Prostate Adenocarcinoma: 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 124 patients, 2 significant findings detected with P value < 0.05.

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

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOADJUVANT.THERAPY'.

- 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 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 | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Fisher's exact test
METHLYATION CNMF | 1 | 0.00334 | 1 | 0.315
RNAseq CNMF subtypes | 1 | 0.057 | 0.61 | 0.252
RNAseq cHierClus subtypes | 1 | 0.0522 | 1 | 0.0124
MIRseq CNMF subtypes | 1 | 0.306 | 1 | 0.249
MIRseq cHierClus subtypes | 1 | 0.138 | 1 | 0.145

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 | 36 | 41 | 47

'METHLYATION CNMF' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 124 | 1 | 0.3 - 66.0 (19.1)
subtype1 | 36 | 0 | 0.3 - 65.9 (21.0)
subtype2 | 41 | 0 | 1.0 - 62.4 (17.6)
subtype3 | 47 | 1 | 1.0 - 66.0 (19.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00334 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 124 | 61.2 (6.6)
subtype1 | 36 | 62.9 (5.7)
subtype2 | 41 | 58.4 (7.0)
subtype3 | 47 | 62.3 (6.2)

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

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

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 119
subtype1 | 1 | 35
subtype2 | 2 | 39
subtype3 | 2 | 45

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.315 (Fisher's exact test)

Table S5. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 120
subtype1 | 1 | 35
subtype2 | 0 | 41
subtype3 | 3 | 44

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

Clustering Approach #2: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 7 | 3 | 7 | 7 | 8 | 5 | 4 | 8 | 4

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 53 | 0 | 1.0 - 65.9 (21.8)
subtype1 | 7 | 0 | 3.0 - 47.4 (15.9)
subtype2 | 3 | 0 | 1.0 - 44.0 (7.0)
subtype3 | 7 | 0 | 2.0 - 35.0 (24.0)
subtype4 | 7 | 0 | 1.0 - 65.9 (19.5)
subtype5 | 8 | 0 | 1.0 - 34.9 (13.9)
subtype6 | 5 | 0 | 12.0 - 27.9 (26.4)
subtype7 | 4 | 0 | 1.0 - 33.0 (11.3)
subtype8 | 8 | 0 | 13.1 - 46.1 (22.2)
subtype9 | 4 | 0 | 17.5 - 54.9 (35.2)

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 = 0.057 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 53 | 61.3 (7.2)
subtype1 | 7 | 64.6 (4.7)
subtype2 | 3 | 67.3 (6.4)
subtype3 | 7 | 65.9 (8.3)
subtype4 | 7 | 58.9 (4.5)
subtype5 | 8 | 55.8 (6.3)
subtype6 | 5 | 63.6 (8.6)
subtype7 | 4 | 62.2 (9.1)
subtype8 | 8 | 57.9 (6.7)
subtype9 | 4 | 61.5 (4.7)

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

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

P value = 0.61 (Chi-square test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 49
subtype1 | 1 | 6
subtype2 | 1 | 2
subtype3 | 0 | 7
subtype4 | 1 | 6
subtype5 | 1 | 7
subtype6 | 0 | 5
subtype7 | 0 | 4
subtype8 | 0 | 8
subtype9 | 0 | 4

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.252 (Chi-square test)

Table S10. Clustering Approach #2: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 50
subtype1 | 0 | 7
subtype2 | 0 | 3
subtype3 | 2 | 5
subtype4 | 1 | 6
subtype5 | 0 | 8
subtype6 | 0 | 5
subtype7 | 0 | 4
subtype8 | 0 | 8
subtype9 | 0 | 4

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

Clustering Approach #3: 'RNAseq cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 11 | 10 | 6 | 26

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 53 | 0 | 1.0 - 65.9 (21.8)
subtype1 | 11 | 0 | 1.0 - 47.4 (15.9)
subtype2 | 10 | 0 | 1.0 - 65.9 (14.8)
subtype3 | 6 | 0 | 1.0 - 35.0 (24.0)
subtype4 | 26 | 0 | 1.0 - 54.9 (23.5)

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 = 0.0522 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 53 | 61.3 (7.2)
subtype1 | 11 | 62.0 (5.7)
subtype2 | 10 | 60.1 (8.3)
subtype3 | 6 | 68.5 (2.4)
subtype4 | 26 | 59.8 (7.3)

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

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

P value = 1 (Fisher's exact test)

Table S14. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 49
subtype1 | 1 | 10
subtype2 | 1 | 9
subtype3 | 0 | 6
subtype4 | 2 | 24

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0124 (Fisher's exact test)

Table S15. Clustering Approach #3: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 50
subtype1 | 0 | 11
subtype2 | 1 | 9
subtype3 | 2 | 4
subtype4 | 0 | 26

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

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
--- | --- | --- | --- | ---
Number of samples | 18 | 28 | 19 | 16

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 81 | 0 | 0.9 - 65.9 (23.0)
subtype1 | 18 | 0 | 1.0 - 65.9 (20.8)
subtype2 | 28 | 0 | 0.9 - 39.3 (21.2)
subtype3 | 19 | 0 | 11.7 - 54.9 (19.9)
subtype4 | 16 | 0 | 8.0 - 47.4 (26.2)

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 = 0.306 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 81 | 61.1 (6.7)
subtype1 | 18 | 62.7 (6.7)
subtype2 | 28 | 61.6 (6.3)
subtype3 | 19 | 58.7 (7.1)
subtype4 | 16 | 61.4 (6.7)

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

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

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 76
subtype1 | 1 | 17
subtype2 | 2 | 26
subtype3 | 1 | 18
subtype4 | 1 | 15

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.249 (Fisher's exact test)

Table S20. Clustering Approach #4: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 78
subtype1 | 0 | 18
subtype2 | 3 | 25
subtype3 | 0 | 19
subtype4 | 0 | 16

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

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 | 15 | 32 | 34

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 81 | 0 | 0.9 - 65.9 (23.0)
subtype1 | 15 | 0 | 3.0 - 47.4 (25.9)
subtype2 | 32 | 0 | 0.9 - 65.9 (22.5)
subtype3 | 34 | 0 | 1.0 - 54.9 (19.7)

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 = 0.138 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 81 | 61.1 (6.7)
subtype1 | 15 | 61.7 (6.1)
subtype2 | 32 | 62.6 (6.9)
subtype3 | 34 | 59.4 (6.6)

Figure S18. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 1 (Fisher's exact test)

Table S24. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 76
subtype1 | 1 | 14
subtype2 | 2 | 30
subtype3 | 2 | 32

Figure S19. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.145 (Fisher's exact test)

Table S25. Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 78
subtype1 | 0 | 15
subtype2 | 3 | 29
subtype3 | 0 | 34

Figure S20. Get High-res Image Clustering Approach #5: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = PRAD.mergedcluster.txt

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

- Number of patients = 124

- 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

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)
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