This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 50 arm-level events and 6 clinical features across 160 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Table 1. Get Full Table Overview of the association between significant copy number variation of 50 arm-level events and 6 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
Clinical Features |
Time to Death |
AGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
COMPLETENESS OF RESECTION |
NUMBER OF LYMPH NODES |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | |
1P GAIN MUTATION ANALYSIS | 4 (2%) | 156 |
100 (1.00) |
0.937 (1.00) |
0.0341 (1.00) |
0.366 (1.00) |
1 (1.00) |
0.849 (1.00) |
1Q GAIN MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.996 (1.00) |
0.0944 (1.00) |
0.553 (1.00) |
0.682 (1.00) |
0.717 (1.00) |
3P GAIN MUTATION ANALYSIS | 12 (8%) | 148 |
100 (1.00) |
0.0107 (1.00) |
0.00486 (1.00) |
1 (1.00) |
0.776 (1.00) |
0.248 (1.00) |
3Q GAIN MUTATION ANALYSIS | 16 (10%) | 144 |
100 (1.00) |
0.00344 (0.986) |
0.0058 (1.00) |
0.68 (1.00) |
0.517 (1.00) |
0.488 (1.00) |
4P GAIN MUTATION ANALYSIS | 4 (2%) | 156 |
100 (1.00) |
0.0834 (1.00) |
0.673 (1.00) |
1 (1.00) |
0.613 (1.00) |
0.00152 (0.45) |
4Q GAIN MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.247 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.00152 (0.45) |
5P GAIN MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.728 (1.00) |
0.322 (1.00) |
0.288 (1.00) |
0.16 (1.00) |
0.722 (1.00) |
7P GAIN MUTATION ANALYSIS | 30 (19%) | 130 |
100 (1.00) |
0.257 (1.00) |
0.182 (1.00) |
0.487 (1.00) |
0.121 (1.00) |
0.312 (1.00) |
7Q GAIN MUTATION ANALYSIS | 27 (17%) | 133 |
100 (1.00) |
0.472 (1.00) |
0.206 (1.00) |
1 (1.00) |
0.15 (1.00) |
0.138 (1.00) |
8P GAIN MUTATION ANALYSIS | 19 (12%) | 141 |
100 (1.00) |
0.275 (1.00) |
0.301 (1.00) |
0.121 (1.00) |
1 (1.00) |
0.908 (1.00) |
8Q GAIN MUTATION ANALYSIS | 30 (19%) | 130 |
100 (1.00) |
0.905 (1.00) |
0.439 (1.00) |
0.498 (1.00) |
0.366 (1.00) |
0.491 (1.00) |
9P GAIN MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.217 (1.00) |
0.571 (1.00) |
0.0266 (1.00) |
0.649 (1.00) |
0.24 (1.00) |
9Q GAIN MUTATION ANALYSIS | 14 (9%) | 146 |
100 (1.00) |
0.244 (1.00) |
0.137 (1.00) |
0.00535 (1.00) |
0.283 (1.00) |
0.156 (1.00) |
10P GAIN MUTATION ANALYSIS | 6 (4%) | 154 |
100 (1.00) |
0.671 (1.00) |
0.746 (1.00) |
1 (1.00) |
0.083 (1.00) |
0.00152 (0.45) |
10Q GAIN MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.56 (1.00) |
0.571 (1.00) |
0.497 (1.00) |
0.0783 (1.00) |
0.441 (1.00) |
11P GAIN MUTATION ANALYSIS | 5 (3%) | 155 |
100 (1.00) |
0.664 (1.00) |
0.473 (1.00) |
0.435 (1.00) |
0.344 (1.00) |
0.995 (1.00) |
11Q GAIN MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.967 (1.00) |
0.0944 (1.00) |
0.162 (1.00) |
0.266 (1.00) |
0.652 (1.00) |
16P GAIN MUTATION ANALYSIS | 8 (5%) | 152 |
100 (1.00) |
0.573 (1.00) |
0.0247 (1.00) |
0.553 (1.00) |
0.325 (1.00) |
0.717 (1.00) |
16Q GAIN MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.148 (1.00) |
0.322 (1.00) |
1 (1.00) |
1 (1.00) |
|
18P GAIN MUTATION ANALYSIS | 6 (4%) | 154 |
100 (1.00) |
0.312 (1.00) |
0.362 (1.00) |
0.0878 (1.00) |
1 (1.00) |
0.45 (1.00) |
20Q GAIN MUTATION ANALYSIS | 5 (3%) | 155 |
100 (1.00) |
0.0647 (1.00) |
0.0258 (1.00) |
1 (1.00) |
0.344 (1.00) |
0.00152 (0.45) |
21Q GAIN MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.0824 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.00152 (0.45) |
1P LOSS MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.621 (1.00) |
0.322 (1.00) |
1 (1.00) |
0.532 (1.00) |
0.00152 (0.45) |
5P LOSS MUTATION ANALYSIS | 4 (2%) | 156 |
100 (1.00) |
0.448 (1.00) |
0.0341 (1.00) |
0.0561 (1.00) |
1 (1.00) |
0.365 (1.00) |
5Q LOSS MUTATION ANALYSIS | 5 (3%) | 155 |
100 (1.00) |
0.468 (1.00) |
0.0258 (1.00) |
0.0878 (1.00) |
1 (1.00) |
0.359 (1.00) |
6P LOSS MUTATION ANALYSIS | 5 (3%) | 155 |
100 (1.00) |
0.0618 (1.00) |
0.473 (1.00) |
0.0878 (1.00) |
0.613 (1.00) |
0.273 (1.00) |
6Q LOSS MUTATION ANALYSIS | 10 (6%) | 150 |
100 (1.00) |
0.538 (1.00) |
0.348 (1.00) |
0.0744 (1.00) |
0.739 (1.00) |
0.179 (1.00) |
8P LOSS MUTATION ANALYSIS | 53 (33%) | 107 |
100 (1.00) |
0.156 (1.00) |
0.04 (1.00) |
0.565 (1.00) |
0.175 (1.00) |
0.143 (1.00) |
8Q LOSS MUTATION ANALYSIS | 10 (6%) | 150 |
100 (1.00) |
0.0526 (1.00) |
0.0305 (1.00) |
0.0744 (1.00) |
0.537 (1.00) |
0.157 (1.00) |
9P LOSS MUTATION ANALYSIS | 6 (4%) | 154 |
100 (1.00) |
0.889 (1.00) |
0.102 (1.00) |
0.0163 (1.00) |
1 (1.00) |
0.205 (1.00) |
10P LOSS MUTATION ANALYSIS | 10 (6%) | 150 |
100 (1.00) |
0.53 (1.00) |
0.00136 (0.406) |
0.0744 (1.00) |
0.537 (1.00) |
0.304 (1.00) |
10Q LOSS MUTATION ANALYSIS | 12 (8%) | 148 |
100 (1.00) |
0.945 (1.00) |
0.0611 (1.00) |
0.025 (1.00) |
0.776 (1.00) |
0.262 (1.00) |
12P LOSS MUTATION ANALYSIS | 14 (9%) | 146 |
100 (1.00) |
0.849 (1.00) |
0.539 (1.00) |
0.00779 (1.00) |
0.478 (1.00) |
0.178 (1.00) |
12Q LOSS MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.529 (1.00) |
0.24 (1.00) |
0.0266 (1.00) |
1 (1.00) |
0.29 (1.00) |
13Q LOSS MUTATION ANALYSIS | 25 (16%) | 135 |
100 (1.00) |
0.102 (1.00) |
0.788 (1.00) |
0.0143 (1.00) |
1 (1.00) |
0.169 (1.00) |
14Q LOSS MUTATION ANALYSIS | 8 (5%) | 152 |
100 (1.00) |
0.282 (1.00) |
0.0247 (1.00) |
0.203 (1.00) |
0.266 (1.00) |
0.509 (1.00) |
15Q LOSS MUTATION ANALYSIS | 10 (6%) | 150 |
100 (1.00) |
0.806 (1.00) |
0.00366 (1.00) |
0.00127 (0.38) |
1 (1.00) |
0.0595 (1.00) |
16P LOSS MUTATION ANALYSIS | 13 (8%) | 147 |
100 (1.00) |
0.73 (1.00) |
0.376 (1.00) |
1 (1.00) |
0.228 (1.00) |
0.248 (1.00) |
16Q LOSS MUTATION ANALYSIS | 34 (21%) | 126 |
100 (1.00) |
0.312 (1.00) |
0.28 (1.00) |
0.331 (1.00) |
0.649 (1.00) |
0.368 (1.00) |
17P LOSS MUTATION ANALYSIS | 25 (16%) | 135 |
100 (1.00) |
0.726 (1.00) |
0.0741 (1.00) |
0.136 (1.00) |
1 (1.00) |
0.199 (1.00) |
17Q LOSS MUTATION ANALYSIS | 5 (3%) | 155 |
100 (1.00) |
0.765 (1.00) |
0.0258 (1.00) |
0.435 (1.00) |
0.344 (1.00) |
0.995 (1.00) |
18P LOSS MUTATION ANALYSIS | 23 (14%) | 137 |
100 (1.00) |
0.262 (1.00) |
0.124 (1.00) |
0.254 (1.00) |
0.606 (1.00) |
0.198 (1.00) |
18Q LOSS MUTATION ANALYSIS | 33 (21%) | 127 |
100 (1.00) |
0.324 (1.00) |
0.226 (1.00) |
0.0834 (1.00) |
0.398 (1.00) |
0.251 (1.00) |
19P LOSS MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.0335 (1.00) |
0.625 (1.00) |
1 (1.00) |
1 (1.00) |
0.00152 (0.45) |
19Q LOSS MUTATION ANALYSIS | 4 (2%) | 156 |
100 (1.00) |
0.00278 (0.802) |
0.107 (1.00) |
0.366 (1.00) |
1 (1.00) |
0.849 (1.00) |
20P LOSS MUTATION ANALYSIS | 6 (4%) | 154 |
100 (1.00) |
0.72 (1.00) |
0.028 (1.00) |
0.435 (1.00) |
0.344 (1.00) |
0.632 (1.00) |
20Q LOSS MUTATION ANALYSIS | 3 (2%) | 157 |
100 (1.00) |
0.581 (1.00) |
0.0474 (1.00) |
1 (1.00) |
0.16 (1.00) |
0.00152 (0.45) |
21Q LOSS MUTATION ANALYSIS | 8 (5%) | 152 |
100 (1.00) |
0.688 (1.00) |
0.175 (1.00) |
0.00248 (0.717) |
1 (1.00) |
0.102 (1.00) |
22Q LOSS MUTATION ANALYSIS | 14 (9%) | 146 |
100 (1.00) |
0.662 (1.00) |
0.00747 (1.00) |
0.0442 (1.00) |
0.618 (1.00) |
0.297 (1.00) |
XQ LOSS MUTATION ANALYSIS | 7 (4%) | 153 |
100 (1.00) |
0.913 (1.00) |
0.308 (1.00) |
0.162 (1.00) |
1 (1.00) |
0.298 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 160
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Number of significantly arm-level cnvs = 50
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Number of selected clinical features = 6
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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 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.
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.