Correlation between gene mutation status and selected clinical features
Rectum Adenocarcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1M61HH1
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 selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1M61HH1
Overview
Introduction

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

Summary

Testing the association between mutation status of 16 genes and 8 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 16 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 | GENDER | HISTOLOGICAL TYPE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M) | TUMOR STAGE
| nMutated (%) | nWild-Type | logrank test | t-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
APC | 57 (83%) | 12 | 0.588 (1.00) | 0.897 (1.00) | 1 (1.00) | 0.634 (1.00) | 0.0753 (1.00) | 0.813 (1.00) | 0.362 (1.00) | 0.0491 (1.00)
KRAS | 38 (55%) | 31 | 0.0279 (1.00) | 0.534 (1.00) | 0.329 (1.00) | 0.27 (1.00) | 0.0973 (1.00) | 0.832 (1.00) | 0.327 (1.00) | 0.309 (1.00)
TP53 | 45 (65%) | 24 | 0.924 (1.00) | 0.976 (1.00) | 0.803 (1.00) | 0.238 (1.00) | 0.0498 (1.00) | 0.935 (1.00) | 0.73 (1.00) | 0.297 (1.00)
SMAD4 | 8 (12%) | 61 | 0.447 (1.00) | 0.668 (1.00) | 1 (1.00) | 0.00581 (0.732) | 1 (1.00) | 0.645 (1.00) | 0.327 (1.00) | 0.594 (1.00)
KIAA1804 | 9 (13%) | 60 | 0.447 (1.00) | 0.24 (1.00) | 1 (1.00) | 1 (1.00) | 0.707 (1.00) | 1 (1.00) | 1 (1.00) | 0.957 (1.00)
FBXW7 | 9 (13%) | 60 | 0.497 (1.00) | 0.92 (1.00) | 0.722 (1.00) | 1 (1.00) | 0.579 (1.00) | 0.199 (1.00) | 0.338 (1.00) | 0.341 (1.00)
NRAS | 5 (7%) | 64 | 0.116 (1.00) | 0.0221 (1.00) | 1 (1.00) | 1 (1.00) | 0.0521 (1.00) | 0.53 (1.00) | 0.555 (1.00) | 0.887 (1.00)
TCF7L2 | 7 (10%) | 62 | 0.665 (1.00) | 0.744 (1.00) | 1 (1.00) | 1 (1.00) | 0.794 (1.00) | 0.85 (1.00) | 0.266 (1.00) | 0.514 (1.00)
PIK3CA | 7 (10%) | 62 | 0.497 (1.00) | 0.432 (1.00) | 1 (1.00) | 0.0348 (1.00) | 1 (1.00) | 0.62 (1.00) | 0.582 (1.00) | 0.63 (1.00)
OPCML | 6 (9%) | 63 | 0.497 (1.00) | 0.966 (1.00) | 0.69 (1.00) | 1 (1.00) | 0.148 (1.00) | 1 (1.00) | 1 (1.00) | 0.504 (1.00)
SMAD2 | 5 (7%) | 64 | 0.497 (1.00) | 0.834 (1.00) | 1 (1.00) | 1 (1.00) | 0.525 (1.00) | 0.342 (1.00) | 1 (1.00) | 0.536 (1.00)
SPATA8 | 3 (4%) | 66 |  | 0.194 (1.00) | 0.0775 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.737 (1.00)
ERBB2 | 4 (6%) | 65 |  | 0.273 (1.00) | 0.127 (1.00) | 1 (1.00) | 0.416 (1.00) | 0.453 (1.00) | 1 (1.00) | 0.662 (1.00)
IL1RAPL2 | 5 (7%) | 64 | 0.765 (1.00) | 0.639 (1.00) | 1 (1.00) | 0.11 (1.00) | 1 (1.00) | 0.816 (1.00) | 1 (1.00) | 0.852 (1.00)
FAM123B | 6 (9%) | 63 | 0.484 (1.00) | 0.485 (1.00) | 0.69 (1.00) | 1 (1.00) | 0.0463 (1.00) | 1 (1.00) | 0.207 (1.00) | 0.0851 (1.00)
ZIM3 | 5 (7%) | 64 | 0.069 (1.00) | 0.235 (1.00) | 1 (1.00) | 0.493 (1.00) | 0.366 (1.00) | 0.53 (1.00) | 1 (1.00) | 0.666 (1.00)

Methods & Data
Input

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

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

- Number of patients = 69

- Number of significantly mutated genes = 16

- 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

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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