(primary solid tumor 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 25 arm-level results and 5 clinical features across 154 patients, 4 significant findings detected with Q value < 0.25.
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1q gain cnv correlated to 'NUMBER.OF.LYMPH.NODES'.
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3p gain cnv correlated to 'NUMBER.OF.LYMPH.NODES'.
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5q loss cnv correlated to 'NUMBER.OF.LYMPH.NODES'.
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20p loss cnv correlated to 'NUMBER.OF.LYMPH.NODES'.
Table 1. Get Full Table Overview of the association between significant copy number variation of 25 arm-level results and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 4 significant findings detected.
Clinical Features |
Time to Death |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | t-test | |
1q gain | 4 (3%) | 150 |
1 (1.00) |
0.302 (1.00) |
1 (1.00) |
0.228 (1.00) |
0.00151 (0.187) |
3p gain | 4 (3%) | 150 |
1 (1.00) |
0.648 (1.00) |
1 (1.00) |
0.604 (1.00) |
0.00151 (0.187) |
5q loss | 3 (2%) | 151 |
1 (1.00) |
0.217 (1.00) |
1 (1.00) |
1 (1.00) |
0.00151 (0.187) |
20p loss | 4 (3%) | 150 |
1 (1.00) |
0.455 (1.00) |
0.125 (1.00) |
0.228 (1.00) |
0.00151 (0.187) |
3q gain | 5 (3%) | 149 |
1 (1.00) |
0.362 (1.00) |
1 (1.00) |
1 (1.00) |
0.439 (1.00) |
7p gain | 14 (9%) | 140 |
1 (1.00) |
0.0192 (1.00) |
1 (1.00) |
0.774 (1.00) |
0.17 (1.00) |
7q gain | 12 (8%) | 142 |
1 (1.00) |
0.0695 (1.00) |
1 (1.00) |
1 (1.00) |
0.958 (1.00) |
8p gain | 7 (5%) | 147 |
1 (1.00) |
0.636 (1.00) |
1 (1.00) |
0.405 (1.00) |
0.685 (1.00) |
8q gain | 14 (9%) | 140 |
1 (1.00) |
0.694 (1.00) |
1 (1.00) |
0.427 (1.00) |
0.856 (1.00) |
9p gain | 3 (2%) | 151 |
1 (1.00) |
0.725 (1.00) |
1 (1.00) |
0.555 (1.00) |
|
9q gain | 4 (3%) | 150 |
1 (1.00) |
0.7 (1.00) |
1 (1.00) |
1 (1.00) |
0.555 (1.00) |
10q gain | 3 (2%) | 151 |
1 (1.00) |
0.146 (1.00) |
1 (1.00) |
1 (1.00) |
0.456 (1.00) |
6q loss | 7 (5%) | 147 |
1 (1.00) |
0.261 (1.00) |
1 (1.00) |
1 (1.00) |
0.609 (1.00) |
8p loss | 39 (25%) | 115 |
1 (1.00) |
0.0895 (1.00) |
0.331 (1.00) |
0.495 (1.00) |
0.0935 (1.00) |
8q loss | 4 (3%) | 150 |
1 (1.00) |
0.651 (1.00) |
1 (1.00) |
1 (1.00) |
0.248 (1.00) |
10p loss | 4 (3%) | 150 |
1 (1.00) |
0.674 (1.00) |
1 (1.00) |
1 (1.00) |
0.875 (1.00) |
10q loss | 4 (3%) | 150 |
1 (1.00) |
0.395 (1.00) |
1 (1.00) |
0.604 (1.00) |
0.328 (1.00) |
12p loss | 7 (5%) | 147 |
1 (1.00) |
0.671 (1.00) |
1 (1.00) |
0.405 (1.00) |
0.142 (1.00) |
13q loss | 11 (7%) | 143 |
1 (1.00) |
0.819 (1.00) |
1 (1.00) |
1 (1.00) |
0.1 (1.00) |
16q loss | 20 (13%) | 134 |
1 (1.00) |
0.286 (1.00) |
1 (1.00) |
0.638 (1.00) |
0.207 (1.00) |
17p loss | 19 (12%) | 135 |
1 (1.00) |
0.464 (1.00) |
1 (1.00) |
0.517 (1.00) |
0.129 (1.00) |
18p loss | 14 (9%) | 140 |
1 (1.00) |
0.861 (1.00) |
1 (1.00) |
0.544 (1.00) |
0.487 (1.00) |
18q loss | 20 (13%) | 134 |
1 (1.00) |
0.72 (1.00) |
1 (1.00) |
0.517 (1.00) |
0.242 (1.00) |
21q loss | 3 (2%) | 151 |
1 (1.00) |
0.405 (1.00) |
1 (1.00) |
1 (1.00) |
0.503 (1.00) |
22q loss | 4 (3%) | 150 |
1 (1.00) |
0.467 (1.00) |
0.125 (1.00) |
0.228 (1.00) |
0.511 (1.00) |
P value = 0.00151 (t-test), Q value = 0.19
Table S1. Gene #1: '1q gain mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 135 | 0.2 (0.7) |
1Q GAIN MUTATED | 4 | 0.0 (0.0) |
1Q GAIN WILD-TYPE | 131 | 0.2 (0.8) |
Figure S1. Get High-res Image Gene #1: '1q gain mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
![](D1V5.png)
P value = 0.00151 (t-test), Q value = 0.19
Table S2. Gene #2: '3p gain mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 135 | 0.2 (0.7) |
3P GAIN MUTATED | 4 | 0.0 (0.0) |
3P GAIN WILD-TYPE | 131 | 0.2 (0.8) |
Figure S2. Get High-res Image Gene #2: '3p gain mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
![](D2V5.png)
P value = 0.00151 (t-test), Q value = 0.19
Table S3. Gene #11: '5q loss mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 135 | 0.2 (0.7) |
5Q LOSS MUTATED | 3 | 0.0 (0.0) |
5Q LOSS WILD-TYPE | 132 | 0.2 (0.8) |
Figure S3. Get High-res Image Gene #11: '5q loss mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
![](D11V5.png)
P value = 0.00151 (t-test), Q value = 0.19
Table S4. Gene #23: '20p loss mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 135 | 0.2 (0.7) |
20P LOSS MUTATED | 4 | 0.0 (0.0) |
20P LOSS WILD-TYPE | 131 | 0.2 (0.8) |
Figure S4. Get High-res Image Gene #23: '20p loss mutation analysis' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'
![](D23V5.png)
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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 154
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Number of significantly arm-level cnvs = 25
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Number of selected clinical features = 5
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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.