Elshad Hasanov, Assistant Professor of Internal Medicine/Medical Oncology at The Ohio State University, shared a post on LinkedIn:
“Excited to share our ASCO26 presentation from the OSUPIIO HasanovLab Biomedical Informatics PhD Student Peng Li and collaborators!
Machine learning-based transcriptomic signatures predict treatment outcomes across targeted therapy and immunotherapy regimens in renal cell carcinoma
Key findings:
- RNA-based ML signatures outperformed IMDC risk groups for PFS and OS stratification
- Treatment-specific transcriptomic models predicted binary outcome disease control vs progression
- Molecular signatures provided prognostic information beyond standard clinical risk models
- Supports development of precision oncology tools to personalize RCC treatment selection
Congratulations to Peng Li and the entire The Ohio State University The Ohio State University College of Medicine Buckeye team: Khalid Niazi, Eric A. Singer, Fuat Bicer, Kimryn Rathmell, Zuhair Majeed, Semiha Özgül.”