Introduction

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.

Publications

  1. MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations.
    Cite this
    Hua X, Hyland PL, Huang J, Song L, Zhu B, Caporaso NE, Landi MT, Chatterjee N, Shi J, 2016-03-01 - American Journal of Human Genetics

Credits

  1. Xing Hua
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  2. Paula L Hyland
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  3. Jing Huang
    Developer

    Center for Cancer Research, National Cancer Institute, United States of America

  4. Lei Song
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  5. Bin Zhu
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  6. Neil E Caporaso
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  7. Maria Teresa Landi
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  8. Nilanjan Chatterjee
    Developer

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

  9. Jianxin Shi
    Investigator

    Division of Cancer Epidemiology and Genetics, National Cancer Institute, United States of America

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT004907
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJianxin Shi