While breast cancer has an overall 5-year survival rate of 89%, the rate for patients with stage 4 metastatic disease is only 26%. Immunotherapies have the potential to improve the prognosis for these patients while also providing better treatment options for all breast cancer patients since they have fewer side effects enabling longer treatment times and the use of combination therapies and reduced chances of developing resistance. Currently these treatments are tested in standard 2D cell cultures that are inaccurate in mimicking in vivo drug response or animal models where the immune system differs from humans in numerous ways including T-cell subsets, cytokine receptors, and costimulatory molecule expression. We have developed 3D models of human breast cancer that span the subtypes, ER+, HER2+, and triple negative, incorporate numerous stromal cell types, fibroblasts and adipocytes, and include different immune cells, macrophages and T-cells under either static or perfusion culture systems. These models have been used to examine how tumor cells influence macrophage differentiation using undifferentiated peripheral blood mononuclear cells (PBMCs), how M1 and M2 macrophages influence tumor cell survival and proliferation, how the combination of these cell types influence cytokine secretion, and how the microenvironment affects macrophage invasion. We have also used these complex models to examine response of tumor cells and T-cells to checkpoint inhibitors through standard viability assays and flow cytometry. These models have several potential uses which include the ability to quickly answer whether a particular immunotherapy agent is effective for that particular patient-specific manner and to screen potential novel immunotherapeutic candidates and/or combinations prior to clinical use.

Citation Format: Qi Guo, Stephen Shuford, Brian McKinley, Mary Rippon, Wendy Cornett, Mark O'Rourke, David Schammel, Jeff Edenfield, David L. Kaplan, Hal E. Crosswell, Teresa Desrochers. 3D modeling of immune cell interactions in breast cancer and prediction of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4834. doi:10.1158/1538-7445.AM2017-4834