Abstract 232: Multi-dimensional patient-derived organoid analysis highly correlates with progression-free survival of pancreatic cancer patients in a retrospective clinical trial

Abstract Patient-derived tumor organoids (PDOs) are promising cancer models, since they preserve the clonal heterogeneity, mutational landscape, and histological architecture of the originating patient tumor1-2. Recent studies have even provided first evidence that PDOs responded similarly to the corresponding patient when treated with the same standard of care therapy, but these studies were only able to predict clinical responses in a subset of patients3, 4. This restriction was largely due to the limitation of current analysis methods. The gold-standard analysis method for PDO-based research, CellTiter-Glo 3D, 5, 6 fails to account for the heterogeneity of PDOs by relying on a bulk lysis approach, and thus only extracts a fraction of the clinically-relevant information that PDOs could provide7. Therefore, we hypothesized that using higher-dimensional analysis methods, will further unlock the predictive performance of PDOs and facilitate translation of research. In this study, we developed an artificial intelligence-driven analysis platform for live-PDO imaging. By combining drug screening metrics with dynamic quantification of organoids on a single-organoid resolution, we unraveled patient-specific tumor heterogeneity and PDAC aggressiveness in response to therapies8. Using a fully characterized a PDAC organoid panel (n=8) we matched our PDO analysis with retrospective clinical patient response to standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX). Our results demonstrated that our PDO analysis method identified patient-specific sensitives to therapy that were in-line with clinical outcomes. Moreover, our PDO readouts highly correlated with progression-free survival of matched patients (R=0. 97), which was a significant improvement to the current, conventional drug response readouts. Taken together, our approach increased the clinical translatability of PDOs by more accurately recapitulating and measuring the complexity of human tumors. This work highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and clinical care, in the emerging era of personalized medicine. References: 1. Seppala, T. T. , et al. Ann Surg 272 (2020) 2. Hadj Bachir, E. , et al. Biol Cell 114 (2021) 3. Ooft, S. N. , et al. Sci Transl Med 11 (2019). 4. Driehuis, E. , et al. Proc Natl Acad Sci 116 (2019) 5. Sachs, N. , et al. Cell 172 (2018) 6. Kijima, T. , et al. Cell Mol Gastroenterol Hepatol 7 (2019) 7. Phan, N. , et al. Commun Biol 2 (2019) 8. Le Compte, M. , et al. npj Precis Oncol (2023) Citation Format: Abraham Lin, Maxim Le Compte, Tyler Gilcrest, Edgar Cardenas De La Hoz, Geert Roeyen, Filip Lardon, Christophe Deben. Multi-dimensional patient-derived organoid analysis highly correlates with progression-free survival of pancreatic cancer patients in a retrospective clinical trial 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 232.