Abstract PO006: Heating up the fight against cancer using 3D bioprinting

Abstract

Abstract Tumor growth is regulated by complex interactions of tumor cells with the tumor microenvironment (TME) which consists of cancer associated fibroblasts (CAFs), various immune cells including tumor infiltrating lymphocytes (TILs) and extracellular matrix components (1). Accurately modeling the TME in vitro is challenging as there is significant genetic (2, 3) and spatial (4) heterogeneity within TME components. Triple-negative breast cancer (TNBC) patients that have tumors with TILs restricted to the margins are called immune “cold” and have poor prognosis (5, 6). Interfering with the mechanisms that exclude TILs from these tumors may improve immunotherapy response and TNBC patient outcome. A major barrier to anti-cancer drug development is the availability of in vitro models that accurately reflect the in vivo TME. Our goal is to establish and validate a 3D bioprinted tumor model that reproduces the spatial relationship between tumor, CAFs and TILs. We have generated a biobank of TNBC patient-derived organoids and tissue-matched TME components. The tumor immune microenvironment molecular subtypes were assigned to each tumor sample using transcriptomic data and immunohistochemistry analysis. We have used microfluidic 3D bioprinting (RX1™ bioprinter, Aspect Biosystems) to print a core-shell fibre design that recapitulates the immune “cold” TME and permits monitoring of immune cell infiltration into the cancer cell core of the fibre. Clinically proven checkpoint inhibitor therapy will be applied to our model to validate its biological function. This innovative bioprinted tumor model will enable phenotypic screening to efficiently identify new targets that transform hard-to-treat immune “cold” tumors into a state where immune cells can infiltrate the tumor. Using patient-derived tissue-matched TME components increases the clinical relevance of the bioprinted tumor model and enables personalized anti-cancer drug screening in vitro to accurately predict patient response to therapy. 1. Balkwill FR, Capasso M, Hagemann T. The tumor microenvironment at a glance. J Cell Sci. 2012; 125: 5591-5596. 2. Finak G, Bertos N, Pepin F, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008; 14: 518-527. 3. Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011; 121: 2750-2767. 4. Gruosso T, Gigoux M, Manem VSK, et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J Clin Invest. 2019;129(4):1785-1800. 5. Ali HR, Provenzano E, Dawson SJ, et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann Oncol. 2014; 25: 1536-1543. 6. Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol. 2015; 26: 259-271. Citation Format: Anne-Marie Fortier, Erin Bedford, Salvador Flores Torres, Spiro Getsios, J. Matt Kinsella, Sam Wadsworth, Morag Park. Heating up the fight against cancer using 3D bioprinting [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 PO006.