Correlation between gene mutation status and selected clinical features
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C19S1PNZ
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/C19S1PNZ
Overview
Introduction

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

Summary

Testing the association between mutation status of 4 genes and 8 clinical features across 36 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 4 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 | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | RADIATIONS RADIATION REGIMENINDICATION | NUMBERPACKYEARSSMOKED | NUMBER OF LYMPH NODES
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | t-test
PIK3CA | 8 (22%) | 28 | 0.294 (1.00) | 0.0838 (1.00) | 0.612 (1.00) | 0.169 (1.00) | 1 (1.00) | 0.441 (1.00) | 0.199 (1.00) | 0.631 (1.00)
TMCC1 | 4 (11%) | 32 | 0.0994 (1.00) | 0.24 (1.00) | 0.546 (1.00) | 0.598 (1.00) | 1 (1.00) | 0.634 (1.00) |  | 0.17 (1.00)
UGT3A2 | 3 (8%) | 33 | 0.15 (1.00) | 0.455 (1.00) | 0.557 (1.00) | 1 (1.00) | 0.238 (1.00) | 1 (1.00) |  | 0.518 (1.00)
MAPK1 | 3 (8%) | 33 | 0.64 (1.00) | 0.831 (1.00) |  | 1 (1.00) | 0.492 (1.00) | 0.267 (1.00) |  |

Methods & Data
Input

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

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

- Number of patients = 36

- Number of significantly mutated genes = 4

- 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

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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