Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation.

ABSTRACT: Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyr

SUBMITTER: Balasooriya ER 

PROVIDER: S-EPMC10831333 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature