Abstract 08: Advancing precision medicine combination drug screening: A miniaturized single-cell imaging platform for evaluating immunotherapy-small molecule combination therapeutics in individuals
Authors: Thomas Jacob, Yunqi Yan, Yoko Kosaka, Sophia Jena, Stephen E. Kurtz, Andy Kaempf, Tomi Mori, Young Hwan Chang, Bill H. Chang, Uma Borate, Elie Traer, Shannon K. McWeeney, Jody Martin, Jeffrey W. Tyner, Evan F. Lind, Tania Q. Vu
Journal: Clinical Cancer Research (2020)

Abstract Major efforts are under way to develop combination therapies to target multiple biologic pathways for effective synergistic cell killing and decrease the risk of cancer relapse. However, a major technical challenge is the lack of screening platforms that allow assessment of optimal combinations directly in individuals. Flow cytometry is a widely used option; however, the large amount of sample required limits the number of drug combinations that can be tested on primary patient samples. Moreover, many protein targets, especially intracellular proteins (e.g., phosphoproteins, immune modulatory molecules) are often present at low levels, making it challenging to detect via flow cytometry or other means—especially in conditions of drug inhibition whereby signal cannot be conclusively discriminated from background noise. We have developed a next-gen miniaturized single-cell imaging platform that evaluates the effect of drug combinations in primary patient tumor and immune cells, with quantitative detection sensitivity and single-cell granularity. We demonstrate the use of this platform technology to screen interactions between targeted agents and immune checkpoint inhibitors (ICIs) in individuals with acute myeloid leukemia (AML). In many tumor types, including AML, targeting tumor cells with small-molecule drugs while concomitantly inducing an antitumor immune response has the possibility of synergistic activities that avoid therapeutic resistance (1). However, many of the pathways of proliferation and survival that are targeted with small-molecule drugs are also important for the ability of tumor-reactive T cells to expand and function (2). For this reason, there is a distinct possibility that many drugs designed to kill tumor cells will also impair T-cell responses and thus not be compatible with immunotherapies such as ICIs. We show how platform functional readouts of ex vivo T-cell activation and tumor-cell killing, along with conventional and machine learning image-based single-cell analysis, provide new information on the effect of specific combination/single agents in individuals. We report observations of ICI rescue of T-cell proliferation and the synergistic effects of TIM3, MEK, and other combination agents. These results demonstrate the advantages of this precision technology to obtain new functional information that helps identify promising combinations—and to do so directly on samples that represent the functional and genetic diversity seen in AML (3). References: 1. Hughes PE et al. Targeted therapy and checkpoint immunotherapy combinations for the treatment of cancer. Trends in Immunotherapy 2016. 2. Zitvogel L et al. Immunological aspect of cancer chemotherapy. Nature Reviews Immunology 2008. 3. Tyner JW et al. Functional genomic landscape of acute myeloid leukaemia. Nature 2018. Note: This abstract was not presented at the conference. Citation Format: Thomas Jacob, Yunqi Yan, Yoko Kosaka, Sophia Jena, Stephen E. Kurtz, Andy Kaempf, Tomi Mori, Young Hwan Chang, Bill H. Chang, Uma Borate, Elie Traer, Shannon K. McWeeney, Jody Martin, Jeffrey W. Tyner, Evan F. Lind, Tania Q. Vu. Advancing precision medicine combination drug screening: A miniaturized single-cell imaging platform for evaluating immunotherapy-small molecule combination therapeutics in individuals [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 08.