Abstract 5673: Complex, patient-derived, multi-cell type, 3D models of breast cancer for personalized prediction of therapeutic response

Autor: C. David Williams, Brian McKinley, John Rinkliff, Lacey E. Dobrolecki, James Epling, Teresa M. DesRochers, Eric McGill, Jeff Edenfield, David L. Kaplan, Nick Erdman, Linda Gray, Howland E. Crosswell, Michael T. Lewis, Christine Wilhelm, Christopher L. Corless, Wendy R. Cornett, Barbara Garner, Qi Guo, David P. Schammel, Mary Rippon, Jeff Hanna, Amanda Scopteuolo, Ashley Elrod, Melissa Millard
Rok vydání: 2018
Předmět:
Zdroj: Cancer Research. 78:5673-5673
ISSN: 1538-7445
0008-5472
DOI: 10.1158/1538-7445.am2018-5673
Popis: Breast cancer survival has drastically improved over the past decades; however, drug resistance and subsequent disease progression is responsible for the incurability of advanced disease. While the focus of many drug response studies is the transformed tumor cells, there is increasing evidence suggesting a role for stromal cells in tumorigenesis and drug resistance. Microenvironmental components, including extracellular matrix, fibroblasts, leukocytes, and adipocytes, all contribute to physiological mammary gland biogenesis. Accordingly, these stromal elements contribute to disease progression and resistance. However, many in vitro drug response studies still utilize 2D monolayer cultures with purified breast tumor cells. In vivo studies remain the gold standard for drug development, even though they are performed with immune-compromised mice that may not reflect the physiological tumor microenvironment and have been repeatedly shown to be a poor representation of clinical outcomes. Thus, there is a need for more complex in vitro models to test drug response effectively and accurately. We have previously demonstrated the benefits of using a patient-derived, tri-culture (3x), 3D perfusion microtumor (3DpMT) system. To further replicate the complex tumor microenvironment, we have expanded to a penta-culture (5x) model by incorporating macrophages and lymphocytes alongside the tumor cells, fibroblasts, and adipocytes of the 3x model. We have accrued over 207 primary tumor samples, including both resected tumor and core biopsies, from which we have generated 12 stable PDX models (~50% ER+) and >20 3x, 4x, and 5x 3DpMT with a focus on triple negative (TNBC). The 5x patient-derived 3DpMT tissues represent our most complex breast cancer in vitro model and have been cultured successfully for up to 5 weeks allowing for high-throughput, long term drug response testing with different dosing strategies. They have been characterized by flow cytometry, IHC, RNA expression, NGS, DNA methylation patterns, and cytokine/chemokine secretion. When possible, marker expression has been compared to the primary tumor. Furthermore, the accuracy of our models to replicate clinical tissue is evident in the similar toxicities of chemotherapies observed in clinical use. With these models we can replicate physiological processes including cell migration, polarization of macrophages, activation of lymphocytes, and changes in molecular profiles throughout the duration of our 5x culture assays. Our model has the potential to test a myriad of drugs, from conventional chemotherapies to novel immunotherapies over extended time periods with different dosing strategies in order to provide a more accurate prediction of patient-specific clinical response. Citation Format: Qi Guo, Melissa Millard, Christine Wilhelm, Ashley Elrod, Nick Erdman, Lacey E. Dobrolecki, Brian McKinley, Mary Rippon, Wendy Cornett, John Rinkliff, Amanda Scopteuolo, Linda Gray, James Epling, Barbara Garner, Jeff Hanna, Eric McGill, C. David Williams, David Schammel, David L. Kaplan, Christopher Corless, Jeff Edenfield, Michael T. Lewis, Howland E. Crosswell, Teresa M. DesRochers. Complex, patient-derived, multi-cell type, 3D models of breast cancer for personalized prediction of therapeutic response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5673.
Databáze: OpenAIRE
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