Correlation between gene mutation status and selected clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1HM56DP
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1HM56DP
Overview
Introduction

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

Summary

Testing the association between mutation status of 7 genes and 8 clinical features across 101 patients, one significant finding detected with Q value < 0.25.

- IL32 mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.

|  | Clinical Features | Time to Death | AGE | GENDER | KARNOFSKY PERFORMANCE SCORE | PATHOLOGY T | PATHOLOGY N | PATHOLOGICSPREAD(M) | TUMOR STAGE
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
IL32 | 4 (4%) | 97 | 9.44e-15 (4.15e-13) | 0.366 (1.00) | 0.3 (1.00) |  | 0.776 (1.00) |  | 0.22 (1.00) | 0.667 (1.00)
MET | 7 (7%) | 94 | 0.695 (1.00) | 0.881 (1.00) | 1 (1.00) |  | 1 (1.00) |  | 1 (1.00) | 0.799 (1.00)
CDC27 | 4 (4%) | 97 | 0.598 (1.00) | 0.78 (1.00) | 0.595 (1.00) |  | 0.776 (1.00) |  | 0.132 (1.00) | 0.345 (1.00)
NF2 | 7 (7%) | 94 | 0.598 (1.00) | 0.3 (1.00) | 1 (1.00) |  | 0.437 (1.00) | 0.0583 (1.00) | 0.0363 (1.00) | 0.162 (1.00)
SFRS2IP | 5 (5%) | 96 | 0.226 (1.00) | 0.121 (1.00) | 0.661 (1.00) |  | 0.102 (1.00) | 1 (1.00) | 0.0829 (1.00) | 0.316 (1.00)
LGI4 | 4 (4%) | 97 | 0.19 (1.00) | 0.91 (1.00) | 0.3 (1.00) |  | 0.776 (1.00) |  | 0.715 (1.00) | 0.667 (1.00)
BHMT | 3 (3%) | 98 | 0.383 (1.00) | 0.703 (1.00) | 0.549 (1.00) |  | 1 (1.00) |  | 0.627 (1.00) | 0.562 (1.00)

'IL32 MUTATION STATUS' versus 'Time to Death'

P value = 9.44e-15 (logrank test), Q value = 4.2e-13

Table S1. Gene #2: 'IL32 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 94 | 14 | 0.0 - 182.7 (13.9)
IL32 MUTATED | 4 | 1 | 3.6 - 7.9 (4.8)
IL32 WILD-TYPE | 90 | 13 | 0.0 - 182.7 (14.6)

Figure S1. Get High-res Image Gene #2: 'IL32 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

- Number of patients = 101

- Number of significantly mutated genes = 7

- 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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