Correlation between gene mutation status and molecular subtypes

Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

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/C1GM85CN

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

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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