Correlation between copy number variation genes (focal events) and selected clinical features

Kidney Chromophobe (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12B8WBD

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 2 focal events and 10 clinical features across 64 patients, one significant finding detected with Q value < 0.25.

AMP PEAK 1(8Q11.23) MUTATION ANALYSIS cnv correlated to 'YEAROFTOBACCOSMOKINGONSET'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 2 focal events and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.


		Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	KARNOFSKY PERFORMANCE SCORE	NUMBERPACKYEARSSMOKED	YEAROFTOBACCOSMOKINGONSET
	nCNV (%)	nWild-Type	logrank test	t-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test	t-test	t-test	t-test
AMP PEAK 1(8Q11 23) MUTATION ANALYSIS	17 (27%)	47	0.167 (1.00)	0.306 (1.00)	0.0913 (1.00)	0.683 (1.00)	0.315 (1.00)	0.0939 (1.00)	0.155 (1.00)	0.863 (1.00)	0.473 (1.00)	0.0084 (0.168)
AMP PEAK 2(15Q22 31) MUTATION ANALYSIS	21 (33%)	43	0.0203 (0.386)	0.628 (1.00)	0.177 (1.00)	0.565 (1.00)	0.141 (1.00)	0.107 (1.00)	0.592 (1.00)	0.863 (1.00)	0.551 (1.00)	0.0341 (0.614)

'AMP PEAK 1(8Q11.23) MUTATION STATUS' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0084 (t-test), Q value = 0.17

Table S1. Gene #1: 'AMP PEAK 1(8Q11.23) MUTATION STATUS' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	8	1973.8 (15.4)
AMP PEAK 1(8Q11.23) MUTATED	3	1989.0 (7.0)
AMP PEAK 1(8Q11.23) WILD-TYPE	5	1964.6 (10.6)

Figure S1. Get High-res Image Gene #1: 'AMP PEAK 1(8Q11.23) MUTATION STATUS' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = KICH-TP.clin.merged.picked.txt
Number of patients = 64
Number of significantly focal cnvs = 2
Number of selected clinical features = 10
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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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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