City of Hope and UC Berkeley Researchers Teach AI to Spot Cancer Risk by Squeezing Individual Breast Cells Scientists have developed a groundbreaking method to assess breast cancer risk by analyzing the mechanical properties of individual breast cells. Researchers at City of Hope, a leading cancer research and treatment organization, and the University of California, Berkeley, have created a microfluidic platform called MechanoAge, which uses machine learning to identify individuals at higher risk for breast cancer based on how their cells respond to stress. The study, published in eBioMedicine, marks a significant advancement in personalized cancer risk assessment. The technique, known as mechano-node pore sensing (Mechano-NPS), involves squeezing individual breast epithelial cells through a narrow channel to measure their deformation, recovery, and behavior under pressure. By analyzing these physical responses, the platform generates a quantifiable "mechanical age" for cells, which correlates with cancer risk. This approach addresses a critical gap in risk assessment, as over 90% of women lack known genetic predispositions or family histories of breast cancer, making traditional methods less effective. Dr. Mark LaBarge, a professor at City of Hope, emphasized the importance of this tool for women without genetic risk factors. "For everyone else, you’re left wondering, ‘Am I at high risk?’" he said. "By translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors—not just risk estimates, but evidence drawn directly from their own cells." The platform’s machine learning algorithm identifies cells exhibiting signs of accelerated aging, assigning a risk score based on mechanical and physical properties.#university_of_california_berkeley #mechanoage #city_of_hope #dr_mark_labarge #dr_lydia_sohn
