Abstract C061: Therapeutic profiling, patient response prediction, and tumor evolution in pancreatic cancer organoids
Authors: Gout Johann , Resheq Yazid J. , Lindenmayer Jessica , Ettrich Thomas , Perkhofer Lukas , Seufferlein Thomas , Kleger Alexander 
Published: January 2024

Abstract
Pancreatic cancer (PC) is characterized by an aggressive biology and high tumor heterogeneity causing considerable variations in therapy response. Patient-derived organoids (PDOs) reflect parental tumor features and represent a powerful preclinical tool to predict drug response and harness personalized treatment. We have derived &gt;100 PDO lines from treatment-naïve and pretreated PC patient primary tumor and metastases with a reliable efficacy and previously developed a pharmacotyping-guided prediction model to prognosticate patient therapy response (Beutel, 2021). Following up our initial feasibility trial, we now validated the model accuracy in real-life in a higher number of PC patients (43 cases). Additionally, a limited set of 7 PDO pairs were derived from the same patients at two distinct timepoints, followed by pharmacotyping and whole exome sequencing (WES). Our model allowed overall a successful drug-response prediction in naïve patients with an accuracy of 85.7% for first and second-line regimens. Prediction power was nevertheless lower in pretreated patients with a precision of 57.1% for subsequent chemotherapy lines. Intriguingly, a trend was observed towards a better performance of our system in prognosticating chemoresponsiveness vs. unresponsiveness (89.5% vs. 68.8%). Retrospective analysis of patient clinical data finally showed that the administration of a regimen predicted to be efficient ultimately translated into a longer progression-free survival. Importantly, the implementation of an automated pipetting system and the subsequent miniaturization of drug screenings enhanced our process capacity, conferring the possibility to extend our panel to a second list of approved targeted substances and significantly reducing the time before pharmacotyping. The access to longitudinal biopsies allowed to conduct WES on 14 PDOs to capture a comprehensive genetic profiling over the time of treatment, notably revealing a CHEK2-mutated patient responding over time upon PARP inhibitor maintenance therapy, in line with our PDO-based prediction, further highlighting the robustness of our method and algorithm. WES revealed a lower mutational burden and amount of nonsynonymous mutations upon chemotherapy, while tracking clonal evolution also revealed therapy-linked changes. Finally, to decipher the molecular mechanisms driving PC chemoresistance with an unprecedented level of accuracy, we took advantage of our phenotypically and therapeutically-profiled PDO living biobank and employed a true single-nucleus multiome approach to fully capture the transcriptome, as well as the chromatin accessibility landscape, of chemotherapy responsive and non-responsive PC PDOs. Overall, we report a robust and clinically-relevant preclinical tool for drug-response prediction, paving the way towards a PDO therapeutic profiling-guided true precision medicine in clinical routine. The identification of gene-regulatory networks and expression signatures driving chemoresistance may dramatically improve patient stratification and thus, foster true precision oncology.
Citation Format: Johann Gout, Yazid J. Resheq, Jessica Lindenmayer, Thomas Ettrich, Lukas Perkhofer, Thomas Seufferlein, Alexander Kleger. Therapeutic profiling, patient response prediction, and tumor evolution in pancreatic cancer organoids [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C061.