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Created by: Hugo Miniere, and Thomas Yankeelov

Issue 290: Tumor diversity is a major roadblock in fighting cancer, often making treatments less effective. To tackle this challenge, we need tools that can spot and predict these differences within and between tumors. Enter our solution: a cool blend of experimentation, biology and math that merged together into a straightforward pipeline for personalized therapies. Our new approach looks at breast cancer cells in the lab, tracking how they respond to treatment over time and space. We've divided the cells into two groups: the surviving population (in red) that resists treatment and the sensitive population (in blue) that is ultimately destroyed. By analyzing images and longitudinal cell counts from experiments with doxorubicin, we're building a map of how these cells behave under pressure, and then predicting the growth and development of cell clusters. Those predictions can then be leveraged to inform future treatment decisions!