Correlation between gene mutation status and molecular subtypes
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C10G3HF2
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 molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C10G3HF2
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 6 genes and 8 molecular subtypes across 39 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

- No gene mutations related to molecuar subtypes.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 6 genes and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

|  | Clinical Features | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRSEQ MATURE CNMF | MIRSEQ MATURE CHIERARCHICAL
| nMutated (%) | nWild-Type | Fisher's exact test | Chi-square test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
PIK3CA | 9 (23%) | 30 | 0.0409 (1.00) | 0.469 (1.00) | 0.36 (1.00) | 0.567 (1.00) | 0.88 (1.00) | 0.669 (1.00) | 1 (1.00) | 0.0855 (1.00)
TMCC1 | 4 (10%) | 35 |  | 0.293 (1.00) | 0.698 (1.00) | 0.599 (1.00) | 0.628 (1.00) | 0.556 (1.00) | 0.378 (1.00) | 0.458 (1.00)
UGT3A2 | 3 (8%) | 36 |  | 0.548 (1.00) | 0.118 (1.00) | 0.52 (1.00) | 0.55 (1.00) | 0.556 (1.00) | 0.397 (1.00) | 0.0312 (1.00)
CDC27 | 5 (13%) | 34 | 0.435 (1.00) | 0.514 (1.00) | 0.545 (1.00) | 0.174 (1.00) | 0.456 (1.00) | 0.302 (1.00) | 0.578 (1.00) | 0.0279 (1.00)
MAPK1 | 3 (8%) | 36 | 0.0387 (1.00) | 0.641 (1.00) | 0.655 (1.00) | 0.73 (1.00) | 0.0885 (1.00) | 0.556 (1.00) | 0.397 (1.00) | 0.783 (1.00)
UGT2B10 | 3 (8%) | 36 |  | 0.408 (1.00) | 0.655 (1.00) | 0.52 (1.00) | 1 (1.00) | 1 (1.00) | 0.397 (1.00) | 0.146 (1.00)

Methods & Data
Input

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

- Molecular subtypes file = CESC-TP.transferedmergedcluster.txt

- Number of patients = 39

- Number of significantly mutated genes = 6

- Number of Molecular subtypes = 8

- Exclude genes that fewer than K tumors have mutations, K = 3

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] 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)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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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