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 67 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 3 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- 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 | 0.0943 | 0.592 | 0.133 | 0.713
RNAseq CNMF subtypes | 0.0406 | 0.452 | 0.443 | 0.68
RNAseq cHierClus subtypes | 0.341 | 0.948 | 0.523 | 0.404
MIRseq CNMF subtypes | 0.00309 | 0.52 | 0.927 | 0.573
MIRseq cHierClus subtypes | 0.5 | 0.107 | 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 | 17 | 27 | 21

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0943 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 65 | 24 | 0.4 - 118.9 (7.8)
subtype1 | 17 | 4 | 1.5 - 118.9 (8.3)
subtype2 | 27 | 13 | 0.4 - 75.3 (8.3)
subtype3 | 21 | 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 = 0.592 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 65 | 68.0 (10.5)
subtype1 | 17 | 65.8 (10.2)
subtype2 | 27 | 69.1 (10.5)
subtype3 | 21 | 68.2 (10.7)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.133 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 23 | 42
subtype1 | 8 | 9
subtype2 | 11 | 16
subtype3 | 4 | 17

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

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

P value = 0.713 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 79.4 (17.7)
subtype1 | 3 | 86.7 (5.8)
subtype2 | 4 | 75.0 (23.8)
subtype3 | 9 | 78.9 (18.3)

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 | 3
--- | --- | --- | ---
Number of samples | 26 | 16 | 22

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0406 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 64 | 23 | 0.4 - 118.9 (7.7)
subtype1 | 26 | 7 | 0.5 - 100.5 (7.1)
subtype2 | 16 | 9 | 1.8 - 118.9 (6.7)
subtype3 | 22 | 7 | 0.4 - 75.3 (8.7)

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 = 0.452 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 67.5 (10.6)
subtype1 | 26 | 66.1 (10.4)
subtype2 | 16 | 70.3 (9.5)
subtype3 | 22 | 67.1 (11.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.443 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 42
subtype1 | 7 | 19
subtype2 | 5 | 11
subtype3 | 10 | 12

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

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

P value = 0.68 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 80.0 (17.3)
subtype1 | 7 | 84.3 (11.3)
subtype2 | 3 | 80.0 (10.0)
subtype3 | 7 | 75.7 (24.4)

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

Clustering Approach #3: 'RNAseq cHierClus subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 23 | 7 | 34

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.341 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 64 | 23 | 0.4 - 118.9 (7.7)
subtype1 | 23 | 8 | 0.4 - 75.3 (8.6)
subtype2 | 7 | 5 | 5.1 - 118.9 (6.7)
subtype3 | 34 | 10 | 0.5 - 100.5 (6.9)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.948 (ANOVA)

Table S13. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 67.5 (10.6)
subtype1 | 23 | 66.9 (11.6)
subtype2 | 7 | 67.9 (9.0)
subtype3 | 34 | 67.8 (10.5)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.523 (Fisher's exact test)

Table S14. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 22 | 42
subtype1 | 9 | 14
subtype2 | 1 | 6
subtype3 | 12 | 22

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

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

P value = 0.404 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 17 | 80.0 (17.3)
subtype1 | 6 | 73.3 (25.8)
subtype2 | 2 | 85.0 (7.1)
subtype3 | 9 | 83.3 (11.2)

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

Clustering Approach #4: 'MIRseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 12 | 29 | 10 | 3 | 12

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00309 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 66 | 25 | 0.4 - 118.9 (7.7)
subtype1 | 12 | 4 | 1.5 - 118.9 (9.5)
subtype2 | 29 | 14 | 0.4 - 49.2 (8.2)
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 S13. Get High-res Image Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.52 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 66 | 67.9 (10.4)
subtype1 | 12 | 70.6 (8.5)
subtype2 | 29 | 67.7 (10.5)
subtype3 | 10 | 63.1 (11.7)
subtype4 | 3 | 71.0 (10.6)
subtype5 | 12 | 68.8 (11.0)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.927 (Chi-square test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 23 | 43
subtype1 | 3 | 9
subtype2 | 10 | 19
subtype3 | 4 | 6
subtype4 | 1 | 2
subtype5 | 5 | 7

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

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

P value = 0.573 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 16 | 79.4 (17.7)
subtype2 | 8 | 76.2 (22.6)
subtype3 | 5 | 82.0 (13.0)
subtype4 | 1 | 90.0 (NA)
subtype5 | 2 | 80.0 (14.1)

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

Clustering Approach #5: 'MIRseq cHierClus subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 2 | 12 | 52

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.5 (logrank test)

Table S22. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 64 | 24 | 0.4 - 118.9 (8.0)
subtype2 | 12 | 3 | 1.5 - 118.9 (7.8)
subtype3 | 52 | 21 | 0.4 - 100.5 (8.0)

Figure S17. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.107 (t-test)

Table S23. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 64 | 68.1 (10.5)
subtype2 | 12 | 72.0 (8.5)
subtype3 | 52 | 67.2 (10.8)

Figure S18. 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 S24. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 23 | 41
subtype2 | 4 | 8
subtype3 | 19 | 33

Figure S19. 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 = 67

- 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

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. 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] 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)
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