Correlation between gene mutation status and molecular subtypes
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1V69GWR
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 gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1V69GWR
Overview
Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 7 genes and 6 molecular subtypes across 196 patients, 15 significant findings detected with P value < 0.05 and Q value < 0.25.

- DNMT3A mutation correlated to 'METHLYATION_CNMF', 'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

- FLT3 mutation correlated to 'METHLYATION_CNMF', 'MRNASEQ_CNMF', 'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

- IDH2 mutation correlated to 'METHLYATION_CNMF'.

- IDH1 mutation correlated to 'METHLYATION_CNMF'.

- NPM1 mutation correlated to 'CN_CNMF', 'METHLYATION_CNMF', 'MRNASEQ_CNMF', 'MRNASEQ_CHIERARCHICAL', 'MIRSEQ_CNMF', and 'MIRSEQ_CHIERARCHICAL'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 7 genes and 6 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 15 significant findings detected.

|  | Clinical Features | CN CNMF | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL
| nMutated (%) | nWild-Type | Chi-square test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test
NPM1 | 54 (28%) | 142 | 0.000852 (0.0259) | 3.26e-20 (1.34e-18) | 1.79e-13 (6.99e-12) | 0.00649 (0.182) | 6.58e-14 (2.63e-12) | 1.07e-22 (4.51e-21)
FLT3 | 56 (29%) | 140 | 0.0152 (0.39) | 2.48e-05 (0.000844) | 1.86e-07 (7.08e-06) | 0.0209 (0.502) | 0.000471 (0.0151) | 4.22e-05 (0.00139)
DNMT3A | 51 (26%) | 145 | 0.977 (1.00) | 4e-07 (1.48e-05) | 0.0122 (0.329) | 0.195 (1.00) | 0.000837 (0.0259) | 3.94e-06 (0.000138)
IDH2 | 20 (10%) | 176 | 0.274 (1.00) | 1.59e-06 (5.74e-05) | 0.725 (1.00) | 1 (1.00) | 0.739 (1.00) | 0.599 (1.00)
IDH1 | 19 (10%) | 177 | 0.969 (1.00) | 0.00563 (0.163) | 0.423 (1.00) | 0.268 (1.00) | 0.284 (1.00) | 0.436 (1.00)
U2AF1 | 8 (4%) | 188 | 0.776 (1.00) | 0.128 (1.00) | 0.232 (1.00) | 0.426 (1.00) | 0.0716 (1.00) | 0.234 (1.00)
NRAS | 15 (8%) | 181 | 0.015 (0.39) | 0.892 (1.00) | 0.645 (1.00) | 0.539 (1.00) | 0.825 (1.00) | 0.765 (1.00)

'DNMT3A MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 4e-07 (Chi-square test), Q value = 1.5e-05

Table S1. Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 47 | 44 | 65 | 14 | 20
DNMT3A MUTATED | 25 | 1 | 19 | 1 | 3
DNMT3A WILD-TYPE | 22 | 43 | 46 | 13 | 17

Figure S1. Get High-res Image Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'DNMT3A MUTATION STATUS' versus 'MIRSEQ_CNMF'

P value = 0.000837 (Fisher's exact test), Q value = 0.026

Table S2. Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 58 | 38 | 42 | 46
DNMT3A MUTATED | 24 | 4 | 5 | 13
DNMT3A WILD-TYPE | 34 | 34 | 37 | 33

Figure S2. Get High-res Image Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

'DNMT3A MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 3.94e-06 (Fisher's exact test), Q value = 0.00014

Table S3. Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 36 | 80 | 68
DNMT3A MUTATED | 2 | 34 | 10
DNMT3A WILD-TYPE | 34 | 46 | 58

Figure S3. Get High-res Image Gene #1: 'DNMT3A MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

'FLT3 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 2.48e-05 (Chi-square test), Q value = 0.00084

Table S4. Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 47 | 44 | 65 | 14 | 20
FLT3 MUTATED | 26 | 12 | 8 | 2 | 7
FLT3 WILD-TYPE | 21 | 32 | 57 | 12 | 13

Figure S4. Get High-res Image Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'FLT3 MUTATION STATUS' versus 'MRNASEQ_CNMF'

P value = 1.86e-07 (Fisher's exact test), Q value = 7.1e-06

Table S5. Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 73 | 52 | 45
FLT3 MUTATED | 6 | 20 | 23
FLT3 WILD-TYPE | 67 | 32 | 22

Figure S5. Get High-res Image Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'FLT3 MUTATION STATUS' versus 'MIRSEQ_CNMF'

P value = 0.000471 (Fisher's exact test), Q value = 0.015

Table S6. Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 58 | 38 | 42 | 46
FLT3 MUTATED | 25 | 4 | 7 | 18
FLT3 WILD-TYPE | 33 | 34 | 35 | 28

Figure S6. Get High-res Image Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

'FLT3 MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 4.22e-05 (Fisher's exact test), Q value = 0.0014

Table S7. Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 36 | 80 | 68
FLT3 MUTATED | 4 | 37 | 13
FLT3 WILD-TYPE | 32 | 43 | 55

Figure S7. Get High-res Image Gene #3: 'FLT3 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

'IDH2 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 1.59e-06 (Chi-square test), Q value = 5.7e-05

Table S8. Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 47 | 44 | 65 | 14 | 20
IDH2 MUTATED | 0 | 0 | 11 | 6 | 1
IDH2 WILD-TYPE | 47 | 44 | 54 | 8 | 19

Figure S8. Get High-res Image Gene #4: 'IDH2 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'IDH1 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 0.00563 (Chi-square test), Q value = 0.16

Table S9. Gene #5: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 47 | 44 | 65 | 14 | 20
IDH1 MUTATED | 5 | 0 | 10 | 4 | 0
IDH1 WILD-TYPE | 42 | 44 | 55 | 10 | 20

Figure S9. Get High-res Image Gene #5: 'IDH1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'NPM1 MUTATION STATUS' versus 'CN_CNMF'

P value = 0.000852 (Chi-square test), Q value = 0.026

Table S10. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 138 | 15 | 15 | 18 | 1
NPM1 MUTATED | 50 | 0 | 1 | 1 | 0
NPM1 WILD-TYPE | 88 | 15 | 14 | 17 | 1

Figure S10. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #1: 'CN_CNMF'

'NPM1 MUTATION STATUS' versus 'METHLYATION_CNMF'

P value = 3.26e-20 (Chi-square test), Q value = 1.3e-18

Table S11. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4 | CLUS_5
--- | --- | --- | --- | --- | ---
ALL | 47 | 44 | 65 | 14 | 20
NPM1 MUTATED | 36 | 0 | 4 | 9 | 4
NPM1 WILD-TYPE | 11 | 44 | 61 | 5 | 16

Figure S11. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #2: 'METHLYATION_CNMF'

'NPM1 MUTATION STATUS' versus 'MRNASEQ_CNMF'

P value = 1.79e-13 (Fisher's exact test), Q value = 7e-12

Table S12. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 73 | 52 | 45
NPM1 MUTATED | 1 | 22 | 25
NPM1 WILD-TYPE | 72 | 30 | 20

Figure S12. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #3: 'MRNASEQ_CNMF'

'NPM1 MUTATION STATUS' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.00649 (Fisher's exact test), Q value = 0.18

Table S13. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2
--- | --- | ---
ALL | 57 | 113
NPM1 MUTATED | 24 | 24
NPM1 WILD-TYPE | 33 | 89

Figure S13. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #4: 'MRNASEQ_CHIERARCHICAL'

'NPM1 MUTATION STATUS' versus 'MIRSEQ_CNMF'

P value = 6.58e-14 (Fisher's exact test), Q value = 2.6e-12

Table S14. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

nPatients | CLUS_1 | CLUS_2 | CLUS_3 | CLUS_4
--- | --- | --- | --- | ---
ALL | 58 | 38 | 42 | 46
NPM1 MUTATED | 37 | 2 | 1 | 12
NPM1 WILD-TYPE | 21 | 36 | 41 | 34

Figure S14. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #5: 'MIRSEQ_CNMF'

'NPM1 MUTATION STATUS' versus 'MIRSEQ_CHIERARCHICAL'

P value = 1.07e-22 (Fisher's exact test), Q value = 4.5e-21

Table S15. Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

nPatients | CLUS_1 | CLUS_2 | CLUS_3
--- | --- | --- | ---
ALL | 36 | 80 | 68
NPM1 MUTATED | 0 | 51 | 1
NPM1 WILD-TYPE | 36 | 29 | 67

Figure S15. Get High-res Image Gene #6: 'NPM1 MUTATION STATUS' versus Clinical Feature #6: 'MIRSEQ_CHIERARCHICAL'

Methods & Data
Input

- Mutation data file = LAML-TB.mutsig.cluster.txt

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

- Number of patients = 196

- Number of significantly mutated genes = 7

- 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

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.

- 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)
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