This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.
Testing the association between copy number variation 29 arm-level results and 6 molecular subtypes across 187 patients, 8 significant findings detected with Q value < 0.25.
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7p gain cnv correlated to 'CN_CNMF'.
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8q gain cnv correlated to 'CN_CNMF'.
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8p loss cnv correlated to 'METHLYATION_CNMF'.
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13q loss cnv correlated to 'CN_CNMF'.
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16q loss cnv correlated to 'CN_CNMF'.
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17p loss cnv correlated to 'CN_CNMF'.
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18p loss cnv correlated to 'CN_CNMF'.
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18q loss cnv correlated to 'CN_CNMF'.
Table 1. Get Full Table Overview of the association between significant copy number variation of 29 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.
Molecular subtypes |
CN CNMF |
METHLYATION CNMF |
MRNASEQ CNMF |
MRNASEQ CHIERARCHICAL |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
||
nCNV (%) | nWild-Type | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
7p gain | 0 (0%) | 171 |
0.000281 (0.0461) |
0.0775 (1.00) |
0.109 (1.00) |
0.139 (1.00) |
0.17 (1.00) |
0.592 (1.00) |
8q gain | 0 (0%) | 168 |
5.57e-05 (0.00924) |
0.462 (1.00) |
0.0856 (1.00) |
0.243 (1.00) |
0.12 (1.00) |
0.00324 (0.518) |
8p loss | 0 (0%) | 140 |
0.00278 (0.448) |
0.000129 (0.0214) |
0.00326 (0.518) |
0.0331 (1.00) |
0.0193 (1.00) |
1 (1.00) |
13q loss | 0 (0%) | 175 |
0.00145 (0.236) |
0.214 (1.00) |
0.0284 (1.00) |
0.0349 (1.00) |
0.449 (1.00) |
0.648 (1.00) |
16q loss | 0 (0%) | 163 |
3.66e-05 (0.00611) |
0.0391 (1.00) |
0.18 (1.00) |
0.199 (1.00) |
0.0754 (1.00) |
0.103 (1.00) |
17p loss | 0 (0%) | 166 |
6.43e-06 (0.00109) |
0.0967 (1.00) |
0.0996 (1.00) |
0.0171 (1.00) |
0.705 (1.00) |
0.907 (1.00) |
18p loss | 0 (0%) | 169 |
2.52e-05 (0.00423) |
0.213 (1.00) |
0.363 (1.00) |
0.257 (1.00) |
0.0156 (1.00) |
0.122 (1.00) |
18q loss | 0 (0%) | 163 |
1.1e-08 (1.88e-06) |
0.165 (1.00) |
0.0886 (1.00) |
0.219 (1.00) |
0.0398 (1.00) |
0.182 (1.00) |
1p gain | 0 (0%) | 184 |
0.0678 (1.00) |
0.0489 (1.00) |
0.496 (1.00) |
0.41 (1.00) |
||
1q gain | 0 (0%) | 182 |
0.139 (1.00) |
0.275 (1.00) |
0.551 (1.00) |
0.3 (1.00) |
0.324 (1.00) |
0.201 (1.00) |
3p gain | 0 (0%) | 182 |
0.254 (1.00) |
0.519 (1.00) |
0.0637 (1.00) |
0.362 (1.00) |
0.17 (1.00) |
0.155 (1.00) |
3q gain | 0 (0%) | 181 |
0.138 (1.00) |
0.664 (1.00) |
0.0198 (1.00) |
0.00561 (0.887) |
0.055 (1.00) |
0.0354 (1.00) |
7q gain | 0 (0%) | 173 |
0.00226 (0.367) |
0.101 (1.00) |
0.182 (1.00) |
0.227 (1.00) |
0.477 (1.00) |
0.598 (1.00) |
8p gain | 0 (0%) | 179 |
0.0103 (1.00) |
0.347 (1.00) |
0.396 (1.00) |
0.0935 (1.00) |
0.852 (1.00) |
0.395 (1.00) |
9p gain | 0 (0%) | 184 |
0.0678 (1.00) |
0.483 (1.00) |
0.644 (1.00) |
0.625 (1.00) |
0.862 (1.00) |
0.41 (1.00) |
9q gain | 0 (0%) | 181 |
0.0762 (1.00) |
0.326 (1.00) |
0.132 (1.00) |
0.229 (1.00) |
0.569 (1.00) |
0.56 (1.00) |
10q gain | 0 (0%) | 183 |
0.278 (1.00) |
0.566 (1.00) |
0.211 (1.00) |
0.3 (1.00) |
0.55 (1.00) |
0.172 (1.00) |
12q gain | 0 (0%) | 184 |
0.201 (1.00) |
0.269 (1.00) |
0.567 (1.00) |
1 (1.00) |
||
16p gain | 0 (0%) | 184 |
0.598 (1.00) |
0.783 (1.00) |
0.776 (1.00) |
0.261 (1.00) |
0.496 (1.00) |
1 (1.00) |
16q gain | 0 (0%) | 184 |
0.598 (1.00) |
0.783 (1.00) |
0.776 (1.00) |
0.261 (1.00) |
0.496 (1.00) |
1 (1.00) |
5q loss | 0 (0%) | 182 |
0.0138 (1.00) |
0.0647 (1.00) |
0.0637 (1.00) |
0.362 (1.00) |
0.09 (1.00) |
0.0892 (1.00) |
6q loss | 0 (0%) | 180 |
0.0157 (1.00) |
0.116 (1.00) |
0.0714 (1.00) |
0.0935 (1.00) |
0.319 (1.00) |
0.176 (1.00) |
8q loss | 0 (0%) | 183 |
0.278 (1.00) |
0.389 (1.00) |
0.329 (1.00) |
0.388 (1.00) |
0.834 (1.00) |
0.309 (1.00) |
10p loss | 0 (0%) | 182 |
0.439 (1.00) |
0.396 (1.00) |
0.551 (1.00) |
0.3 (1.00) |
0.234 (1.00) |
0.0246 (1.00) |
10q loss | 0 (0%) | 182 |
0.439 (1.00) |
0.735 (1.00) |
0.625 (1.00) |
0.866 (1.00) |
0.545 (1.00) |
1 (1.00) |
12p loss | 0 (0%) | 177 |
0.198 (1.00) |
0.0197 (1.00) |
0.139 (1.00) |
0.0123 (1.00) |
0.0682 (1.00) |
0.252 (1.00) |
20p loss | 0 (0%) | 182 |
0.363 (1.00) |
0.0647 (1.00) |
0.211 (1.00) |
0.133 (1.00) |
0.0473 (1.00) |
0.0246 (1.00) |
21q loss | 0 (0%) | 183 |
0.278 (1.00) |
0.566 (1.00) |
0.329 (1.00) |
0.0963 (1.00) |
0.352 (1.00) |
0.83 (1.00) |
22q loss | 0 (0%) | 182 |
0.0646 (1.00) |
0.396 (1.00) |
0.377 (1.00) |
0.452 (1.00) |
1 (1.00) |
1 (1.00) |
P value = 0.000281 (Fisher's exact test), Q value = 0.046
Table S1. Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
7P GAIN CNV | 1 | 2 | 13 | 0 |
7P GAIN WILD-TYPE | 33 | 90 | 46 | 2 |
Figure S1. Get High-res Image Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'
![](D5V1.png)
P value = 5.57e-05 (Fisher's exact test), Q value = 0.0092
Table S2. Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
8Q GAIN CNV | 2 | 2 | 15 | 0 |
8Q GAIN WILD-TYPE | 32 | 90 | 44 | 2 |
Figure S2. Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'
![](D8V1.png)
P value = 0.000129 (Fisher's exact test), Q value = 0.021
Table S3. Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 |
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ALL | 55 | 77 | 55 |
8P LOSS CNV | 11 | 31 | 5 |
8P LOSS WILD-TYPE | 44 | 46 | 50 |
Figure S3. Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'
![](D17V2.png)
P value = 0.00145 (Fisher's exact test), Q value = 0.24
Table S4. Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
13Q LOSS CNV | 1 | 1 | 10 | 0 |
13Q LOSS WILD-TYPE | 33 | 91 | 49 | 2 |
Figure S4. Get High-res Image Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'
![](D22V1.png)
P value = 3.66e-05 (Fisher's exact test), Q value = 0.0061
Table S5. Gene #23: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
16Q LOSS CNV | 2 | 4 | 18 | 0 |
16Q LOSS WILD-TYPE | 32 | 88 | 41 | 2 |
Figure S5. Get High-res Image Gene #23: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'
![](D23V1.png)
P value = 6.43e-06 (Fisher's exact test), Q value = 0.0011
Table S6. Gene #24: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
17P LOSS CNV | 0 | 4 | 17 | 0 |
17P LOSS WILD-TYPE | 34 | 88 | 42 | 2 |
Figure S6. Get High-res Image Gene #24: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'
![](D24V1.png)
P value = 2.52e-05 (Fisher's exact test), Q value = 0.0042
Table S7. Gene #25: '18p loss' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
18P LOSS CNV | 1 | 2 | 15 | 0 |
18P LOSS WILD-TYPE | 33 | 90 | 44 | 2 |
Figure S7. Get High-res Image Gene #25: '18p loss' versus Molecular Subtype #1: 'CN_CNMF'
![](D25V1.png)
P value = 1.1e-08 (Fisher's exact test), Q value = 1.9e-06
Table S8. Gene #26: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'
nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 |
---|---|---|---|---|
ALL | 34 | 92 | 59 | 2 |
18Q LOSS CNV | 0 | 3 | 21 | 0 |
18Q LOSS WILD-TYPE | 34 | 89 | 38 | 2 |
Figure S8. Get High-res Image Gene #26: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'
![](D26V1.png)
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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Molecular subtypes file = PRAD-TP.transferedmergedcluster.txt
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Number of patients = 187
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Number of significantly arm-level cnvs = 29
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Number of molecular subtypes = 6
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Exclude genes 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 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.