Abstract 4255: A systematic framework for efficient generation of expandable patient-derived 3D sarcoma models

Abstract Introduction: Sarcomas are rare connective tissue tumors with over 100 described subtypes. High heterogeneity within and among sarcoma sub-entities and low incidence contribute to poor therapeutic success. Patient-derived 3D models (PDMs), i. e. organoids and spheroids, closely recapitulate features of the original tumor, e. g. cell-to-cell interaction, differentiation potential and heterogeneity. However, PDMs are rarely available or even lacking for most sarcoma sub-entities, constituting a major hurdle for sarcoma precision oncology and research. Here, we present a highly efficient and systematic framework to generate long-term 3D models for distinct sarcoma subtypes, which we developed within the HEROES-AYA consortium. Methods: Tumors were received directly after surgery and processed within 24 hours. After enzymatic dissociation, cell clumps were seeded in 384-well plates with and without Matrigel. Each tumor was cultivated on an array of 140 distinct culture conditions, differing in contribution and concentration of pathway inhibitors and activators. Organoid and spheroid growth was assessed over time via imaging and AI-based algorithms to identify best-growing conditions. Results: Initially, we combined Ewing sarcoma (EwS) secreted factors and potentially relevant cytokines to generate a matrix of 140 diverse media, named Media Test Plate (MTP). The MTP was tested on five EwS tumors, allowing the identification of optimal culture conditions with 100% efficiency. When transplanted in NSG mice, the established PDMs showed tumorigenicity potential and Ewing sarcoma-specific CD99 marker expression. The MTP was then tested on four synovial sarcomas (SyS) and five myxoid liposarcomas with a 75% and 50% success rate, respectively. The optimal media, as identified by MTP, allowed the cultivation of one EwS and one SyS out of two, respectively, even with limited cell count. Additional PDMs generated include one undifferentiated, one alveolar soft tissue and one spindle cell sarcoma. Established cultures were characterized by Ki67 positivity, ranging from 40% to 70%, and expression of sarcoma subtype-specific markers. EwS models resulted 100% CD99 positive, while TLE1 positivity in SyS models was ≈90%. DNA and RNA sequencing of PDMs will show whether they resemble the genetic and transcriptional features of the original tumors. Conclusions: We here present a novel systematic protocol to generate organoids and spheroids from different sarcoma subtypes. To date, no such systematic protocol is available for sarcoma models generation. Our success rate is comparable to the one of intestinal and pancreatic organoids and exceeds those of 2D and PDX sarcoma cultures, which is ≈20%. Current work aims to adapt the MTP to a broad range of sarcoma subtypes, including rare entities. The efficient generation of expandable PDMs from a wide range of sarcomas will further fuel basic and translational research. Citation Format: Claudia Dagostino, Waqar Hussain, Jasmina Paluncic, Marcelo Zoccoler, Jessica Pablik, Daniela Richter, Lisanne Knol, Ana Banito, Johanna Wagner, Ivona Mateska, Daniel E. Stange, Ina Oehme, Stephan Richter, Stefan M. Pfister, Stefan Fröhling, Claudia Scholl, Jürgen Weitz, Klaus-Dieter Schaser, Christoph Heining, Hanno Glimm, Claudia R. Ball. A systematic framework for efficient generation of expandable patient-derived 3D sarcoma models abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4255.