Ovarian Serous Cystadenocarcinoma: Correlation between gene mutation status and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between mutation status of 9 genes and 4 clinical features across 316 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 9 genes and 4 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 | KARNOFSKY PERFORMANCE SCORE | TUMOR STAGE
| nMutated (%) | nWild-Type | logrank test | t-test | t-test | Fisher's exact test
TP53 | 276 (87%) | 40 | 0.389 (1.00) | 0.649 (1.00) | 0.585 (1.00) | 0.744 (1.00)
SRC | 4 (1%) | 312 | 0.197 (1.00) | 0.621 (1.00) |  | 0.287 (1.00)
TBP | 4 (1%) | 312 | 0.323 (1.00) | 0.191 (1.00) |  | 0.618 (1.00)
RB1 | 9 (3%) | 307 | 0.241 (1.00) | 0.204 (1.00) |  | 1 (1.00)
GABRA6 | 6 (2%) | 310 | 0.815 (1.00) | 0.0121 (0.363) |  | 1 (1.00)
C9ORF171 | 5 (2%) | 311 | 0.895 (1.00) | 0.121 (1.00) | 0.746 (1.00) | 0.374 (1.00)
CSMD3 | 18 (6%) | 298 | 0.262 (1.00) | 0.61 (1.00) | 0.285 (1.00) | 0.613 (1.00)
NF1 | 14 (4%) | 302 | 0.2 (1.00) | 0.606 (1.00) |  | 0.623 (1.00)
BRCA1 | 12 (4%) | 304 | 0.957 (1.00) | 0.65 (1.00) |  | 0.827 (1.00)

Methods & Data
Input

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

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

- Number of patients = 316

- Number of significantly mutated genes = 9

- Number of selected clinical features = 4

- 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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