Kidney Renal Clear Cell Carcinoma: Correlation between gene mutation status 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 significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 11 genes and 8 clinical features across 293 patients, no significant finding detected with Q value < 0.25.

- No gene mutations related to clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 11 genes and 8 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 | GENDER | KARNOFSKY PERFORMANCE SCORE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M) | NEOADJUVANT THERAPY
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
CT47B1 | 3 (1%) | 290 |  | 0.685 (1.00) | 1 (1.00) |  | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
PCDHA10 | 5 (2%) | 288 | 0.844 (1.00) | 0.432 (1.00) | 1 (1.00) |  | 0.262 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
WDR46 | 3 (1%) | 290 | 0.94 (1.00) | 0.584 (1.00) | 1 (1.00) |  | 0.244 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
PCDHGA1 | 4 (1%) | 289 | 0.681 (1.00) | 0.737 (1.00) | 1 (1.00) |  | 0.606 (1.00) | 0.0753 (1.00) | 0.437 (1.00) | 1 (1.00)
USP48 | 3 (1%) | 290 | 0.674 (1.00) | 0.607 (1.00) | 1 (1.00) |  | 1 (1.00) | 0.145 (1.00) | 0.35 (1.00) | 1 (1.00)
ABCC10 | 3 (1%) | 290 | 0.216 (1.00) | 0.596 (1.00) | 1 (1.00) |  | 0.0917 (1.00) | 1 (1.00) | 0.0476 (1.00) | 1 (1.00)
PCDHGA8 | 4 (1%) | 289 | 0.251 (1.00) | 0.207 (1.00) | 1 (1.00) |  | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
KHK | 3 (1%) | 290 | 0.0253 (1.00) | 0.18 (1.00) | 1 (1.00) |  | 0.718 (1.00) | 1 (1.00) | 0.35 (1.00) | 1 (1.00)
PCDHB10 | 3 (1%) | 290 | 0.389 (1.00) | 0.254 (1.00) | 0.278 (1.00) |  | 1 (1.00) | 1 (1.00) | 0.35 (1.00) | 1 (1.00)
PCDHA6 | 4 (1%) | 289 | 0.0349 (1.00) | 0.0761 (1.00) | 0.612 (1.00) |  | 0.0613 (1.00) | 0.211 (1.00) | 0.0871 (1.00) | 1 (1.00)
PCDHB7 | 4 (1%) | 289 | 0.24 (1.00) | 0.543 (1.00) | 1 (1.00) |  | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

Methods & Data
Input

- Mutation data file = KIRC.mutsig.cluster.txt

- Clinical data file = KIRC.clin.merged.picked.txt

- Number of patients = 293

- Number of significantly mutated genes = 11

- Number of selected clinical features = 8

- 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.

- 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 © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle