Medicine and Health Sciences Commons^™

Employing A Machine Learning Approach In Precision Oncology To Predict Pik3ca Functional Status And Identify Phenocopying Variants Of Deleterious Pik3ca Mutations, Javier Castillo

CMC Senior Theses

The following proposal describes a modular machine learning approach that detects malfunctioning genes and pathways in cancer using the transcriptome of cancer patients. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Applied to the PI3K/AKT/mTOR pathway, this method can be used to predict PIK3CA functional status and identify phenocopying variants of deleterious PIK3CA mutations. The classifier will do this by integrating RNA-seq, copy number, and mutation data from tumors to determine the functional status of PIK3CA using a set of learned gene-specific weights. The classifier will then be …