Correlation between copy number variations of arm-level result and molecular subtypes
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1SX6BD9
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/C1SX6BD9
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 22 arm-level results and 6 molecular subtypes across 191 patients, 20 significant findings detected with Q value < 0.25.

- 1p gain cnv correlated to 'CN_CNMF'.

- 4p gain cnv correlated to 'CN_CNMF'.

- 4q gain cnv correlated to 'CN_CNMF'.

- 8p gain cnv correlated to 'CN_CNMF'.

- 8q gain cnv correlated to 'CN_CNMF'.

- 10q gain cnv correlated to 'CN_CNMF'.

- 11p gain cnv correlated to 'CN_CNMF'.

- 11q gain cnv correlated to 'CN_CNMF'.

- 17q gain cnv correlated to 'CN_CNMF'.

- 21q gain cnv correlated to 'CN_CNMF'.

- 5q loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 7p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 7q loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 17p loss cnv correlated to 'CN_CNMF' and 'METHLYATION_CNMF'.

- 17q loss cnv correlated to 'CN_CNMF'.

- 18p 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 22 arm-level results and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 20 significant findings detected.

|  | Molecular subtypes | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nCNV (%) | nWild-Type | Chi-square test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
5q loss | 6 (3%) | 185 | 6.1e-11 (7.75e-09) | 0.00215 (0.239) | 0.224 (1.00) | 0.664 (1.00) | 0.0202 (1.00) | 0.022 (1.00)
7p loss | 15 (8%) | 176 | 1.61e-06 (0.000197) | 4.49e-05 (0.00535) | 0.00277 (0.305) | 0.00279 (0.305) | 0.00845 (0.816) | 0.00711 (0.704)
7q loss | 18 (9%) | 173 | 6.33e-08 (7.86e-06) | 5.19e-07 (6.38e-05) | 0.0272 (1.00) | 0.0211 (1.00) | 0.00337 (0.358) | 0.00338 (0.358)
17p loss | 10 (5%) | 181 | 2.2e-10 (2.77e-08) | 0.000181 (0.0206) | 0.0752 (1.00) | 0.167 (1.00) | 0.0144 (1.00) | 0.00495 (0.5)
1p gain | 3 (2%) | 188 | 0.00135 (0.152) | 0.101 (1.00) | 0.186 (1.00) | 0.552 (1.00) | 0.147 (1.00) | 0.094 (1.00)
4p gain | 4 (2%) | 187 | 9.82e-05 (0.0114) | 0.383 (1.00) |  | 0.55 (1.00) | 0.142 (1.00) | 0.825 (1.00)
4q gain | 4 (2%) | 187 | 9.82e-05 (0.0114) | 0.383 (1.00) |  | 0.55 (1.00) | 0.142 (1.00) | 0.825 (1.00)
8p gain | 20 (10%) | 171 | 2.44e-05 (0.00293) | 0.0504 (1.00) | 0.0674 (1.00) | 0.0126 (1.00) | 0.0611 (1.00) | 0.0617 (1.00)
8q gain | 21 (11%) | 170 | 6.42e-05 (0.00751) | 0.0999 (1.00) | 0.0434 (1.00) | 0.0125 (1.00) | 0.115 (1.00) | 0.0937 (1.00)
10q gain | 3 (2%) | 188 | 0.000979 (0.111) | 0.439 (1.00) | 0.186 (1.00) | 0.552 (1.00) | 0.79 (1.00) | 0.227 (1.00)
11p gain | 4 (2%) | 187 | 3.32e-15 (4.29e-13) | 0.263 (1.00) | 0.467 (1.00) | 1 (1.00) | 0.343 (1.00) | 0.0737 (1.00)
11q gain | 7 (4%) | 184 | 9.42e-12 (1.21e-09) | 0.0104 (0.991) | 0.0668 (1.00) | 0.425 (1.00) | 0.278 (1.00) | 0.371 (1.00)
17q gain | 3 (2%) | 188 | 6.81e-06 (0.000825) | 0.529 (1.00) | 1 (1.00) | 0.552 (1.00) | 0.452 (1.00) | 0.227 (1.00)
21q gain | 8 (4%) | 183 | 2.62e-15 (3.41e-13) | 0.454 (1.00) | 0.124 (1.00) | 1 (1.00) | 0.197 (1.00) | 0.2 (1.00)
17q loss | 5 (3%) | 186 | 4.94e-10 (6.18e-08) | 0.00833 (0.816) | 0.224 (1.00) | 0.664 (1.00) | 0.00632 (0.632) | 0.00322 (0.345)
18p loss | 5 (3%) | 186 | 5.17e-05 (0.00611) | 0.00833 (0.816) | 1 (1.00) | 1 (1.00) | 0.343 (1.00) | 0.423 (1.00)
13q gain | 3 (2%) | 188 | 0.00355 (0.369) | 0.101 (1.00) | 0.186 (1.00) | 0.552 (1.00) | 0.452 (1.00) | 0.227 (1.00)
19p gain | 5 (3%) | 186 | 0.0342 (1.00) | 0.772 (1.00) | 1 (1.00) | 1 (1.00) | 0.511 (1.00) | 0.521 (1.00)
19q gain | 5 (3%) | 186 | 0.0342 (1.00) | 0.772 (1.00) | 1 (1.00) | 1 (1.00) | 0.511 (1.00) | 0.521 (1.00)
22q gain | 8 (4%) | 183 | 0.00406 (0.414) | 0.00288 (0.311) | 0.0349 (1.00) | 0.269 (1.00) | 0.167 (1.00) | 0.0822 (1.00)
12p loss | 3 (2%) | 188 | 0.0191 (1.00) | 0.431 (1.00) | 0.618 (1.00) | 0.552 (1.00) | 0.79 (1.00) | 0.227 (1.00)
18q loss | 3 (2%) | 188 | 0.00355 (0.369) | 0.101 (1.00) | 0.786 (1.00) | 1 (1.00) | 0.0841 (1.00) | 0.094 (1.00)

'1p gain mutation analysis' versus 'CN_CNMF'

P value = 0.00135 (Chi-square test), Q value = 0.15

Table S1. Gene #1: '1p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
1P GAIN MUTATED | 0 | 2 | 0 | 1 | 0
1P GAIN WILD-TYPE | 142 | 13 | 15 | 17 | 1

Figure S1. Get High-res Image Gene #1: '1p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'4p gain mutation analysis' versus 'CN_CNMF'

P value = 9.82e-05 (Chi-square test), Q value = 0.011

Table S2. Gene #2: '4p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
4P GAIN MUTATED | 0 | 0 | 1 | 3 | 0
4P GAIN WILD-TYPE | 142 | 15 | 14 | 15 | 1

Figure S2. Get High-res Image Gene #2: '4p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'4q gain mutation analysis' versus 'CN_CNMF'

P value = 9.82e-05 (Chi-square test), Q value = 0.011

Table S3. Gene #3: '4q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
4Q GAIN MUTATED | 0 | 0 | 1 | 3 | 0
4Q GAIN WILD-TYPE | 142 | 15 | 14 | 15 | 1

Figure S3. Get High-res Image Gene #3: '4q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8p gain mutation analysis' versus 'CN_CNMF'

P value = 2.44e-05 (Chi-square test), Q value = 0.0029

Table S4. Gene #4: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
8P GAIN MUTATED | 8 | 1 | 4 | 6 | 1
8P GAIN WILD-TYPE | 134 | 14 | 11 | 12 | 0

Figure S4. Get High-res Image Gene #4: '8p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 6.42e-05 (Chi-square test), Q value = 0.0075

Table S5. Gene #5: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
8Q GAIN MUTATED | 9 | 1 | 4 | 6 | 1
8Q GAIN WILD-TYPE | 133 | 14 | 11 | 12 | 0

Figure S5. Get High-res Image Gene #5: '8q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'10q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000979 (Chi-square test), Q value = 0.11

Table S6. Gene #6: '10q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
10Q GAIN MUTATED | 0 | 1 | 2 | 0 | 0
10Q GAIN WILD-TYPE | 142 | 14 | 13 | 18 | 1

Figure S6. Get High-res Image Gene #6: '10q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'11p gain mutation analysis' versus 'CN_CNMF'

P value = 3.32e-15 (Chi-square test), Q value = 4.3e-13

Table S7. Gene #7: '11p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
11P GAIN MUTATED | 0 | 3 | 0 | 0 | 1
11P GAIN WILD-TYPE | 142 | 12 | 15 | 18 | 0

Figure S7. Get High-res Image Gene #7: '11p gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'11q gain mutation analysis' versus 'CN_CNMF'

P value = 9.42e-12 (Chi-square test), Q value = 1.2e-09

Table S8. Gene #8: '11q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
11Q GAIN MUTATED | 0 | 4 | 0 | 2 | 1
11Q GAIN WILD-TYPE | 142 | 11 | 15 | 16 | 0

Figure S8. Get High-res Image Gene #8: '11q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'17q gain mutation analysis' versus 'CN_CNMF'

P value = 6.81e-06 (Chi-square test), Q value = 0.00082

Table S9. Gene #10: '17q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
17Q GAIN MUTATED | 0 | 0 | 0 | 3 | 0
17Q GAIN WILD-TYPE | 142 | 15 | 15 | 15 | 1

Figure S9. Get High-res Image Gene #10: '17q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'21q gain mutation analysis' versus 'CN_CNMF'

P value = 2.62e-15 (Chi-square test), Q value = 3.4e-13

Table S10. Gene #13: '21q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
21Q GAIN MUTATED | 0 | 0 | 7 | 1 | 0
21Q GAIN WILD-TYPE | 142 | 15 | 8 | 17 | 1

Figure S10. Get High-res Image Gene #13: '21q gain mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'5q loss mutation analysis' versus 'CN_CNMF'

P value = 6.1e-11 (Chi-square test), Q value = 7.7e-09

Table S11. Gene #15: '5q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
5Q LOSS MUTATED | 0 | 0 | 3 | 2 | 1
5Q LOSS WILD-TYPE | 142 | 15 | 12 | 16 | 0

Figure S11. Get High-res Image Gene #15: '5q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'5q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.00215 (Chi-square test), Q value = 0.24

Table S12. Gene #15: '5q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5 | CLUS_6
--- | --- | --- | --- | --- | --- | ---
ALL | 46 | 18 | 26 | 36 | 46 | 13
5Q LOSS MUTATED | 0 | 0 | 0 | 0 | 6 | 0
5Q LOSS WILD-TYPE | 46 | 18 | 26 | 36 | 40 | 13

Figure S12. Get High-res Image Gene #15: '5q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'7p loss mutation analysis' versus 'CN_CNMF'

P value = 1.61e-06 (Chi-square test), Q value = 2e-04

Table S13. Gene #16: '7p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
7P LOSS MUTATED | 6 | 0 | 2 | 6 | 1
7P LOSS WILD-TYPE | 136 | 15 | 13 | 12 | 0

Figure S13. Get High-res Image Gene #16: '7p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'7p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 4.49e-05 (Chi-square test), Q value = 0.0053

Table S14. Gene #16: '7p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5 | CLUS_6
--- | --- | --- | --- | --- | --- | ---
ALL | 46 | 18 | 26 | 36 | 46 | 13
7P LOSS MUTATED | 1 | 0 | 1 | 0 | 12 | 1
7P LOSS WILD-TYPE | 45 | 18 | 25 | 36 | 34 | 12

Figure S14. Get High-res Image Gene #16: '7p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'7q loss mutation analysis' versus 'CN_CNMF'

P value = 6.33e-08 (Chi-square test), Q value = 7.9e-06

Table S15. Gene #17: '7q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
7Q LOSS MUTATED | 6 | 0 | 4 | 7 | 1
7Q LOSS WILD-TYPE | 136 | 15 | 11 | 11 | 0

Figure S15. Get High-res Image Gene #17: '7q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'7q loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 5.19e-07 (Chi-square test), Q value = 6.4e-05

Table S16. Gene #17: '7q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5 | CLUS_6
--- | --- | --- | --- | --- | --- | ---
ALL | 46 | 18 | 26 | 36 | 46 | 13
7Q LOSS MUTATED | 1 | 0 | 1 | 0 | 15 | 1
7Q LOSS WILD-TYPE | 45 | 18 | 25 | 36 | 31 | 12

Figure S16. Get High-res Image Gene #17: '7q loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'17p loss mutation analysis' versus 'CN_CNMF'

P value = 2.2e-10 (Chi-square test), Q value = 2.8e-08

Table S17. Gene #19: '17p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
17P LOSS MUTATED | 0 | 1 | 3 | 5 | 1
17P LOSS WILD-TYPE | 142 | 14 | 12 | 13 | 0

Figure S17. Get High-res Image Gene #19: '17p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'17p loss mutation analysis' versus 'METHLYATION_CNMF'

P value = 0.000181 (Chi-square test), Q value = 0.021

Table S18. Gene #19: '17p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5 | CLUS_6
--- | --- | --- | --- | --- | --- | ---
ALL | 46 | 18 | 26 | 36 | 46 | 13
17P LOSS MUTATED | 0 | 0 | 0 | 1 | 9 | 0
17P LOSS WILD-TYPE | 46 | 18 | 26 | 35 | 37 | 13

Figure S18. Get High-res Image Gene #19: '17p loss mutation analysis' versus Clinical Feature #2: 'METHLYATION_CNMF'

'17q loss mutation analysis' versus 'CN_CNMF'

P value = 4.94e-10 (Chi-square test), Q value = 6.2e-08

Table S19. Gene #20: '17q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
17Q LOSS MUTATED | 0 | 1 | 2 | 1 | 1
17Q LOSS WILD-TYPE | 142 | 14 | 13 | 17 | 0

Figure S19. Get High-res Image Gene #20: '17q loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

'18p loss mutation analysis' versus 'CN_CNMF'

P value = 5.17e-05 (Chi-square test), Q value = 0.0061

Table S20. Gene #21: '18p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 142 | 15 | 15 | 18 | 1
18P LOSS MUTATED | 0 | 0 | 2 | 3 | 0
18P LOSS WILD-TYPE | 142 | 15 | 13 | 15 | 1

Figure S20. Get High-res Image Gene #21: '18p loss mutation analysis' versus Clinical Feature #1: 'CN_CNMF'

Methods & Data
Input

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

- Molecular subtypes file = LAML-TB.transferedmergedcluster.txt

- Number of patients = 191

- Number of significantly arm-level cnvs = 22

- Number of molecular subtypes = 6

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

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

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

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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[2] 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)
[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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle