Acute Myeloid Leukemia: Correlation between gene mutation status and selected clinical features
(primary blood tumor (peripheral) cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between mutation status of 24 genes and 3 clinical features across 197 patients, 5 significant findings detected with Q value < 0.25.

- DNMT3A mutation correlated to 'Time to Death'.

- IDH2 mutation correlated to 'AGE'.

- U2AF1 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 24 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 5 significant findings detected.

|  | Clinical Features | Time to Death | AGE | GENDER
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test
TP53 | 15 (8%) | 182 | 1.22e-05 (0.000844) | 0.000379 (0.0257) | 0.177 (1.00)
DNMT3A | 51 (26%) | 146 | 0.00051 (0.0342) | 0.061 (1.00) | 0.328 (1.00)
IDH2 | 20 (10%) | 177 | 0.463 (1.00) | 1.02e-05 (0.000713) | 0.814 (1.00)
U2AF1 | 8 (4%) | 189 | 0.63 (1.00) | 0.00134 (0.0885) | 0.0711 (1.00)
NRAS | 15 (8%) | 182 | 0.834 (1.00) | 0.282 (1.00) | 1 (1.00)
WT1 | 12 (6%) | 185 | 0.713 (1.00) | 0.181 (1.00) | 0.776 (1.00)
RUNX1 | 18 (9%) | 179 | 0.0531 (1.00) | 0.0378 (1.00) | 0.623 (1.00)
FLT3 | 56 (28%) | 141 | 0.095 (1.00) | 0.582 (1.00) | 0.753 (1.00)
IDH1 | 19 (10%) | 178 | 0.794 (1.00) | 0.312 (1.00) | 0.632 (1.00)
NPM1 | 54 (27%) | 143 | 0.124 (1.00) | 0.99 (1.00) | 0.204 (1.00)
KRAS | 8 (4%) | 189 | 0.429 (1.00) | 0.0741 (1.00) | 0.147 (1.00)
PTPN11 | 9 (5%) | 188 | 0.406 (1.00) | 0.42 (1.00) | 1 (1.00)
TET2 | 17 (9%) | 180 | 0.704 (1.00) | 0.106 (1.00) | 0.316 (1.00)
KIT | 8 (4%) | 189 | 0.593 (1.00) | 0.541 (1.00) | 0.475 (1.00)
PHF6 | 6 (3%) | 191 | 0.857 (1.00) | 0.232 (1.00) | 0.0316 (1.00)
SMC1A | 7 (4%) | 190 | 0.119 (1.00) | 0.0645 (1.00) | 0.126 (1.00)
SMC3 | 7 (4%) | 190 | 0.0624 (1.00) | 0.658 (1.00) | 0.706 (1.00)
RAD21 | 5 (3%) | 192 | 0.988 (1.00) | 0.288 (1.00) | 1 (1.00)
STAG2 | 6 (3%) | 191 | 0.415 (1.00) | 0.1 (1.00) | 0.418 (1.00)
FAM5C | 5 (3%) | 192 | 0.114 (1.00) | 0.0454 (1.00) | 0.376 (1.00)
EZH2 | 3 (2%) | 194 |  | 0.116 (1.00) | 1 (1.00)
ASXL1 | 5 (3%) | 192 | 0.186 (1.00) | 0.0643 (1.00) | 0.664 (1.00)
PHACTR1 | 3 (2%) | 194 | 0.359 (1.00) | 0.887 (1.00) | 0.596 (1.00)
DIS3 | 3 (2%) | 194 |  | 0.726 (1.00) | 0.596 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.00051 (logrank test), Q value = 0.034

Table S1. Gene #4: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 173 | 108 | 0.9 - 94.1 (12.0)
DNMT3A MUTATED | 46 | 35 | 0.9 - 37.0 (9.0)
DNMT3A WILD-TYPE | 127 | 73 | 0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #4: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 1.02e-05 (t-test), Q value = 0.00071

Table S2. Gene #7: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 197 | 55.0 (16.2)
IDH2 MUTATED | 20 | 64.8 (8.0)
IDH2 WILD-TYPE | 177 | 53.9 (16.5)

Figure S2. Get High-res Image Gene #7: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'U2AF1 MUTATION STATUS' versus 'AGE'

P value = 0.00134 (t-test), Q value = 0.088

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 197 | 55.0 (16.2)
U2AF1 MUTATED | 8 | 69.9 (9.0)
U2AF1 WILD-TYPE | 189 | 54.4 (16.1)

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

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 1.22e-05 (logrank test), Q value = 0.00084

Table S4. Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 173 | 108 | 0.9 - 94.1 (12.0)
TP53 MUTATED | 12 | 12 | 1.0 - 17.0 (8.0)
TP53 WILD-TYPE | 161 | 96 | 0.9 - 94.1 (13.0)

Figure S4. Get High-res Image Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'TP53 MUTATION STATUS' versus 'AGE'

P value = 0.000379 (t-test), Q value = 0.026

Table S5. Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 197 | 55.0 (16.2)
TP53 MUTATED | 15 | 66.9 (10.7)
TP53 WILD-TYPE | 182 | 54.1 (16.2)

Figure S5. Get High-res Image Gene #10: 'TP53 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

- Mutation data file = LAML-TB.mutsig.cluster.txt

- Clinical data file = LAML-TB.clin.merged.picked.txt

- Number of patients = 197

- Number of significantly mutated genes = 24

- 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

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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