Correlation between copy number variation genes (focal events) and selected clinical features
Kidney Chromophobe (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1V986GQ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1V986GQ
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 66 patients, one significant finding detected with Q value < 0.25.

- amp_8q11.23 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 8q11 23 | 19 (29%) | 47 | 0.418 (1.00) | 0.296 (1.00) | 0.12 (1.00) | 0.839 (1.00) | 0.33 (1.00) | 0.135 (1.00) | 0.412 (1.00) | 0.863 (1.00) | 0.291 (1.00) | 0.0084 (0.168)
amp 15q22 31 | 23 (35%) | 43 | 0.0699 (1.00) | 0.396 (1.00) | 0.127 (1.00) | 0.397 (1.00) | 0.141 (1.00) | 0.112 (1.00) | 0.601 (1.00) | 0.863 (1.00) | 0.776 (1.00) | 0.0341 (0.649)

'amp_8q11.23' versus 'YEAROFTOBACCOSMOKINGONSET'

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

Table S1. Gene #1: 'amp_8q11.23' 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_8q11.23' versus Clinical Feature #10: 'YEAROFTOBACCOSMOKINGONSET'

Methods & Data
Input

- Copy number data file = transformed.cor.cli.txt

- Clinical data file = KICH-TP.merged_data.txt

- Number of patients = 66

- 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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle