Abstract 4971: Integrating artificial intelligence and functional precision oncology for individualized cancer therapies

Abstract Precision oncology seeks to tailor cancer treatments to individual patients. Patient-derived xenografts (PDXs) have emerged as a promising platform for selecting efficacious, personalized therapies and developing new oncology drugs. Here, we present a workflow for selecting and validating treatments for patients with colorectal cancer (CRC) that integrates orthotopic PDX (O-PDX) models, molecular profiling, machine learning, and in vivo pharmacological validation. Of the nine tumor biopsies collected, seven were successfully developed into O-PDX models (78%) with a median time to establishment of 119 days. Models were molecularly profiled for gene expression and were serially passaged to perform orthotopic pharmacology studies to validate test agents selected by oncologists as well those suggested by CertisAITM, a novel ensemble of machine learning models for the prediction of drug response in human cancers. The correlation between CertisAI's predicted therapy responses and the actual observed tumor growth inhibition (TGI) was r = 0. 7, encompassing 37 distinct treatments across studies from six patients. This research collectively illustrates that the integration of artificial intelligence with functional in vivo assays offers a powerful platform for precision oncology, enabling the identification and validation of tailored treatments for individual cancer patients. Citation Format: Fernando Eguiarte-Solomon, Rowan Prendergast, Bianca Carapia, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Jose Lopez-Ramos, Itzel Gutierrez, Rafaella Pippa, Yuan-Hung Chien, Warren Andrews, Long Do, Jantzen Sperry, Jonathan K. Nakashima. Integrating artificial intelligence and functional precision oncology for individualized cancer therapies 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 (6Suppl): Abstract nr 4971.