This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 4 genes and 8 clinical features across 101 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 |
DISTANT METASTASIS |
LYMPH NODE METASTASIS |
TUMOR STAGECODE |
NEOPLASM DISEASESTAGE |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | t-test | Fisher's exact test | Fisher's exact test | t-test | Fisher's exact test | |
IL32 | 4 (4%) | 97 |
9.44e-15 (2.26e-13) |
0.366 (1.00) |
0.3 (1.00) |
0.217 (1.00) |
0.62 (1.00) |
0.667 (1.00) |
||
CDC27 | 4 (4%) | 97 |
0.598 (1.00) |
0.78 (1.00) |
0.595 (1.00) |
0.13 (1.00) |
0.153 (1.00) |
0.345 (1.00) |
||
NF2 | 7 (7%) | 94 |
0.598 (1.00) |
0.3 (1.00) |
1 (1.00) |
0.0347 (0.798) |
0.075 (1.00) |
0.162 (1.00) |
||
SFRS2IP | 5 (5%) | 96 |
0.226 (1.00) |
0.121 (1.00) |
0.661 (1.00) |
0.082 (1.00) |
0.385 (1.00) |
0.316 (1.00) |
P value = 9.44e-15 (logrank test), Q value = 2.3e-13
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 94 | 14 | 0.0 - 182.7 (13.9) |
IL32 MUTATED | 4 | 1 | 3.6 - 7.9 (4.8) |
IL32 WILD-TYPE | 90 | 13 | 0.0 - 182.7 (14.6) |
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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 = 101
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Number of significantly mutated genes = 4
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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.