A Scoring System that Leverages Single Cell RNA-Sequencing Data to Predict Cell Therapy Outcome in Solid Tumor Treatment

Cancer, a prominent global health concern, involves the uncontrolled growth and dissemination of abnormal cells, forming tumors that can infiltrate nearby tissues and metastasize to distant sites. Early and precise tumor detection and treatments are crucial to impede their spread and mitigate disease progression. This study presents the development of a scoring system that harnesses single-cell RNA sequencing (scRNA-seq) data to predict the suitability of solid tumors, specifically glioblastoma (GBM) and lymphoma, for advanced cell therapies such as CAR-T, NK cell, and TIL therapies. By integrating key cellular factorsincluding tumor-infiltrating cells, immunosuppressive cells, and immune escape markersthis system provides a quantitative assessment of a tumors potential responsiveness to these therapies. Validation with patient-derived samples has demonstrated the system's potential to improve precision in therapy selection, offering a promising tool for personalized oncology. This research not only aims to enhance the effectiveness of cell therapies for cancer patients but also lays the groundwork for applying similar methodologies to other cancer types, advancing the integration of precision medicine in cancer treatment.