Correlation between gene mutation status and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1RB737C
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 gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RB737C
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.

Summary

Testing the association between mutation status of 7 genes and 10 clinical features across 155 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 7 genes and 10 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 | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER | KARNOFSKY PERFORMANCE SCORE | NUMBERPACKYEARSSMOKED | YEAROFTOBACCOSMOKINGONSET
| nMutated (%) | 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
SKI | 6 (4%) | 149 | 0.657 (1.00) | 0.714 (1.00) | 0.739 (1.00) | 0.678 (1.00) |  | 1 (1.00) | 0.668 (1.00) |  |  |
NEFH | 9 (6%) | 146 | 0.397 (1.00) | 0.427 (1.00) | 0.262 (1.00) | 0.0671 (1.00) |  | 0.196 (1.00) | 1 (1.00) |  |  |
HNRNPM | 11 (7%) | 144 | 0.192 (1.00) | 0.307 (1.00) | 0.853 (1.00) | 0.718 (1.00) |  | 0.838 (1.00) | 0.506 (1.00) | 0.289 (1.00) |  |
NF2 | 11 (7%) | 144 | 0.174 (1.00) | 0.332 (1.00) | 0.04 (1.00) | 0.0984 (1.00) | 0.0166 (0.813) | 0.108 (1.00) | 0.31 (1.00) |  |  |
ZNF598 | 12 (8%) | 143 | 0.336 (1.00) | 0.302 (1.00) | 0.358 (1.00) | 0.488 (1.00) | 0.598 (1.00) | 0.126 (1.00) | 0.755 (1.00) | 0.604 (1.00) |  |
BMS1 | 13 (8%) | 142 | 0.129 (1.00) | 0.211 (1.00) | 0.853 (1.00) | 0.0902 (1.00) |  | 0.198 (1.00) | 0.755 (1.00) | 0.504 (1.00) |  |
MUC2 | 25 (16%) | 130 | 0.0641 (1.00) | 0.144 (1.00) | 0.182 (1.00) | 0.823 (1.00) | 0.361 (1.00) | 0.131 (1.00) | 0.635 (1.00) | 0.025 (1.00) |  |

Methods & Data
Input

- Mutation data file = transformed.cor.cli.txt

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

- Number of patients = 155

- Number of significantly mutated genes = 7

- 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)
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