Bladder Urothelial Carinoma: 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 4 clinical features across 56 patients, 2 significant findings detected with P value < 0.05.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death'.

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

- 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 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 2 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA
RNAseq CNMF subtypes | 0.0369 | 0.284 | 0.187 | 0.746
RNAseq cHierClus subtypes | 0.799 | 0.733 | 0.398 | 1
MIRseq CNMF subtypes | 0.387 | 0.423 | 0.405 | 0.557
MIRseq cHierClus subtypes | 0.0258 | 0.367 | 0.734 | 0.376

Clustering Approach #1: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 22 | 9 | 16 | 6

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0369 (logrank test)

Table S2. Clustering Approach #1: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 53 | 19 | 0.4 - 118.9 (8.3)
subtype1 | 22 | 6 | 0.5 - 100.5 (7.4)
subtype2 | 9 | 6 | 5.1 - 118.9 (12.2)
subtype3 | 16 | 5 | 0.4 - 75.3 (8.9)
subtype4 | 6 | 2 | 1.8 - 10.6 (3.8)

Figure S1. Get High-res Image Clustering Approach #1: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.284 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 53 | 68.4 (10.2)
subtype1 | 22 | 66.8 (10.6)
subtype2 | 9 | 67.1 (9.9)
subtype3 | 16 | 68.5 (10.0)
subtype4 | 6 | 75.8 (8.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.187 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 31 | 22
subtype1 | 16 | 6
subtype2 | 6 | 3
subtype3 | 7 | 9
subtype4 | 2 | 4

Figure S3. Get High-res Image Clustering Approach #1: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.746 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 80.0 (16.7)
subtype1 | 4 | 82.5 (15.0)
subtype2 | 2 | 85.0 (7.1)
subtype3 | 4 | 77.5 (25.0)
subtype4 | 1 | 70.0 (NA)

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

Clustering Approach #2: 'RNAseq cHierClus subtypes'

Table S6. Get Full Table Description of clustering approach #2: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 22 | 31

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.799 (logrank test)

Table S7. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 53 | 19 | 0.4 - 118.9 (8.3)
subtype1 | 22 | 10 | 0.4 - 75.3 (8.9)
subtype2 | 31 | 9 | 0.5 - 118.9 (7.0)

Figure S5. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.733 (t-test)

Table S8. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 53 | 68.4 (10.2)
subtype1 | 22 | 67.8 (10.4)
subtype2 | 31 | 68.8 (10.3)

Figure S6. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.398 (Fisher's exact test)

Table S9. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 31 | 22
subtype1 | 11 | 11
subtype2 | 20 | 11

Figure S7. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 1 (t-test)

Table S10. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 11 | 80.0 (16.7)
subtype1 | 6 | 80.0 (20.0)
subtype2 | 5 | 80.0 (14.1)

Figure S8. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #3: 'MIRseq CNMF subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 10 | 27 | 15

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.387 (logrank test)

Table S12. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 52 | 21 | 0.4 - 118.9 (8.0)
subtype1 | 10 | 3 | 1.5 - 118.9 (7.8)
subtype2 | 27 | 14 | 0.4 - 49.2 (8.6)
subtype3 | 15 | 4 | 0.5 - 100.5 (7.2)

Figure S9. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.423 (ANOVA)

Table S13. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 52 | 68.7 (10.0)
subtype1 | 10 | 71.1 (9.0)
subtype2 | 27 | 66.9 (10.0)
subtype3 | 15 | 70.2 (10.8)

Figure S10. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.405 (Fisher's exact test)

Table S14. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 19
subtype1 | 8 | 2
subtype2 | 17 | 10
subtype3 | 8 | 7

Figure S11. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.557 (ANOVA)

Table S15. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 79.0 (17.3)
subtype2 | 7 | 77.1 (19.8)
subtype3 | 3 | 83.3 (11.5)

Figure S12. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #4: 'MIRseq cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 9 | 12 | 12 | 19

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0258 (logrank test)

Table S17. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 52 | 21 | 0.4 - 118.9 (8.0)
subtype1 | 9 | 7 | 2.0 - 26.9 (9.0)
subtype2 | 12 | 3 | 0.4 - 49.2 (10.0)
subtype3 | 12 | 4 | 1.5 - 118.9 (9.5)
subtype4 | 19 | 7 | 0.5 - 100.5 (7.2)

Figure S13. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.367 (ANOVA)

Table S18. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 52 | 68.7 (10.0)
subtype1 | 9 | 69.6 (7.5)
subtype2 | 12 | 64.2 (11.8)
subtype3 | 12 | 70.6 (8.5)
subtype4 | 19 | 69.9 (10.6)

Figure S14. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.734 (Fisher's exact test)

Table S19. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 33 | 19
subtype1 | 5 | 4
subtype2 | 8 | 4
subtype3 | 9 | 3
subtype4 | 11 | 8

Figure S15. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.376 (ANOVA)

Table S20. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 10 | 79.0 (17.3)
subtype1 | 1 | 80.0 (NA)
subtype2 | 5 | 74.0 (23.0)
subtype4 | 4 | 85.0 (10.0)

Figure S16. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data
Input

- Cluster data file = BLCA.mergedcluster.txt

- Clinical data file = BLCA.clin.merged.picked.txt

- Number of patients = 56

- Number of clustering approaches = 4

- Number of selected clinical features = 4

- 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

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

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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] 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.
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