This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 28 genes and 11 clinical features across 165 patients, one significant finding detected with Q value < 0.25.
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SETD2 mutation correlated to 'Time to Death'.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
KARNOFSKY PERFORMANCE SCORE |
NUMBERPACKYEARSSMOKED | 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 | Fisher's exact test | Fisher's exact test | |
SETD2 | 15 (9%) | 150 |
0.00027 (0.0621) |
0.00948 (1.00) |
0.0225 (1.00) |
0.0126 (1.00) |
0.173 (1.00) |
0.151 (1.00) |
0.774 (1.00) |
1 (1.00) |
1 (1.00) |
||
HNRNPM | 11 (7%) | 154 |
0.468 (1.00) |
0.478 (1.00) |
0.8 (1.00) |
0.721 (1.00) |
0.835 (1.00) |
0.507 (1.00) |
0.959 (1.00) |
1 (1.00) |
1 (1.00) |
||
NF2 | 12 (7%) | 153 |
0.248 (1.00) |
0.647 (1.00) |
0.0196 (1.00) |
0.0538 (1.00) |
0.0266 (1.00) |
0.0731 (1.00) |
0.514 (1.00) |
0.799 (1.00) |
0.287 (1.00) |
||
NEFH | 9 (5%) | 156 |
0.62 (1.00) |
0.145 (1.00) |
0.246 (1.00) |
0.0807 (1.00) |
0.147 (1.00) |
1 (1.00) |
0.377 (1.00) |
1 (1.00) |
|||
TDG | 5 (3%) | 160 |
0.631 (1.00) |
0.365 (1.00) |
0.58 (1.00) |
0.79 (1.00) |
1 (1.00) |
0.718 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
ZNF598 | 13 (8%) | 152 |
0.352 (1.00) |
0.446 (1.00) |
0.444 (1.00) |
0.751 (1.00) |
0.582 (1.00) |
0.109 (1.00) |
1 (1.00) |
0.429 (1.00) |
1 (1.00) |
0.356 (1.00) |
|
SKI | 6 (4%) | 159 |
0.554 (1.00) |
0.76 (1.00) |
0.652 (1.00) |
0.675 (1.00) |
1 (1.00) |
0.669 (1.00) |
1 (1.00) |
1 (1.00) |
|||
CSGALNACT2 | 5 (3%) | 160 |
0.761 (1.00) |
0.0714 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.164 (1.00) |
0.395 (1.00) |
1 (1.00) |
|||
MET | 15 (9%) | 150 |
0.484 (1.00) |
0.729 (1.00) |
0.958 (1.00) |
0.845 (1.00) |
0.172 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.418 (1.00) |
||
MUC5B | 18 (11%) | 147 |
0.549 (1.00) |
0.043 (1.00) |
0.696 (1.00) |
0.478 (1.00) |
1 (1.00) |
0.179 (1.00) |
0.422 (1.00) |
0.187 (1.00) |
0.567 (1.00) |
1 (1.00) |
|
KDM6A | 9 (5%) | 156 |
0.619 (1.00) |
0.987 (1.00) |
0.624 (1.00) |
0.881 (1.00) |
1 (1.00) |
0.279 (1.00) |
0.0714 (1.00) |
1 (1.00) |
|||
ZNF814 | 8 (5%) | 157 |
0.0866 (1.00) |
0.0933 (1.00) |
0.754 (1.00) |
0.638 (1.00) |
0.0463 (1.00) |
0.107 (1.00) |
0.751 (1.00) |
1 (1.00) |
|||
OR2L8 | 4 (2%) | 161 |
0.833 (1.00) |
0.318 (1.00) |
1 (1.00) |
0.181 (1.00) |
1 (1.00) |
0.391 (1.00) |
1 (1.00) |
||||
MYH6 | 8 (5%) | 157 |
0.632 (1.00) |
0.671 (1.00) |
0.701 (1.00) |
0.455 (1.00) |
0.776 (1.00) |
0.699 (1.00) |
0.72 (1.00) |
1 (1.00) |
|||
UNC13A | 9 (5%) | 156 |
0.487 (1.00) |
0.627 (1.00) |
0.726 (1.00) |
0.5 (1.00) |
1 (1.00) |
0.443 (1.00) |
0.724 (1.00) |
0.548 (1.00) |
1 (1.00) |
||
MED16 | 4 (2%) | 161 |
0.017 (1.00) |
0.632 (1.00) |
0.156 (1.00) |
0.115 (1.00) |
1 (1.00) |
0.585 (1.00) |
1 (1.00) |
||||
GLUD2 | 11 (7%) | 154 |
0.534 (1.00) |
0.447 (1.00) |
0.875 (1.00) |
0.513 (1.00) |
1 (1.00) |
0.356 (1.00) |
0.0908 (1.00) |
0.778 (1.00) |
0.0168 (1.00) |
||
BMS1 | 13 (8%) | 152 |
0.44 (1.00) |
0.174 (1.00) |
0.8 (1.00) |
0.113 (1.00) |
0.189 (1.00) |
0.756 (1.00) |
0.73 (1.00) |
0.241 (1.00) |
1 (1.00) |
||
FUS | 3 (2%) | 162 |
0.931 (1.00) |
0.208 (1.00) |
0.44 (1.00) |
0.633 (1.00) |
1 (1.00) |
1 (1.00) |
|||||
TP53 | 7 (4%) | 158 |
0.736 (1.00) |
0.169 (1.00) |
0.0342 (1.00) |
0.315 (1.00) |
1 (1.00) |
0.0089 (1.00) |
0.434 (1.00) |
0.33 (1.00) |
1 (1.00) |
||
ACADL | 4 (2%) | 161 |
0.726 (1.00) |
0.957 (1.00) |
0.026 (1.00) |
0.0206 (1.00) |
0.425 (1.00) |
1 (1.00) |
0.59 (1.00) |
1 (1.00) |
|||
AHNAK2 | 10 (6%) | 155 |
0.611 (1.00) |
0.48 (1.00) |
1 (1.00) |
0.89 (1.00) |
0.666 (1.00) |
1 (1.00) |
0.137 (1.00) |
1 (1.00) |
|||
SMARCB1 | 5 (3%) | 160 |
0.596 (1.00) |
0.0649 (1.00) |
0.361 (1.00) |
0.206 (1.00) |
0.719 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|||
TEKT1 | 4 (2%) | 161 |
0.326 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.0834 (1.00) |
0.589 (1.00) |
1 (1.00) |
||||
MKL1 | 9 (5%) | 156 |
0.54 (1.00) |
0.436 (1.00) |
0.518 (1.00) |
0.223 (1.00) |
1 (1.00) |
1 (1.00) |
0.724 (1.00) |
0.778 (1.00) |
1 (1.00) |
||
MYH7 | 6 (4%) | 159 |
0.0588 (1.00) |
0.979 (1.00) |
0.417 (1.00) |
0.828 (1.00) |
1 (1.00) |
0.669 (1.00) |
0.155 (1.00) |
1 (1.00) |
|||
MAP4K3 | 5 (3%) | 160 |
0.775 (1.00) |
0.378 (1.00) |
0.761 (1.00) |
0.791 (1.00) |
0.723 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|||
NEK2 | 3 (2%) | 162 |
0.361 (1.00) |
0.178 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
P value = 0.00027 (logrank test), Q value = 0.062
nPatients | nDeath | Duration Range (Median), Year | |
---|---|---|---|
ALL | 142 | 5 | 2.0 - 5925.0 (566.0) |
SETD2 MUTATED | 11 | 2 | 3.0 - 2639.0 (140.0) |
SETD2 WILD-TYPE | 131 | 3 | 2.0 - 5925.0 (595.0) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = KIRP-TP.merged_data.txt
-
Number of patients = 165
-
Number of significantly mutated genes = 28
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Number of selected clinical features = 11
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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.