ROCKVILLE, MD, July 30, 2025 – A research team funded by the National Foundation for Cancer Research (NFCR) has developed a powerful software that simulates cancer cell behavior, enabling scientists to model tumor growth, immune responses, and treatment outcomes in a virtual setting.
Published this week in Cell , the study combines genomic data with advanced computational modeling to forecast how cancer and immune cells interact and evolve, providing researchers with a powerful, computer-based tool to explore treatment options alongside lab models and clinical trials, without the cost or risk to patients.
The study was co-led by NFCR-funded collaborating team , Dr. Lisa M. Coussens of Oregon Health & Science University (OHSU), an expert in tumor immunology, and Dr. Elana J. Fertig of the University of Maryland School of Medicine (UMSOM), a leader in mathematical modeling and genomics. Dr. Coussens contributed single-cell atlases of tumor environments, while Dr. Fertig’s team built simulations to test how tumors in preclinical models might respond to various treatment scenarios.
“This gives us a new, in silico approach to explore complex cancer dynamics,” said Dr. Fertig. “It’s like a weather model—but for cancer biology.”
The study was the result of extensive cross-institutional collaboration, with other lead researchers from Indiana University, Johns Hopkins University, OHSU and UMSOM. A highlight of the work is the “hypothesis grammar” developed by Dr. Paul Macklin’s team at Indiana University, which translates biological theory into computational logic.
“Breast cancer is notoriously difficult to treat,” said Dr. Coussens. “By modeling how immune cells like macrophages impact tumor growth, we can better predict which treatment combinations may be most effective.”
Dr. Sujuan Ba, President and CEO of NFCR, praised the project’s cross-disciplinary teamwork: “This project embodies our mission—bringing together scientific minds across disciplines to accelerate the discovery of better treatments and cures. Computational modeling is a powerful addition to the cancer research toolbox.”
Read the full article here: https://www.cell.com/cell/fulltext/S0092-8674(25)00750-0
Cell
Computational simulation/modeling
Cells
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