CHASMplus: predicting which missense mutations drive human cancers

Author:Collin Tokheim, Rachel Karchin
Contact:ctokhei1 # alumni DOT jh DOT edu
Lab:Karchin Lab
Source code:GitHub
Q&A:Biostars (tag: CHASMplus)

Large-scale DNA sequencing studies of patients’ tumors have revealed that most driver mutations occur only in a few patients, which presents a challenge for precision medicine. CHASMplus is a machine learning method that accurately distinguishes between driver and passenger missense mutations, even for those found at low frequencies or are cancer type-specific. Unlike previous approaches that focus on identifying driver genes, CHASMplus identifies whether individual mutations are cancer drivers. CHASMplus can be used by both bioinformaticians and biolgists by using a graphical user interface or a command line tool.


CHASMplus is available through a graphical user interface [see the Quick start (OpenCRAVAT & CHASMplus)]

Prominent papers using CHASMplus:

  • Reiter et al., Minimal functional driver gene heterogeneity among untreated metastases. Science
  • Anagnostou, Niknafs et al., Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. Nature Cancer
  • Reiter et al., An analysis of genetic heterogeneity in untreated cancers. Nature Reviews Cancer



Please cite our paper:

Tokheim and Karchin, CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers, Cell Systems (2019),