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 11 genes and 5 clinical features across 57 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 11 genes and 5 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 		YEARS
TO
BIRTH 		RADIATION
THERAPY 		HISTOLOGICAL
TYPE 		RACE
nMutated (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test
TP53 51 (89%) 6 0.271
(0.778) 		0.0288
(0.711) 		0.399
(0.778) 		0.474
(0.809) 		0.22
(0.755)
FBXW7 22 (39%) 35 0.517
(0.809) 		0.407
(0.778) 		0.579
(0.809) 		0.165
(0.755) 		1
(1.00)
PPP2R1A 16 (28%) 41 0.659
(0.809) 		0.631
(0.809) 		0.384
(0.778) 		0.268
(0.778) 		0.535
(0.809)
KRAS 7 (12%) 50 0.359
(0.778) 		0.576
(0.809) 		0.692
(0.809) 		0.0824
(0.755) 		0.557
(0.809)
PTEN 11 (19%) 46 0.389
(0.778) 		0.504
(0.809) 		0.736
(0.844) 		0.686
(0.809) 		0.678
(0.809)
RB1 6 (11%) 51 0.169
(0.755) 		0.355
(0.778) 		0.0849
(0.755) 		0.0154
(0.711) 		0.329
(0.778)
ZBTB7B 6 (11%) 51 0.134
(0.755) 		0.649
(0.809) 		0.399
(0.778) 		1
(1.00) 		0.218
(0.755)
PIK3R1 6 (11%) 51 0.6
(0.809) 		0.207
(0.755) 		0.653
(0.809) 		0.863
(0.949) 		0.219
(0.755)
ARHGAP35 6 (11%) 51 0.539
(0.809) 		0.0983
(0.755) 		0.675
(0.809) 		0.41
(0.778) 		0.0388
(0.711)
PIK3CA 20 (35%) 37 0.206
(0.755) 		0.136
(0.755) 		1
(1.00) 		0.106
(0.755) 		0.755
(0.848)
MAMLD1 4 (7%) 53 0.984
(1.00) 		0.364
(0.778) 		1
(1.00) 		0.436
(0.799) 		0.304
(0.778)
Methods & Data
Input

  • Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline

  • Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/UCS-TP/22569432/transformed.cor.cli.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/UCS-TP/22507158/UCS-TP.merged_data.txt

  • Number of patients = 57

  • Number of significantly mutated genes = 11

  • Number of selected clinical features = 5

  • 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.

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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