Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data

• Main Authors: Schürch, Manuel, Boos, Laura, Heinzelmann-Schwarz, Viola, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas, Consortium, Tumor Profiler
• Format: Journal Article
• Language: English
• Published: 19-02-2024
• Subjects: Computer Science - Learning; Quantitative Biology - Quantitative Methods; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2402.12190

AI-driven precision oncology has the transformative potential to reshape
cancer treatment by leveraging the power of AI models to analyze the
interaction between complex patient characteristics and their corresponding
treatment outcomes. New technological platforms have facilitated the timely
acquisition of multimodal data on tumor biology at an unprecedented resolution,
such as single-cell multi-omics data, making this quality and quantity of data
available for data-driven improved clinical decision-making. In this work, we
propose a modular machine learning framework designed for personalized
counterfactual cancer treatment suggestions based on an ensemble of machine
learning experts trained on diverse multi-omics technologies. These specialized
counterfactual experts per technology are consistently aggregated into a more
powerful expert with superior performance and can provide both confidence and
an explanation of its decision. The framework is tailored to address critical
challenges inherent in data-driven cancer research, including the
high-dimensional nature of the data, and the presence of treatment assignment
bias in the retrospective observational data. The framework is showcased
through comprehensive demonstrations using data from in-vitro and in-vivo
treatment responses from a cohort of patients with ovarian cancer. Our method
aims to empower clinicians with a reality-centric decision-support tool
including probabilistic treatment suggestions with calibrated confidence and
personalized explanations for tailoring treatment strategies to multi-omics
characteristics of individual cancer patients.