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 4 different clustering approaches and 7 clinical features across 30 patients, 4 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'.

- CNMF clustering analysis on sequencing-based miR expression data identified 2 subtypes that do not correlate to any clinical features.

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 4 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.0944 | 0.126 | 0.576 | 0.0158
AGE | ANOVA | 0.326 | 0.467 | 0.389 | 0.988
GENDER | Fisher's exact test | 0.101 | 0.172 | 1 | 0.605
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.441 | 0.441 | 0.0754 | 0.39
HISTOLOGICAL TYPE | Chi-square test | 0.0226 | 0.0136 | 0.486 | 0.00614
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.384 | 0.757 | 0.7 | 0.886
NEOADJUVANT THERAPY | Fisher's exact test | 0.883 | 0.685 | 1 | 0.707

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 | 26 | 9 | 8.0 - 134.3 (47.3)
subtype1 | 9 | 4 | 10.6 - 130.8 (43.9)
subtype2 | 9 | 3 | 8.0 - 78.2 (41.1)
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 | 10 | 17
subtype1 | 4 | 5
subtype2 | 3 | 7
subtype3 | 3 | 5

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 | 26 | 9 | 8.0 - 134.3 (47.3)
subtype1 | 9 | 4 | 10.6 - 130.8 (43.9)
subtype2 | 7 | 2 | 14.4 - 134.3 (52.4)
subtype3 | 10 | 3 | 8.0 - 78.2 (43.9)

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 | 10 | 17
subtype1 | 4 | 5
subtype2 | 3 | 4
subtype3 | 3 | 8

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

Clustering Approach #3: 'MIRseq CNMF subtypes'

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

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 15 | 15

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.576 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 29 | 10 | 7.7 - 134.3 (44.5)
subtype1 | 15 | 5 | 7.7 - 134.3 (46.6)
subtype2 | 14 | 5 | 9.5 - 130.8 (36.5)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.389 (t-test)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 30 | 40.9 (10.5)
subtype1 | 15 | 42.6 (13.6)
subtype2 | 15 | 39.2 (6.3)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 21 | 9
subtype1 | 11 | 4
subtype2 | 10 | 5

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

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

P value = 0.0754 (t-test)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 89.5 (11.8)
subtype1 | 9 | 94.4 (7.3)
subtype2 | 10 | 85.0 (13.5)

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

'MIRseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.486 (Chi-square test)

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

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 12 | 9 | 9
subtype1 | 5 | 4 | 6
subtype2 | 7 | 5 | 3

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

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

P value = 0.7 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 10 | 20
subtype1 | 6 | 9
subtype2 | 4 | 11

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 19 | 11
subtype1 | 9 | 6
subtype2 | 10 | 5

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

Clustering Approach #4: 'MIRseq cHierClus subtypes'

Table S25. Get Full Table Description of clustering approach #4: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 12 | 11 | 7

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0158 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 29 | 10 | 7.7 - 134.3 (44.5)
subtype1 | 12 | 2 | 8.0 - 134.3 (51.3)
subtype2 | 11 | 5 | 7.7 - 130.8 (46.6)
subtype3 | 6 | 3 | 9.5 - 78.2 (25.9)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.988 (ANOVA)

Table S27. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 30 | 40.9 (10.5)
subtype1 | 12 | 40.8 (13.1)
subtype2 | 11 | 40.6 (10.4)
subtype3 | 7 | 41.4 (6.3)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.605 (Fisher's exact test)

Table S28. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 21 | 9
subtype1 | 8 | 4
subtype2 | 9 | 2
subtype3 | 4 | 3

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

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

P value = 0.39 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 19 | 89.5 (11.8)
subtype1 | 7 | 92.9 (9.5)
subtype2 | 8 | 85.0 (15.1)
subtype3 | 4 | 92.5 (5.0)

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

'MIRseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00614 (Chi-square test)

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

nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA
--- | --- | --- | ---
ALL | 12 | 9 | 9
subtype1 | 1 | 5 | 6
subtype2 | 9 | 2 | 0
subtype3 | 2 | 2 | 3

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

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

P value = 0.886 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 10 | 20
subtype1 | 4 | 8
subtype2 | 3 | 8
subtype3 | 3 | 4

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.707 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 19 | 11
subtype1 | 7 | 5
subtype2 | 8 | 3
subtype3 | 4 | 3

Figure S28. Get High-res Image Clustering Approach #4: '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 = 30

- Number of clustering approaches = 4

- 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

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

Download Results

This is an experimental feature. Location of data archives could not be determined.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[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)
Meta

- Maintainer = TCGA GDAC Team

Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle