Acute Myeloid Leukemia: Correlation between copy number variations of arm-level result and molecular subtypes
(primary blood tumor (peripheral) cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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.

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)
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