Correlation between copy number variations of arm-level result and selected clinical features

Prostate Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9G51

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between subtypes identified by 22 different clustering approaches and 3 clinical features across 146 patients, no significant finding detected with Q value < 0.25.

2 subtypes identified in current cancer cohort by '1q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '3p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '3q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '7p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '8p gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '8q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '9q gain mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '5q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '6q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '8p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '8q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '10p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '10q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '12p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '13q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '16q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '17p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '18p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '18q loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '20p loss mutation analysis'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by '22q loss mutation analysis'. These subtypes 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 22 different clustering approaches and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


Clinical Features	Time to Death	AGE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	t-test	Fisher's exact test
1q gain	1 (1.00)	0.299 (1.00)	1 (1.00)
3p gain	1 (1.00)	0.646 (1.00)	1 (1.00)
3q gain	1 (1.00)	0.329 (1.00)	1 (1.00)
7p gain	1 (1.00)	0.0153 (1.00)	1 (1.00)
7q gain	1 (1.00)	0.0682 (1.00)	1 (1.00)
8p gain	1 (1.00)	0.537 (1.00)	1 (1.00)
8q gain	1 (1.00)	0.603 (1.00)	1 (1.00)
9q gain	1 (1.00)	0.276 (1.00)	1 (1.00)
5q loss	1 (1.00)	0.214 (1.00)	1 (1.00)
6q loss	1 (1.00)	0.258 (1.00)	1 (1.00)
8p loss	1 (1.00)	0.0476 (1.00)	0.327 (1.00)
8q loss	1 (1.00)	0.635 (1.00)	1 (1.00)
10p loss	1 (1.00)	0.91 (1.00)	1 (1.00)
10q loss	1 (1.00)	0.396 (1.00)	1 (1.00)
12p loss	1 (1.00)	0.674 (1.00)	1 (1.00)
13q loss	1 (1.00)	0.82 (1.00)	1 (1.00)
16q loss	1 (1.00)	0.146 (1.00)	1 (1.00)
17p loss	1 (1.00)	0.529 (1.00)	1 (1.00)
18p loss	1 (1.00)	0.621 (1.00)	1 (1.00)
18q loss	1 (1.00)	0.3 (1.00)	1 (1.00)
20p loss	1 (1.00)	0.451 (1.00)	0.131 (1.00)
22q loss	1 (1.00)	0.274 (1.00)	0.162 (1.00)

Clustering Approach #1: '1q gain mutation analysis'

Table S1. Get Full Table Description of clustering approach #1: '1q gain mutation analysis'

Cluster Labels	1Q GAIN MUTATED	1Q GAIN WILD-TYPE
Number of samples	4	142

Clustering Approach #2: '3p gain mutation analysis'

Table S2. Get Full Table Description of clustering approach #2: '3p gain mutation analysis'

Cluster Labels	3P GAIN MUTATED	3P GAIN WILD-TYPE
Number of samples	4	142

Clustering Approach #3: '3q gain mutation analysis'

Table S3. Get Full Table Description of clustering approach #3: '3q gain mutation analysis'

Cluster Labels	3Q GAIN MUTATED	3Q GAIN WILD-TYPE
Number of samples	6	140

Clustering Approach #4: '7p gain mutation analysis'

Table S4. Get Full Table Description of clustering approach #4: '7p gain mutation analysis'

Cluster Labels	7P GAIN MUTATED	7P GAIN WILD-TYPE
Number of samples	15	131

Clustering Approach #5: '7q gain mutation analysis'

Table S5. Get Full Table Description of clustering approach #5: '7q gain mutation analysis'

Cluster Labels	7Q GAIN MUTATED	7Q GAIN WILD-TYPE
Number of samples	12	134

Clustering Approach #6: '8p gain mutation analysis'

Table S6. Get Full Table Description of clustering approach #6: '8p gain mutation analysis'

Cluster Labels	8P GAIN MUTATED	8P GAIN WILD-TYPE
Number of samples	6	140

Clustering Approach #7: '8q gain mutation analysis'

Table S7. Get Full Table Description of clustering approach #7: '8q gain mutation analysis'

Cluster Labels	8Q GAIN MUTATED	8Q GAIN WILD-TYPE
Number of samples	13	133

Clustering Approach #8: '9q gain mutation analysis'

Table S8. Get Full Table Description of clustering approach #8: '9q gain mutation analysis'

Cluster Labels	9Q GAIN MUTATED	9Q GAIN WILD-TYPE
Number of samples	3	143

Clustering Approach #9: '5q loss mutation analysis'

Table S9. Get Full Table Description of clustering approach #9: '5q loss mutation analysis'

Cluster Labels	5Q LOSS MUTATED	5Q LOSS WILD-TYPE
Number of samples	3	143

Clustering Approach #10: '6q loss mutation analysis'

Table S10. Get Full Table Description of clustering approach #10: '6q loss mutation analysis'

Cluster Labels	6Q LOSS MUTATED	6Q LOSS WILD-TYPE
Number of samples	7	139

Clustering Approach #11: '8p loss mutation analysis'

Table S11. Get Full Table Description of clustering approach #11: '8p loss mutation analysis'

Cluster Labels	8P LOSS MUTATED	8P LOSS WILD-TYPE
Number of samples	38	108

Clustering Approach #12: '8q loss mutation analysis'

Table S12. Get Full Table Description of clustering approach #12: '8q loss mutation analysis'

Cluster Labels	8Q LOSS MUTATED	8Q LOSS WILD-TYPE
Number of samples	4	142

Clustering Approach #13: '10p loss mutation analysis'

Table S13. Get Full Table Description of clustering approach #13: '10p loss mutation analysis'

Cluster Labels	10P LOSS MUTATED	10P LOSS WILD-TYPE
Number of samples	5	141

Clustering Approach #14: '10q loss mutation analysis'

Table S14. Get Full Table Description of clustering approach #14: '10q loss mutation analysis'

Cluster Labels	10Q LOSS MUTATED	10Q LOSS WILD-TYPE
Number of samples	4	142

Clustering Approach #15: '12p loss mutation analysis'

Table S15. Get Full Table Description of clustering approach #15: '12p loss mutation analysis'

Cluster Labels	12P LOSS MUTATED	12P LOSS WILD-TYPE
Number of samples	7	139

Clustering Approach #16: '13q loss mutation analysis'

Table S16. Get Full Table Description of clustering approach #16: '13q loss mutation analysis'

Cluster Labels	13Q LOSS MUTATED	13Q LOSS WILD-TYPE
Number of samples	11	135

Clustering Approach #17: '16q loss mutation analysis'

Table S17. Get Full Table Description of clustering approach #17: '16q loss mutation analysis'

Cluster Labels	16Q LOSS MUTATED	16Q LOSS WILD-TYPE
Number of samples	18	128

Clustering Approach #18: '17p loss mutation analysis'

Table S18. Get Full Table Description of clustering approach #18: '17p loss mutation analysis'

Cluster Labels	17P LOSS MUTATED	17P LOSS WILD-TYPE
Number of samples	17	129

Clustering Approach #19: '18p loss mutation analysis'

Table S19. Get Full Table Description of clustering approach #19: '18p loss mutation analysis'

Cluster Labels	18P LOSS MUTATED	18P LOSS WILD-TYPE
Number of samples	14	132

Clustering Approach #20: '18q loss mutation analysis'

Table S20. Get Full Table Description of clustering approach #20: '18q loss mutation analysis'

Cluster Labels	18Q LOSS MUTATED	18Q LOSS WILD-TYPE
Number of samples	19	127

Clustering Approach #21: '20p loss mutation analysis'

Table S21. Get Full Table Description of clustering approach #21: '20p loss mutation analysis'

Cluster Labels	20P LOSS MUTATED	20P LOSS WILD-TYPE
Number of samples	4	142

Clustering Approach #22: '22q loss mutation analysis'

Table S22. Get Full Table Description of clustering approach #22: '22q loss mutation analysis'

Cluster Labels	22Q LOSS MUTATED	22Q LOSS WILD-TYPE
Number of samples	5	141

Methods & Data

Input

Cluster data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 146
Number of clustering approaches = 22
Number of selected clinical features = 3
Exclude small clusters that include fewer than K patients, K = 3

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' 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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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)

[4] 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)

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