CHASMplus: predicting which missense mutations drive human cancers¶
- Author:
Collin Tokheim, Rachel Karchin
- Contact:
ctokhei1 # alumni DOT jh DOT edu
- Lab:
- Source code:
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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.
Note
You can run CHASMplus without installing anything by submitting your data to the OpenCRAVAT webserver (details here). After creating a user account, you’ll just need to check the box for CHASMplus and hit the annotate button (OpenCRAVAT webserver). Also, you can install locally 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
Saito et al., Landscape and function of multiple mutations within individual oncogenes. Nature
Reiter et al., An analysis of genetic heterogeneity in untreated cancers. Nature Reviews Cancer
Hu et al., Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nature Genetics
Sakomoto et al., The Evolutionary Origins of Recurrent Pancreatic Cancer, Cancer Discovery
Contents:
Citation¶
Please cite our paper:
Tokheim and Karchin, CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers, Cell Systems (2019), https://doi.org/10.1016/j.cels.2019.05.005