This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
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.
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DNMT3A mutation correlated to 'Time to Death'.
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IDH2 mutation correlated to 'YEARS_TO_BIRTH'.
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U2AF1 mutation correlated to 'YEARS_TO_BIRTH'.
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RUNX1 mutation correlated to 'YEARS_TO_BIRTH'.
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CEBPA mutation correlated to 'YEARS_TO_BIRTH'.
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TP53 mutation correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.
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) |
0.384 (0.939) |
0.158 (0.724) |
IDH2 | 20 (10%) | 173 |
0.685 (1.00) |
0.00164 (0.0471) |
0.815 (1.00) |
0.358 (0.894) |
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) |
0.0667 (0.616) |
0.563 (1.00) |
NPM1 | 33 (17%) | 160 |
0.705 (1.00) |
0.172 (0.724) |
0.57 (1.00) |
0.523 (1.00) |
1 (1.00) |
IDH1 | 18 (9%) | 175 |
0.638 (1.00) |
0.326 (0.865) |
0.465 (1.00) |
0.704 (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) |
0.672 (1.00) |
1 (1.00) |
WT1 | 12 (6%) | 181 |
0.574 (1.00) |
0.168 (0.724) |
0.774 (1.00) |
0.649 (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) |
0.0187 (0.269) |
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) |
0.432 (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) |
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'
![](D2V1.png)
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'
![](D4V2.png)
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'
![](D9V2.png)
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'
![](D10V2.png)
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'
![](D11V2.png)
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'
![](D12V1.png)
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'
![](D12V2.png)
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Mutation data file = sample_sig_gene_table.txt from Mutsig_2CV pipeline
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Processed Mutation data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/LAML-TB/20018555/transformed.cor.cli.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/19775300/LAML-TB.merged_data.txt
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Number of patients = 193
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Number of significantly mutated genes = 23
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Number of selected clinical features = 5
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Exclude genes that fewer than K tumors have mutations, K = 3
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
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
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.
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.