Correlation between gene mutation status and selected clinical features
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 2 clinical features across 32 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 2 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
nMutated (%) nWild-Type logrank test t-test
FBXW7 13 (41%) 19 0.583
(1.00) 		0.311
(1.00)
KRAS 3 (9%) 29 0.87
(1.00) 		0.663
(1.00)
TP53 28 (88%) 4 0.981
(1.00) 		0.0281
(0.507)
PPP2R1A 10 (31%) 22 0.714
(1.00) 		0.841
(1.00)
PIK3CA 11 (34%) 21 0.882
(1.00) 		0.472
(1.00)
PTEN 5 (16%) 27 0.974
(1.00) 		0.375
(1.00)
PCDHAC2 4 (12%) 28 0.221
(1.00) 		0.688
(1.00)
PIK3R1 5 (16%) 27 0.415
(1.00) 		0.246
(1.00)
ZBTB7B 4 (12%) 28 0.114
(1.00) 		0.586
(1.00)
Methods & Data
Input

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

  • Clinical data file = UCS-TP.merged_data.txt

  • Number of patients = 32

  • Number of significantly mutated genes = 9

  • Number of selected clinical features = 2

  • 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

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.

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] 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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