Bladder Urothelial Carcinoma: 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 5 different clustering approaches and 4 clinical features across 65 patients, 2 significant findings detected with P value < 0.05.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.

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

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

- CNMF clustering analysis on sequencing-based miR expression data identified 5 subtypes that correlate to 'Time to Death'.

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 5 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 | t-test | Fisher's exact test | t-test
METHLYATION CNMF | 0.0846 | 0.64 | 0.207 | 0.553
RNAseq CNMF subtypes | 0.0614 | 0.966 | 0.267 |
RNAseq cHierClus subtypes | 0.0098 | 0.929 | 0.189 |
MIRseq CNMF subtypes | 0.00313 | 0.482 | 0.928 | 0.951
MIRseq cHierClus subtypes | 0.498 | 0.138 | 1 |

Clustering Approach #1: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 18 | 26 | 20

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0846 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 64 | 24 | 0.4 - 118.9 (8.0)
subtype1 | 18 | 5 | 1.5 - 118.9 (8.7)
subtype2 | 26 | 12 | 0.4 - 75.3 (8.3)
subtype3 | 20 | 7 | 0.5 - 26.1 (7.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.64 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 68.3 (10.1)
subtype1 | 18 | 66.4 (10.3)
subtype2 | 26 | 68.8 (10.6)
subtype3 | 20 | 69.4 (9.6)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.207 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 41 | 23
subtype1 | 10 | 8
subtype2 | 15 | 11
subtype3 | 16 | 4

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

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

P value = 0.553 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 15 | 82.0 (14.7)
subtype1 | 4 | 85.0 (5.8)
subtype2 | 3 | 73.3 (28.9)
subtype3 | 8 | 83.8 (11.9)

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2
--- | --- | ---
Number of samples | 18 | 14

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0614 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 32 | 15 | 0.4 - 118.9 (7.8)
subtype1 | 18 | 6 | 0.5 - 118.9 (7.8)
subtype2 | 14 | 9 | 0.4 - 26.1 (8.0)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.966 (t-test)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 32 | 70.3 (8.9)
subtype1 | 18 | 70.2 (9.6)
subtype2 | 14 | 70.4 (8.3)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.267 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 10
subtype1 | 14 | 4
subtype2 | 8 | 6

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

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S10. Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 12 | 8 | 12

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0098 (logrank test)

Table S11. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 32 | 15 | 0.4 - 118.9 (7.8)
subtype1 | 12 | 7 | 0.4 - 19.0 (8.0)
subtype2 | 8 | 5 | 0.5 - 19.5 (6.8)
subtype3 | 12 | 3 | 2.0 - 118.9 (8.7)

Figure S8. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.929 (ANOVA)

Table S12. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 32 | 70.3 (8.9)
subtype1 | 12 | 69.5 (8.5)
subtype2 | 8 | 70.5 (10.1)
subtype3 | 12 | 70.9 (9.2)

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.189 (Fisher's exact test)

Table S13. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 10
subtype1 | 6 | 6
subtype2 | 7 | 1
subtype3 | 9 | 3

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

Clustering Approach #4: 'MIRseq CNMF subtypes'

Table S14. Get Full Table Description of clustering approach #4: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 12 | 28 | 10 | 3 | 12

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00313 (logrank test)

Table S15. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 65 | 25 | 0.4 - 118.9 (7.8)
subtype1 | 12 | 4 | 1.5 - 118.9 (9.5)
subtype2 | 28 | 14 | 0.4 - 49.2 (8.3)
subtype3 | 10 | 3 | 0.7 - 36.4 (10.4)
subtype4 | 3 | 2 | 5.1 - 6.6 (6.2)
subtype5 | 12 | 2 | 0.5 - 100.5 (6.8)

Figure S11. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.482 (ANOVA)

Table S16. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 65 | 68.2 (10.1)
subtype1 | 12 | 70.6 (8.5)
subtype2 | 28 | 68.5 (9.7)
subtype3 | 10 | 63.1 (11.7)
subtype4 | 3 | 71.0 (10.6)
subtype5 | 12 | 68.8 (11.0)

Figure S12. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.928 (Chi-square test)

Table S17. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 42 | 23
subtype1 | 9 | 3
subtype2 | 18 | 10
subtype3 | 6 | 4
subtype4 | 2 | 1
subtype5 | 7 | 5

Figure S13. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.951 (ANOVA)

Table S18. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 15 | 82.0 (14.7)
subtype2 | 7 | 81.4 (18.6)
subtype3 | 5 | 82.0 (13.0)
subtype4 | 1 | 90.0 (NA)
subtype5 | 2 | 80.0 (14.1)

Figure S14. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S19. Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 2 | 12 | 51

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.498 (logrank test)

Table S20. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 24 | 0.4 - 118.9 (8.2)
subtype2 | 12 | 3 | 1.5 - 118.9 (7.8)
subtype3 | 51 | 21 | 0.4 - 100.5 (8.2)

Figure S15. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.138 (t-test)

Table S21. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 63 | 68.4 (10.2)
subtype2 | 12 | 72.0 (8.5)
subtype3 | 51 | 67.6 (10.4)

Figure S16. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

Table S22. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 40 | 23
subtype2 | 8 | 4
subtype3 | 32 | 19

Figure S17. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

Methods & Data
Input

- Cluster data file = BLCA.mergedcluster.txt

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

- Number of patients = 65

- Number of clustering approaches = 5

- 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

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
[7] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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