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Abstract PO042: Microfluidic modeling of tumor-macrophage signaling in ovarian cancer
Abstract
Abstract In ovarian cancer, the presence of macrophages in the tumor microenvironment (TME) has been associated with poor clinical outcomes. Understanding mechanisms of macrophage recruitment and signaling with tumor cells in the TME may uncover novel therapeutic targets to block the pro-tumor effects of macrophages and improve patient outcomes. Several tumor secreted factors, such as mCSF, CCL2, IL8, and periostin have been implicated in the recruitment of macrophages to the tumor site. However, previous studies of macrophage recruitment have utilized culture systems that fail to recapitulate TME complexity. Therefore, a 3D physiologically relevant microfluidic platform was used in this work that can accurately control cell-cell interactions. Our microfluidic design incorporates chemokine concentration gradients and allows for the study of the spatiotemporal macrophage infiltration dynamics towards ovarian cancer cells using live cell microscopy. We first studied the abilities of a panel of high grade serous ovarian cancer models comprised of established cancer cell lines (murine ID8 and human CaOV3, OHSAHO, OVCAR8) and patient-derived high-grade serous ovarian cancer xenografts (4 PDX models) to recruit macrophages via microfluidic and collagen-droplet based 3D assays. The presence of tumor cells enhanced macrophage infiltration into collagen 3-fold after 48 hours. We found that ovarian cancer models that efficiently recruited macrophages expressed higher levels of mCSF, CCL2, and IL8 cytokines. Furthermore, tumor cell secreted factors more potently increased macrophage recruitment compared to chemotactic gradients of CCL2 and mCSF, indicating a complex signaling network between tumor cells and macrophages. RAW264.7 murine macrophages additionally induced resistance to cisplatin-induced cell death in ID8 cells proportional to increasing macrophage:tumor cell ratios. We also studied the ability of CSF1R inhibition to block macrophage infiltration. Treatment with BLZ945, a potent CSF1R inhibitor that is evaluated in clinical trials, effectively blocked the recruitment of the RAW264.7 macrophages towards ID8 ovarian cancer cells. We subsequently characterized the attachment and invasion profiles of the human and murine ovarian cancer models in syngeneic and immunocompromised xenografts in vivo. We found that tumor cells attached and invaded the ovaries, abdominal organs, and fatty tissue. In a preliminary analysis, one of the PDX models that efficiently recruited macrophages also formed invasive implants, while another PDX model with poor macrophage infiltration did not exhibit invasive implant formation. This work demonstrates the capability of a microfluidic platform to model macrophage infiltration in ovarian cancer. In ongoing studies using this microfluidic platform we are investigating the effects of chemotherapy-induced changes in the tumor cell secretome on macrophage infiltration and the resulting effects on tumor cell chemotherapy sensitivity. Citation Format: Alexis L. Scott, Oluwadamilola Ayoola, Ioannis K. Zervantonakis. Microfluidic modeling of tumor-macrophage signaling in ovarian cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO042.
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Microfluidic systems have been used to study macrophage recruitment by ovarian cancer cells via integration of chemokine concentration gradients, with both HGSOC cell lines and patient-derived xenografts.
“Microfluidic systems have also been used to study macrophage recruitment by ovarian cancer cells via integration of chemokine concentration gradients to mimic macrophage infiltration into the tumor with both HGSOC cell lines and patient-derived xenografts (Scott et al., 2021).”
From Three-dimensional modelling of ovarian cancer: from cell lines to organoids for discovery and personalized medicine (2022) · cites this paper as Scott et al., 2021
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Yee, C., Dickson, K.-A., Muntasir, M. N., Ma, Y., & Marsh, D. J. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.836984 · Cited by 66
“An extensive review of ovarian cancer PDX models by Scott et al. (2013), highlighted a number of gaps in the field related to variations in methodologies, genetic stability over multiple generations, representation of few ovarian cancer histotypes and the limitation of using immunocompromised mice…”
Background
Metadata
- Authors
- Scott, A. L., Ayoola, O., & Zervantonakis, I. K.
- Affiliations
- University of Pittsburgh (US)
- Venue
- Cancer Research
- Published
- 2021-03-01
- Volume
- 81
- Issue
- 5_Supplement
- Pages
- PO042-PO042
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- OpenAlex
- W3134708119
- Cited by
- 1
- References
- 0
- Citations by year
- 2022: 1
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- Cancer Research (publishedVersion)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Stress, Anesthesia, and Immune Response
- Keywords
- ovarian cancer, tumor-associated macrophages, tumor microenvironment, macrophage recruitment, high-grade serous ovarian cancer, chemokine gradients, MCP-1, interleukin-8, periostin, CSF1R inhibition, BLZ945, cisplatin resistance, patient-derived xenografts, macrophage infiltration
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