(All_Primary cohort)
This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 38 arm-level results and 7 clinical features across 29 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE |
PRIMARY SITE OF DISEASE |
GENDER |
LYMPH NODE METASTASIS |
TUMOR STAGECODE |
NEOPLASM DISEASESTAGE |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Chi-square test | t-test | Chi-square test | |
1p gain | 4 (14%) | 25 |
0.0976 (1.00) |
0.549 (1.00) |
0.467 (1.00) |
1 (1.00) |
0.0369 (1.00) |
0.3 (1.00) |
|
1q gain | 6 (21%) | 23 |
0.341 (1.00) |
0.725 (1.00) |
0.18 (1.00) |
1 (1.00) |
0.0559 (1.00) |
0.269 (1.00) |
|
5p gain | 4 (14%) | 25 |
1 (1.00) |
0.726 (1.00) |
1 (1.00) |
0.076 (1.00) |
0.775 (1.00) |
0.334 (1.00) |
|
6p gain | 8 (28%) | 21 |
0.666 (1.00) |
0.965 (1.00) |
0.748 (1.00) |
0.371 (1.00) |
0.419 (1.00) |
0.524 (1.00) |
|
6q gain | 3 (10%) | 26 |
0.813 (1.00) |
0.6 (1.00) |
1 (1.00) |
0.532 (1.00) |
0.127 (1.00) |
0.191 (1.00) |
|
7p gain | 13 (45%) | 16 |
0.962 (1.00) |
0.832 (1.00) |
1 (1.00) |
0.13 (1.00) |
0.366 (1.00) |
0.497 (1.00) |
|
7q gain | 13 (45%) | 16 |
0.962 (1.00) |
0.832 (1.00) |
1 (1.00) |
0.13 (1.00) |
0.366 (1.00) |
0.497 (1.00) |
|
8p gain | 5 (17%) | 24 |
0.341 (1.00) |
0.81 (1.00) |
0.127 (1.00) |
1 (1.00) |
0.0839 (1.00) |
0.202 (1.00) |
|
8q gain | 9 (31%) | 20 |
0.216 (1.00) |
0.301 (1.00) |
0.364 (1.00) |
0.396 (1.00) |
0.613 (1.00) |
0.73 (1.00) |
|
13q gain | 3 (10%) | 26 |
1 (1.00) |
0.948 (1.00) |
1 (1.00) |
0.532 (1.00) |
0.419 (1.00) |
0.865 (1.00) |
|
18p gain | 3 (10%) | 26 |
0.371 (1.00) |
1 (1.00) |
|||||
18q gain | 3 (10%) | 26 |
0.371 (1.00) |
1 (1.00) |
|||||
20p gain | 4 (14%) | 25 |
0.0917 (1.00) |
1 (1.00) |
0.28 (1.00) |
0.299 (1.00) |
0.3 (1.00) |
||
20q gain | 5 (17%) | 24 |
0.0173 (1.00) |
1 (1.00) |
1 (1.00) |
0.419 (1.00) |
0.121 (1.00) |
||
22q gain | 3 (10%) | 26 |
0.133 (1.00) |
1 (1.00) |
1 (1.00) |
0.893 (1.00) |
0.191 (1.00) |
||
2p loss | 4 (14%) | 25 |
0.633 (1.00) |
0.08 (1.00) |
1 (1.00) |
0.0167 (1.00) |
0.148 (1.00) |
||
2q loss | 3 (10%) | 26 |
0.633 (1.00) |
0.371 (1.00) |
0.532 (1.00) |
0.0501 (1.00) |
|||
4p loss | 4 (14%) | 25 |
1 (1.00) |
0.786 (1.00) |
1 (1.00) |
0.28 (1.00) |
0.889 (1.00) |
0.334 (1.00) |
|
4q loss | 6 (21%) | 23 |
1 (1.00) |
0.985 (1.00) |
1 (1.00) |
0.633 (1.00) |
0.47 (1.00) |
0.358 (1.00) |
|
5q loss | 4 (14%) | 25 |
0.649 (1.00) |
0.486 (1.00) |
0.467 (1.00) |
0.28 (1.00) |
0.219 (1.00) |
0.191 (1.00) |
|
6q loss | 8 (28%) | 21 |
0.519 (1.00) |
0.569 (1.00) |
0.748 (1.00) |
1 (1.00) |
0.975 (1.00) |
0.739 (1.00) |
|
8p loss | 6 (21%) | 23 |
0.519 (1.00) |
0.932 (1.00) |
1 (1.00) |
0.633 (1.00) |
0.47 (1.00) |
0.848 (1.00) |
|
8q loss | 3 (10%) | 26 |
1 (1.00) |
0.312 (1.00) |
1 (1.00) |
1 (1.00) |
0.893 (1.00) |
0.911 (1.00) |
|
9p loss | 17 (59%) | 12 |
0.229 (1.00) |
0.412 (1.00) |
0.147 (1.00) |
0.422 (1.00) |
0.582 (1.00) |
0.637 (1.00) |
|
9q loss | 9 (31%) | 20 |
0.649 (1.00) |
0.679 (1.00) |
1 (1.00) |
0.675 (1.00) |
0.94 (1.00) |
0.0738 (1.00) |
|
10p loss | 13 (45%) | 16 |
0.981 (1.00) |
0.328 (1.00) |
0.0301 (1.00) |
1 (1.00) |
0.346 (1.00) |
0.269 (1.00) |
|
10q loss | 15 (52%) | 14 |
0.981 (1.00) |
0.259 (1.00) |
0.0996 (1.00) |
1 (1.00) |
0.652 (1.00) |
0.497 (1.00) |
|
11p loss | 7 (24%) | 22 |
1 (1.00) |
0.583 (1.00) |
0.238 (1.00) |
1 (1.00) |
0.325 (1.00) |
0.05 (1.00) |
|
11q loss | 8 (28%) | 21 |
1 (1.00) |
0.694 (1.00) |
0.3 (1.00) |
0.675 (1.00) |
0.489 (1.00) |
0.104 (1.00) |
|
12p loss | 3 (10%) | 26 |
0.633 (1.00) |
0.371 (1.00) |
1 (1.00) |
0.127 (1.00) |
|||
12q loss | 5 (17%) | 24 |
1 (1.00) |
0.689 (1.00) |
0.553 (1.00) |
0.633 (1.00) |
0.419 (1.00) |
0.334 (1.00) |
|
13q loss | 5 (17%) | 24 |
0.341 (1.00) |
0.00425 (0.885) |
1 (1.00) |
0.633 (1.00) |
0.419 (1.00) |
0.525 (1.00) |
|
14q loss | 4 (14%) | 25 |
1 (1.00) |
0.56 (1.00) |
1 (1.00) |
0.568 (1.00) |
0.889 (1.00) |
0.958 (1.00) |
|
16q loss | 8 (28%) | 21 |
0.0143 (1.00) |
0.454 (1.00) |
0.748 (1.00) |
1 (1.00) |
0.644 (1.00) |
0.883 (1.00) |
|
17p loss | 4 (14%) | 25 |
1 (1.00) |
0.609 (1.00) |
1 (1.00) |
1 (1.00) |
0.775 (1.00) |
0.807 (1.00) |
|
18p loss | 3 (10%) | 26 |
0.912 (1.00) |
0.371 (1.00) |
1 (1.00) |
0.893 (1.00) |
|||
19q loss | 3 (10%) | 26 |
1 (1.00) |
0.532 (1.00) |
0.893 (1.00) |
0.911 (1.00) |
|||
21q loss | 3 (10%) | 26 |
0.341 (1.00) |
0.852 (1.00) |
1 (1.00) |
0.532 (1.00) |
0.127 (1.00) |
0.148 (1.00) |
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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
-
Number of patients = 29
-
Number of significantly arm-level cnvs = 38
-
Number of selected clinical features = 7
-
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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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.