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.

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] 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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