This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 30 arm-level events and 5 clinical features across 191 patients, 2 significant findings detected with Q value < 0.25.
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5q loss cnv correlated to 'Time to Death'.
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18q loss cnv correlated to 'Time to Death'.
Clinical Features |
Time to Death |
AGE | GENDER | RACE | ETHNICITY | ||
nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
5q loss | 6 (3%) | 185 |
0.000704 (0.101) |
0.0519 (1.00) |
0.0325 (1.00) |
0.416 (1.00) |
1 (1.00) |
18q loss | 4 (2%) | 187 |
0.000836 (0.119) |
0.0121 (1.00) |
0.127 (1.00) |
1 (1.00) |
1 (1.00) |
1p gain | 3 (2%) | 188 |
0.115 (1.00) |
0.592 (1.00) |
1 (1.00) |
1 (1.00) |
|
4p gain | 4 (2%) | 187 |
0.583 (1.00) |
0.328 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
4q gain | 4 (2%) | 187 |
0.583 (1.00) |
0.328 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
8p gain | 22 (12%) | 169 |
0.514 (1.00) |
0.112 (1.00) |
0.0734 (1.00) |
1 (1.00) |
0.313 (1.00) |
8q gain | 23 (12%) | 168 |
0.56 (1.00) |
0.167 (1.00) |
0.0723 (1.00) |
1 (1.00) |
0.325 (1.00) |
10q gain | 3 (2%) | 188 |
0.654 (1.00) |
0.252 (1.00) |
0.233 (1.00) |
1 (1.00) |
|
11p gain | 4 (2%) | 187 |
0.173 (1.00) |
1 (1.00) |
1 (1.00) |
0.0628 (1.00) |
|
11q gain | 7 (4%) | 184 |
0.566 (1.00) |
0.047 (1.00) |
1 (1.00) |
1 (1.00) |
0.108 (1.00) |
13q gain | 6 (3%) | 185 |
0.826 (1.00) |
0.12 (1.00) |
0.69 (1.00) |
1 (1.00) |
1 (1.00) |
17q gain | 3 (2%) | 188 |
0.737 (1.00) |
0.113 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
19p gain | 5 (3%) | 186 |
0.741 (1.00) |
0.715 (1.00) |
0.378 (1.00) |
1 (1.00) |
1 (1.00) |
19q gain | 5 (3%) | 186 |
0.741 (1.00) |
0.715 (1.00) |
0.378 (1.00) |
1 (1.00) |
1 (1.00) |
21q gain | 8 (4%) | 183 |
0.0186 (1.00) |
0.401 (1.00) |
0.0732 (1.00) |
0.186 (1.00) |
1 (1.00) |
22q gain | 9 (5%) | 182 |
0.728 (1.00) |
0.0629 (1.00) |
0.513 (1.00) |
1 (1.00) |
1 (1.00) |
xq gain | 3 (2%) | 188 |
0.108 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
|
3p loss | 3 (2%) | 188 |
0.0208 (1.00) |
0.252 (1.00) |
1 (1.00) |
1 (1.00) |
|
3q loss | 3 (2%) | 188 |
0.0208 (1.00) |
0.252 (1.00) |
1 (1.00) |
1 (1.00) |
|
7p loss | 17 (9%) | 174 |
0.0328 (1.00) |
0.458 (1.00) |
0.802 (1.00) |
1 (1.00) |
1 (1.00) |
7q loss | 20 (10%) | 171 |
0.015 (1.00) |
0.513 (1.00) |
1 (1.00) |
0.718 (1.00) |
1 (1.00) |
12p loss | 4 (2%) | 187 |
0.869 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
|
15q loss | 4 (2%) | 187 |
0.679 (1.00) |
0.677 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
16q loss | 4 (2%) | 187 |
0.0589 (1.00) |
0.388 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
17p loss | 13 (7%) | 178 |
0.109 (1.00) |
0.765 (1.00) |
0.147 (1.00) |
0.655 (1.00) |
1 (1.00) |
17q loss | 7 (4%) | 184 |
0.403 (1.00) |
0.196 (1.00) |
0.458 (1.00) |
1 (1.00) |
1 (1.00) |
18p loss | 5 (3%) | 186 |
0.00203 (0.287) |
0.367 (1.00) |
0.0642 (1.00) |
0.362 (1.00) |
1 (1.00) |
19p loss | 4 (2%) | 187 |
0.0991 (1.00) |
0.0472 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
19q loss | 4 (2%) | 187 |
0.0991 (1.00) |
0.0472 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
xq loss | 5 (3%) | 186 |
0.181 (1.00) |
0.0283 (1.00) |
0.0642 (1.00) |
1 (1.00) |
1 (1.00) |
P value = 0.000704 (logrank test), Q value = 0.1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 168 | 106 | 0.9 - 94.1 (12.0) |
5Q LOSS MUTATED | 6 | 6 | 1.0 - 12.0 (7.0) |
5Q LOSS WILD-TYPE | 162 | 100 | 0.9 - 94.1 (12.5) |
P value = 0.000836 (logrank test), Q value = 0.12
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 168 | 106 | 0.9 - 94.1 (12.0) |
18Q LOSS MUTATED | 4 | 4 | 1.0 - 10.0 (4.5) |
18Q LOSS WILD-TYPE | 164 | 102 | 0.9 - 94.1 (12.0) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = LAML-TB.merged_data.txt
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Number of patients = 191
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Number of significantly arm-level cnvs = 30
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Number of selected clinical features = 5
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Exclude regions 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.