Human 3D ovarian cancer models reveal malignant cell intrinsic and extrinsic factors that influence CAR T cell activity.

In vitro preclinical testing of chimeric antigen receptor (CAR) T cells is mostly carried out in monolayer cell cultures. However, alternative strategies are needed to take into account the complexity and the effects of the tumor microenvironment (TME). Here, we describe the modulation of CAR T cell activity by malignant cells and fibroblasts in human 3D in vitro cell models of increasing complexity. In models combining mucin-1 (MUC1) and TnMUC1 CAR T cells with human high-grade serous ovarian cancer (HGSOC) cell spheroids, malignant cell-intrinsic resistance to CAR T cell killing was due to defective death receptor signaling involving TNFα. Adding primary human fibroblasts to spheroids unexpectedly increased the ability of CAR T cells to kill resistant malignant cells as CCL2 produced by fibroblasts activated CCR2/4+ CAR T cells. However, culturing malignant cells and fibroblasts in collagen gels engendered production of a dense extracellular matrix that impeded CAR T cell activity in a TGFß-dependent manner. A vascularized microfluidic device was developed that allowed CAR T cells to flow through the vessels and penetrate the gels in a more physiological way, killing malignant cells in a TNFα-dependent manner. Complex 3D human cell models may provide an efficient way of screening multiple cytotoxic human immune cell constructs while also enabling evaluation of mechanisms of resistance involving cell-cell and cell-matrix interactions, thus accelerating preclinical research on cytotoxic immune cell therapies in solid tumors.

Mouse Ovarian Cancer Models Recapitulate the Human Tumor Microenvironment and Patient Response to Treatment.

Although there are many prospective targets in the tumor microenvironment (TME) of high-grade serous ovarian cancer (HGSOC), pre-clinical testing is challenging, especially as there is limited information on the murine TME. Here, we characterize the TME of six orthotopic, transplantable syngeneic murine HGSOC lines established from genetic models and compare these to patient biopsies. We identify significant correlations between the transcriptome, host cell infiltrates, matrisome, vasculature, and tissue modulus of mouse and human TMEs, with several stromal and malignant targets in common. However, each model shows distinct differences and potential vulnerabilities that enabled us to test predictions about response to chemotherapy and an anti-IL-6 antibody. Using machine learning, the transcriptional profiles of the mouse tumors that differed in chemotherapy response are able to classify chemotherapy-sensitive and -refractory patient tumors. These models provide useful pre-clinical tools and may help identify subgroups of HGSOC patients who are most likely to respond to specific therapies.