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Abstract With the emergence of precision oncology as a new paradigm in cancer care, there is an urgent need to develop tools capable of mining the massive amounts of rich omic data generated every year. To support target discovery programs at scale, we developed a pan-cancer bioinformatics platform combining patient data with extensive biological and pharmaceutical knowledge for the identification and prioritization of novel antigen targets. Our pipeline was first validated with the discovery of new antigen targets amenable to CAR-T therapy for relapsed/refractory multiple myeloma. Here, we are exploring AML, a type of leukemia with several unmet needs. First, we identified and integrated 36 relevant microarray datasets from the GEO database using a proprietary data identification and integration pipeline. The clinical data was curated using our proprietary oncology ontology, machine learning models, and domain expert quality control processes. The molecular data was normalized and the datasets aggregated into a virtual patient cohort of unprecedented size and quality, comprising 2, 995 AML patients and 220 healthy controls. To both find a clinically relevant patient sub-population and reduce the cohort heterogeneity, AML patients were stratified based on their transcriptomic profile using a consensus clustering analysis, which we interpreted thanks to the curated clinical data. Among multiple others, we identified a highly stable cluster enriched in AML-M3 patients, a very distinct and aggressive subtype of AML caused by a t (15;17) translocation. A differential gene expression analysis was performed comparing this cluster with the control group and 574 genes were found to be overexpressed. We then applied proteomic filters to exclusively focus on cell surface-bound protein targets demonstrating an acceptable level of anticipated cytotoxicity. Finally, the 23 short-listed antigen targets were prioritized with additional multi-omic patient and cell line data to optimize their safety and efficacy profiles as well as expression robustness. Interestingly, well-known targets, such as CD96 and ABCC1, were found in the top targets. Developing scalable pipelines will be instrumental in the advent of precision oncology. Combining unbiased data-driven tools with cancer biology-driven approaches, our state-of-the-art pipeline can be used for any cancer type and antigen-targeting modality, including CAR-T and antibody-based therapies. The present study illustrates the potential of our platform when applied to AML, one of the most heterogeneous groups of neoplastic disorders. Leveraging our large and unique AML patient cohort, we were not only able to detect a relevant subgroup of patients, but also identified novel antigen target candidates for this specific population, which were then prioritized based on domain expertise. A broader study addressing other cancer indications, including solid tumors, is underway. Citation Format: Eleonore Fox, Guillaume Appe, Abdelkader Behdenna, Lea Meunier, Akpeli Nordor, Solene Weill, Camille Marijon. A scalable pancancer antigen target discovery platform for precision oncology 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 1915.
Fox et al. (Fri,) studied this question.