This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 32 arm-level events and 5 clinical features across 191 patients, 8 significant findings detected with Q value < 0.25.
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10q gain cnv correlated to 'Time to Death'.
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3p loss cnv correlated to 'Time to Death'.
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3q loss cnv correlated to 'Time to Death'.
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5q loss cnv correlated to 'Time to Death'.
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12p loss cnv correlated to 'Time to Death'.
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18p loss cnv correlated to 'Time to Death'.
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18q loss cnv correlated to 'Time to Death' and 'YEARS_TO_BIRTH'.
Table 1. Get Full Table Overview of the association between significant copy number variation of 32 arm-level events and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.
Clinical Features |
Time to Death |
YEARS TO BIRTH |
GENDER | RACE | ETHNICITY | ||
nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
18q loss | 4 (2%) | 187 |
0.00664 (0.177) |
0.0121 (0.241) |
0.127 (0.508) |
1 (1.00) |
1 (1.00) |
10q gain | 3 (2%) | 188 |
0.00577 (0.177) |
0.654 (1.00) |
0.252 (0.791) |
0.233 (0.778) |
1 (1.00) |
3p loss | 3 (2%) | 188 |
0.000763 (0.061) |
0.0208 (0.3) |
0.252 (0.791) |
1 (1.00) |
1 (1.00) |
3q loss | 3 (2%) | 188 |
0.000763 (0.061) |
0.0208 (0.3) |
0.252 (0.791) |
1 (1.00) |
1 (1.00) |
5q loss | 6 (3%) | 185 |
0.00532 (0.177) |
0.0519 (0.419) |
0.0325 (0.346) |
0.416 (1.00) |
1 (1.00) |
12p loss | 4 (2%) | 187 |
0.00166 (0.0886) |
0.869 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
18p loss | 5 (3%) | 186 |
0.011 (0.241) |
0.367 (0.993) |
0.0642 (0.419) |
0.363 (0.993) |
1 (1.00) |
1p gain | 3 (2%) | 188 |
0.115 (0.486) |
0.592 (1.00) |
1 (1.00) |
1 (1.00) |
|
4p gain | 4 (2%) | 187 |
0.506 (1.00) |
0.328 (0.937) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
4q gain | 4 (2%) | 187 |
0.506 (1.00) |
0.328 (0.937) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
8p gain | 22 (12%) | 169 |
0.412 (1.00) |
0.112 (0.486) |
0.0734 (0.419) |
1 (1.00) |
0.313 (0.937) |
8q gain | 23 (12%) | 168 |
0.464 (1.00) |
0.167 (0.609) |
0.0723 (0.419) |
1 (1.00) |
0.325 (0.937) |
11p gain | 4 (2%) | 187 |
0.89 (1.00) |
0.173 (0.615) |
1 (1.00) |
1 (1.00) |
0.0628 (0.419) |
11q gain | 7 (4%) | 184 |
0.37 (0.993) |
0.047 (0.419) |
1 (1.00) |
1 (1.00) |
0.108 (0.486) |
13q gain | 6 (3%) | 185 |
0.582 (1.00) |
0.12 (0.493) |
0.69 (1.00) |
1 (1.00) |
1 (1.00) |
17q gain | 3 (2%) | 188 |
0.856 (1.00) |
0.113 (0.486) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
19p gain | 5 (3%) | 186 |
0.91 (1.00) |
0.715 (1.00) |
0.378 (0.993) |
1 (1.00) |
1 (1.00) |
19q gain | 5 (3%) | 186 |
0.91 (1.00) |
0.715 (1.00) |
0.378 (0.993) |
1 (1.00) |
1 (1.00) |
21q gain | 8 (4%) | 183 |
0.0661 (0.419) |
0.401 (1.00) |
0.0732 (0.419) |
0.187 (0.649) |
1 (1.00) |
22q gain | 9 (5%) | 182 |
0.564 (1.00) |
0.0629 (0.419) |
0.513 (1.00) |
1 (1.00) |
1 (1.00) |
xp gain | 3 (2%) | 188 |
0.452 (1.00) |
0.108 (0.486) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
xq gain | 3 (2%) | 188 |
0.452 (1.00) |
0.108 (0.486) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
7p loss | 17 (9%) | 174 |
0.0916 (0.473) |
0.458 (1.00) |
0.802 (1.00) |
1 (1.00) |
1 (1.00) |
7q loss | 20 (10%) | 171 |
0.0225 (0.3) |
0.513 (1.00) |
1 (1.00) |
0.721 (1.00) |
1 (1.00) |
15q loss | 4 (2%) | 187 |
0.8 (1.00) |
0.677 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
16q loss | 4 (2%) | 187 |
0.0189 (0.3) |
0.388 (1.00) |
0.627 (1.00) |
1 (1.00) |
1 (1.00) |
17p loss | 13 (7%) | 178 |
0.112 (0.486) |
0.765 (1.00) |
0.147 (0.575) |
0.657 (1.00) |
1 (1.00) |
17q loss | 7 (4%) | 184 |
0.274 (0.844) |
0.196 (0.668) |
0.458 (1.00) |
1 (1.00) |
1 (1.00) |
19p loss | 4 (2%) | 187 |
0.168 (0.609) |
0.0472 (0.419) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
19q loss | 4 (2%) | 187 |
0.168 (0.609) |
0.0472 (0.419) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
xp loss | 5 (3%) | 186 |
0.081 (0.432) |
0.0283 (0.323) |
0.0642 (0.419) |
1 (1.00) |
1 (1.00) |
xq loss | 5 (3%) | 186 |
0.081 (0.432) |
0.0283 (0.323) |
0.0642 (0.419) |
1 (1.00) |
1 (1.00) |
P value = 0.00577 (logrank test), Q value = 0.18
Table S1. Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
10Q GAIN MUTATED | 3 | 2 | 0.0 - 2.0 (1.9) |
10Q GAIN WILD-TYPE | 175 | 114 | 0.0 - 94.1 (12.0) |
Figure S1. Get High-res Image Gene #6: '10q gain' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.000763 (logrank test), Q value = 0.061
Table S2. Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
3P LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (1.0) |
3P LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0) |
Figure S2. Get High-res Image Gene #17: '3p loss' versus Clinical Feature #1: 'Time to Death'
![](D17V1.png)
P value = 0.000763 (logrank test), Q value = 0.061
Table S3. Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
3Q LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (1.0) |
3Q LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0) |
Figure S3. Get High-res Image Gene #18: '3q loss' versus Clinical Feature #1: 'Time to Death'
![](D18V1.png)
P value = 0.00532 (logrank test), Q value = 0.18
Table S4. Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
5Q LOSS MUTATED | 6 | 6 | 1.0 - 12.0 (7.0) |
5Q LOSS WILD-TYPE | 172 | 110 | 0.0 - 94.1 (12.0) |
Figure S4. Get High-res Image Gene #19: '5q loss' versus Clinical Feature #1: 'Time to Death'
![](D19V1.png)
P value = 0.00166 (logrank test), Q value = 0.089
Table S5. Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
12P LOSS MUTATED | 3 | 3 | 0.0 - 7.0 (4.0) |
12P LOSS WILD-TYPE | 175 | 113 | 0.0 - 94.1 (12.0) |
Figure S5. Get High-res Image Gene #22: '12p loss' versus Clinical Feature #1: 'Time to Death'
![](D22V1.png)
P value = 0.011 (logrank test), Q value = 0.24
Table S6. Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
18P LOSS MUTATED | 5 | 5 | 1.0 - 12.0 (7.0) |
18P LOSS WILD-TYPE | 173 | 111 | 0.0 - 94.1 (12.0) |
Figure S6. Get High-res Image Gene #27: '18p loss' versus Clinical Feature #1: 'Time to Death'
![](D27V1.png)
P value = 0.00664 (logrank test), Q value = 0.18
Table S7. Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 178 | 116 | 0.0 - 94.1 (11.0) |
18Q LOSS MUTATED | 4 | 4 | 1.0 - 10.0 (4.5) |
18Q LOSS WILD-TYPE | 174 | 112 | 0.0 - 94.1 (12.0) |
Figure S7. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #1: 'Time to Death'
![](D28V1.png)
P value = 0.0121 (Wilcoxon-test), Q value = 0.24
Table S8. Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 191 | 55.2 (16.1) |
18Q LOSS MUTATED | 4 | 72.8 (5.4) |
18Q LOSS WILD-TYPE | 187 | 54.9 (16.0) |
Figure S8. Get High-res Image Gene #28: '18q loss' versus Clinical Feature #2: 'YEARS_TO_BIRTH'
![](D28V2.png)
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Copy number data file = broad_values_by_arm.txt from GISTIC pipeline
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Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/LAML-TB/15087096/transformed.cor.cli.txt
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Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/15082450/LAML-TB.merged_data.txt
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Number of patients = 191
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Number of significantly arm-level cnvs = 32
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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 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 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 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.