Correlation between gene mutation status and selected clinical features
Uterine Carcinosarcoma (Primary solid tumor)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1NK3D55
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NK3D55
Overview
Introduction

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

Summary

Testing the association between mutation status of 13 genes and 3 clinical features across 57 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 13 genes and 3 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 | YEARS TO BIRTH | RACE
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test
TP53 | 51 (89%) | 6 | 0.31 (0.748) | 0.0288 (0.64) | 0.219 (0.64)
FBXW7 | 22 (39%) | 35 | 0.594 (0.773) | 0.407 (0.755) | 1 (1.00)
PPP2R1A | 16 (28%) | 41 | 0.579 (0.773) | 0.631 (0.779) | 0.534 (0.773)
PTEN | 11 (19%) | 46 | 0.442 (0.755) | 0.504 (0.773) | 0.679 (0.779)
KRAS | 7 (12%) | 50 | 0.445 (0.755) | 0.576 (0.773) | 0.556 (0.773)
ZBTB7B | 6 (11%) | 51 | 0.122 (0.64) | 0.649 (0.779) | 0.218 (0.64)
CHD4 | 10 (18%) | 47 | 0.437 (0.755) | 0.223 (0.64) | 0.23 (0.64)
PIK3R1 | 6 (11%) | 51 | 0.659 (0.779) | 0.207 (0.64) | 0.217 (0.64)
ARHGAP35 | 6 (11%) | 51 | 0.584 (0.773) | 0.0983 (0.64) | 0.0398 (0.64)
PIK3CA | 20 (35%) | 37 | 0.188 (0.64) | 0.136 (0.64) | 0.755 (0.841)
MAMLD1 | 4 (7%) | 53 | 0.976 (1.00) | 0.364 (0.748) | 0.306 (0.748)
RB1 | 6 (11%) | 51 | 0.147 (0.64) | 0.355 (0.748) | 0.332 (0.748)
LYPLA2 | 3 (5%) | 54 | 0.988 (1.00) | 0.138 (0.64) | 1 (1.00)

Methods & Data
Input

- Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

- Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/UCS-TP/15165005/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCS-TP/15096025/UCS-TP.merged_data.txt

- Number of patients = 57

- Number of significantly mutated genes = 13

- Number of selected clinical features = 3

- 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

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] 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)
[3] 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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