Cancer mutational signatures identification in clinical assays using neural embedding-based representations

Abstract

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3 S249C . In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients. • AI neural embedding model creates specific mutational and signature representations • Dominant signature prediction across >60,000 targeted panel tumors • Signatures can be useful biomarkers for drug response, drug resistance, and prognosis • We identify APOBEC-related EGFR-TKI resistance in NSCLC Yaacov et al. develop a neural embedding model for predicting mutational signatures in targeted gene panels, the main sequencing technology used in clinical settings. The model, MESiCA, enables prediction of dominant signatures with only a few mutations. MESiCA demonstrates that mutational signatures are useful biomarkers for drug response, drug resistance, and prognostication.

Topics & Concepts