Paracrine Ovarian Cancer Cell-Derived CSF1 Signaling Regulates Macrophage Migration Dynamics in a 3D Microfluidic Model that Recapitulates In Vivo Infiltration Patterns in Patient-Derived Xenografts.

A high density of macrophages in the ovarian cancer microenvironment is associated with disease progression and poor outcomes. Understanding cancer-macrophage interaction mechanisms that establish this pro-tumorigenic microenvironment is critical for developing macrophage-targeted therapies. Here, 3D microfluidic assays and patient-derived xenografts are utilized to define the role of cancer-derived colony stimulating factor 1 (CSF1) on macrophage infiltration dynamics toward ovarian cancer cells. It is demonstrated that multiple ovarian cancer models promote the infiltration of macrophages into a 3D extracellular matrix in vitro in a cell density-dependent manner. Macrophages exhibit directional migration and increased migration speed under both direct interactions with cancer cells embedded within the matrix and paracrine crosstalk with cancer cells seeded in an independent microchannel. It is also found that platinum-based chemotherapy increases macrophage recruitment and the levels of cancer cell-derived CSF1. Targeting CSF1 signaling under baseline or chemotherapy-treatment conditions reduces the number of infiltrated macrophages. It is further shown that results obtained with the 3D microfluidic model reflect the recruitment profiles of macrophages in patient-derived xenografts in vivo. These findings highlight the role of CSF1 signaling in establishing macrophage-rich ovarian cancer microenvironments, as well as the utility of microfluidic models in recapitulating 3D tumor ecosystems and dissecting cancer-macrophage signaling.

Cancer-mesothelial and cancer-macrophage interactions in the ovarian cancer microenvironment.

The metastatic ovarian cancer microenvironment is characterized by an intricate interaction network between cancer cells and host cells. This complex heterotypic cancer-host cell crosstalk results in an environment that promotes cancer cell metastasis and treatment resistance, leading to poor patient prognosis and survival. In this review, we focus on two host cell types found in the ovarian cancer microenvironment: mesothelial cells and tumor-associated macrophages. Mesothelial cells make up the protective lining of organs in the abdominal cavity. Cancer cells attach and invade through the mesothelial monolayer to form metastatic lesions. Crosstalk between mesothelial and cancer cells can contribute to metastatic progression and chemotherapy resistance. Tumor-associated macrophages are the most abundant immune cell type in the ovarian cancer microenvironment with heterogeneous subpopulations exhibiting protumor or antitumor functions. Macrophage reprogramming toward a protumor or antitumor state can be influenced by chemotherapy and communication with cancer cells, resulting in cancer cell invasion and treatment resistance. A better understanding of cancer-mesothelial and cancer-macrophage crosstalk will uncover biomarkers of metastatic progression and therapeutic targets to restore chemotherapy sensitivity.

The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters.

OBJECTIVE: Model-based metabolic tests require accurate identification of subject-specific parameters from measured assays. Insulin assays are used to identify insulin kinetics parameters, such as general and first-pass hepatic clearances. This study assesses the impact of intravenous insulin boluses on parameter identification precision. METHOD: Insulin and C-peptide data from two intravenous glucose tolerance test (IVGTT) trials of healthy adults (N = 10 × 2; denoted A and B), with (A) and without (B) insulin modification, were used to identify insulin kinetics parameters using a grid search. Monte Carlo analysis (N = 1000) quantifies variation in simulation error for insulin assay errors of 5%. A region of parameter values around the optimum was identified whose errors are within variation due to assay error. A smaller optimal region indicates more precise practical identifiability. Trial results were compared to assess identifiability and precision. RESULTS: Trial B, without insulin modification, has optimal parameter regions 4.7 times larger on average than Trial A, with 1-U insulin bolus modification. Ranges of optimal parameter values between trials A and B increase from 0.04 to 0.12 min-1 for hepatic clearance and from 0.07 to 0.14 for first-pass clearance on average. Trial B's optimal values frequently lie outside physiological ranges, further indicating lack of distinct identifiability. CONCLUSIONS: A small 1-U insulin bolus improves identification of hepatic clearance parameters by providing a smaller region of optimal parameter values. Adding an insulin bolus in metabolic tests can significantly improve identifiability and outcome test precision. Assay errors necessitate insulin modification in clinical tests to ensure identifiability and precision.