KRAS inhibitors (KRASi) have the potential to transform treatment for non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and pancreatic cancer (PDAC). However, current outcomes show the need for predictive biomarkers to improve response rates. Genialis™ krasID uses machine learning to model KRAS biology and predict tumor response. It identifies G12C KRASi sensitivity in real-world NSCLC patients, potential ICI (immune checkpoint inhibitor) combination responders, and therapeutically actionable biologies in the relapse setting. krasID also predicts pan-RAS inhibitor sensitivity in preclinical models, enabling stratification for monotherapy or ICI combination therapy in CRC and PDAC patient cohorts. These findings underscore the potential for krasID to personalize treatment for KRAS-driven cancers.

Published at the 6th Annual RAS-Targeted Drug Development Summit 2024.