Correlation between gene mutation status and selected clinical features
Rectum Adenocarcinoma (Primary solid tumor)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C11N7ZX8
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/C11N7ZX8
Overview
Introduction

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

Summary

Testing the association between mutation status of 12 genes and 10 clinical features across 69 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 12 genes and 10 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 | NEOPLASM DISEASESTAGE | PATHOLOGY T STAGE | PATHOLOGY N STAGE | PATHOLOGY M STAGE | GENDER | HISTOLOGICAL TYPE | COMPLETENESS OF RESECTION | NUMBER OF LYMPH NODES
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Wilcoxon-test
APC | 56 (81%) | 13 | 0.0906 (1.00) | 0.83 (1.00) | 0.433 (1.00) | 0.267 (1.00) | 0.747 (1.00) | 0.385 (1.00) | 0.764 (1.00) | 0.655 (1.00) | 0.713 (1.00) | 0.48 (1.00)
TP53 | 44 (64%) | 25 | 0.105 (1.00) | 0.955 (1.00) | 0.768 (1.00) | 0.0792 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.42 (1.00) | 0.639 (1.00) | 0.96 (1.00)
KRAS | 38 (55%) | 31 | 0.236 (1.00) | 0.612 (1.00) | 0.63 (1.00) | 0.0986 (1.00) | 0.83 (1.00) | 0.327 (1.00) | 0.329 (1.00) | 0.27 (1.00) | 0.0402 (1.00) | 0.593 (1.00)
SMAD4 | 8 (12%) | 61 | 0.255 (1.00) | 0.68 (1.00) | 0.882 (1.00) | 1 (1.00) | 0.644 (1.00) | 0.327 (1.00) | 1 (1.00) | 0.00581 (0.697) | 1 (1.00) | 0.532 (1.00)
FBXW7 | 9 (13%) | 60 | 0.764 (1.00) | 0.721 (1.00) | 0.343 (1.00) | 0.582 (1.00) | 0.201 (1.00) | 0.338 (1.00) | 0.722 (1.00) | 1 (1.00) | 0.631 (1.00) | 0.155 (1.00)
NRAS | 5 (7%) | 64 | 0.698 (1.00) | 0.0212 (1.00) | 0.955 (1.00) | 0.053 (1.00) | 0.532 (1.00) | 0.555 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.79 (1.00)
ARID1A | 5 (7%) | 64 | 0.819 (1.00) | 0.826 (1.00) | 0.598 (1.00) | 0.0353 (1.00) | 0.342 (1.00) | 1 (1.00) | 0.646 (1.00) | 1 (1.00) | 1 (1.00) | 0.0862 (1.00)
RBM10 | 4 (6%) | 65 | 0.0359 (1.00) | 0.0828 (1.00) | 0.163 (1.00) | 0.684 (1.00) | 0.0278 (1.00) | 0.0975 (1.00) | 1 (1.00) | 0.417 (1.00) | 0.442 (1.00) | 0.023 (1.00)
TCF7L2 | 7 (10%) | 62 | 0.882 (1.00) | 0.929 (1.00) | 0.967 (1.00) | 0.794 (1.00) | 0.849 (1.00) | 0.266 (1.00) | 1 (1.00) | 1 (1.00) | 0.277 (1.00) | 0.954 (1.00)
ERBB2 | 4 (6%) | 65 | 0.381 (1.00) | 0.738 (1.00) | 0.768 (1.00) | 1 (1.00) | 0.452 (1.00) | 1 (1.00) | 0.127 (1.00) | 1 (1.00) | 1 (1.00) | 0.129 (1.00)
KIAA1804 | 9 (13%) | 60 | 0.644 (1.00) | 0.349 (1.00) | 0.745 (1.00) | 0.708 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.645 (1.00)
CCDC88C | 3 (4%) | 66 | 0.521 (1.00) | 0.895 (1.00) | 0.247 (1.00) | 0.181 (1.00) | 0.523 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.879 (1.00)

Methods & Data
Input

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

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

- Number of patients = 69

- Number of significantly mutated genes = 12

- 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

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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