This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 16 genes and 10 clinical features across 69 patients, 2 significant findings detected with Q value < 0.25.
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SMAD2 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
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ERBB2 mutation correlated to 'NUMBER.OF.LYMPH.NODES'.
Table 1. Get Full Table Overview of the association between mutation status of 16 genes and 10 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 2 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
HISTOLOGICAL TYPE |
PATHOLOGY T |
PATHOLOGY N |
PATHOLOGICSPREAD(M) |
TUMOR STAGE |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | |
SMAD2 | 5 (7%) | 64 |
0.497 (1.00) |
0.834 (1.00) |
1 (1.00) |
1 (1.00) |
0.525 (1.00) |
0.342 (1.00) |
1 (1.00) |
0.536 (1.00) |
1 (1.00) |
0.000868 (0.137) |
ERBB2 | 4 (6%) | 65 |
0.273 (1.00) |
0.127 (1.00) |
1 (1.00) |
0.416 (1.00) |
0.453 (1.00) |
1 (1.00) |
0.662 (1.00) |
1 (1.00) |
0.000874 (0.137) |
|
APC | 57 (83%) | 12 |
0.588 (1.00) |
0.897 (1.00) |
1 (1.00) |
0.634 (1.00) |
0.0753 (1.00) |
0.813 (1.00) |
0.362 (1.00) |
0.0491 (1.00) |
0.693 (1.00) |
0.495 (1.00) |
KRAS | 38 (55%) | 31 |
0.0279 (1.00) |
0.534 (1.00) |
0.329 (1.00) |
0.27 (1.00) |
0.0973 (1.00) |
0.832 (1.00) |
0.327 (1.00) |
0.309 (1.00) |
0.0405 (1.00) |
0.34 (1.00) |
TP53 | 45 (65%) | 24 |
0.924 (1.00) |
0.976 (1.00) |
0.803 (1.00) |
0.238 (1.00) |
0.0498 (1.00) |
0.935 (1.00) |
0.73 (1.00) |
0.297 (1.00) |
0.624 (1.00) |
0.476 (1.00) |
SMAD4 | 8 (12%) | 61 |
0.447 (1.00) |
0.668 (1.00) |
1 (1.00) |
0.00581 (0.907) |
1 (1.00) |
0.645 (1.00) |
0.327 (1.00) |
0.594 (1.00) |
1 (1.00) |
0.586 (1.00) |
KIAA1804 | 9 (13%) | 60 |
0.447 (1.00) |
0.24 (1.00) |
1 (1.00) |
1 (1.00) |
0.707 (1.00) |
1 (1.00) |
1 (1.00) |
0.957 (1.00) |
1 (1.00) |
0.0883 (1.00) |
FBXW7 | 9 (13%) | 60 |
0.497 (1.00) |
0.92 (1.00) |
0.722 (1.00) |
1 (1.00) |
0.579 (1.00) |
0.199 (1.00) |
0.338 (1.00) |
0.341 (1.00) |
0.631 (1.00) |
0.721 (1.00) |
NRAS | 5 (7%) | 64 |
0.116 (1.00) |
0.0221 (1.00) |
1 (1.00) |
1 (1.00) |
0.0521 (1.00) |
0.53 (1.00) |
0.555 (1.00) |
0.887 (1.00) |
1 (1.00) |
0.0288 (1.00) |
TCF7L2 | 7 (10%) | 62 |
0.665 (1.00) |
0.744 (1.00) |
1 (1.00) |
1 (1.00) |
0.794 (1.00) |
0.85 (1.00) |
0.266 (1.00) |
0.514 (1.00) |
0.276 (1.00) |
0.21 (1.00) |
PIK3CA | 7 (10%) | 62 |
0.497 (1.00) |
0.432 (1.00) |
1 (1.00) |
0.0348 (1.00) |
1 (1.00) |
0.62 (1.00) |
0.582 (1.00) |
0.63 (1.00) |
0.628 (1.00) |
0.0101 (1.00) |
OPCML | 6 (9%) | 63 |
0.497 (1.00) |
0.966 (1.00) |
0.69 (1.00) |
1 (1.00) |
0.148 (1.00) |
1 (1.00) |
1 (1.00) |
0.504 (1.00) |
1 (1.00) |
0.724 (1.00) |
SPATA8 | 3 (4%) | 66 |
0.194 (1.00) |
0.0775 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.737 (1.00) |
1 (1.00) |
0.314 (1.00) |
|
IL1RAPL2 | 5 (7%) | 64 |
0.765 (1.00) |
0.639 (1.00) |
1 (1.00) |
0.11 (1.00) |
1 (1.00) |
0.816 (1.00) |
1 (1.00) |
0.852 (1.00) |
1 (1.00) |
0.0153 (1.00) |
FAM123B | 6 (9%) | 63 |
0.484 (1.00) |
0.485 (1.00) |
0.69 (1.00) |
1 (1.00) |
0.0463 (1.00) |
1 (1.00) |
0.207 (1.00) |
0.0851 (1.00) |
0.222 (1.00) |
0.639 (1.00) |
ZIM3 | 5 (7%) | 64 |
0.069 (1.00) |
0.235 (1.00) |
1 (1.00) |
0.493 (1.00) |
0.366 (1.00) |
0.53 (1.00) |
1 (1.00) |
0.666 (1.00) |
0.0831 (1.00) |
0.00803 (1.00) |
P value = 0.000868 (t-test), Q value = 0.14
Table S1. Gene #11: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 68 | 2.3 (5.5) |
SMAD2 MUTATED | 5 | 0.0 (0.0) |
SMAD2 WILD-TYPE | 63 | 2.5 (5.7) |
Figure S1. Get High-res Image Gene #11: 'SMAD2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D11V10.png)
P value = 0.000874 (t-test), Q value = 0.14
Table S2. Gene #13: 'ERBB2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 68 | 2.3 (5.5) |
ERBB2 MUTATED | 4 | 0.0 (0.0) |
ERBB2 WILD-TYPE | 64 | 2.5 (5.7) |
Figure S2. Get High-res Image Gene #13: 'ERBB2 MUTATION STATUS' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D13V10.png)
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Mutation data file = READ-TP.mutsig.cluster.txt
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Clinical data file = READ-TP.clin.merged.picked.txt
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Number of patients = 69
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Number of significantly mutated genes = 16
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Number of selected clinical features = 10
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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.