
ALL 239 55 0.0 - 211.2 (16.2)
subtype1 47 5 0.1 - 82.0 (15.1)
subtype2 57 29 0.1 - 156.2 (16.2)
subtype3 65 10 0.0 - 211.2 (19.2)
subtype4 70 11 0.1 - 138.2 (15.8)

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 = 0.0251 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 239 42.7 (13.3)
subtype1 47 38.4 (11.5)
subtype2 57 46.2 (13.7)
subtype3 65 43.3 (13.0)
subtype4 70 42.3 (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 = 0.00813 (Fisher's exact test), Q value = 0.42

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

nPatients FEMALE MALE
ALL 106 133
subtype1 17 30
subtype2 33 24
subtype3 20 45
subtype4 36 34

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 = 0.127 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 87.6 (11.4)
subtype1 26 89.2 (8.9)
subtype2 28 84.6 (14.5)
subtype3 25 91.2 (10.9)
subtype4 28 85.7 (9.6)

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 = 0.000274 (Chi-square test), Q value = 0.015

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 73 69 97
subtype1 16 21 10
subtype2 27 11 19
subtype3 17 20 28
subtype4 13 17 40

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 = 0.136 (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 84 155
subtype1 15 32
subtype2 20 37
subtype3 30 35
subtype4 19 51

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 7 121 111
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.379 (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 239 55 0.0 - 211.2 (16.2)
subtype1 7 1 0.1 - 75.2 (8.3)
subtype2 121 38 0.0 - 211.2 (18.1)
subtype3 111 16 0.1 - 138.2 (15.0)

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 = 0.0699 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 239 42.7 (13.3)
subtype1 7 39.6 (6.8)
subtype2 121 44.7 (13.5)
subtype3 111 40.8 (13.2)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.675 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 106 133
subtype1 2 5
subtype2 53 68
subtype3 51 60

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 = 0.613 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 107 87.6 (11.4)
subtype1 1 80.0 (NA)
subtype2 55 88.2 (12.5)
subtype3 51 87.1 (10.3)

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 = 0.185 (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 73 69 97
subtype1 2 1 4
subtype2 45 32 44
subtype3 26 36 49

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 = 0.223 (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 84 155
subtype1 4 3
subtype2 46 75
subtype3 34 77

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 74 53 46 85 6 13
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.4e-07 (logrank test), Q value = 1.5e-05

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

nPatients nDeath Duration Range (Median), Month
ALL 276 59 0.0 - 211.2 (15.4)
subtype1 74 15 0.0 - 130.8 (19.1)
subtype2 53 19 0.1 - 211.2 (11.5)
subtype3 46 9 0.1 - 182.3 (14.2)
subtype4 85 11 0.1 - 156.2 (16.0)
subtype5 6 5 11.6 - 46.6 (18.1)
subtype6 12 0 0.2 - 31.4 (14.8)

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 = 7.11e-06 (ANOVA), Q value = 0.00042

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

nPatients Mean (Std.Dev)
ALL 277 43.1 (13.3)
subtype1 74 37.6 (11.0)
subtype2 53 49.4 (14.1)
subtype3 46 47.3 (12.7)
subtype4 85 41.8 (13.5)
subtype5 6 44.5 (12.3)
subtype6 13 40.8 (10.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.263 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 127 150
subtype1 28 46
subtype2 30 23
subtype3 25 21
subtype4 36 49
subtype5 3 3
subtype6 5 8

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 = 0.0187 (ANOVA), Q value = 0.88

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

nPatients Mean (Std.Dev)
ALL 133 87.8 (10.8)
subtype1 45 92.0 (8.9)
subtype2 25 84.0 (11.2)
subtype3 22 88.6 (10.8)
subtype4 31 84.5 (11.5)
subtype5 3 90.0 (0.0)
subtype6 7 85.7 (12.7)

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 = 5.37e-14 (Chi-square test), Q value = 3.8e-12

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 88 77 112
subtype1 32 26 16
subtype2 35 11 7
subtype3 1 10 35
subtype4 15 25 45
subtype5 4 1 1
subtype6 1 4 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 = 0.0214 (Chi-square test), Q value = 0.94

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

nPatients NO YES
ALL 89 188
subtype1 29 45
subtype2 18 35
subtype3 10 36
subtype4 25 60
subtype5 5 1
subtype6 2 11

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 42 136 99
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0125 (logrank test), Q value = 0.6

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

nPatients nDeath Duration Range (Median), Month
ALL 276 59 0.0 - 211.2 (15.4)
subtype1 42 8 0.1 - 182.3 (14.5)
subtype2 135 37 0.0 - 211.2 (15.4)
subtype3 99 14 0.1 - 156.2 (16.0)

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 = 0.0671 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 277 43.1 (13.3)
subtype1 42 47.4 (12.6)
subtype2 136 42.5 (13.5)
subtype3 99 42.0 (13.2)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 127 150
subtype1 22 20
subtype2 60 76
subtype3 45 54

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 = 0.151 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 133 87.8 (10.8)
subtype1 22 86.8 (13.6)
subtype2 72 89.4 (10.3)
subtype3 39 85.4 (9.7)

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 = 3.51e-14 (Chi-square test), Q value = 2.5e-12

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 88 77 112
subtype1 1 7 34
subtype2 69 40 27
subtype3 18 30 51

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 = 0.179 (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 89 188
subtype1 11 31
subtype2 51 85
subtype3 27 72

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 68 53 91 17 46
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.199 (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 274 58 0.0 - 211.2 (15.2)
subtype1 67 16 0.0 - 117.4 (14.9)
subtype2 53 14 0.1 - 211.2 (15.4)
subtype3 91 12 0.1 - 156.2 (14.5)
subtype4 17 3 0.1 - 90.8 (12.8)
subtype5 46 13 0.1 - 182.3 (17.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.305 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 275 43.2 (13.3)
subtype1 68 42.7 (14.2)
subtype2 53 42.6 (12.4)
subtype3 91 42.6 (12.8)
subtype4 17 50.0 (11.1)
subtype5 46 43.3 (14.6)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.738 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 127 148
subtype1 32 36
subtype2 20 33
subtype3 44 47
subtype4 8 9
subtype5 23 23

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

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

P value = 0.173 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 131 87.6 (10.8)
subtype1 33 90.6 (10.0)
subtype2 31 85.2 (11.8)
subtype3 38 86.3 (9.7)
subtype4 8 92.5 (4.6)
subtype5 21 87.1 (13.1)

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 = 5.8e-08 (Chi-square test), Q value = 3.7e-06

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 86 76 113
subtype1 30 19 19
subtype2 31 13 9
subtype3 16 26 49
subtype4 1 3 13
subtype5 8 15 23

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 = 0.27 (Chi-square test), Q value = 1

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

nPatients NO YES
ALL 88 187
subtype1 19 49
subtype2 22 31
subtype3 24 67
subtype4 5 12
subtype5 18 28

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 1 154 120
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0273 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 273 57 0.0 - 211.2 (15.3)
subtype2 153 38 0.0 - 182.3 (15.7)
subtype3 120 19 0.1 - 211.2 (14.7)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.585 (t-test), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 274 43.1 (13.3)
subtype2 154 42.7 (13.2)
subtype3 120 43.6 (13.6)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0202 (Fisher's exact test), Q value = 0.93

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

nPatients FEMALE MALE
ALL 126 148
subtype2 61 93
subtype3 65 55

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

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

P value = 0.35 (t-test), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 131 87.6 (10.8)
subtype2 75 88.4 (10.8)
subtype3 56 86.6 (10.8)

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 = 8.79e-05 (Chi-square test), Q value = 0.005

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 85 76 113
subtype2 63 42 49
subtype3 22 34 64

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 = 0.244 (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 88 186
subtype2 54 100
subtype3 34 86

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 46 41 36 75 27 33 4 13
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 3.57e-08 (logrank test), Q value = 2.3e-06

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

nPatients nDeath Duration Range (Median), Month
ALL 274 58 0.0 - 211.2 (15.2)
subtype1 46 5 0.0 - 117.4 (15.5)
subtype2 41 16 0.1 - 75.2 (10.6)
subtype3 36 7 0.1 - 130.8 (15.6)
subtype4 75 9 0.1 - 156.2 (14.5)
subtype5 27 4 0.1 - 154.1 (14.3)
subtype6 33 16 1.2 - 211.2 (18.9)
subtype7 4 1 12.4 - 23.6 (18.4)
subtype8 12 0 4.8 - 72.9 (17.6)

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 = 0.000496 (ANOVA), Q value = 0.027

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

nPatients Mean (Std.Dev)
ALL 275 43.2 (13.3)
subtype1 46 36.8 (11.8)
subtype2 41 50.0 (14.6)
subtype3 36 42.0 (10.7)
subtype4 75 42.7 (13.5)
subtype5 27 44.0 (13.8)
subtype6 33 46.8 (13.3)
subtype7 4 42.8 (9.7)
subtype8 13 39.6 (9.6)

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 = 0.192 (Chi-square test), Q value = 1

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

nPatients FEMALE MALE
ALL 127 148
subtype1 13 33
subtype2 18 23
subtype3 18 18
subtype4 37 38
subtype5 15 12
subtype6 19 14
subtype7 1 3
subtype8 6 7

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 = 0.0758 (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 131 87.6 (10.8)
subtype1 21 91.9 (9.3)
subtype2 15 84.0 (9.9)
subtype3 23 86.5 (13.7)
subtype4 28 83.6 (9.5)
subtype5 15 92.0 (9.4)
subtype6 19 87.9 (12.3)
subtype7 3 90.0 (0.0)
subtype8 7 91.4 (3.8)

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 = 1.54e-07 (Chi-square test), Q value = 9.7e-06

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 86 76 113
subtype1 19 16 11
subtype2 26 7 8
subtype3 15 12 9
subtype4 13 22 40
subtype5 3 3 21
subtype6 8 10 15
subtype7 2 1 1
subtype8 0 5 8

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 = 0.000423 (Chi-square test), Q value = 0.023

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

nPatients NO YES
ALL 88 187
subtype1 14 32
subtype2 12 29
subtype3 10 26
subtype4 18 57
subtype5 7 20
subtype6 22 11
subtype7 3 1
subtype8 2 11

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 10 153 112
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0642 (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 274 58 0.0 - 211.2 (15.2)
subtype1 10 5 5.3 - 211.2 (12.3)
subtype2 152 41 0.0 - 182.3 (16.2)
subtype3 112 12 0.1 - 156.2 (14.5)

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 = 0.549 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 275 43.2 (13.3)
subtype1 10 46.9 (14.7)
subtype2 153 42.6 (13.4)
subtype3 112 43.6 (13.2)

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 = 0.0107 (Fisher's exact test), Q value = 0.54

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

nPatients FEMALE MALE
ALL 127 148
subtype1 7 3
subtype2 59 94
subtype3 61 51

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 = 0.547 (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 131 87.6 (10.8)
subtype1 8 86.2 (7.4)
subtype2 75 88.5 (10.7)
subtype3 48 86.5 (11.4)

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 = 1.57e-07 (Chi-square test), Q value = 9.7e-06

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 86 76 113
subtype1 6 3 1
subtype2 65 43 45
subtype3 15 30 67

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 = 0.0765 (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 88 187
subtype1 5 5
subtype2 55 98
subtype3 28 84

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 = 281

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[8] 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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