Correlation between copy number variations of arm-level result and molecular subtypes
Prostate Adenocarcinoma (Primary solid tumor)
23 May 2013 | analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C17W698S
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C17W698S
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 29 arm-level results and 8 molecular subtypes across 187 patients, 7 significant findings detected with Q value < 0.25.

- 7p gain cnv correlated to 'CN_CNMF'.

- 8q gain cnv correlated to 'CN_CNMF'.

- 8p loss cnv correlated to 'METHLYATION_CNMF'.

- 16q loss cnv correlated to 'CN_CNMF'.

- 17p loss cnv correlated to 'CN_CNMF'.

- 18p loss cnv correlated to 'CN_CNMF'.

- 18q loss cnv correlated to 'CN_CNMF'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 29 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

|  | Molecular subtypes | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRSEQ MATURE CNMF | MIRSEQ MATURE 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 | Chi-square test | Fisher's exact test
7p gain | 0 (0%) | 171 | 0.000281 (0.0624) | 0.126 (1.00) | 0.109 (1.00) | 0.139 (1.00) | 0.17 (1.00) | 0.592 (1.00) | 0.36 (1.00) | 0.499 (1.00)
8q gain | 0 (0%) | 168 | 5.57e-05 (0.0124) | 0.0536 (1.00) | 0.0856 (1.00) | 0.243 (1.00) | 0.12 (1.00) | 0.00324 (0.706) | 0.0495 (1.00) | 0.549 (1.00)
8p loss | 0 (0%) | 140 | 0.00278 (0.609) | 2.75e-05 (0.0062) | 0.00326 (0.707) | 0.0331 (1.00) | 0.0193 (1.00) | 1 (1.00) | 0.0158 (1.00) | 0.623 (1.00)
16q loss | 0 (0%) | 163 | 3.66e-05 (0.0082) | 0.00974 (1.00) | 0.18 (1.00) | 0.199 (1.00) | 0.0754 (1.00) | 0.103 (1.00) | 0.028 (1.00) | 0.897 (1.00)
17p loss | 0 (0%) | 166 | 6.43e-06 (0.00146) | 0.0292 (1.00) | 0.0996 (1.00) | 0.0171 (1.00) | 0.705 (1.00) | 0.907 (1.00) | 0.0711 (1.00) | 0.276 (1.00)
18p loss | 0 (0%) | 169 | 2.52e-05 (0.00569) | 0.0512 (1.00) | 0.363 (1.00) | 0.257 (1.00) | 0.0156 (1.00) | 0.122 (1.00) | 0.185 (1.00) | 0.0973 (1.00)
18q loss | 0 (0%) | 163 | 1.1e-08 (2.51e-06) | 0.015 (1.00) | 0.0886 (1.00) | 0.219 (1.00) | 0.0398 (1.00) | 0.182 (1.00) | 0.153 (1.00) | 0.32 (1.00)
1p gain | 0 (0%) | 184 | 0.0678 (1.00) | 0.182 (1.00) |  |  | 0.496 (1.00) | 0.41 (1.00) | 0.439 (1.00) | 0.625 (1.00)
1q gain | 0 (0%) | 182 | 0.139 (1.00) | 0.476 (1.00) | 0.551 (1.00) | 0.3 (1.00) | 0.324 (1.00) | 0.201 (1.00) | 0.256 (1.00) | 0.7 (1.00)
3p gain | 0 (0%) | 182 | 0.254 (1.00) | 0.773 (1.00) | 0.0637 (1.00) | 0.362 (1.00) | 0.17 (1.00) | 0.155 (1.00) | 0.23 (1.00) | 0.7 (1.00)
3q gain | 0 (0%) | 181 | 0.138 (1.00) | 0.931 (1.00) | 0.0198 (1.00) | 0.00561 (1.00) | 0.055 (1.00) | 0.0354 (1.00) | 0.605 (1.00) | 1 (1.00)
7q gain | 0 (0%) | 173 | 0.00226 (0.498) | 0.125 (1.00) | 0.182 (1.00) | 0.227 (1.00) | 0.477 (1.00) | 0.598 (1.00) | 0.543 (1.00) | 0.832 (1.00)
8p gain | 0 (0%) | 179 | 0.0103 (1.00) | 0.126 (1.00) | 0.396 (1.00) | 0.0935 (1.00) | 0.852 (1.00) | 0.395 (1.00) | 0.396 (1.00) | 0.77 (1.00)
9p gain | 0 (0%) | 184 | 0.0678 (1.00) | 0.128 (1.00) | 0.644 (1.00) | 0.625 (1.00) | 0.862 (1.00) | 0.41 (1.00) | 0.206 (1.00) | 1 (1.00)
9q gain | 0 (0%) | 181 | 0.0762 (1.00) | 0.355 (1.00) | 0.132 (1.00) | 0.229 (1.00) | 0.569 (1.00) | 0.56 (1.00) | 0.674 (1.00) | 1 (1.00)
10q gain | 0 (0%) | 183 | 0.278 (1.00) | 0.419 (1.00) | 0.211 (1.00) | 0.3 (1.00) | 0.55 (1.00) | 0.172 (1.00) | 0.441 (1.00) | 1 (1.00)
12q gain | 0 (0%) | 184 | 0.201 (1.00) | 0.664 (1.00) |  |  | 0.567 (1.00) | 1 (1.00) | 0.181 (1.00) | 0.625 (1.00)
16p gain | 0 (0%) | 184 | 0.598 (1.00) | 0.342 (1.00) | 0.776 (1.00) | 0.261 (1.00) | 0.496 (1.00) | 1 (1.00) | 0.439 (1.00) | 0.625 (1.00)
16q gain | 0 (0%) | 184 | 0.598 (1.00) | 0.342 (1.00) | 0.776 (1.00) | 0.261 (1.00) | 0.496 (1.00) | 1 (1.00) | 0.439 (1.00) | 0.625 (1.00)
5q loss | 0 (0%) | 182 | 0.0138 (1.00) | 0.147 (1.00) | 0.0637 (1.00) | 0.362 (1.00) | 0.09 (1.00) | 0.0892 (1.00) | 0.749 (1.00) | 1 (1.00)
6q loss | 0 (0%) | 180 | 0.0157 (1.00) | 0.215 (1.00) | 0.0714 (1.00) | 0.0935 (1.00) | 0.319 (1.00) | 0.176 (1.00) | 0.663 (1.00) | 1 (1.00)
8q loss | 0 (0%) | 183 | 0.278 (1.00) | 0.486 (1.00) | 0.329 (1.00) | 0.388 (1.00) | 0.834 (1.00) | 0.309 (1.00) | 0.423 (1.00) | 0.396 (1.00)
10p loss | 0 (0%) | 182 | 0.439 (1.00) | 0.424 (1.00) | 0.551 (1.00) | 0.3 (1.00) | 0.234 (1.00) | 0.0246 (1.00) | 0.705 (1.00) | 1 (1.00)
10q loss | 0 (0%) | 182 | 0.439 (1.00) | 0.671 (1.00) | 0.625 (1.00) | 0.866 (1.00) | 0.545 (1.00) | 1 (1.00) | 0.705 (1.00) | 1 (1.00)
12p loss | 0 (0%) | 177 | 0.198 (1.00) | 0.0307 (1.00) | 0.139 (1.00) | 0.0123 (1.00) | 0.0682 (1.00) | 0.252 (1.00) | 0.0135 (1.00) | 0.615 (1.00)
13q loss | 0 (0%) | 175 | 0.00145 (0.32) | 0.311 (1.00) | 0.0284 (1.00) | 0.0349 (1.00) | 0.449 (1.00) | 0.648 (1.00) | 0.343 (1.00) | 0.81 (1.00)
20p loss | 0 (0%) | 182 | 0.363 (1.00) | 0.0317 (1.00) | 0.211 (1.00) | 0.133 (1.00) | 0.0473 (1.00) | 0.0246 (1.00) | 0.0171 (1.00) | 0.271 (1.00)
21q loss | 0 (0%) | 183 | 0.278 (1.00) | 0.634 (1.00) | 0.329 (1.00) | 0.0963 (1.00) | 0.352 (1.00) | 0.83 (1.00) | 0.638 (1.00) | 1 (1.00)
22q loss | 0 (0%) | 182 | 0.0646 (1.00) | 0.476 (1.00) | 0.377 (1.00) | 0.452 (1.00) | 1 (1.00) | 1 (1.00) | 0.338 (1.00) | 1 (1.00)

'7p gain' versus 'CN_CNMF'

P value = 0.000281 (Fisher's exact test), Q value = 0.062

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'

'8q gain' versus 'CN_CNMF'

P value = 5.57e-05 (Fisher's exact test), Q value = 0.012

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'

'8p loss' versus 'METHLYATION_CNMF'

P value = 2.75e-05 (Fisher's exact test), Q value = 0.0062

Table S3. Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 57 | 57 | 55 | 18
8P LOSS CNV | 12 | 21 | 4 | 10
8P LOSS WILD-TYPE | 45 | 36 | 51 | 8

Figure S3. Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 3.66e-05 (Fisher's exact test), Q value = 0.0082

Table S4. 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 S4. Get High-res Image Gene #23: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

'17p loss' versus 'CN_CNMF'

P value = 6.43e-06 (Fisher's exact test), Q value = 0.0015

Table S5. 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 S5. Get High-res Image Gene #24: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18p loss' versus 'CN_CNMF'

P value = 2.52e-05 (Fisher's exact test), Q value = 0.0057

Table S6. 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 S6. Get High-res Image Gene #25: '18p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18q loss' versus 'CN_CNMF'

P value = 1.1e-08 (Fisher's exact test), Q value = 2.5e-06

Table S7. 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 S7. Get High-res Image Gene #26: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

- Mutation data file = broad_values_by_arm.mutsig.cluster.txt

- Molecular subtypes file = PRAD-TP.transferedmergedcluster.txt

- Number of patients = 187

- Number of significantly arm-level cnvs = 29

- Number of molecular subtypes = 8

- Exclude genes that fewer than K tumors have mutations, K = 3

Fisher's exact test

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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