Real-life experience of pharmacotyping and response prediction using pancreatic cancer patient-derived organoids

Introduction Pancreatic cancer (PC) is characterized by an exceptionally 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. In clinical practice, PDO-guided selection of effective drugs may provide substantial benefit and improve outcomes.

Methods We have derived>40 PDOs from treatment-naïve and pretreated PC patient primary tumor and metastases (after ultrasound-guided biopsy, endoscopic ultrasound-guided biopsy or surgical resection) with a reliable efficacy. PDO drug response was evaluated using a cytotoxicity assay. Using Jenks natural breaks classification method, we clustered response (AUC) for each drug and correlated it to patient response.

Results The implementation of an automated pipetting system and the subsequent miniaturization of drug screenings significantly enhanced our process capacity, conferring the possibility to extend our panel to a second list of approved targeted substances and further reducing the time before pharmacotyping. The validity of our approach was assessed via re-pharmacotyping a set of PDOs. Following up our feasibility trial (Beutel, 2021), we validated the accuracy of our model on a greater number of PC patients. Our model allowed overall a successful drug-response prediction in naïve patients with an accuracy of 85.7% for both first and second-line regimens. Prediction power remained lower for pretreated patients, with a precision of 57.1% for subsequent lines. A trend was also observed towards a better performance of the system in prognosticating chemoresponsiveness vs unresponsiveness (89.5% vs 68.8%). Finally, the administration of a regimen predicted to be efficient translated into a significantly longer progression-free survival. The access to longitudinal biopsies allowed us to conduct whole exome sequencing on 14 PDOs to capture a comprehensive genetic profiling over the time of treatment, notably revealing a CHEK2-mutated patient who responded over time upon PARP inhibitor maintenance therapy, in line with our PDO-based response prediction, further highlighting the robustness of our method and algorithm.

Conclusion Overall, we report a robust and clinically-relevant preclinical tool for drug response prediction, paving the way towards an PDO-based true precision medicine in clinical routine.

präsentiert in der Sitzung: Tumorboard: Interdisziplinäre FalldiskussionDonnerstag, 14. September 2023, 09:30–11:00, Saal 4

 

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Artikel online veröffentlicht:
28. August 2023