Abstract 5370: New oncology target identification and validation platform combining artificial intelligence and preclinical pharmacology

Abstract

Abstract Despite major advances in cancer therapy in the last decades, treatment resistance can develop over time. Precision medicine allows for the successful implementation of targeted therapies and stratification of patients, but treatment resistance remains a major obstacle in patient management. The identification and validation of new targets associated with cancer resistance remains a major challenge. The great diversity of molecular mechanisms involved in treatment resistance phenomena, whether intrinsic (de novo or primary) or acquired (secondary), constitutes a real therapeutic challenge for patient care. A better understanding of resistance mechanisms would allow to explore new therapeutic strategies to circumvent these phenomena in different types of cancer. The OncoSNIPE® project was developed in this context as part of a multicenter and collaborative clinical study (NCT04548960) in more than 800 chemo-naive adult patients. The objective of this project was to identify early and/or late markers of treatment resistance in three different pathologies for which resistance problems are encountered: triple negative breast cancer (TNBC) or luminal B, locally advanced or metastatic non-small cell lung cancer (NSCLC) and pancreatic ductal adenocarcinoma (PDAC). The program included traditional clinical and whole exome sequencing (WES) monitoring of patient biopsies (Exom-seq and RNA-seq) at diagnosis and relapse, monitoring of blood markers (RNA-seq and Proteomics – Cytokine) at diagnosis, and the evaluation of best therapeutic responses and relapse. The program used bioinformatics, artificial intelligence, statistical learning and semantic enrichment approaches to discover the diversity of mechanisms involved in these resistances and to identify new therapeutic targets, through hetero-modal data including clinical, genomic, transcriptomic, immunological and radiomic dimensions. Subsequently, a specific flowchart for target validation was applied to the resulting list, considering the target's developmental potential, its essentiality, prior knowledge (database mining) and home-made score of the link between the target and the disease. Finally, new targets were prioritized using weighting parameter and heuristic approximation based on the Crank algorithm. Experimental work on multiple targets began in the laboratory, initially in vitro, using 2D and 3D cell culture (including cells from patient-derived xenografts) and molecular interference. A wide variety of intrinsic or acquired molecular mechanisms involved in treatment resistance are being evaluated as candidates for diagnostic and therapeutic development. Citation Format: Sebastien Vachenc, Nicolas Ancellin, Didier Grillot, Kenji Shoji, Joanna Giemza, Nathalie Jeanray, Leila Outemzabet, Salvatore Raieli, Lamine Toure, Olivier Duchamp, Fabrice Viviani, Philippe Genne, Jan Hoflack, Stéphane Gerart. New oncology target identification and validation platform combining artificial intelligence and preclinical pharmacology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5370.