Overcoming limitations in current measures of drug response may enable AI-driven precision oncology
Katja Ovchinnikova, Jannis Born, Panagiotis Chouvardas, Maria Anna Rapsomaniki, Marianna Kruithof‐de Julio
Abstract
Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models - they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.
Topics & Concepts
Precision oncologyPrecision medicineDrug responseDrugDrug developmentAnticancer drugSensitivity (control systems)OncologyMedicineClinical OncologyCancer drugsMedical physicsInternal medicineComputer scienceMachine learningCancerPharmacologyPathologyEngineeringElectronic engineeringComputational Drug Discovery MethodsMachine Learning in Materials ScienceRadiomics and Machine Learning in Medical Imaging