Prostate Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 6 different clustering approaches and 3 clinical features across 147 patients, 2 significant findings detected with P value < 0.05.

- 3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'AGE'.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.

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

- 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 4 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 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 6 different clustering approaches and 3 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 | RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests | logrank test | ANOVA | Fisher's exact test
CN CNMF | 100 | 0.0257 | 0.853
METHLYATION CNMF | 100 | 0.00439 | 0.861
RNAseq CNMF subtypes | 100 | 0.207 | 0.86
RNAseq cHierClus subtypes | 100 | 0.302 | 1
MIRseq CNMF subtypes | 100 | 0.0775 | 0.631
MIRseq cHierClus subtypes | 100 | 0.46 | 0.197

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 33 | 66 | 46 | 1

'CN CNMF' versus 'Time to Death'

P value = 100 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 145 | 1 | 0.3 - 66.0 (16.8)
subtype1 | 33 | 0 | 0.3 - 63.3 (9.4)
subtype2 | 66 | 0 | 1.0 - 65.9 (17.6)
subtype3 | 46 | 1 | 0.9 - 66.0 (18.5)

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

'CN CNMF' versus 'AGE'

P value = 0.0257 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 145 | 60.5 (6.9)
subtype1 | 33 | 61.9 (6.1)
subtype2 | 66 | 58.8 (7.5)
subtype3 | 46 | 61.9 (6.1)

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

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.853 (Fisher's exact test)

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 140
subtype1 | 1 | 32
subtype2 | 3 | 63
subtype3 | 1 | 45

Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #2: 'METHLYATION CNMF'

Table S5. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 47 | 43 | 57

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test)

Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 147 | 1 | 0.3 - 66.0 (16.6)
subtype1 | 47 | 0 | 0.3 - 65.9 (15.9)
subtype2 | 43 | 0 | 1.1 - 54.9 (13.1)
subtype3 | 57 | 1 | 1.0 - 66.0 (17.5)

Figure S4. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00439 (ANOVA)

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 147 | 60.4 (7.0)
subtype1 | 47 | 61.9 (6.3)
subtype2 | 43 | 57.5 (7.2)
subtype3 | 57 | 61.4 (6.8)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.861 (Fisher's exact test)

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 142
subtype1 | 1 | 46
subtype2 | 2 | 41
subtype3 | 2 | 55

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S9. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 40 | 35 | 42

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S10. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 117 | 1 | 0.3 - 66.0 (14.8)
subtype1 | 40 | 0 | 0.3 - 65.9 (16.0)
subtype2 | 35 | 0 | 1.0 - 54.9 (13.0)
subtype3 | 42 | 1 | 1.0 - 66.0 (14.8)

Figure S7. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.207 (ANOVA)

Table S11. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 117 | 60.7 (7.2)
subtype1 | 40 | 61.5 (6.9)
subtype2 | 35 | 58.9 (7.3)
subtype3 | 42 | 61.4 (7.2)

Figure S8. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.86 (Fisher's exact test)

Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 112
subtype1 | 1 | 39
subtype2 | 2 | 33
subtype3 | 2 | 40

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S13. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 34 | 36 | 47

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S14. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 117 | 1 | 0.3 - 66.0 (14.8)
subtype1 | 34 | 0 | 1.0 - 65.9 (15.5)
subtype2 | 36 | 1 | 1.0 - 66.0 (13.0)
subtype3 | 47 | 0 | 0.3 - 54.9 (13.1)

Figure S10. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.302 (ANOVA)

Table S15. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 117 | 60.7 (7.2)
subtype1 | 34 | 60.9 (7.1)
subtype2 | 36 | 62.0 (7.5)
subtype3 | 47 | 59.5 (6.9)

Figure S11. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S16. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 112
subtype1 | 1 | 33
subtype2 | 2 | 34
subtype3 | 2 | 45

Figure S12. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'MIRseq CNMF subtypes'

Table S17. Get Full Table Description of clustering approach #5: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 41 | 40 | 26 | 38

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S18. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 145 | 1 | 0.3 - 66.0 (16.6)
subtype1 | 41 | 0 | 0.9 - 66.0 (22.1)
subtype2 | 40 | 0 | 3.0 - 54.9 (19.7)
subtype3 | 26 | 0 | 0.3 - 64.1 (16.0)
subtype4 | 38 | 1 | 1.0 - 66.0 (5.7)

Figure S13. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.0775 (ANOVA)

Table S19. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 145 | 60.5 (6.9)
subtype1 | 41 | 62.7 (6.1)
subtype2 | 40 | 58.9 (7.0)
subtype3 | 26 | 59.4 (6.8)
subtype4 | 38 | 60.4 (7.4)

Figure S14. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.631 (Fisher's exact test)

Table S20. Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 140
subtype1 | 2 | 39
subtype2 | 2 | 38
subtype3 | 1 | 25
subtype4 | 0 | 38

Figure S15. Get High-res Image Clustering Approach #5: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'MIRseq cHierClus subtypes'

Table S21. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 25 | 68 | 52

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test)

Table S22. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 145 | 1 | 0.3 - 66.0 (16.6)
subtype1 | 25 | 0 | 0.3 - 64.1 (18.2)
subtype2 | 68 | 0 | 0.9 - 65.9 (22.6)
subtype3 | 52 | 1 | 1.0 - 66.0 (6.0)

Figure S16. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.46 (ANOVA)

Table S23. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 145 | 60.5 (6.9)
subtype1 | 25 | 59.0 (6.7)
subtype2 | 68 | 61.0 (6.9)
subtype3 | 52 | 60.4 (7.2)

Figure S17. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.197 (Fisher's exact test)

Table S24. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 140
subtype1 | 1 | 24
subtype2 | 4 | 64
subtype3 | 0 | 52

Figure S18. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

- Cluster data file = PRAD-TP.mergedcluster.txt

- Clinical data file = PRAD-TP.clin.merged.picked.txt

- Number of patients = 147

- Number of clustering approaches = 6

- Number of selected clinical features = 3

- 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

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)
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