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 124 patients, 2 significant findings 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 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 6 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
CN CNMF 1 0.471 1 0.666
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: 'CN CNMF'

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

Cluster Labels 1 2 3
Number of samples 24 61 38
'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 123 1 0.3 - 66.0 (19.5)
subtype1 24 0 0.3 - 61.1 (12.0)
subtype2 61 0 1.0 - 65.9 (24.0)
subtype3 38 1 0.9 - 66.0 (17.9)

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

'CN CNMF' versus 'AGE'

P value = 0.471 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 123 61.1 (6.6)
subtype1 24 61.6 (6.7)
subtype2 61 60.4 (7.0)
subtype3 38 62.0 (6.0)

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

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

P value = 1 (Fisher's exact test)

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

nPatients NO YES
ALL 5 118
subtype1 1 23
subtype2 3 58
subtype3 1 37

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 = 0.666 (Fisher's exact test)

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

nPatients NO YES
ALL 4 119
subtype1 0 24
subtype2 2 59
subtype3 2 36

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 36 41 47
'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 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 S5.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00334 (ANOVA)

Table S8.  Clustering Approach #2: '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 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 5 119
subtype1 1 35
subtype2 2 39
subtype3 2 45

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 = 0.315 (Fisher's exact test)

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

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

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 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 S12.  Clustering Approach #3: '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 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 = 0.057 (ANOVA)

Table S13.  Clustering Approach #3: '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 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 = 0.61 (Chi-square test)

Table S14.  Clustering Approach #3: '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 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 = 0.252 (Chi-square test)

Table S15.  Clustering Approach #3: '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 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 4
Number of samples 11 10 6 26
'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 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 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 = 0.0522 (ANOVA)

Table S18.  Clustering Approach #4: '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 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 4 49
subtype1 1 10
subtype2 1 9
subtype3 0 6
subtype4 2 24

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 = 0.0124 (Fisher's exact test)

Table S20.  Clustering Approach #4: '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 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 18 28 19 16
'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 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 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 = 0.306 (ANOVA)

Table S23.  Clustering Approach #5: '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 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 = 1 (Fisher's exact test)

Table S24.  Clustering Approach #5: '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 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 = 0.249 (Fisher's exact test)

Table S25.  Clustering Approach #5: '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 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 15 32 34
'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 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 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 = 0.138 (ANOVA)

Table S28.  Clustering Approach #6: '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 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 = 1 (Fisher's exact test)

Table S29.  Clustering Approach #6: '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 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 = 0.145 (Fisher's exact test)

Table S30.  Clustering Approach #6: '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 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 = 124

  • 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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
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