Prostate Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Prostate Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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, one significant finding detected with P value < 0.05 and Q value < 0.25.

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 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, one significant finding detected.


Clinical Features	Time to Death	AGE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Fisher's exact test
CN CNMF	100 (1.00)	0.0257 (0.436)	0.853 (1.00)
METHLYATION CNMF	100 (1.00)	0.00439 (0.0789)	0.861 (1.00)
RNAseq CNMF subtypes	100 (1.00)	0.207 (1.00)	0.86 (1.00)
RNAseq cHierClus subtypes	100 (1.00)	0.302 (1.00)	1 (1.00)
MIRseq CNMF subtypes	100 (1.00)	0.0775 (1.00)	0.631 (1.00)
MIRseq cHierClus subtypes	100 (1.00)	0.46 (1.00)	0.197 (1.00)

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), Q value = 1

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), Q value = 0.44

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), Q value = 1

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), Q value = 1

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), Q value = 0.079

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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), Q value = 1

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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)

[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] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

Made with Nozzle