A platform of patient-derived microtumors identifies individual treatment responses and therapeutic vulnerabilities in ovarian cancer
Creators: name: Anderle, Nicole
name: Koch, André
name: Gierke, Berthold
name: Keller, Anna-Lena
name: Staebler, Annette
name: Hartkopf, Andreas
name: Brucker, Sara Y.
name: Pawlak, Michael
name: Schenke-Layland, Katja
name: Schmees, Christian
Publisher: name: Universität Ulm
Year: 2022
Subjects: subject: patient-derived tumor model
subject: ovarian cancer
subject: anti-cancer drug sensitivity
subject: RPPA protein profiling
subject: cancer immunotherapy
subject: DDC 610 / Medicine &amp; health
subject: Ovarian neoplasms; Drug therapy
subject: Immunotherapy
subject: Protein array analysis
subject: Proteomics
subject: Eierstockkrebs
subject: Immuntherapie
subject: Chemotherapie
subject: Proteomanalyse

description: Simple Summary For personalized oncology, it is crucial to develop appropriate patient-derived tumor models that allow individualized validation of the most effective cancer therapy. The objective of this study was to develop and characterize a new patient-derived ovarian cancer tumor model composed of patient-derived microtumors (PDM) and autologous tumor-infiltrating lymphocytes (TIL). In contrast to other preclinical tumor models, such as patient-derived organoids, PDM are generated within 24 h from fresh ovarian tumor samples. From immunohistochemical comparison with the original primary tumor, we conclude that the histopathological features of the original tumor are essentially preserved. Importantly, we successfully identified treatment-sensitive and treatment-resistant tumor models for standard platinum-based therapy by reverse-phase protein array (RPPA) analysis of PDM. Furthermore, we were able to evaluate the efficacy of cancer immunotherapy by co-culturing PDM and autologous TILs. PDM and TILs may therefore serve as a preclinical platform to identify individualized, tailored cancer treatments in the future. Abstract In light of the frequent development of therapeutic resistance in cancer treatment, there is a strong need for personalized model systems representing patient tumor heterogeneity, while enabling parallel drug testing and identification of appropriate treatment responses in individual patients. Using ovarian cancer as a prime example of a heterogeneous tumor disease, we developed a 3D preclinical tumor model comprised of patient-derived microtumors (PDM) and autologous tumor-infiltrating lymphocytes (TILs) to identify individual treatment vulnerabilities and validate chemo-, immuno- and targeted therapy efficacies. Enzymatic digestion of primary ovarian cancer tissue and cultivation in defined serum-free media allowed rapid and efficient recovery of PDM, while preserving histopathological features of corresponding patient tumor tissue. Reverse-phase protein array (RPPA)-analyses of &gt;110 total and phospho-proteins enabled the identification of patient-specific sensitivities to standard, platinum-based therapy and thereby the prediction of potential treatment-responders. Co-cultures of PDM and autologous TILs for individual efficacy testing of immune checkpoint inhibitor treatment demonstrated patient-specific enhancement of cytotoxic TIL activity by this therapeutic approach. Combining protein pathway analysis and drug efficacy testing of PDM enables drug mode-of-action analyses and therapeutic sensitivity prediction within a clinically relevant time frame after surgery. Follow-up studies in larger cohorts are currently under way to further evaluate the applicability of this platform to support clinical decision making.