Prostate Adenocarcinoma: Correlation between copy number variations of arm-level result 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 significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 20 arm-level results and 4 clinical features across 123 patients, 2 significant findings detected with Q value < 0.25.

7p gain cnv correlated to 'AGE'.
7q gain cnv correlated to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 20 arm-level results and 4 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.


		Clinical Features	Time to Death	AGE	RADIATIONS RADIATION REGIMENINDICATION	NEOADJUVANT THERAPY
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test
7p gain	14 (11%)	109	1 (1.00)	0.000303 (0.0242)	1 (1.00)	0.0635 (1.00)
7q gain	11 (9%)	112	1 (1.00)	0.00208 (0.164)	1 (1.00)	0.316 (1.00)
1q gain	4 (3%)	119	1 (1.00)	0.439 (1.00)	1 (1.00)	1 (1.00)
3p gain	5 (4%)	118	1 (1.00)	0.681 (1.00)	1 (1.00)	1 (1.00)
3q gain	7 (6%)	116	1 (1.00)	0.965 (1.00)	1 (1.00)	0.211 (1.00)
8p gain	4 (3%)	119	1 (1.00)	0.501 (1.00)	1 (1.00)	1 (1.00)
8q gain	11 (9%)	112	1 (1.00)	0.345 (1.00)	1 (1.00)	0.316 (1.00)
9q gain	3 (2%)	120	1 (1.00)	0.352 (1.00)	1 (1.00)	1 (1.00)
6q loss	5 (4%)	118	1 (1.00)	0.265 (1.00)	1 (1.00)	1 (1.00)
8p loss	36 (29%)	87	1 (1.00)	0.215 (1.00)	0.32 (1.00)	0.58 (1.00)
8q loss	4 (3%)	119	1 (1.00)	0.475 (1.00)	1 (1.00)	1 (1.00)
10p loss	3 (2%)	120	1 (1.00)	0.0488 (1.00)	1 (1.00)	0.0952 (1.00)
12p loss	5 (4%)	118	1 (1.00)	0.987 (1.00)	1 (1.00)	1 (1.00)
13q loss	9 (7%)	114	1 (1.00)	0.853 (1.00)	1 (1.00)	0.265 (1.00)
16q loss	14 (11%)	109	1 (1.00)	0.085 (1.00)	1 (1.00)	0.0635 (1.00)
17p loss	14 (11%)	109	1 (1.00)	0.542 (1.00)	1 (1.00)	1 (1.00)
18p loss	10 (8%)	113	1 (1.00)	0.928 (1.00)	1 (1.00)	0.291 (1.00)
18q loss	15 (12%)	108	1 (1.00)	0.714 (1.00)	1 (1.00)	0.41 (1.00)
20p loss	4 (3%)	119	1 (1.00)	0.676 (1.00)	0.155 (1.00)	1 (1.00)
22q loss	5 (4%)	118	1 (1.00)	0.406 (1.00)	0.19 (1.00)	1 (1.00)

'7p gain mutation analysis' versus 'AGE'

P value = 0.000303 (t-test), Q value = 0.024

Table S1. Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	123	61.1 (6.6)
7P GAIN MUTATED	14	64.9 (3.0)
7P GAIN WILD-TYPE	109	60.7 (6.8)

Figure S1. Get High-res Image Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'7q gain mutation analysis' versus 'AGE'

P value = 0.00208 (t-test), Q value = 0.16

Table S2. Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	123	61.1 (6.6)
7Q GAIN MUTATED	11	64.7 (3.1)
7Q GAIN WILD-TYPE	112	60.8 (6.8)

Figure S2. Get High-res Image Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = PRAD.clin.merged.picked.txt
Number of patients = 123
Number of significantly arm-level cnvs = 20
Number of selected clinical features = 4
Exclude genes that fewer than K tumors have mutations, 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 tumors with and without gene mutations using 't.test' function in R

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), 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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