Abstract 3086: Organoids standardized to a clinically validated drug response assay for truly predictive in vitro drug response profiling

Abstract

Abstract Unlike cell lines, organoids maintain most of the biological properties of the parental tissue from which the starting cells were isolated including the histology and gene expression. When organoids include clinical annotation, they become a useful, renewable tool for clinical correlation studies, but to be truly predictive the drug profiling assays utilized to screen organoid response must have measurable correlation with patient response. 3D Predict™ is a highly accurate assay that is 89% and 85% predictive of response in first-line ovarian cancer and high-grade gliomas (HGG) respectively. We have developed a panel of organoids that are clinically annotated, include correlative primary tissue 3D Predict™ drug response data, and have been assessed for the recapitulation of primary tissue histology and genomics. Additionally, our organoid models incorporate matched immune cells, a key component of the tumor microenvironment, making them an ideal model for immune-oncology studies. Here we present data on 15 available organoid models across HGG, breast, colorectal and bladder cancer. We have applied these models to drug response studies, including checkpoint inhibitors and shown correlation to primary patient response. The assurance of predictive capacity is unique to KIYATEC’s organoids and is significant because it avoids the pitfalls of comparing drug responses across non-concordant assay platforms while providing assurance that the models are reflective of individual patient response and outcomes. Citation Format: Melissa Millard, Natalie A. Williams, Ashley K. Elrod, Teresa M. DesRochers. Organoids standardized to a clinically validated drug response assay for truly predictive in vitro drug response profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3086.