Multiscale Systems Biology Modeling to Exploit Tumor-stromal Metabolic Crosstalk in Colorectal Cancer
- Multiscale Systems Biology Modeling to Exploit Tumor-stromal Metabolic Crosstalk in Colorectal Cancer
List of Collaborating Institutions
- University of Southern California
The survival rate for patients with metastatic colorectal cancer (CRC) is less than 10%, and resistance to standard therapies is a key contributor to this high rate of morbidity and mortality. Cancer-associated fibroblasts (CAFs), a dominant cellular component of the tumor stroma, play a significant role in drug resistance by contributing to the altered metabolism that is a hallmark of CRC. Despite many computational models of colorectal cancer growth and progression, there is currently no quantitative spatiotemporal description of the interactions between colon cancer cells and CAF cells, or the metabolic dependencies of these two cell populations. Thus quantifying the collective cell dynamics (i.e. cooperation or competition) of tumor and CAF cells in their metabolic ecosystem may provide insight needed to develop optimal cancer therapies.
Our U01 addresses this limitation by applying a systems biology approach comprised of novel mathematical frameworks across scales, quantitative imaging techniques, and physiologically-relevant preclinical models to investigate reciprocal metabolic reprogramming that occurs between tumor cells and CAFs. We hypothesize that exploiting tumor-stromal metabolic dependencies will enhance the effects of therapeutic strategies to inhibit tumor growth. We will test this hypothesis by pursuing three aims that combine computational and experimental studies: (1) Develop computational models of intracellular metabolic pathways in CRC cells and CAFs that promote colon cancer proliferation; (2) Develop a spatial multiscale model of colon cancer cell growth, integrating the pathway models of tumor-CAF metabolic crosstalk; and (3) explore these data-driven models with supercomputing resources to identify and validate treatment strategies that exploit tumor and CAF metabolism.
Stacey Finley, Ph.D.
Stacey Finley is an Assistant Professor of Biomedical Engineering at the USC Viterbi School of Engineering. She is a computational systems biologist with many years of experience in modeling metabolic and signaling networks, based on her training in chemical engineering and biomedical engineering. Dr. Finley received her PhD from Northwestern University, where she used computational tools to predict novel metabolic pathways, with a focus on metabolizing environmental pollutants. Her postdoctoral fellowship at Johns Hopkins University continued in the area of developing and applying computational systems biology tools to study biological signaling pathways relevant to human health and disease. Dr. Finley currently directs the Computational Systems Biology Laboratory at USC. Her research program works to quantitatively understand the dynamics of key signaling and metabolic networks in cancer, providing detailed insight needed to answer outstanding questions in cancer. She applies a systems biology approach to study biological networks: building multiscale computational models to examine individual biochemical reactions within their broader signaling and metabolic pathways at the cellular, tissue, and whole-body levels. She complements the modeling with quantitative experimental studies to obtain data to inform, refine, and validate her models. Dr. Finley’s research program studies biochemical networks in three areas: metabolism, immune cell signaling, and tumor angiogenesis.
Paul Macklin, Ph.D.
Paul Macklin is an Associate Professor of Intelligent Systems Engineering at Indiana University. He has over fifteen years of experience in multiscale modeling of cancer and tissue engineering, data standards, open source software, and high-throughput computing (HTC). He received his PhD in Mathematics from the University of California Irvine and his postdoctoral training at the University of Texas Health Science Center. Dr. Macklin has developed a comprehensive, open source computing platform for simulating multicellular behavior in complex 3-D tissues, with calibration to high-content screening systems. BioFVM simulates tens of diffusing growth substrates, metabolites, and signaling factors in 3-D tissues. PhysiCell extends BioFVM to simulate hundreds of thousands of cell agents as they interact and change phenotypes in response to 3-D tissue environments. This software can simulate large multicellular systems on desktop workstations, or run hundreds of virtual experiments on HTC resources. PhysiCell has been used to study critical problems in breast and metastatic colon cancer, cancer immunology, cryobiology, tissue engineering, synthetic systems, and in design of next-generation cancer nanotherapies. All of Dr. Macklin’s software is freely available at http://OpenSource.MathCancer.org.
Shannon Mumenthaler, Ph.D.
Shannon Mumenthaler is an Assistant Professor of Medicine at the USC Keck School of Medicine and Laboratory Director for the Lawrence J. Ellison Institute for Transformative Medicine. Dr. Mumenthaler has a strong cancer cell biology background with over ten years of experience working with various tumor models and cell biology techniques, including: 3D cell culture models (i.e. patient-derived organoids and microfluidic organs-on-chips), xenograft mouse models, quantitative high-content cell-based imaging, and drug screening assays. She received her PhD in Cellular and Molecular Pathology from UCLA where she studied single-gene defects involved in prostate cancer progression. During her postdoctoral training at Cedars-Sinai Medical Center and at USC, she investigated mechanisms of response and resistance to various therapeutic agents and developed quantitative measures of cancer cell behavior using novel imaging tools. Her current research interests lie in understanding the evolutionary dynamics of tumor progression and drug resistance with specific investigations into the influence of microenvironmental selective pressures on tumor cell behavior. She applies a multidisciplinary approach to her research, collaborating closely with mathematicians, engineers, physicists, and oncologists to pursue various translational cancer research projects.