Abstract C053: Pancreatic cancer patient-derived organoids, preclinical tools for therapeutic profiling, patient response prediction, and tumor evolution

Abstract Pancreatic cancer (PC) is characterized by an aggressive biology and an exceptionally high tumor heterogeneity that causes considerable variations in response to chemotherapies, targeted agents, and immunotherapies. Patient-derived organoids (PDOs) accurately reflect parental tumor biological and molecular features and may represent powerful preclinical avatars to predict drug response and support clinical decision-making. We generated a living organoid biobank of 100 PC PDO lines from treatment-naïve and pretreated primary tumor and metastases with a reliable efficacy (60. 8%), and previously developed a pharmacotyping-guided prediction model to prognosticate patient therapy response in an initial feasibility trial (Beutel AK et al, 2021). Real-life chemotherapy responses in 46 patients (for a total of 93 therapy lines) matched to pharmacotyping-informed predictions with overall 73. 1% accuracy, 85. 4% sensitivity (responsiveness), and 63. 5% specificity (non-responsiveness). Overall, our model allowed a successful drug-response prediction in naïve patients with an accuracy of 73. 0% and 70. 0% for first and second-line regimens. Prediction power was nevertheless lower in pretreated and heavily pretreated patients with a precision of 68. 2% and 50. 0% for subsequent chemotherapy lines, respectively. Interestingly, PDO-based pharmacotyping also precisely predicted patient clinical outcome in the neoadjuvant and adjuvant settings, with respective accuracies of 83. 3% and 71. 4%. Retrospective analysis of patient clinical data in palliative setting finally showed that the administration of a regimen predicted to be efficient ultimately translated into a significantly longer progression-free survival. Implementing automation and drug screening miniaturization to our workflow also significantly enhanced our process capacity, reduced time before pharmacotyping, and improved compliance to internal quality controls. Tracking clonal evolution in longitudinal biopsies (a set of seven PDO pairs derived from the same patients at two distinct timepoints) revealed lower mutational burden and therapy-driven genetic alterations upon progression. Notably, this approach revealed a CHEK2-mutated patient responded over time to PARP inhibitor maintenance therapy, aligning with our predictions and underscoring the robustness of our method. Single nucleus-resolved RNA and ATAC-seq analysis of a unique longitudinally-collected case uncovered transcriptomic and epigenetic dynamics associated with an oncogenic FGFR2 fusion and with a subsequent resistance-mediating mutation upon targeted treatment with FGFR2 inhibitors. Particularly, FGFR2 constitutive activation correlated with aberrant epigenetic trace and transcription programs of downstream targets as PI3K-AKT and RAS family members and of RNA polymerase II transcription machinery. Here, we report a robust and clinically-relevant preclinical tool for drug-response prediction, one more step towards a PDO therapeutic profiling-guided personalized medicine in clinical routine. Citation Format: Johann Gout, Yazid J Resheq, Jessica Lindenmayer, Julian D Schwab, Elodie Roger, Johann M Kraus, Thomas Ettrich, Alica K Beutel, Lukas Perkhofer, Hans A Kestler, Thomas Seufferlein, Alexander Kleger. Pancreatic cancer patient-derived organoids, preclinical tools for therapeutic profiling, patient response prediction, and tumor evolution abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84 (17 Suppl₂): Abstract nr C053.