Abstract 192: A novel and rapid patient-derived organoid breast cancer platform for precision medicine

Abstract

An increasing number of studies performing correlative drug screens on patient-derived organoids (PDO) are revealing enormous potential for these models in predicting patient response to therapy. Despite this, their future use in a clinical setting is hindered by intrinsic limitations of PDO models, namely low success rates in establishing growing cell cultures from tumor tissue samples and long return times for drug response data that fall outside timescales of clinical actionability. To address these limitations, we leveraged technological advances in emulsion-based microfluidics and droplet generators to develop MicroOrganoSpheres (MOS) - microscale environments containing tumorspheres that retain structural, cellular, and genetic properties of an individual patient’s diseased tissue. Here, we focused on generating MOS from breast cancer tissue biopsies and resections across different subtypes of breast cancer. We established over 30 unique patients’ breast MOS samples at an average of 80% success, with the majority of these samples being hormone receptor positive (ER+/PR+) - the most clinically lethal and challenging subtype to establish. We evaluated MOS dose-responses across several first line standard of care chemotherapies for breast cancer. For this, we created a semi-automated workflow paired with high-content imaging for quantifying dose responses and tracking longitudinal cell dynamics during dosing. We generated dose response data for MOS samples, being able to move from primary tissue samples to MOS to drug dosing data within a clinically relevant timescale of 2-3 weeks. We conclude that our platform demonstrates the feasibility of 1) efficiently establishing MOS from breast cancer patient tumor samples from different subtypes and 2) performing drug dosing studies on these samples with improved turnaround times that enable clinical actionability for the patient. Together, these findings provide a foundation for evaluating this technology as a diagnostic tool in future clinical settings. Citation Format: David M. Graham, Gabrielle Rupprecht, Eric D. Bankaitis, Jeremy M. Force, Wylie Watlington, Steven W. Metzger, Xiling Shen, David Hsu. A novel and rapid patient-derived organoid breast cancer platform for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 192.