Acute Myeloid Leukemia: Correlation between gene mutation status and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 48 genes and 3 clinical features across 199 patients, 6 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'.

- ZAN mutation correlated to 'AGE'.

- FLJ43860 mutation correlated to 'Time to Death'.

- NINL mutation correlated to 'AGE'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 48 genes and 3 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 | AGE | GENDER
| nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test
DNMT3A | 50 (25%) | 149 | 0.000611 (0.0795) | 0.0569 (1.00) | 0.192 (1.00)
IDH2 | 20 (10%) | 179 | 0.629 (1.00) | 2.17e-05 (0.00287) | 0.814 (1.00)
TP53 | 11 (6%) | 188 | 7.67e-06 (0.00102) | 0.00316 (0.404) | 0.233 (1.00)
ZAN | 6 (3%) | 193 | 0.263 (1.00) | 0.00108 (0.139) | 0.0951 (1.00)
FLJ43860 | 3 (2%) | 196 | 0.000592 (0.0775) | 0.756 (1.00) | 0.252 (1.00)
NINL | 3 (2%) | 196 | 0.811 (1.00) | 9.99e-09 (1.34e-06) | 1 (1.00)
IDH1 | 20 (10%) | 179 | 0.932 (1.00) | 0.331 (1.00) | 0.479 (1.00)
U2AF1 | 10 (5%) | 189 | 0.459 (1.00) | 0.00364 (0.462) | 0.0228 (1.00)
KRAS | 9 (5%) | 190 | 0.587 (1.00) | 0.453 (1.00) | 0.306 (1.00)
TET2 | 15 (8%) | 184 | 0.865 (1.00) | 0.175 (1.00) | 0.289 (1.00)
FLT3 | 52 (26%) | 147 | 0.206 (1.00) | 0.352 (1.00) | 0.519 (1.00)
NPM1 | 47 (24%) | 152 | 0.222 (1.00) | 0.967 (1.00) | 0.136 (1.00)
NRAS | 18 (9%) | 181 | 0.875 (1.00) | 0.531 (1.00) | 0.625 (1.00)
OR5H6 | 4 (2%) | 195 |  | 0.0986 (1.00) | 0.334 (1.00)
RUNX1 | 17 (9%) | 182 | 0.233 (1.00) | 0.108 (1.00) | 0.802 (1.00)
WT1 | 13 (7%) | 186 | 0.672 (1.00) | 0.0301 (1.00) | 1 (1.00)
KIT | 7 (4%) | 192 | 0.901 (1.00) | 0.49 (1.00) | 0.705 (1.00)
PHF6 | 6 (3%) | 193 | 0.873 (1.00) | 0.232 (1.00) | 0.0323 (1.00)
AP3S1 | 3 (2%) | 196 | 0.74 (1.00) | 0.537 (1.00) | 0.594 (1.00)
SCRN3 | 3 (2%) | 196 |  | 0.0541 (1.00) | 0.594 (1.00)
MPRIP | 3 (2%) | 196 |  | 0.65 (1.00) | 1 (1.00)
CYP21A2 | 4 (2%) | 195 | 0.0181 (1.00) | 0.556 (1.00) | 1 (1.00)
PTPN11 | 7 (4%) | 192 | 0.37 (1.00) | 0.673 (1.00) | 1 (1.00)
ETV6 | 5 (3%) | 194 | 0.472 (1.00) | 0.276 (1.00) | 0.378 (1.00)
NFKBIZ | 3 (2%) | 196 |  | 0.0353 (1.00) | 0.0939 (1.00)
LILRA3 | 3 (2%) | 196 |  | 0.875 (1.00) | 0.594 (1.00)
C17ORF97 | 5 (3%) | 194 | 0.629 (1.00) | 0.406 (1.00) | 0.378 (1.00)
MUC4 | 7 (4%) | 192 | 0.149 (1.00) | 0.497 (1.00) | 1 (1.00)
SMC3 | 6 (3%) | 193 | 0.177 (1.00) | 0.589 (1.00) | 0.415 (1.00)
NOTCH2NL | 3 (2%) | 196 |  | 0.804 (1.00) | 0.252 (1.00)
FAM5C | 5 (3%) | 194 | 0.119 (1.00) | 0.045 (1.00) | 0.378 (1.00)
PRUNE2 | 6 (3%) | 193 | 0.418 (1.00) | 0.0919 (1.00) | 0.00834 (1.00)
SMC1A | 5 (3%) | 194 | 0.474 (1.00) | 0.0224 (1.00) | 0.378 (1.00)
ZNF275 | 3 (2%) | 196 |  | 0.0723 (1.00) | 0.594 (1.00)
C5ORF25 | 3 (2%) | 196 | 0.0889 (1.00) | 0.194 (1.00) | 0.0939 (1.00)
MAP3K4 | 4 (2%) | 195 | 0.236 (1.00) | 0.138 (1.00) | 0.334 (1.00)
CSPG4 | 3 (2%) | 196 | 0.179 (1.00) | 0.569 (1.00) | 0.594 (1.00)
TRIM48 | 3 (2%) | 196 |  | 0.204 (1.00) | 0.594 (1.00)
ASXL1 | 5 (3%) | 194 | 0.193 (1.00) | 0.0641 (1.00) | 0.662 (1.00)
CSMD1 | 6 (3%) | 193 | 0.384 (1.00) | 0.499 (1.00) | 1 (1.00)
CPAMD8 | 3 (2%) | 196 |  | 0.0723 (1.00) | 0.594 (1.00)
PKD1L2 | 4 (2%) | 195 | 0.00538 (0.678) | 0.851 (1.00) | 1 (1.00)
STAG2 | 4 (2%) | 195 | 0.396 (1.00) | 0.328 (1.00) | 0.334 (1.00)
EZH2 | 3 (2%) | 196 |  | 0.116 (1.00) | 1 (1.00)
OR11H12 | 3 (2%) | 196 | 0.531 (1.00) | 0.376 (1.00) | 0.594 (1.00)
ANKRD24 | 3 (2%) | 196 | 0.178 (1.00) | 0.757 (1.00) | 1 (1.00)
CCDC74A | 3 (2%) | 196 | 0.526 (1.00) | 0.349 (1.00) | 0.252 (1.00)
QRICH2 | 4 (2%) | 195 | 0.609 (1.00) | 0.0273 (1.00) | 1 (1.00)

'DNMT3A MUTATION STATUS' versus 'Time to Death'

P value = 0.000611 (logrank test), Q value = 0.079

Table S1. Gene #5: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 175 | 110 | 0.9 - 94.1 (12.0)
DNMT3A MUTATED | 46 | 35 | 0.9 - 37.0 (9.0)
DNMT3A WILD-TYPE | 129 | 75 | 0.9 - 94.1 (15.0)

Figure S1. Get High-res Image Gene #5: 'DNMT3A MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'IDH2 MUTATION STATUS' versus 'AGE'

P value = 2.17e-05 (t-test), Q value = 0.0029

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 199 | 55.0 (16.1)
IDH2 MUTATED | 20 | 64.5 (8.0)
IDH2 WILD-TYPE | 179 | 54.0 (16.4)

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

'TP53 MUTATION STATUS' versus 'Time to Death'

P value = 7.67e-06 (logrank test), Q value = 0.001

Table S3. Gene #12: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 175 | 110 | 0.9 - 94.1 (12.0)
TP53 MUTATED | 10 | 10 | 1.0 - 17.0 (6.0)
TP53 WILD-TYPE | 165 | 100 | 0.9 - 94.1 (13.0)

Figure S3. Get High-res Image Gene #12: 'TP53 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'ZAN MUTATION STATUS' versus 'AGE'

P value = 0.00108 (t-test), Q value = 0.14

Table S4. Gene #40: 'ZAN MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 199 | 55.0 (16.1)
ZAN MUTATED | 6 | 70.3 (6.7)
ZAN WILD-TYPE | 193 | 54.5 (16.1)

Figure S4. Get High-res Image Gene #40: 'ZAN MUTATION STATUS' versus Clinical Feature #2: 'AGE'

'FLJ43860 MUTATION STATUS' versus 'Time to Death'

P value = 0.000592 (logrank test), Q value = 0.078

Table S5. Gene #46: 'FLJ43860 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 175 | 110 | 0.9 - 94.1 (12.0)
FLJ43860 MUTATED | 3 | 3 | 1.0 - 9.0 (2.0)
FLJ43860 WILD-TYPE | 172 | 107 | 0.9 - 94.1 (12.5)

Figure S5. Get High-res Image Gene #46: 'FLJ43860 MUTATION STATUS' versus Clinical Feature #1: 'Time to Death'

'NINL MUTATION STATUS' versus 'AGE'

P value = 9.99e-09 (t-test), Q value = 1.3e-06

Table S6. Gene #48: 'NINL MUTATION STATUS' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 199 | 55.0 (16.1)
NINL MUTATED | 3 | 44.7 (1.2)
NINL WILD-TYPE | 196 | 55.2 (16.2)

Figure S6. Get High-res Image Gene #48: 'NINL MUTATION STATUS' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

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

- Number of patients = 199

- Number of significantly mutated genes = 48

- 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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