Correlation between gene mutation status and selected clinical features
Cervical Squamous Cell Carcinoma (Primary solid tumor)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1KK98PW
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Cervical Squamous Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KK98PW
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 9 clinical features across 21 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 9 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 | RADIATIONS RADIATION REGIMENINDICATION | NUMBERPACKYEARSSMOKED | TOBACCOSMOKINGHISTORYINDICATOR | DISTANT METASTASIS | LYMPH NODE METASTASIS | NUMBER OF LYMPH NODES | TUMOR STAGECODE
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | t-test | Fisher's exact test | Fisher's exact test | t-test | t-test
PIK3CA | 4 (19%) | 17 | 0.277 (1.00) | 0.462 (1.00) | 1 (1.00) |  | 0.0368 (1.00) | 1 (1.00) | 0.559 (1.00) | 0.479 (1.00) |
TMCC1 | 3 (14%) | 18 | 0.207 (1.00) | 0.408 (1.00) | 0.586 (1.00) |  | 0.364 (1.00) | 1 (1.00) | 1 (1.00) | 0.933 (1.00) |
PRG4 | 4 (19%) | 17 | 0.173 (1.00) | 0.814 (1.00) | 0.0351 (1.00) |  | 0.313 (1.00) | 1 (1.00) | 1 (1.00) |  |
CDC27 | 4 (19%) | 17 | 0.182 (1.00) | 0.333 (1.00) | 0.311 (1.00) |  | 0.313 (1.00) | 1 (1.00) | 0.275 (1.00) | 0.185 (1.00) |
NFE2L2 | 4 (19%) | 17 | 0.736 (1.00) | 0.687 (1.00) | 1 (1.00) |  | 0.679 (1.00) | 0.522 (1.00) | 0.0686 (1.00) | 0.885 (1.00) |
UGT3A2 | 3 (14%) | 18 | 0.207 (1.00) | 0.72 (1.00) | 0.586 (1.00) |  | 0.364 (1.00) | 0.522 (1.00) | 0.559 (1.00) | 0.518 (1.00) |
PRB2 | 3 (14%) | 18 | 0.207 (1.00) | 0.919 (1.00) | 0.0902 (1.00) |  | 0.364 (1.00) | 0.522 (1.00) | 1 (1.00) | 0.933 (1.00) |

Methods & Data
Input

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

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

- Number of patients = 21

- Number of significantly mutated genes = 7

- Number of selected clinical features = 9

- 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)
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