This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 21 focal events and 5 clinical features across 191 patients, 3 significant findings detected with Q value < 0.25.
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amp_21q22.2 cnv correlated to 'Time to Death'.
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del_3p13 cnv correlated to 'Time to Death'.
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del_12p13.2 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 | |
amp 21q22 2 | 14 (7%) | 177 |
0.000747 (0.0762) |
0.0236 (1.00) |
0.0922 (1.00) |
0.383 (1.00) |
1 (1.00) |
del 3p13 | 9 (5%) | 182 |
3.8e-05 (0.00391) |
0.0494 (1.00) |
0.185 (1.00) |
0.114 (1.00) |
1 (1.00) |
del 12p13 2 | 10 (5%) | 181 |
0.0011 (0.112) |
0.367 (1.00) |
0.351 (1.00) |
1 (1.00) |
1 (1.00) |
amp 1p33 | 7 (4%) | 184 |
0.337 (1.00) |
0.119 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
amp 1q43 | 7 (4%) | 184 |
0.734 (1.00) |
0.0209 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
amp 11q23 3 | 17 (9%) | 174 |
0.122 (1.00) |
0.0171 (1.00) |
0.45 (1.00) |
1 (1.00) |
0.249 (1.00) |
amp 13q31 3 | 7 (4%) | 184 |
0.931 (1.00) |
0.0648 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
amp 19p13 2 | 6 (3%) | 185 |
0.978 (1.00) |
0.793 (1.00) |
0.223 (1.00) |
1 (1.00) |
1 (1.00) |
amp 20q11 21 | 3 (2%) | 188 |
0.114 (1.00) |
0.346 (1.00) |
0.252 (1.00) |
0.234 (1.00) |
1 (1.00) |
del 3q26 31 | 3 (2%) | 188 |
0.0208 (1.00) |
0.252 (1.00) |
1 (1.00) |
1 (1.00) |
|
del 5q31 2 | 18 (9%) | 173 |
0.00338 (0.338) |
0.0108 (1.00) |
0.0464 (1.00) |
1 (1.00) |
1 (1.00) |
del 7p12 1 | 16 (8%) | 175 |
0.0737 (1.00) |
0.484 (1.00) |
0.604 (1.00) |
1 (1.00) |
1 (1.00) |
del 7q32 3 | 23 (12%) | 168 |
0.0282 (1.00) |
0.189 (1.00) |
0.656 (1.00) |
0.756 (1.00) |
1 (1.00) |
del 7q34 | 24 (13%) | 167 |
0.0705 (1.00) |
0.197 (1.00) |
0.512 (1.00) |
0.77 (1.00) |
1 (1.00) |
del 9q21 32 | 5 (3%) | 186 |
0.9 (1.00) |
0.417 (1.00) |
0.378 (1.00) |
0.362 (1.00) |
1 (1.00) |
del 12q21 33 | 3 (2%) | 188 |
0.0366 (1.00) |
0.252 (1.00) |
1 (1.00) |
1 (1.00) |
|
del 16q23 1 | 9 (5%) | 182 |
0.124 (1.00) |
0.304 (1.00) |
0.513 (1.00) |
0.558 (1.00) |
1 (1.00) |
del 17p13 2 | 15 (8%) | 176 |
0.0435 (1.00) |
0.129 (1.00) |
0.0565 (1.00) |
0.667 (1.00) |
1 (1.00) |
del 17q11 2 | 13 (7%) | 178 |
0.0318 (1.00) |
0.321 (1.00) |
0.775 (1.00) |
0.655 (1.00) |
1 (1.00) |
del 18p11 21 | 9 (5%) | 182 |
0.00546 (0.541) |
0.427 (1.00) |
0.185 (1.00) |
0.556 (1.00) |
1 (1.00) |
del 20q13 13 | 4 (2%) | 187 |
0.0388 (1.00) |
0.0674 (1.00) |
0.627 (1.00) |
0.302 (1.00) |
1 (1.00) |
P value = 0.000747 (logrank test), Q value = 0.076
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 168 | 106 | 0.9 - 94.1 (12.0) |
AMP PEAK 7(21Q22.2) MUTATED | 12 | 10 | 1.0 - 24.0 (5.5) |
AMP PEAK 7(21Q22.2) WILD-TYPE | 156 | 96 | 0.9 - 94.1 (12.5) |
P value = 3.8e-05 (logrank test), Q value = 0.0039
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 168 | 106 | 0.9 - 94.1 (12.0) |
DEL PEAK 2(3P13) MUTATED | 8 | 7 | 1.0 - 14.0 (2.0) |
DEL PEAK 2(3P13) WILD-TYPE | 160 | 99 | 0.9 - 94.1 (12.5) |
P value = 0.0011 (logrank test), Q value = 0.11
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 168 | 106 | 0.9 - 94.1 (12.0) |
DEL PEAK 10(12P13.2) MUTATED | 8 | 8 | 1.0 - 22.1 (7.0) |
DEL PEAK 10(12P13.2) WILD-TYPE | 160 | 98 | 0.9 - 94.1 (12.5) |
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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 focal cnvs = 21
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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 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.