Immune Mechanisms of Resistance to Cediranib in Ovarian Cancer.

This article investigates mechanisms of resistance to the VEGF receptor inhibitor cediranib in high-grade serous ovarian cancer (HGSOC), and defines rational combination therapies. We used three different syngeneic orthotopic mouse HGSOC models that replicated the human tumor microenvironment (TME). After 4 to 5 weeks treatment of established tumors, cediranib had antitumor activity with increased tumor T-cell infiltrates and alterations in myeloid cells. However, continued cediranib treatment did not change overall survival or the immune microenvironment in two of the three models. Moreover, treated mice developed additional peritoneal metastases not seen in controls. Cediranib-resistant tumors had intrinsically high levels of IL6 and JAK/STAT signaling and treatment increased endothelial STAT3 activation. Combination of cediranib with a murine anti-IL6 antibody was superior to monotherapy, increasing mouse survival, reducing blood vessel density, and pSTAT3, with increased T-cell infiltrates in both models. In a third HGSOC model, that had lower inherent IL6 JAK/STAT3 signaling in the TME but high programmed cell death protein 1 (PD-1) signaling, long-term cediranib treatment significantly increased overall survival. When the mice eventually relapsed, pSTAT3 was still reduced in the tumors but there were high levels of immune cell PD-1 and Programmed death-ligand 1. Combining cediranib with an anti-PD-1 antibody was superior to monotherapy in this model, increasing T cells and decreasing blood vessel densities. Bioinformatics analysis of two human HGSOC transcriptional datasets revealed distinct clusters of tumors with IL6 and PD-1 pathway expression patterns that replicated the mouse tumors. Combination of anti-IL6 or anti-PD-1 in these patients may increase activity of VEGFR inhibitors and prolong disease-free survival.

Chemotherapy Induces Tumor-Associated Macrophages that Aid Adaptive Immune Responses in Ovarian Cancer.

Neoadjuvant chemotherapy (NACT) may stimulate anticancer adaptive immune responses in high-grade serous ovarian cancer (HGSOC), but little is known about effects on innate immunity. Using omental biopsies from HGSOC, and omental tumors from orthotopic mouse HGSOC models that replicate the human tumor microenvironment, we studied the impact of platinum-based NACT on tumor-associated macrophages (TAM). We found that chemotherapy reduces markers associated with alternative macrophage activation while increasing expression of proinflammatory pathways, with evidence of inflammasome activation. Further evidence of a shift in TAM functions came from macrophage depletion via CSF1R inhibitors (CSF1Ri) in the mouse models. Although macrophage depletion in established disease had no impact on tumor weight or survival, CSF1Ri treatment after chemotherapy significantly decreased disease-free and overall survival. This decrease in survival was accompanied by significant inhibition of adaptive immune response pathways in the tumors. We conclude that chemotherapy skews the TAM population in HSGOC toward an antitumor phenotype that may aid adaptive immune responses, and therapies that enhance or sustain this during remission may delay relapse.

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.