OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data

Nathalia Ferreira; Ajinkya Kulkarni; David Agorku; Teona Midelashvili; Olaf Hardt; Tobias J Legler; Philipp Ströbel; Lena-Christin Conradi; Frauke Alves; Fernanda Ramos-Gomes; M Andrea Markus

doi:10.1007/s13402-024-00958-2

OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data

Cell Oncol (Dordr). 2024 May 28. doi: 10.1007/s13402-024-00958-2. Online ahead of print.

Authors

Affiliations

¹ Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany.
² Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany.
³ Institute of Pathology, University Medical Center Göttingen, Göttingen, Germany.
⁴ Department of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Robert-Koch-Straβe 40, 37075, Göttingen, Germany.
⁵ Department of Transfusion Medicine, University Medical Center Göttingen, Göttingen, Germany.
⁶ Clinic of Hematology and Medical Oncology, Department of Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.
⁷ Translational Molecular Imaging, Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, Germany. markus@mpinat.mpg.de.

^# Contributed equally.

PMID: 38805131
DOI: 10.1007/s13402-024-00958-2

Abstract

Purpose: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based models in combination with accurate real-time monitoring are required.

Methods: We established stable live-imaging organoid/peripheral blood mononuclear cells (PBMCs) co-cultures and introduced OrganoIDNet, a deep-learning-based algorithm, capable of analyzing bright-field images of murine and human patient-derived PDAC organoids acquired with live-cell imaging. We investigated the response to the chemotherapy gemcitabine in PDAC organoids and the PD-L1 inhibitor Atezolizumab, cultured with or without HLA-matched PBMCs over time. Results obtained with OrganoIDNet were validated with the endpoint proliferation assay CellTiter-Glo.

Results: Live cell imaging in combination with OrganoIDNet accurately detected size-specific drug responses of organoids to gemcitabine over time, showing that large organoids were more prone to cytotoxic effects. This approach also allowed distinguishing between healthy and unhealthy status and measuring eccentricity as organoids' reaction to therapy. Furthermore, imaging of a new organoids/PBMCs sandwich-based co-culture enabled longitudinal analysis of organoid responses to Atezolizumab, showing an increased potency of PBMCs tumor-killing in an organoid-individual manner when Atezolizumab was added.

Conclusion: Optimized PDAC organoid imaging analyzed by OrganoIDNet represents a platform capable of accurately detecting organoid responses to standard PDAC chemotherapy over time. Moreover, organoid/immune cell co-cultures allow monitoring of organoid responses to immunotherapy, offering dynamic insights into treatment behavior within a co-culture setting with PBMCs. This setup holds promise for real-time assessment of immunotherapeutic effects in individual patient-derived PDAC organoids.

Keywords: Artificial intelligence; Co-cultures; Gemcitabine; Immunotherapy; Organoids; PDAC.