Abstract 6446: ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics

Abstract

Abstract Breakthroughs in targeted KRAS therapeutics (KRASi) have the potential to transform the treatment landscape for several of the most common cancers including lung, colorectal, and pancreatic. Despite the recent approvals of KRASi and the anticipation of more to come, both the rate of patient response and the durability of these responses remain significant areas requiring improvement. Biomarkers that can predict response to KRASi and guide effective patient selection and drug combination strategies will be key to realizing the full potential of this emerging therapeutic field. While most biomarkers predominantly rely on a single analyte (e.g. KRAS mutation status), Genialis’ biomarkers are constructed using high-dimensional and/or multimodal data that capture the underlying biological complexity unique to each individual patient. Genialis' ResponderID™ is a machine learning-based biomarker discovery framework that models fundamental aspects of cancer biology to predict the clinical benefit based on the patient’s own biology. Here we report progress towards the development of a first-in-class, RNA-based biomarker, ResponderID™ KRAS, capable of stratifying KRAS G12C inhibitor response in lung cancer patients using RNA sequencing data. Trained on thousands of lung cancer samples, our biomarker models therapeutic response by unifying two core KRAS biologic axes, dependency and activation, to identify those patients most likely to respond. The performance characteristics of ResponderID™ KRAS thus far has been evaluated on a real world dataset of lung cancer patients treated with Sotorasib. ResponderID™ KRAS serves as an independent biomarker designed to inform clinical trial design, select for therapeutic efficacy, identify rational combination strategies, and expedite approvals across various therapeutic contexts. Citation Format: Josh Wheeler, Anže Lovše, Klemen Žiberna, Miha Štajdohar, Luka Ausec, Janez Kokošar, Daniel Pointing, Aditya Pai, Rafael Rosengarten, Mark Uhlik. ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6446.