Colorectal cancer (CRC) remains one of the deadliest cancers in the United States, with a 5-year survival rate of 10% for patients with metastatic disease. Over 120,000 people are diagnosed with CRC each year, leading to approximately 50,000 deaths. Even with the current standard of care, CRC patients have a high rate of relapse, and resistance to therapy is a key contributor to their high morbidity and mortality. Interactions between tumor and stromal cells are a source of acquired drug resistance. Cancer-associated fibroblasts (CAFs) are a dominant cellular component of the tumor stroma and play a significant role in drug resistance by contributing to the altered metabolism that is a hallmark of CRC. Recent studies suggest reciprocal metabolic reprogramming among CRC cells and CAFs. However, questions still remain regarding the metabolic dependencies of these two cell populations in the context of treatment response. 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. 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 stromal cells, or the metabolic dependencies of these two cell populations. The proposed research addresses this limitation by developing an experiment-based, multiscale computational model of tumor-stromal metabolic interactions in colon cancer. 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 using a systems biology approach and 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) Identify and validate treatment strategies that exploit tumor and CAF metabolism. This work applies a systems biology approach comprised of novel mathematical frameworks across scales, quantitative imaging techniques, and physiologically-relevant preclinical models. We have assembled a dynamic team of Principal Investigators to successfully complete this project, integrating expertise in computational systems biology (lead by Finley) and modeling multicellular interactions in biochemical signaling environments (lead by Macklin), driven by cutting-edge high-throughput experimental data in realistic conditions (lead by Mumenthaler). As a result, this work will generate the first multiscale model that explicitly accounts for molecular interactions between tumor and stromal cells in the context of colorectal cancer. We will apply the model to identify novel strategies that inhibit tumor growth by exploiting the tumor-stromal cell metabolic interactions, and the model predictions will be validated experimentally.

Public Health Relevance

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. We propose a systems biology approach to investigate reciprocal metabolic reprogramming that occurs between tumor cells and CAFs and identify therapeutic strategies that inhibit tumor growth by further understanding and exploiting tumor-CAF metabolism.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1)
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Hughes, Shannon K
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University of Southern California
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
Los Angeles
United States
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