This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 23 genes and 8 clinical features across 465 patients, no significant finding detected with Q value < 0.25.
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No gene mutations related to clinical features.
Table 1. Get Full Table Overview of the association between mutation status of 23 genes and 8 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
Clinical Features |
Time to Death |
YEARS TO BIRTH |
PRIMARY SITE OF DISEASE |
KARNOFSKY PERFORMANCE SCORE |
RADIATIONS RADIATION REGIMENINDICATION |
COMPLETENESS OF RESECTION |
RACE | ETHNICITY | ||
nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
TP53 | 382 (82%) | 83 |
0.286 (1.00) |
0.535 (1.00) |
0.446 (1.00) |
0.908 (1.00) |
0.446 (1.00) |
0.283 (1.00) |
0.12 (1.00) |
0.0615 (1.00) |
BRCA1 | 19 (4%) | 446 |
0.741 (1.00) |
0.704 (1.00) |
1 (1.00) |
1 (1.00) |
0.785 (1.00) |
1 (1.00) |
||
NF1 | 24 (5%) | 441 |
0.389 (1.00) |
0.219 (1.00) |
1 (1.00) |
0.147 (1.00) |
1 (1.00) |
1 (1.00) |
0.396 (1.00) |
|
RB1 | 15 (3%) | 450 |
0.273 (1.00) |
0.238 (1.00) |
1 (1.00) |
1 (1.00) |
0.272 (1.00) |
1 (1.00) |
||
BRCA2 | 13 (3%) | 452 |
0.00699 (0.812) |
0.588 (1.00) |
1 (1.00) |
1 (1.00) |
0.713 (1.00) |
1 (1.00) |
||
IL21R | 8 (2%) | 457 |
0.134 (1.00) |
0.815 (1.00) |
1 (1.00) |
1 (1.00) |
0.266 (1.00) |
1 (1.00) |
||
KRAS | 5 (1%) | 460 |
0.706 (1.00) |
0.576 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
YSK4 | 10 (2%) | 455 |
0.629 (1.00) |
0.302 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.00882 (0.812) |
||
ANKRD35 | 9 (2%) | 456 |
0.518 (1.00) |
0.984 (1.00) |
1 (1.00) |
1 (1.00) |
0.384 (1.00) |
1 (1.00) |
||
C9ORF171 | 5 (1%) | 460 |
0.78 (1.00) |
0.0961 (1.00) |
1 (1.00) |
0.705 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|
MTA2 | 4 (1%) | 461 |
0.0758 (1.00) |
0.126 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
CYP11B1 | 8 (2%) | 457 |
0.923 (1.00) |
0.263 (1.00) |
1 (1.00) |
0.0508 (1.00) |
0.535 (1.00) |
1 (1.00) |
||
NRAS | 4 (1%) | 461 |
0.941 (1.00) |
0.124 (1.00) |
1 (1.00) |
1 (1.00) |
0.181 (1.00) |
1 (1.00) |
||
ACBD4 | 3 (1%) | 462 |
0.846 (1.00) |
0.483 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
EFEMP1 | 7 (2%) | 458 |
0.462 (1.00) |
0.0712 (1.00) |
1 (1.00) |
1 (1.00) |
0.306 (1.00) |
1 (1.00) |
||
PODN | 6 (1%) | 459 |
0.0722 (1.00) |
0.0671 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.124 (1.00) |
||
TOP2A | 8 (2%) | 457 |
0.949 (1.00) |
0.211 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.18 (1.00) |
||
NCOA3 | 5 (1%) | 460 |
0.0175 (1.00) |
0.628 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
TP53TG5 | 3 (1%) | 462 |
0.0417 (1.00) |
0.436 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|||
RPGRIP1 | 7 (2%) | 458 |
0.345 (1.00) |
0.657 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
PTEN | 5 (1%) | 460 |
0.662 (1.00) |
0.916 (1.00) |
1 (1.00) |
1 (1.00) |
0.375 (1.00) |
1 (1.00) |
||
RB1CC1 | 9 (2%) | 456 |
0.348 (1.00) |
0.178 (1.00) |
1 (1.00) |
1 (1.00) |
0.577 (1.00) |
1 (1.00) |
||
LPAR3 | 4 (1%) | 461 |
0.172 (1.00) |
0.17 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
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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/OV-TP/15650402/transformed.cor.cli.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/OV-TP/15085780/OV-TP.merged_data.txt
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Number of patients = 465
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Number of significantly mutated genes = 23
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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 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.