Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model.

BACKGROUND: Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer. METHODS: Green Fluorescent Protein (GFP)-ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10-week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients. RESULTS: Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell-mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8+ , CD4+ , B- , and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment. CONCLUSIONS: We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options.

Rapid screening of engineered microbial therapies in a 3D multicellular model.

Synthetic biology is transforming therapeutic paradigms by engineering living cells and microbes to intelligently sense and respond to diseases including inflammation, infections, metabolic disorders, and cancer. However, the ability to rapidly engineer new therapies far outpaces the throughput of animal-based testing regimes, creating a major bottleneck for clinical translation. In vitro approaches to address this challenge have been limited in scalability and broad applicability. Here, we present a bacteria-in-spheroid coculture (BSCC) platform that simultaneously tests host species, therapeutic payloads, and synthetic gene circuits of engineered bacteria within multicellular spheroids over a timescale of weeks. Long-term monitoring of bacterial dynamics and disease progression enables quantitative comparison of critical therapeutic parameters such as efficacy and biocontainment. Specifically, we screen Salmonella typhimurium strains expressing and delivering a library of antitumor therapeutic molecules via several synthetic gene circuits. We identify candidates exhibiting significant tumor reduction and demonstrate high similarity in their efficacies, using a syngeneic mouse model. Last, we show that our platform can be expanded to dynamically profile diverse microbial species including Listeria monocytogenes, Proteus mirabilis, and Escherichia coli in various host cell types. This high-throughput framework may serve to accelerate synthetic biology for clinical applications and for understanding the host-microbe interactions in disease sites.