Correlation between gene mutation status and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1RB73NR
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1RB73NR
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, 6 significant findings detected with Q value < 0.25.

- DNMT3A mutation correlated to 'Time to Death'.

- IDH2 mutation correlated to 'YEARS_TO_BIRTH'.

- U2AF1 mutation correlated to 'YEARS_TO_BIRTH'.

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

- CEBPA mutation correlated to 'YEARS_TO_BIRTH'.

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, 6 significant findings detected.

|  | Clinical Features | Time to Death | YEARS TO BIRTH | 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 | 6.22e-06 (0.000778) | 0.00204 (0.0638) | 0.177 (0.761) | 1 (1.00) | 1 (1.00)
DNMT3A | 49 (25%) | 146 | 0.00185 (0.0638) | 0.266 (0.844) | 0.188 (0.785) | 0.389 (0.952) | 0.161 (0.75)
IDH2 | 20 (10%) | 175 | 0.724 (1.00) | 0.0016 (0.0638) | 0.815 (1.00) | 0.355 (0.888) | 0.282 (0.844)
U2AF1 | 8 (4%) | 187 | 0.496 (1.00) | 0.00419 (0.105) | 0.0695 (0.595) | 1 (1.00) | 1 (1.00)
CEBPA | 13 (7%) | 182 | 0.737 (1.00) | 0.0116 (0.242) | 0.58 (1.00) | 1 (1.00) | 1 (1.00)
FLT3 | 53 (27%) | 142 | 0.135 (0.75) | 0.328 (0.844) | 0.52 (1.00) | 0.0713 (0.595) | 0.562 (1.00)
NPM1 | 52 (27%) | 143 | 0.044 (0.527) | 0.774 (1.00) | 0.145 (0.75) | 0.231 (0.827) | 0.564 (1.00)
IDH1 | 18 (9%) | 177 | 0.609 (1.00) | 0.322 (0.844) | 0.466 (1.00) | 0.7 (1.00) | 1 (1.00)
RUNX1 | 18 (9%) | 177 | 0.0714 (0.595) | 0.0168 (0.288) | 0.622 (1.00) | 1 (1.00) | 1 (1.00)
TET2 | 17 (9%) | 178 | 0.805 (1.00) | 0.128 (0.75) | 0.319 (0.844) | 1 (1.00) | 1 (1.00)
NRAS | 15 (8%) | 180 | 0.637 (1.00) | 0.257 (0.844) | 1 (1.00) | 0.674 (1.00) | 1 (1.00)
WT1 | 12 (6%) | 183 | 0.55 (1.00) | 0.168 (0.75) | 0.774 (1.00) | 1 (1.00) | 0.163 (0.75)
PHF6 | 6 (3%) | 189 | 0.998 (1.00) | 0.158 (0.75) | 0.0309 (0.429) | 1 (1.00) | 1 (1.00)
KRAS | 8 (4%) | 187 | 0.319 (0.844) | 0.101 (0.745) | 0.149 (0.75) | 1 (1.00) | 0.12 (0.75)
SMC3 | 7 (4%) | 188 | 0.129 (0.75) | 0.782 (1.00) | 0.707 (1.00) | 1 (1.00) | 1 (1.00)
KIT | 8 (4%) | 187 | 0.468 (1.00) | 0.68 (1.00) | 0.477 (1.00) | 1 (1.00) | 1 (1.00)
RAD21 | 5 (3%) | 190 | 0.869 (1.00) | 0.231 (0.827) | 1 (1.00) | 0.0184 (0.288) | 1 (1.00)
EZH2 | 3 (2%) | 192 |  | 0.214 (0.825) | 1 (1.00) | 1 (1.00) | 1 (1.00)
STAG2 | 6 (3%) | 189 | 0.331 (0.844) | 0.214 (0.825) | 0.42 (1.00) | 0.128 (0.75) | 1 (1.00)
PTPN11 | 9 (5%) | 186 | 0.582 (1.00) | 0.294 (0.844) | 1 (1.00) | 0.571 (1.00) | 1 (1.00)
ASXL1 | 5 (3%) | 190 | 0.281 (0.844) | 0.0653 (0.595) | 0.666 (1.00) | 1 (1.00) | 0.0765 (0.598)
SUZ12 | 3 (2%) | 192 | 0.897 (1.00) | 0.947 (1.00) | 0.249 (0.844) |  | 1 (1.00)
PHACTR1 | 3 (2%) | 192 | 0.316 (0.844) | 0.963 (1.00) | 0.599 (1.00) | 1 (1.00) | 0.0464 (0.527)
SMC1A | 6 (3%) | 189 | 0.321 (0.844) | 0.265 (0.844) | 0.218 (0.825) | 0.43 (1.00) | 1 (1.00)
KDM6A | 3 (2%) | 192 | 0.793 (1.00) | 0.893 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.00185 (logrank test), Q value = 0.064

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 181 | 117 | 0.0 - 94.1 (12.0)
DNMT3A MUTATED | 45 | 35 | 0.0 - 34.0 (9.0)
DNMT3A WILD-TYPE | 136 | 82 | 0.0 - 94.1 (12.5)

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

'IDH2 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0016 (Wilcoxon-test), Q value = 0.064

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

| 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: 'YEARS_TO_BIRTH'

'U2AF1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00419 (Wilcoxon-test), Q value = 0.1

Table S3. Gene #10: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 54.9 (16.1)
U2AF1 MUTATED | 8 | 69.9 (9.0)
U2AF1 WILD-TYPE | 187 | 54.3 (16.1)

Figure S3. Get High-res Image Gene #10: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 6.22e-06 (logrank test), Q value = 0.00078

Table S4. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 181 | 117 | 0.0 - 94.1 (12.0)
TP53 MUTATED | 14 | 14 | 0.0 - 17.0 (6.0)
TP53 WILD-TYPE | 167 | 103 | 0.0 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00204 (Wilcoxon-test), Q value = 0.064

Table S5. Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| 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 S5. Get High-res Image Gene #11: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'CEBPA MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0116 (Wilcoxon-test), Q value = 0.24

Table S6. Gene #12: 'CEBPA MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 195 | 54.9 (16.1)
CEBPA MUTATED | 13 | 42.7 (17.6)
CEBPA WILD-TYPE | 182 | 55.8 (15.7)

Figure S6. Get High-res Image Gene #12: 'CEBPA MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

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/LAML-TB/15166008/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/15082450/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)
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