This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 20 arm-level results and 4 clinical features across 126 patients, 2 significant findings detected with Q value < 0.25.
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7p gain cnv correlated to 'AGE'.
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7q gain cnv correlated to 'AGE'.
Table 1. Get Full Table Overview of the association between significant copy number variation of 20 arm-level results and 4 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 |
RADIATIONS RADIATION REGIMENINDICATION |
NEOADJUVANT THERAPY |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | |
7p gain | 14 (11%) | 112 |
1 (1.00) |
0.000168 (0.0134) |
1 (1.00) |
0.0607 (1.00) |
7q gain | 11 (9%) | 115 |
1 (1.00) |
0.00134 (0.106) |
1 (1.00) |
0.309 (1.00) |
1q gain | 4 (3%) | 122 |
1 (1.00) |
0.398 (1.00) |
1 (1.00) |
1 (1.00) |
3p gain | 4 (3%) | 122 |
1 (1.00) |
0.744 (1.00) |
1 (1.00) |
1 (1.00) |
3q gain | 6 (5%) | 120 |
1 (1.00) |
0.425 (1.00) |
1 (1.00) |
0.179 (1.00) |
8p gain | 4 (3%) | 122 |
1 (1.00) |
0.461 (1.00) |
1 (1.00) |
1 (1.00) |
8q gain | 10 (8%) | 116 |
1 (1.00) |
0.405 (1.00) |
1 (1.00) |
0.285 (1.00) |
9q gain | 3 (2%) | 123 |
1 (1.00) |
0.33 (1.00) |
1 (1.00) |
1 (1.00) |
6q loss | 5 (4%) | 121 |
1 (1.00) |
0.244 (1.00) |
1 (1.00) |
1 (1.00) |
8p loss | 37 (29%) | 89 |
1 (1.00) |
0.189 (1.00) |
0.32 (1.00) |
0.58 (1.00) |
8q loss | 4 (3%) | 122 |
1 (1.00) |
0.513 (1.00) |
1 (1.00) |
1 (1.00) |
10p loss | 3 (2%) | 123 |
1 (1.00) |
0.0449 (1.00) |
1 (1.00) |
0.093 (1.00) |
12p loss | 6 (5%) | 120 |
1 (1.00) |
0.849 (1.00) |
1 (1.00) |
1 (1.00) |
13q loss | 9 (7%) | 117 |
1 (1.00) |
0.787 (1.00) |
1 (1.00) |
0.259 (1.00) |
16q loss | 14 (11%) | 112 |
1 (1.00) |
0.0662 (1.00) |
1 (1.00) |
0.0607 (1.00) |
17p loss | 15 (12%) | 111 |
1 (1.00) |
0.749 (1.00) |
1 (1.00) |
1 (1.00) |
18p loss | 10 (8%) | 116 |
1 (1.00) |
0.844 (1.00) |
1 (1.00) |
0.285 (1.00) |
18q loss | 15 (12%) | 111 |
1 (1.00) |
0.618 (1.00) |
1 (1.00) |
0.402 (1.00) |
20p loss | 4 (3%) | 122 |
1 (1.00) |
0.612 (1.00) |
0.151 (1.00) |
1 (1.00) |
22q loss | 5 (4%) | 121 |
1 (1.00) |
0.367 (1.00) |
0.186 (1.00) |
1 (1.00) |
P value = 0.000168 (t-test), Q value = 0.013
Table S1. Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 126 | 60.9 (6.7) |
7P GAIN MUTATED | 14 | 64.9 (3.0) |
7P GAIN WILD-TYPE | 112 | 60.5 (6.9) |
Figure S1. Get High-res Image Gene #4: '7p gain mutation analysis' versus Clinical Feature #2: 'AGE'

P value = 0.00134 (t-test), Q value = 0.11
Table S2. Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 126 | 60.9 (6.7) |
7Q GAIN MUTATED | 11 | 64.7 (3.1) |
7Q GAIN WILD-TYPE | 115 | 60.6 (6.8) |
Figure S2. Get High-res Image Gene #5: '7q gain mutation analysis' versus Clinical Feature #2: 'AGE'

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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = PRAD.clin.merged.picked.txt
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Number of patients = 126
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Number of significantly arm-level cnvs = 20
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Number of selected clinical features = 4
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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.