Correlation between gene mutation status and selected clinical features
Kidney Chromophobe (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1P55KTP
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 gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1P55KTP
Overview
Introduction

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

Summary

Testing the association between mutation status of 2 genes and 10 clinical features across 64 patients, 2 significant findings detected with Q value < 0.25.

- TP53 mutation correlated to 'Time to Death'.

- PTEN mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 2 genes and 10 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 | 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
TP53 | 21 (33%) | 43 | 0.00124 (0.0198) | 0.973 (1.00) | 0.29 (1.00) | 0.359 (1.00) | 0.036 (0.504) | 0.126 (1.00) | 1 (1.00) |  | 0.146 (1.00) | 0.777 (1.00)
PTEN | 6 (9%) | 58 | 0.00338 (0.0508) | 0.402 (1.00) | 0.0594 (0.719) | 0.0553 (0.719) | 0.0874 (0.962) | 0.664 (1.00) | 0.199 (1.00) |  |  |

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 0.00124 (logrank test), Q value = 0.02

Table S1. Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
TP53 MUTATED | 21 | 6 | 10.7 - 114.2 (44.8)
TP53 WILD-TYPE | 42 | 1 | 0.6 - 151.9 (70.3)

Figure S1. Get High-res Image Gene #1: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'PTEN MUTATION STATUS' versus 'Time to Death'

P value = 0.00338 (logrank test), Q value = 0.051

Table S2. Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 63 | 7 | 0.6 - 151.9 (63.9)
PTEN MUTATED | 6 | 3 | 16.7 - 90.5 (46.4)
PTEN WILD-TYPE | 57 | 4 | 0.6 - 151.9 (66.5)

Figure S2. Get High-res Image Gene #2: 'PTEN MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

- Mutation data file = KICH-TP.mutsig.cluster.txt

- Clinical data file = KICH-TP.clin.merged.picked.txt

- Number of patients = 64

- Number of significantly mutated genes = 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)
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