This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 26 genes and 12 clinical features across 282 patients, 2 significant findings detected with Q value < 0.25.
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PBRM1 mutation correlated to 'Time to Death'.
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CALCR mutation correlated to 'Time to Death'.
Clinical Features |
Time to Death |
YEARS TO BIRTH |
PATHOLOGIC STAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
KARNOFSKY PERFORMANCE SCORE |
NUMBER PACK YEARS SMOKED |
YEAR OF TOBACCO SMOKING ONSET |
RACE | ETHNICITY | ||
nMutated (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Wilcoxon-test | Wilcoxon-test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
PBRM1 | 11 (4%) | 271 |
0.000244 (0.0381) |
0.0077 (0.382) |
0.201 (1.00) |
0.113 (1.00) |
0.657 (1.00) |
1 (1.00) |
0.299 (1.00) |
0.512 (1.00) |
0.797 (1.00) |
1 (1.00) |
||
CALCR | 3 (1%) | 279 |
4.15e-06 (0.0013) |
0.166 (1.00) |
0.119 (1.00) |
0.15 (1.00) |
1 (1.00) |
0.0874 (1.00) |
1 (1.00) |
1 (1.00) |
0.129 (1.00) |
|||
SETD2 | 16 (6%) | 266 |
0.0129 (0.382) |
0.0793 (1.00) |
0.00252 (0.262) |
0.00431 (0.269) |
0.431 (1.00) |
0.144 (1.00) |
0.57 (1.00) |
0.499 (1.00) |
0.834 (1.00) |
0.412 (1.00) |
1 (1.00) |
|
NF2 | 10 (4%) | 272 |
0.244 (1.00) |
0.398 (1.00) |
0.00384 (0.269) |
0.00917 (0.382) |
0.0355 (0.651) |
0.31 (1.00) |
0.467 (1.00) |
0.977 (1.00) |
0.454 (1.00) |
0.882 (1.00) |
0.565 (1.00) |
1 (1.00) |
ZNF814 | 9 (3%) | 273 |
0.49 (1.00) |
0.339 (1.00) |
0.739 (1.00) |
1 (1.00) |
1 (1.00) |
0.173 (1.00) |
1 (1.00) |
1 (1.00) |
||||
MET | 21 (7%) | 261 |
0.898 (1.00) |
0.724 (1.00) |
0.971 (1.00) |
1 (1.00) |
0.411 (1.00) |
1 (1.00) |
1 (1.00) |
0.122 (1.00) |
0.339 (1.00) |
0.229 (1.00) |
0.634 (1.00) |
0.553 (1.00) |
NEFH | 6 (2%) | 276 |
0.317 (1.00) |
0.93 (1.00) |
0.488 (1.00) |
0.531 (1.00) |
1 (1.00) |
0.196 (1.00) |
1 (1.00) |
1 (1.00) |
||||
KRAS | 5 (2%) | 277 |
0.476 (1.00) |
0.772 (1.00) |
0.809 (1.00) |
0.629 (1.00) |
1 (1.00) |
0.0197 (0.448) |
0.0274 (0.535) |
1 (1.00) |
||||
CUL3 | 10 (4%) | 272 |
0.621 (1.00) |
0.647 (1.00) |
0.176 (1.00) |
1 (1.00) |
1 (1.00) |
0.242 (1.00) |
1 (1.00) |
0.0135 (0.382) |
0.256 (1.00) |
1 (1.00) |
1 (1.00) |
|
PCF11 | 11 (4%) | 271 |
0.985 (1.00) |
0.332 (1.00) |
1 (1.00) |
0.698 (1.00) |
1 (1.00) |
1 (1.00) |
0.621 (1.00) |
0.619 (1.00) |
0.342 (1.00) |
|||
BCLAF1 | 6 (2%) | 276 |
0.842 (1.00) |
0.562 (1.00) |
0.137 (1.00) |
0.0979 (1.00) |
0.262 (1.00) |
0.242 (1.00) |
1 (1.00) |
0.451 (1.00) |
1 (1.00) |
|||
PAM | 3 (1%) | 279 |
0.423 (1.00) |
0.562 (1.00) |
0.257 (1.00) |
0.694 (1.00) |
1 (1.00) |
0.566 (1.00) |
0.481 (1.00) |
1 (1.00) |
||||
SMARCB1 | 6 (2%) | 276 |
0.26 (1.00) |
0.295 (1.00) |
0.0408 (0.67) |
0.0459 (0.717) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|||
KDM6A | 10 (4%) | 272 |
0.711 (1.00) |
0.0122 (0.382) |
0.74 (1.00) |
1 (1.00) |
0.546 (1.00) |
1 (1.00) |
1 (1.00) |
0.698 (1.00) |
0.181 (1.00) |
0.181 (1.00) |
1 (1.00) |
|
AR | 13 (5%) | 269 |
0.969 (1.00) |
0.43 (1.00) |
0.438 (1.00) |
0.426 (1.00) |
1 (1.00) |
1 (1.00) |
0.346 (1.00) |
0.397 (1.00) |
0.944 (1.00) |
0.977 (1.00) |
0.54 (1.00) |
1 (1.00) |
TP53 | 7 (2%) | 275 |
0.0119 (0.382) |
0.134 (1.00) |
0.0215 (0.448) |
0.0619 (0.877) |
1 (1.00) |
0.0595 (0.877) |
0.679 (1.00) |
0.479 (1.00) |
1 (1.00) |
|||
KRTAP4-5 | 5 (2%) | 277 |
0.325 (1.00) |
0.453 (1.00) |
0.379 (1.00) |
0.0207 (0.448) |
1 (1.00) |
0.328 (1.00) |
0.209 (1.00) |
1 (1.00) |
||||
BRAF | 4 (1%) | 278 |
0.296 (1.00) |
0.983 (1.00) |
0.706 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.626 (1.00) |
1 (1.00) |
||||
KIAA0922 | 5 (2%) | 277 |
0.841 (1.00) |
0.253 (1.00) |
0.809 (1.00) |
0.633 (1.00) |
1 (1.00) |
0.328 (1.00) |
0.627 (1.00) |
1 (1.00) |
||||
KRT2 | 5 (2%) | 277 |
0.362 (1.00) |
0.0903 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.592 (1.00) |
0.299 (1.00) |
0.148 (1.00) |
1 (1.00) |
||
ALMS1 | 8 (3%) | 274 |
0.618 (1.00) |
0.37 (1.00) |
0.738 (1.00) |
1 (1.00) |
1 (1.00) |
0.113 (1.00) |
0.231 (1.00) |
0.277 (1.00) |
||||
GXYLT1 | 4 (1%) | 278 |
0.574 (1.00) |
0.258 (1.00) |
0.769 (1.00) |
0.739 (1.00) |
1 (1.00) |
0.626 (1.00) |
1 (1.00) |
|||||
ATP1B1 | 7 (2%) | 275 |
0.989 (1.00) |
0.583 (1.00) |
0.69 (1.00) |
0.584 (1.00) |
1 (1.00) |
0.0873 (1.00) |
0.354 (1.00) |
0.242 (1.00) |
||||
PTEN | 7 (2%) | 275 |
0.271 (1.00) |
0.04 (0.67) |
0.509 (1.00) |
0.47 (1.00) |
0.546 (1.00) |
1 (1.00) |
0.0168 (0.436) |
0.481 (1.00) |
1 (1.00) |
|||
SAV1 | 6 (2%) | 276 |
0.869 (1.00) |
0.682 (1.00) |
0.325 (1.00) |
1 (1.00) |
1 (1.00) |
0.348 (1.00) |
0.228 (1.00) |
0.109 (1.00) |
1 (1.00) |
|||
IGSF3 | 4 (1%) | 278 |
0.455 (1.00) |
0.245 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.577 (1.00) |
0.311 (1.00) |
1 (1.00) |
P value = 0.000244 (logrank test), Q value = 0.038
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 279 | 41 | 0.1 - 194.8 (25.2) |
PBRM1 MUTATED | 11 | 4 | 0.1 - 67.2 (12.3) |
PBRM1 WILD-TYPE | 268 | 37 | 0.1 - 194.8 (25.5) |
P value = 4.15e-06 (logrank test), Q value = 0.0013
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 279 | 41 | 0.1 - 194.8 (25.2) |
CALCR MUTATED | 3 | 2 | 10.8 - 16.0 (12.9) |
CALCR WILD-TYPE | 276 | 39 | 0.1 - 194.8 (25.3) |
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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/KIRP-TP/22574643/transformed.cor.cli.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIRP-TP/22507257/KIRP-TP.merged_data.txt
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Number of patients = 282
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Number of significantly mutated genes = 26
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Number of selected clinical features = 12
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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.