This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 7 genes and 8 clinical features across 98 patients, one significant finding detected with Q value < 0.25.
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IL32 mutation correlated to 'Time to Death'.
Clinical Features |
Time to Death |
AGE | GENDER |
KARNOFSKY PERFORMANCE SCORE |
PATHOLOGY T |
PATHOLOGY N |
PATHOLOGICSPREAD(M) |
TUMOR STAGE |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
IL32 | 4 (4%) | 94 |
1.58e-14 (7.09e-13) |
0.362 (1.00) |
0.304 (1.00) |
0.78 (1.00) |
0.223 (1.00) |
0.676 (1.00) |
||
CDC27 | 4 (4%) | 94 |
0.603 (1.00) |
0.778 (1.00) |
0.589 (1.00) |
0.78 (1.00) |
0.128 (1.00) |
0.354 (1.00) |
||
MET | 7 (7%) | 91 |
0.69 (1.00) |
0.887 (1.00) |
1 (1.00) |
1 (1.00) |
0.806 (1.00) |
0.894 (1.00) |
||
PCF11 | 7 (7%) | 91 |
0.828 (1.00) |
0.403 (1.00) |
0.426 (1.00) |
0.864 (1.00) |
0.204 (1.00) |
0.584 (1.00) |
1 (1.00) |
|
SFRS2IP | 5 (5%) | 93 |
0.233 (1.00) |
0.12 (1.00) |
0.65 (1.00) |
0.105 (1.00) |
1 (1.00) |
0.0847 (1.00) |
0.324 (1.00) |
|
NF2 | 6 (6%) | 92 |
0.603 (1.00) |
0.137 (1.00) |
1 (1.00) |
0.252 (1.00) |
0.0542 (1.00) |
0.0251 (1.00) |
0.0546 (1.00) |
|
LGI4 | 4 (4%) | 94 |
0.189 (1.00) |
0.905 (1.00) |
0.304 (1.00) |
0.78 (1.00) |
0.703 (1.00) |
0.676 (1.00) |
P value = 1.58e-14 (logrank test), Q value = 7.1e-13
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 91 | 14 | 0.0 - 182.7 (14.1) |
IL32 MUTATED | 4 | 1 | 3.6 - 7.9 (4.8) |
IL32 WILD-TYPE | 87 | 13 | 0.0 - 182.7 (15.5) |
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Mutation data file = KIRP-TP.mutsig.cluster.txt
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Clinical data file = KIRP-TP.clin.merged.picked.txt
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Number of patients = 98
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Number of significantly mutated genes = 7
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Number of selected clinical features = 8
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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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.