Correlation between gene mutation status and selected clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1JQ10FG
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene mutation status and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JQ10FG
Overview
Introduction

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

Summary

Testing the association between mutation status of 23 genes and 5 clinical features across 193 patients, 7 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'.

- RUNX1 mutation correlated to 'YEARS_TO_BIRTH'.

- CEBPA mutation correlated to 'YEARS_TO_BIRTH'.

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 23 genes and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 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%) | 178 | 5.68e-06 (0.000653) | 0.000713 (0.041) | 0.176 (0.724) | 1 (1.00) | 1 (1.00)
DNMT3A | 48 (25%) | 145 | 0.0011 (0.042) | 0.267 (0.837) | 0.136 (0.724) | 0.382 (0.935) | 0.158 (0.724)
IDH2 | 20 (10%) | 173 | 0.685 (1.00) | 0.00164 (0.0471) | 0.815 (1.00) | 0.36 (0.9) | 0.285 (0.86)
U2AF1 | 8 (4%) | 185 | 0.47 (1.00) | 0.00422 (0.097) | 0.0696 (0.616) | 1 (1.00) | 1 (1.00)
RUNX1 | 16 (8%) | 177 | 0.0284 (0.356) | 0.00656 (0.126) | 1 (1.00) | 1 (1.00) | 1 (1.00)
CEBPA | 13 (7%) | 180 | 0.765 (1.00) | 0.0117 (0.193) | 0.58 (1.00) | 1 (1.00) | 1 (1.00)
FLT3 | 52 (27%) | 141 | 0.167 (0.724) | 0.331 (0.865) | 0.627 (1.00) | 0.0693 (0.616) | 0.563 (1.00)
NPM1 | 33 (17%) | 160 | 0.705 (1.00) | 0.172 (0.724) | 0.57 (1.00) | 0.522 (1.00) | 1 (1.00)
IDH1 | 18 (9%) | 175 | 0.638 (1.00) | 0.326 (0.865) | 0.465 (1.00) | 0.701 (1.00) | 1 (1.00)
TET2 | 17 (9%) | 176 | 0.844 (1.00) | 0.127 (0.724) | 0.319 (0.865) | 1 (1.00) | 1 (1.00)
NRAS | 15 (8%) | 178 | 0.666 (1.00) | 0.256 (0.837) | 1 (1.00) | 0.67 (1.00) | 1 (1.00)
WT1 | 12 (6%) | 181 | 0.574 (1.00) | 0.168 (0.724) | 0.774 (1.00) | 0.648 (1.00) | 0.165 (0.724)
KRAS | 8 (4%) | 185 | 0.303 (0.861) | 0.102 (0.724) | 0.149 (0.724) | 1 (1.00) | 0.122 (0.724)
PHF6 | 6 (3%) | 187 | 0.977 (1.00) | 0.158 (0.724) | 0.0309 (0.356) | 1 (1.00) | 1 (1.00)
STAG2 | 6 (3%) | 187 | 0.34 (0.87) | 0.215 (0.808) | 0.42 (1.00) | 0.13 (0.724) | 1 (1.00)
KIT | 8 (4%) | 185 | 0.489 (1.00) | 0.681 (1.00) | 0.476 (1.00) | 1 (1.00) | 1 (1.00)
RAD21 | 5 (3%) | 188 | 0.888 (1.00) | 0.233 (0.837) | 1 (1.00) | 0.0191 (0.275) | 1 (1.00)
EZH2 | 3 (2%) | 190 |  | 0.213 (0.808) | 1 (1.00) | 1 (1.00) | 1 (1.00)
SMC3 | 7 (4%) | 186 | 0.117 (0.724) | 0.783 (1.00) | 0.707 (1.00) | 1 (1.00) | 1 (1.00)
ASXL1 | 5 (3%) | 188 | 0.269 (0.837) | 0.0661 (0.616) | 0.666 (1.00) | 1 (1.00) | 0.0773 (0.635)
SMC1A | 6 (3%) | 187 | 0.307 (0.861) | 0.265 (0.837) | 0.218 (0.808) | 0.435 (1.00) | 1 (1.00)
PTPN11 | 9 (5%) | 184 | 0.554 (1.00) | 0.292 (0.86) | 1 (1.00) | 0.576 (1.00) | 1 (1.00)
SUZ12 | 3 (2%) | 190 | 0.88 (1.00) | 0.946 (1.00) | 0.25 (0.837) |  | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.0011 (logrank test), Q value = 0.042

Table S1. Gene #2: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 115 | 0.0 - 94.1 (12.0)
DNMT3A MUTATED | 44 | 34 | 0.0 - 34.0 (8.5)
DNMT3A WILD-TYPE | 135 | 81 | 0.0 - 94.1 (13.0)

Figure S1. Get High-res Image Gene #2: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00164 (Wilcoxon-test), Q value = 0.047

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 193 | 54.9 (16.2)
IDH2 MUTATED | 20 | 64.9 (8.0)
IDH2 WILD-TYPE | 173 | 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 = 0.00422 (Wilcoxon-test), Q value = 0.097

Table S3. Gene #9: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 193 | 54.9 (16.2)
U2AF1 MUTATED | 8 | 69.9 (9.0)
U2AF1 WILD-TYPE | 185 | 54.3 (16.1)

Figure S3. Get High-res Image Gene #9: 'U2AF1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RUNX1 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.00656 (Wilcoxon-test), Q value = 0.13

Table S4. Gene #10: 'RUNX1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 193 | 54.9 (16.2)
RUNX1 MUTATED | 16 | 64.3 (16.0)
RUNX1 WILD-TYPE | 177 | 54.1 (16.0)

Figure S4. Get High-res Image Gene #10: 'RUNX1 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'CEBPA MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.0117 (Wilcoxon-test), Q value = 0.19

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 193 | 54.9 (16.2)
CEBPA MUTATED | 13 | 42.7 (17.6)
CEBPA WILD-TYPE | 180 | 55.8 (15.8)

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

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 5.68e-06 (logrank test), Q value = 0.00065

Table S6. Gene #12: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 179 | 115 | 0.0 - 94.1 (12.0)
TP53 MUTATED | 14 | 14 | 0.0 - 17.0 (6.0)
TP53 WILD-TYPE | 165 | 101 | 0.0 - 94.1 (12.0)

Figure S6. Get High-res Image Gene #12: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'YEARS_TO_BIRTH'

P value = 0.000713 (Wilcoxon-test), Q value = 0.041

Table S7. Gene #12: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 193 | 54.9 (16.2)
TP53 MUTATED | 15 | 67.8 (10.1)
TP53 WILD-TYPE | 178 | 53.8 (16.2)

Figure S7. Get High-res Image Gene #12: 'TP53 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/22570966/transformed.cor.cli.txt

- Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/22506504/LAML-TB.merged_data.txt

- Number of patients = 193

- Number of significantly mutated genes = 23

- 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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