This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 12 genes and 6 clinical features across 277 patients, 4 significant findings detected with Q value < 0.25.
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IDH1 mutation correlated to 'Time to Death' and 'AGE'.
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TP53 mutation correlated to 'Time to Death'.
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STAG2 mutation correlated to 'Time to Death'.
Clinical Features |
Time to Death |
AGE | GENDER |
KARNOFSKY PERFORMANCE SCORE |
HISTOLOGICAL TYPE |
RADIATIONS RADIATION REGIMENINDICATION |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | |
IDH1 | 14 (5%) | 263 |
0.00018 (0.0128) |
9.49e-05 (0.00683) |
0.392 (1.00) |
0.0655 (1.00) |
0.47 (1.00) |
0.0404 (1.00) |
TP53 | 78 (28%) | 199 |
0.00281 (0.194) |
0.105 (1.00) |
0.677 (1.00) |
0.00657 (0.447) |
0.393 (1.00) |
0.203 (1.00) |
STAG2 | 12 (4%) | 265 |
0.0019 (0.133) |
0.915 (1.00) |
0.761 (1.00) |
0.0956 (1.00) |
1 (1.00) |
0.537 (1.00) |
EGFR | 73 (26%) | 204 |
0.865 (1.00) |
0.727 (1.00) |
0.257 (1.00) |
0.512 (1.00) |
0.503 (1.00) |
0.663 (1.00) |
PIK3R1 | 32 (12%) | 245 |
0.585 (1.00) |
0.972 (1.00) |
0.434 (1.00) |
0.872 (1.00) |
1 (1.00) |
0.69 (1.00) |
BRAF | 5 (2%) | 272 |
0.133 (1.00) |
0.772 (1.00) |
1 (1.00) |
0.158 (1.00) |
0.2 (1.00) |
0.665 (1.00) |
PTEN | 85 (31%) | 192 |
0.507 (1.00) |
0.166 (1.00) |
0.588 (1.00) |
0.98 (1.00) |
0.0888 (1.00) |
0.581 (1.00) |
PIK3CA | 29 (10%) | 248 |
0.527 (1.00) |
0.872 (1.00) |
0.545 (1.00) |
0.98 (1.00) |
0.364 (1.00) |
1 (1.00) |
RB1 | 23 (8%) | 254 |
0.265 (1.00) |
0.813 (1.00) |
0.654 (1.00) |
0.0179 (1.00) |
0.654 (1.00) |
1 (1.00) |
NF1 | 29 (10%) | 248 |
0.204 (1.00) |
0.169 (1.00) |
0.84 (1.00) |
0.18 (1.00) |
0.742 (1.00) |
0.537 (1.00) |
WNT2 | 5 (2%) | 272 |
0.108 (1.00) |
0.236 (1.00) |
0.0587 (1.00) |
0.685 (1.00) |
1 (1.00) |
1 (1.00) |
TPTE2 | 8 (3%) | 269 |
0.439 (1.00) |
0.605 (1.00) |
1 (1.00) |
0.0232 (1.00) |
1 (1.00) |
0.445 (1.00) |
P value = 0.00018 (logrank test), Q value = 0.013
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 277 | 198 | 0.1 - 73.8 (8.7) |
IDH1 MUTATED | 14 | 4 | 3.4 - 50.5 (18.8) |
IDH1 WILD-TYPE | 263 | 194 | 0.1 - 73.8 (8.3) |
P value = 9.49e-05 (t-test), Q value = 0.0068
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 277 | 61.1 (13.0) |
IDH1 MUTATED | 14 | 40.0 (15.1) |
IDH1 WILD-TYPE | 263 | 62.2 (11.9) |
P value = 0.00281 (logrank test), Q value = 0.19
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 277 | 198 | 0.1 - 73.8 (8.7) |
TP53 MUTATED | 78 | 48 | 0.4 - 50.5 (10.4) |
TP53 WILD-TYPE | 199 | 150 | 0.1 - 73.8 (8.3) |
P value = 0.0019 (logrank test), Q value = 0.13
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 277 | 198 | 0.1 - 73.8 (8.7) |
STAG2 MUTATED | 12 | 11 | 0.2 - 17.5 (4.1) |
STAG2 WILD-TYPE | 265 | 187 | 0.1 - 73.8 (8.8) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = GBM-TP.merged_data.txt
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Number of patients = 277
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Number of significantly mutated genes = 12
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Number of selected clinical features = 6
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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 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
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.