MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations
The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations...
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| Published in | American journal of human genetics Vol. 98; no. 3; pp. 442 - 455 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
03.03.2016
Cell Press Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10−4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. |
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| AbstractList | The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10−4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 10 super(-4)). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10(-4)). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10(-4)). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers.The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10(-4)). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes ( FLT3 , IDH2 , NRAS , KIT , and TP53 ) and a MEGS ( NPM1 , TP53 , and RUNX1 ) whose mutation status was strongly associated with survival (p = 6.7 × 10 −4 ). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes ( ARID1A , AKT1 , MED23 , and TBL1XR1 ), providing support for their role as cancer drivers. The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 x 10...). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers. (ProQuest: ... denotes formulae/symbols omitted.) |
| Author | Caporaso, Neil E. Chatterjee, Nilanjan Hua, Xing Hyland, Paula L. Huang, Jing Shi, Jianxin Zhu, Bin Song, Lei Landi, Maria Teresa |
| AuthorAffiliation | 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA 2 Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA |
| AuthorAffiliation_xml | – name: 1 Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – name: 2 Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA |
| Author_xml | – sequence: 1 givenname: Xing surname: Hua fullname: Hua, Xing organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 2 givenname: Paula L. surname: Hyland fullname: Hyland, Paula L. organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 3 givenname: Jing surname: Huang fullname: Huang, Jing organization: Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 4 givenname: Lei surname: Song fullname: Song, Lei organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 5 givenname: Bin surname: Zhu fullname: Zhu, Bin organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 6 givenname: Neil E. surname: Caporaso fullname: Caporaso, Neil E. organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 7 givenname: Maria Teresa surname: Landi fullname: Landi, Maria Teresa organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 8 givenname: Nilanjan surname: Chatterjee fullname: Chatterjee, Nilanjan organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA – sequence: 9 givenname: Jianxin surname: Shi fullname: Shi, Jianxin email: jianxin.shi@nih.gov organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD 20892, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26899600$$D View this record in MEDLINE/PubMed |
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| Copyright | 2016 The American Society of Human Genetics Copyright © 2016 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved. Copyright Cell Press Mar 3, 2016 2016 by The American Society of Human Genetics. All rights reserved. 2016 The American Society of Human Genetics |
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| Keywords | oncogenic pathways mutual exclusivity tumor sequencing driver genes |
| Language | English |
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| SubjectTerms | Breast cancer Breast Neoplasms - genetics Cell Line, Tumor Computational Biology - methods Core Binding Factor Alpha 2 Subunit - genetics driver genes Female fms-Like Tyrosine Kinase 3 - genetics Genes Genome-Wide Association Study Genomes GTP Phosphohydrolases - genetics Humans Isocitrate Dehydrogenase - genetics Leukemia, Myeloid, Acute - diagnosis Leukemia, Myeloid, Acute - genetics Mediator Complex - genetics Membrane Proteins - genetics Models, Molecular Mutation mutual exclusivity Nuclear Proteins - genetics oncogenic pathways Proto-Oncogene Proteins c-akt - genetics Proto-Oncogene Proteins c-kit - genetics Receptors, Cytoplasmic and Nuclear - genetics Repressor Proteins - genetics Sequence Analysis, DNA Simulation Transcription Factors - genetics tumor sequencing Tumor Suppressor Protein p53 - genetics |
| Title | MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations |
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