ArticleAmerican journal of human genetics2016
MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations.
Article in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns.PLoS computational biology · 2025Article
- Gene mutation in diabetic patients with lung adenocarcinoma: a real-world retrospective cohort study.Frontiers in medicine · 2025Article
- Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400).Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2024Article
- Co-occurring Mutations in Different Genes Can Fuel Oncogenic Signaling and Serve as Metastatic Tumor Markers.bioRxiv : the preprint server for biology · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Prognostic value of European LeukemiaNet 2022 criteria and genomic clusters using machine learning in older adults with acute myeloid leukemia.Haematologica · 2024Article
- Adaptation of a mutual exclusivity framework to identify driver mutations within oncogenic pathways.American journal of human genetics · 2024Article
- Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma.Cell reports. Medicine · 2024Article
- A pan-sarcoma landscape of telomeric content shows that alterations in RAD51B and GID4 are associated with higher telomeric content.NPJ genomic medicine · 2023Article
- A pan-cancer landscape of telomeric content shows that RAD21 and HGF alterations are associated with longer telomeres.Genome medicine · 2022Article
- MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations.Bioinformatics (Oxford, England) · 2021Article
- A forward selection algorithm to identify mutually exclusive alterations in cancer studies.Journal of human genetics · 2021Article
- ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network.PLoS computational biology · 2021Article
- A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.Frontiers in genetics · 2021Article
- Validating a targeted next-generation sequencing assay and profiling somatic variants in Chinese non-small cell lung cancer patients.Scientific reports · 2020Article
- Co-occurrence and Mutual Exclusivity Analysis of DNA Methylation Reveals Distinct Subtypes in Multiple Cancers.Frontiers in cell and developmental biology · 2020Article
- The genomic landscape of Epstein-Barr virus-associated pulmonary lymphoepithelioma-like carcinoma.Nature communications · 2019Article
- Review
- Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.Scientific reports · 2019Article
- Characterization of Squamous Cell Lung Cancers from Appalachian Kentucky.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2019Article
Corrections and comments
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Authors and funding
9 authors.
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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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