Skin Cutaneous Melanoma: Correlation between gene mutation status and selected clinical features
(All_Primary cohort)
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 15 genes and 7 clinical features across 23 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 15 genes and 7 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 | PRIMARY SITE OF DISEASE | GENDER | LYMPH NODE METASTASIS | TUMOR STAGECODE | NEOPLASM DISEASESTAGE
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Chi-square test | t-test | Chi-square test
BRAF | 16 (70%) | 7 | 0.936 (1.00) | 0.447 (1.00) | 0.352 (1.00) | 0.626 (1.00) | 0.302 (1.00) |  | 0.681 (1.00)
NRAS | 3 (13%) | 20 |  |  | 0.453 (1.00) | 1 (1.00) |  |  |
PRB2 | 4 (17%) | 19 |  | 0.368 (1.00) | 1 (1.00) | 0.557 (1.00) | 0.343 (1.00) |  | 0.156 (1.00)
FAM135B | 6 (26%) | 17 | 0.45 (1.00) | 0.00415 (0.303) | 1 (1.00) | 0.621 (1.00) | 0.697 (1.00) |  | 0.469 (1.00)
PTEN | 4 (17%) | 19 |  | 0.696 (1.00) | 0.125 (1.00) | 1 (1.00) | 0.343 (1.00) |  | 0.961 (1.00)
TLL1 | 6 (26%) | 17 | 0.197 (1.00) | 0.0736 (1.00) | 1 (1.00) | 1 (1.00) | 0.302 (1.00) |  | 0.469 (1.00)
ARID2 | 4 (17%) | 19 | 0.197 (1.00) | 0.37 (1.00) | 1 (1.00) | 0.273 (1.00) | 0.343 (1.00) |  | 0.961 (1.00)
ZFHX4 | 8 (35%) | 15 | 0.936 (1.00) | 0.734 (1.00) | 0.779 (1.00) | 0.345 (1.00) | 0.404 (1.00) |  | 0.148 (1.00)
CFHR1 | 5 (22%) | 18 | 0.782 (1.00) | 0.302 (1.00) | 1 (1.00) | 0.272 (1.00) | 0.959 (1.00) |  | 0.29 (1.00)
OR52M1 | 3 (13%) | 20 |  | 0.319 (1.00) | 1 (1.00) | 1 (1.00) | 0.804 (1.00) |  | 0.339 (1.00)
PPP6C | 3 (13%) | 20 |  |  | 0.453 (1.00) | 1 (1.00) |  |  |
RUNDC3B | 3 (13%) | 20 |  | 0.154 (1.00) | 0.453 (1.00) | 0.526 (1.00) | 0.191 (1.00) |  |
ADCYAP1R1 | 3 (13%) | 20 | 0.0253 (1.00) | 0.224 (1.00) | 1 (1.00) | 1 (1.00) | 0.839 (1.00) |  | 0.928 (1.00)
KCNC2 | 3 (13%) | 20 |  |  | 0.453 (1.00) | 0.209 (1.00) |  |  |
PIK3R1 | 4 (17%) | 19 | 0.332 (1.00) | 0.754 (1.00) | 1 (1.00) | 0.557 (1.00) | 0.0811 (1.00) |  | 0.505 (1.00)

Methods & Data
Input

- Mutation data file = SKCM-All_Primary.mutsig.cluster.txt

- Clinical data file = SKCM-All_Primary.clin.merged.picked.txt

- Number of patients = 23

- Number of significantly mutated genes = 15

- Number of selected clinical features = 7

- 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

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[5] 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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