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 6 different clustering approaches and 4 clinical features across 127 patients, one significant finding detected with P value < 0.05.

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

- 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 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding 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
CN CNMF | 1 | 0.113 | 0.852 | 0.815
METHLYATION CNMF | 1 | 0.00809 | 1 | 0.385
RNAseq CNMF subtypes | 1 | 0.132 | 0.861 | 0.546
RNAseq cHierClus subtypes | 1 | 0.103 | 1 | 0.129
MIRseq CNMF subtypes | 1 | 0.195 | 0.758 | 0.161
MIRseq cHierClus subtypes | 1 | 0.877 | 0.306 | 0.579

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 28 | 58 | 40

'CN CNMF' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 126 | 1 | 0.3 - 66.0 (18.4)
subtype1 | 28 | 0 | 0.3 - 63.3 (13.8)
subtype2 | 58 | 0 | 1.0 - 65.9 (20.7)
subtype3 | 40 | 1 | 0.9 - 66.0 (22.9)

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

'CN CNMF' versus 'AGE'

P value = 0.113 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 126 | 60.9 (6.7)
subtype1 | 28 | 62.3 (6.1)
subtype2 | 58 | 59.6 (7.3)
subtype3 | 40 | 62.0 (5.9)

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

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

P value = 0.852 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 121
subtype1 | 1 | 27
subtype2 | 3 | 55
subtype3 | 1 | 39

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

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.815 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 122
subtype1 | 0 | 28
subtype2 | 2 | 56
subtype3 | 2 | 38

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 38 | 38 | 51

'METHLYATION CNMF' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 127 | 1 | 0.3 - 66.0 (18.2)
subtype1 | 38 | 0 | 0.3 - 65.9 (23.8)
subtype2 | 38 | 0 | 1.1 - 54.9 (15.6)
subtype3 | 51 | 1 | 1.0 - 66.0 (19.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00809 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 127 | 61.0 (6.7)
subtype1 | 38 | 62.6 (5.8)
subtype2 | 38 | 58.2 (7.3)
subtype3 | 51 | 61.9 (6.3)

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

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

P value = 1 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 122
subtype1 | 1 | 37
subtype2 | 2 | 36
subtype3 | 2 | 49

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

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.385 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 123
subtype1 | 1 | 37
subtype2 | 0 | 38
subtype3 | 3 | 48

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 34 | 31 | 37

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 102 | 1 | 0.3 - 66.0 (16.8)
subtype1 | 34 | 0 | 0.3 - 65.9 (19.6)
subtype2 | 31 | 0 | 1.1 - 54.9 (13.0)
subtype3 | 37 | 1 | 1.0 - 66.0 (17.1)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.132 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 102 | 61.3 (6.8)
subtype1 | 34 | 62.4 (6.0)
subtype2 | 31 | 59.3 (7.4)
subtype3 | 37 | 62.0 (6.6)

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

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

P value = 0.861 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 97
subtype1 | 1 | 33
subtype2 | 2 | 29
subtype3 | 2 | 35

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.546 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 98
subtype1 | 2 | 32
subtype2 | 0 | 31
subtype3 | 2 | 35

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 28 | 32 | 42

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 102 | 1 | 0.3 - 66.0 (16.8)
subtype1 | 28 | 0 | 1.0 - 65.9 (19.6)
subtype2 | 32 | 1 | 1.0 - 66.0 (14.8)
subtype3 | 42 | 0 | 0.3 - 54.9 (15.1)

Figure S13. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.103 (ANOVA)

Table S18. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 102 | 61.3 (6.8)
subtype1 | 28 | 61.7 (6.2)
subtype2 | 32 | 63.1 (6.8)
subtype3 | 42 | 59.8 (6.9)

Figure S14. 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 S19. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 97
subtype1 | 1 | 27
subtype2 | 2 | 30
subtype3 | 2 | 40

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.129 (Fisher's exact test)

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 4 | 98
subtype1 | 1 | 27
subtype2 | 3 | 29
subtype3 | 0 | 42

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

Clustering Approach #5: 'MIRseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 39 | 37 | 21 | 28

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 1 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 125 | 1 | 0.3 - 66.0 (18.2)
subtype1 | 39 | 0 | 0.9 - 65.9 (22.1)
subtype2 | 37 | 0 | 3.0 - 54.9 (19.9)
subtype3 | 21 | 0 | 0.3 - 64.1 (21.8)
subtype4 | 28 | 1 | 1.1 - 66.0 (9.4)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.195 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 125 | 61.0 (6.6)
subtype1 | 39 | 62.6 (6.2)
subtype2 | 37 | 59.3 (6.8)
subtype3 | 21 | 61.0 (6.1)
subtype4 | 28 | 61.2 (7.1)

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

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

P value = 0.758 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 120
subtype1 | 2 | 37
subtype2 | 2 | 35
subtype3 | 1 | 20
subtype4 | 0 | 28

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.161 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 122
subtype1 | 3 | 36
subtype2 | 0 | 37
subtype3 | 0 | 21
subtype4 | 0 | 28

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

Clustering Approach #6: 'MIRseq cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 21 | 67 | 37

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 1 (logrank test)

Table S27. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 125 | 1 | 0.3 - 66.0 (18.2)
subtype1 | 21 | 0 | 0.3 - 64.1 (23.8)
subtype2 | 67 | 0 | 0.9 - 65.9 (23.0)
subtype3 | 37 | 1 | 1.0 - 66.0 (8.5)

Figure S21. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.877 (ANOVA)

Table S28. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 125 | 61.0 (6.6)
subtype1 | 21 | 60.5 (5.8)
subtype2 | 67 | 61.0 (6.9)
subtype3 | 37 | 61.4 (6.7)

Figure S22. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.306 (Fisher's exact test)

Table S29. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 5 | 120
subtype1 | 1 | 20
subtype2 | 4 | 63
subtype3 | 0 | 37

Figure S23. Get High-res Image Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.579 (Fisher's exact test)

Table S30. Clustering Approach #6: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 3 | 122
subtype1 | 0 | 21
subtype2 | 3 | 64
subtype3 | 0 | 37

Figure S24. Get High-res Image Clustering Approach #6: '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 = 127

- Number of clustering approaches = 6

- 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

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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle