Correlation between gene mutation status and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C14748MM
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/C14748MM
Overview
Introduction

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

Summary

Testing the association between mutation status of 25 genes and 5 clinical features across 195 patients, 4 significant findings detected with Q value < 0.25.

- DNMT3A mutation correlated to 'Time to Death'.

- IDH2 mutation correlated to 'AGE'.

- TP53 mutation correlated to 'Time to Death' and 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 25 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER | RACE | ETHNICITY
| nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test
TP53 | 15 (8%) | 180 | 1.18e-05 (0.00144) | 0.00204 (0.243) | 0.177 (1.00) | 1 (1.00) | 1 (1.00)
DNMT3A | 49 (25%) | 146 | 0.000206 (0.0249) | 0.266 (1.00) | 0.188 (1.00) | 0.387 (1.00) | 0.161 (1.00)
IDH2 | 20 (10%) | 175 | 0.481 (1.00) | 0.0016 (0.192) | 0.815 (1.00) | 0.356 (1.00) | 0.282 (1.00)
FLT3 | 53 (27%) | 142 | 0.106 (1.00) | 0.328 (1.00) | 0.52 (1.00) | 0.0698 (1.00) | 0.562 (1.00)
NPM1 | 52 (27%) | 143 | 0.0954 (1.00) | 0.774 (1.00) | 0.145 (1.00) | 0.232 (1.00) | 0.564 (1.00)
IDH1 | 18 (9%) | 177 | 0.947 (1.00) | 0.322 (1.00) | 0.466 (1.00) | 0.702 (1.00) | 1 (1.00)
RUNX1 | 18 (9%) | 177 | 0.0571 (1.00) | 0.0168 (1.00) | 0.622 (1.00) | 1 (1.00) | 1 (1.00)
TET2 | 17 (9%) | 178 | 0.71 (1.00) | 0.128 (1.00) | 0.319 (1.00) | 1 (1.00) | 1 (1.00)
NRAS | 15 (8%) | 180 | 0.826 (1.00) | 0.257 (1.00) | 1 (1.00) | 0.671 (1.00) | 1 (1.00)
WT1 | 12 (6%) | 183 | 0.7 (1.00) | 0.168 (1.00) | 0.774 (1.00) | 1 (1.00) | 0.163 (1.00)
U2AF1 | 8 (4%) | 187 | 0.642 (1.00) | 0.00419 (0.494) | 0.0695 (1.00) | 1 (1.00) | 1 (1.00)
CEBPA | 13 (7%) | 182 | 0.95 (1.00) | 0.0116 (1.00) | 0.58 (1.00) | 1 (1.00) | 1 (1.00)
PHF6 | 6 (3%) | 189 | 0.874 (1.00) | 0.158 (1.00) | 0.0309 (1.00) | 1 (1.00) | 1 (1.00)
KRAS | 8 (4%) | 187 | 0.436 (1.00) | 0.101 (1.00) | 0.149 (1.00) | 1 (1.00) | 0.12 (1.00)
SMC3 | 7 (4%) | 188 | 0.061 (1.00) | 0.782 (1.00) | 0.707 (1.00) | 1 (1.00) | 1 (1.00)
KIT | 8 (4%) | 187 | 0.589 (1.00) | 0.68 (1.00) | 0.477 (1.00) | 1 (1.00) | 1 (1.00)
RAD21 | 5 (3%) | 190 | 0.999 (1.00) | 0.231 (1.00) | 1 (1.00) | 0.0178 (1.00) | 1 (1.00)
EZH2 | 3 (2%) | 192 |  | 0.214 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)
STAG2 | 6 (3%) | 189 | 0.409 (1.00) | 0.214 (1.00) | 0.42 (1.00) | 0.13 (1.00) | 1 (1.00)
PTPN11 | 9 (5%) | 186 | 0.415 (1.00) | 0.294 (1.00) | 1 (1.00) | 0.575 (1.00) | 1 (1.00)
ASXL1 | 5 (3%) | 190 | 0.197 (1.00) | 0.0653 (1.00) | 0.666 (1.00) | 1 (1.00) | 0.0765 (1.00)
SUZ12 | 3 (2%) | 192 |  | 0.947 (1.00) | 0.249 (1.00) |  | 1 (1.00)
PHACTR1 | 3 (2%) | 192 | 0.355 (1.00) | 0.963 (1.00) | 0.599 (1.00) | 1 (1.00) | 0.0464 (1.00)
SMC1A | 6 (3%) | 189 | 0.222 (1.00) | 0.265 (1.00) | 0.218 (1.00) | 0.43 (1.00) | 1 (1.00)
KDM6A | 3 (2%) | 192 | 0.875 (1.00) | 0.893 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000206 (logrank test), Q value = 0.025

Table S1. Gene #3: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 171 | 107 | 0.9 - 94.1 (12.0)
DNMT3A MUTATED | 44 | 34 | 0.9 - 34.0 (9.0)
DNMT3A WILD-TYPE | 127 | 73 | 0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #3: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 0.0016 (Wilcoxon-test), Q value = 0.19

Table S2. Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 54.9 (16.1)
IDH2 MUTATED | 20 | 64.9 (8.0)
IDH2 WILD-TYPE | 175 | 53.8 (16.5)

Figure S2. Get High-res Image Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 1.18e-05 (logrank test), Q value = 0.0014

Table S3. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 171 | 107 | 0.9 - 94.1 (12.0)
TP53 MUTATED | 12 | 12 | 1.0 - 17.0 (8.0)
TP53 WILD-TYPE | 159 | 95 | 0.9 - 94.1 (13.0)

Figure S3. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 0.00204 (Wilcoxon-test), Q value = 0.24

Table S4. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 54.9 (16.1)
TP53 MUTATED | 15 | 66.9 (10.7)
TP53 WILD-TYPE | 180 | 53.9 (16.1)

Figure S4. Get High-res Image Gene #11: 'TP53 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 = 195

- Number of significantly mutated genes = 25

- 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.

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] 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)
Copyright © 2014 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle