Note: Single-source report; awaiting corroboration.
Researchers funded by the National Institutes of Health (NIH) have developed scSurvival, a machine learning framework that predicts cancer patient survival by analyzing large-scale single-cell tumor data.
The model assigns weights to individual tumor cells based on their association with survival outcomes, enabling predictions that preserve detailed cell-level information often lost with traditional averaging methods. This approach was validated on clinical datasets from over 150 patients with melanoma or liver cancer, and achieved more accurate survival predictions than existing techniques.
ScSurvival also enables researchers to link survival predictions to specific cell populations within tumors. In melanoma, certain immune and tumor cell groups identified by the model correlated with immunotherapy responses and patient risk levels, suggesting that differences in tumor cell compositions influence disease progression and treatment outcomes.
According to Anthony Letai, M.D., Ph.D., Director of the NIH's National Cancer Institute, this tool could help clinicians identify high-risk patients and understand the biological reasons for their risk, potentially improving cancer management.
Overall, scSurvival offers a promising approach for cancer risk assessment by leveraging single-cell data, and could guide precision treatment strategies in oncology.