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 41 genes and 3 clinical features across 196 patients, 4 significant findings detected with Q value < 0.25.

- DNMT3A mutation correlated to 'Time to Death'.

- TP53 mutation correlated to 'Time to Death'.

- IDH2 mutation correlated to 'AGE'.

- FLJ43860 mutation correlated to 'Time to Death'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 41 genes and 3 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
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test
DNMT3A | 49 (25%) | 147 | 0.000602 (0.0662) | 0.0517 (1.00) | 0.248 (1.00)
TP53 | 11 (6%) | 185 | 7.75e-06 (0.000876) | 0.00306 (0.334) | 0.351 (1.00)
IDH2 | 18 (9%) | 178 | 0.897 (1.00) | 3.57e-05 (0.004) | 1 (1.00)
FLJ43860 | 3 (2%) | 193 | 0.00055 (0.061) | 0.754 (1.00) | 0.253 (1.00)
NPM1 | 47 (24%) | 149 | 0.223 (1.00) | 0.985 (1.00) | 0.132 (1.00)
FLT3 | 52 (27%) | 144 | 0.204 (1.00) | 0.365 (1.00) | 0.516 (1.00)
IDH1 | 20 (10%) | 176 | 0.925 (1.00) | 0.339 (1.00) | 0.478 (1.00)
NRAS | 18 (9%) | 178 | 0.88 (1.00) | 0.523 (1.00) | 0.626 (1.00)
RUNX1 | 17 (9%) | 179 | 0.232 (1.00) | 0.106 (1.00) | 0.802 (1.00)
WT1 | 12 (6%) | 184 | 0.325 (1.00) | 0.0417 (1.00) | 0.773 (1.00)
KRAS | 8 (4%) | 188 | 0.498 (1.00) | 0.435 (1.00) | 0.472 (1.00)
TET2 | 15 (8%) | 181 | 0.869 (1.00) | 0.171 (1.00) | 0.286 (1.00)
U2AF1 | 10 (5%) | 186 | 0.462 (1.00) | 0.00354 (0.382) | 0.0234 (1.00)
PHF6 | 6 (3%) | 190 | 0.882 (1.00) | 0.234 (1.00) | 0.0328 (1.00)
PTPN11 | 7 (4%) | 189 | 0.373 (1.00) | 0.677 (1.00) | 1 (1.00)
KIT | 7 (4%) | 189 | 0.905 (1.00) | 0.485 (1.00) | 0.704 (1.00)
C17ORF97 | 5 (3%) | 191 | 0.631 (1.00) | 0.408 (1.00) | 0.379 (1.00)
ETV6 | 5 (3%) | 191 | 0.471 (1.00) | 0.278 (1.00) | 0.379 (1.00)
SMC3 | 6 (3%) | 190 | 0.178 (1.00) | 0.584 (1.00) | 0.413 (1.00)
FAM5C | 5 (3%) | 191 | 0.121 (1.00) | 0.044 (1.00) | 0.379 (1.00)
MUC4 | 7 (4%) | 189 | 0.149 (1.00) | 0.5 (1.00) | 1 (1.00)
CYP21A2 | 4 (2%) | 192 | 0.0175 (1.00) | 0.554 (1.00) | 1 (1.00)
NOTCH2NL | 3 (2%) | 193 |  | 0.807 (1.00) | 0.253 (1.00)
AP3S1 | 3 (2%) | 193 | 0.741 (1.00) | 0.534 (1.00) | 0.592 (1.00)
SMC1A | 5 (3%) | 191 | 0.479 (1.00) | 0.0219 (1.00) | 0.379 (1.00)
TRIM48 | 3 (2%) | 193 |  | 0.202 (1.00) | 0.592 (1.00)
C5ORF25 | 3 (2%) | 193 | 0.0882 (1.00) | 0.195 (1.00) | 0.0919 (1.00)
CCDC74A | 3 (2%) | 193 | 0.523 (1.00) | 0.347 (1.00) | 0.253 (1.00)
OR11H12 | 3 (2%) | 193 | 0.528 (1.00) | 0.374 (1.00) | 0.592 (1.00)
OR5H6 | 3 (2%) | 193 |  | 0.0468 (1.00) | 0.592 (1.00)
LILRA3 | 3 (2%) | 193 |  | 0.878 (1.00) | 0.592 (1.00)
NMUR2 | 3 (2%) | 193 |  | 0.928 (1.00) | 1 (1.00)
STAG2 | 4 (2%) | 192 | 0.397 (1.00) | 0.325 (1.00) | 0.331 (1.00)
SUZ12 | 3 (2%) | 193 |  | 0.879 (1.00) | 0.253 (1.00)
SCRN3 | 3 (2%) | 193 |  | 0.0536 (1.00) | 0.592 (1.00)
DIS3 | 3 (2%) | 193 |  | 0.724 (1.00) | 0.592 (1.00)
VPS26B | 3 (2%) | 193 | 0.588 (1.00) | 0.96 (1.00) | 0.253 (1.00)
ZNF275 | 3 (2%) | 193 |  | 0.0715 (1.00) | 0.592 (1.00)
EZH2 | 3 (2%) | 193 |  | 0.115 (1.00) | 1 (1.00)
ANKRD24 | 3 (2%) | 193 | 0.177 (1.00) | 0.761 (1.00) | 1 (1.00)
QRICH2 | 4 (2%) | 192 | 0.608 (1.00) | 0.0269 (1.00) | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000602 (logrank test), Q value = 0.066

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 172 | 108 | 0.9 - 94.1 (12.0)
DNMT3A MUTATED | 45 | 34 | 0.9 - 37.0 (9.0)
DNMT3A WILD-TYPE | 127 | 74 | 0.9 - 94.1 (15.0)

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

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 7.75e-06 (logrank test), Q value = 0.00088

Table S2. Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 172 | 108 | 0.9 - 94.1 (12.0)
TP53 MUTATED | 10 | 10 | 1.0 - 17.0 (6.0)
TP53 WILD-TYPE | 162 | 98 | 0.9 - 94.1 (13.0)

Figure S2. Get High-res Image Gene #5: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 3.57e-05 (t-test), Q value = 0.004

Table S3. Gene #6: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 196 | 55.0 (16.2)
IDH2 MUTATED | 18 | 64.6 (7.9)
IDH2 WILD-TYPE | 178 | 54.0 (16.5)

Figure S3. Get High-res Image Gene #6: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'FLJ43860 MUTATION STATUS' versus 'Time to Death'

P value = 0.00055 (logrank test), Q value = 0.061

Table S4. Gene #34: 'FLJ43860 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 172 | 108 | 0.9 - 94.1 (12.0)
FLJ43860 MUTATED | 3 | 3 | 1.0 - 9.0 (2.0)
FLJ43860 WILD-TYPE | 169 | 105 | 0.9 - 94.1 (12.0)

Figure S4. Get High-res Image Gene #34: 'FLJ43860 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

Methods & Data
Input

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

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

- Number of patients = 196

- Number of significantly mutated genes = 41

- 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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