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

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

Summary

Testing the association between subtypes identified by 7 different clustering approaches and 7 clinical features across 143 patients, 13 significant findings detected with P value < 0.05.

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

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

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

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'AGE',  'GENDER', and 'HISTOLOGICAL.TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
Statistical Tests logrank test ANOVA Fisher's exact test ANOVA Chi-square test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.0944 0.326 0.101 0.441 0.0226 0.384 0.883
mRNA cHierClus subtypes 0.126 0.467 0.172 0.441 0.0136 0.757 0.685
METHLYATION CNMF 1.73e-06 1.38e-05 0.0696 0.845 3.33e-10 6.96e-05 0.459
RNAseq CNMF subtypes 0.0355 0.0914 0.306 0.826 0.000403 0.201 0.661
RNAseq cHierClus subtypes 0.0635 0.0294 0.214 0.842 0.000268 0.366 0.281
MIRseq CNMF subtypes 0.195 0.285 0.172 0.719 0.469 0.79 0.102
MIRseq cHierClus subtypes 0.112 0.0293 0.0238 0.729 0.00256 0.339 0.0789
Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

P value = 0.0944 (logrank test)

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)

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)

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)

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)

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 = 0.384 (Fisher's exact test)

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

nPatients NO YES
ALL 19 8
subtype1 7 2
subtype2 8 2
subtype3 4 4

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

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.883 (Fisher's exact test)

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 17 10
subtype1 5 4
subtype2 7 3
subtype3 5 3

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S9.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 9 7 11
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.126 (logrank test)

Table S10.  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 9 4 10.6 - 130.8 (43.9)
subtype2 7 2 14.4 - 134.3 (52.4)
subtype3 11 3 0.1 - 78.2 (41.1)

Figure S8.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.467 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 27 39.3 (9.1)
subtype1 9 39.2 (6.2)
subtype2 7 36.0 (13.6)
subtype3 11 41.5 (7.6)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.172 (Fisher's exact test)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 9 18
subtype1 2 7
subtype2 1 6
subtype3 6 5

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.441 (ANOVA)

Table S13.  Clustering Approach #2: 'mRNA cHierClus 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 3 90.0 (10.0)
subtype3 7 92.9 (7.6)

Figure S11.  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 = 0.0136 (Chi-square test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

Figure S12.  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 = 0.757 (Fisher's exact test)

Table S15.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 19 8
subtype1 7 2
subtype2 4 3
subtype3 8 3

Figure S13.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.685 (Fisher's exact test)

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 17 10
subtype1 5 4
subtype2 4 3
subtype3 8 3

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'METHLYATION CNMF'

Table S17.  Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'

Cluster Labels 1 2 3 4
Number of samples 63 23 16 38
'METHLYATION CNMF' versus 'Time to Death'

P value = 1.73e-06 (logrank test)

Table S18.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 140 44 0.0 - 211.2 (17.8)
subtype1 63 19 0.0 - 156.2 (23.3)
subtype2 23 15 0.9 - 211.2 (11.5)
subtype3 16 2 0.1 - 97.9 (14.3)
subtype4 38 8 0.1 - 182.3 (26.1)

Figure S15.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 1.38e-05 (ANOVA)

Table S19.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 140 42.6 (13.0)
subtype1 63 38.8 (11.4)
subtype2 23 51.7 (13.7)
subtype3 16 36.2 (13.0)
subtype4 38 46.0 (11.2)

Figure S16.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0696 (Fisher's exact test)

Table S20.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 62 78
subtype1 22 41
subtype2 15 8
subtype3 6 10
subtype4 19 19

Figure S17.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.845 (ANOVA)

Table S21.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 76 89.2 (10.3)
subtype1 38 90.0 (10.1)
subtype2 14 87.9 (5.8)
subtype3 6 86.7 (10.3)
subtype4 18 89.4 (13.5)

Figure S18.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 3.33e-10 (Chi-square test)

Table S22.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 49 34 56
subtype1 27 21 14
subtype2 14 6 3
subtype3 6 5 5
subtype4 2 2 34

Figure S19.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 6.96e-05 (Fisher's exact test)

Table S23.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 83 57
subtype1 48 15
subtype2 16 7
subtype3 5 11
subtype4 14 24

Figure S20.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.459 (Fisher's exact test)

Table S24.  Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 70 70
subtype1 32 31
subtype2 12 11
subtype3 5 11
subtype4 21 17

Figure S21.  Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RNAseq CNMF subtypes'

Table S25.  Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 13 17 17
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0355 (logrank test)

Table S26.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 47 24 1.2 - 211.2 (31.8)
subtype1 13 9 6.7 - 211.2 (16.8)
subtype2 17 8 6.8 - 94.5 (43.9)
subtype3 17 7 1.2 - 182.3 (47.9)

Figure S22.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0914 (ANOVA)

Table S27.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 47 44.0 (12.3)
subtype1 13 46.4 (14.6)
subtype2 17 38.8 (7.4)
subtype3 17 47.3 (13.2)

Figure S23.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.306 (Fisher's exact test)

Table S28.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 25 22
subtype1 9 4
subtype2 7 10
subtype3 9 8

Figure S24.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.826 (ANOVA)

Table S29.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 27 89.6 (10.9)
subtype1 9 90.0 (5.0)
subtype2 10 88.0 (14.8)
subtype3 8 91.2 (11.3)

Figure S25.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000403 (Chi-square test)

Table S30.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 15 14 18
subtype1 8 4 1
subtype2 7 6 4
subtype3 0 4 13

Figure S26.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.201 (Fisher's exact test)

Table S31.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 33 14
subtype1 10 3
subtype2 14 3
subtype3 9 8

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.661 (Fisher's exact test)

Table S32.  Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 26 21
subtype1 7 6
subtype2 8 9
subtype3 11 6

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq cHierClus subtypes'

Table S33.  Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 12 16 19
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0635 (logrank test)

Table S34.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 47 24 1.2 - 211.2 (31.8)
subtype1 12 8 6.7 - 211.2 (15.1)
subtype2 16 6 1.2 - 182.3 (43.1)
subtype3 19 10 6.8 - 114.0 (47.9)

Figure S29.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0294 (ANOVA)

Table S35.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 47 44.0 (12.3)
subtype1 12 46.8 (15.2)
subtype2 16 48.6 (12.3)
subtype3 19 38.4 (7.8)

Figure S30.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.214 (Fisher's exact test)

Table S36.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 25 22
subtype1 9 3
subtype2 8 8
subtype3 8 11

Figure S31.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.842 (ANOVA)

Table S37.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 27 89.6 (10.9)
subtype1 8 90.0 (5.3)
subtype2 7 91.4 (12.1)
subtype3 12 88.3 (13.4)

Figure S32.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000268 (Chi-square test)

Table S38.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 15 14 18
subtype1 7 4 1
subtype2 0 3 13
subtype3 8 7 4

Figure S33.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.366 (Fisher's exact test)

Table S39.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 33 14
subtype1 9 3
subtype2 9 7
subtype3 15 4

Figure S34.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.281 (Fisher's exact test)

Table S40.  Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 26 21
subtype1 7 5
subtype2 11 5
subtype3 8 11

Figure S35.  Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'MIRseq CNMF subtypes'

Table S41.  Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 22 25 20
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.195 (logrank test)

Table S42.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 27 0.1 - 211.2 (23.8)
subtype1 22 12 4.7 - 182.3 (19.4)
subtype2 25 9 0.1 - 134.3 (46.6)
subtype3 20 6 1.2 - 211.2 (16.3)

Figure S36.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA)

Table S43.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 67 43.1 (12.1)
subtype1 22 42.7 (11.2)
subtype2 25 40.8 (9.6)
subtype3 20 46.5 (15.2)

Figure S37.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.172 (Fisher's exact test)

Table S44.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 33 34
subtype1 14 8
subtype2 9 16
subtype3 10 10

Figure S38.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.719 (ANOVA)

Table S45.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 89.8 (9.6)
subtype1 13 89.2 (7.6)
subtype2 16 88.8 (12.6)
subtype3 12 91.7 (7.2)

Figure S39.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.469 (Chi-square test)

Table S46.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 24 20 23
subtype1 8 7 7
subtype2 10 9 6
subtype3 6 4 10

Figure S40.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.79 (Fisher's exact test)

Table S47.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 48 19
subtype1 17 5
subtype2 17 8
subtype3 14 6

Figure S41.  Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.102 (Fisher's exact test)

Table S48.  Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 39 28
subtype1 9 13
subtype2 18 7
subtype3 12 8

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

Clustering Approach #7: 'MIRseq cHierClus subtypes'

Table S49.  Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 25 15 13 14
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.112 (logrank test)

Table S50.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 67 27 0.1 - 211.2 (23.8)
subtype1 25 9 0.1 - 134.3 (46.6)
subtype2 15 8 6.4 - 211.2 (13.4)
subtype3 13 6 4.7 - 114.0 (18.9)
subtype4 14 4 1.2 - 182.3 (24.1)

Figure S43.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.0293 (ANOVA)

Table S51.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 67 43.1 (12.1)
subtype1 25 40.1 (9.1)
subtype2 15 47.8 (12.4)
subtype3 13 38.0 (9.3)
subtype4 14 48.3 (15.6)

Figure S44.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.0238 (Fisher's exact test)

Table S52.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 33 34
subtype1 8 17
subtype2 11 4
subtype3 9 4
subtype4 5 9

Figure S45.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.729 (ANOVA)

Table S53.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 41 89.8 (9.6)
subtype1 16 88.8 (12.6)
subtype2 12 88.3 (5.8)
subtype3 8 92.5 (4.6)
subtype4 5 92.0 (13.0)

Figure S46.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00256 (Chi-square test)

Table S54.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 24 20 23
subtype1 11 9 5
subtype2 9 4 2
subtype3 4 4 5
subtype4 0 3 11

Figure S47.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.339 (Fisher's exact test)

Table S55.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 48 19
subtype1 17 8
subtype2 13 2
subtype3 10 3
subtype4 8 6

Figure S48.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0789 (Fisher's exact test)

Table S56.  Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

nPatients NO YES
ALL 39 28
subtype1 19 6
subtype2 9 6
subtype3 5 8
subtype4 6 8

Figure S49.  Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

  • Cluster data file = LGG.mergedcluster.txt

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

  • Number of patients = 143

  • Number of clustering approaches = 7

  • Number of selected clinical features = 7

  • 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

Download Results

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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