Correlation between gene mutation status and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1BZ64PQ
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1BZ64PQ
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 3 clinical features across 196 patients, 3 significant findings detected with Q value < 0.25.

- DNMT3A mutation correlated to 'Time to Death'.

- U2AF1 mutation correlated to 'AGE'.

- IDH2 mutation correlated to 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 3 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test
DNMT3A | 51 (26%) | 145 | 0.000372 (0.00743) | 0.0662 (1.00) | 0.328 (1.00)
U2AF1 | 8 (4%) | 188 | 0.621 (1.00) | 0.00137 (0.0261) | 0.0703 (1.00)
IDH2 | 20 (10%) | 176 | 0.456 (1.00) | 1.07e-05 (0.000225) | 0.815 (1.00)
FLT3 | 56 (29%) | 140 | 0.0841 (1.00) | 0.566 (1.00) | 0.754 (1.00)
IDH1 | 19 (10%) | 177 | 0.796 (1.00) | 0.303 (1.00) | 0.633 (1.00)
NPM1 | 54 (28%) | 142 | 0.11 (1.00) | 0.972 (1.00) | 0.262 (1.00)
NRAS | 15 (8%) | 181 | 0.852 (1.00) | 0.288 (1.00) | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000372 (logrank test), Q value = 0.0074

Table S1. Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 172 | 107 | 0.9 - 94.1 (12.0)
DNMT3A MUTATED | 46 | 35 | 0.9 - 37.0 (9.0)
DNMT3A WILD-TYPE | 126 | 72 | 0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'U2AF1 MUTATION STATUS' versus 'AGE'

P value = 0.00137 (t-test), Q value = 0.026

Table S2. Gene #2: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 196 | 55.1 (16.2)
U2AF1 MUTATED | 8 | 69.9 (9.0)
U2AF1 WILD-TYPE | 188 | 54.5 (16.1)

Figure S2. Get High-res Image Gene #2: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 1.07e-05 (t-test), Q value = 0.00023

Table S3. Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 196 | 55.1 (16.2)
IDH2 MUTATED | 20 | 64.9 (8.0)
IDH2 WILD-TYPE | 176 | 54.0 (16.5)

Figure S3. Get High-res Image Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

- Clinical data file = LAML-TB.merged_data.txt

- Number of patients = 196

- Number of significantly mutated genes = 7

- Number of selected clinical features = 3

- 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- 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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