Renu Dhanasekaran

Professor Stanford University

Dr. Renumathy Dhanasekaran is Assistant Professor in Gastroenterology and Hepatology at Stanford University School of Medicine, where she earned her PhD in Cancer Biology. Her research focuses on liver cancer’s molecular mechanisms to identify novel biomarkers and therapies using mouse models and human cancer genomics. She oversees a comprehensive liver cancer bio-repository and has developed patient-derived models for preclinical testing. Dr. Dhanasekaran has received funding from ACG, AASLD, Doris Duke Foundation, and NIH/NCI. She completed Internal Medicine residency at University of Florida and fellowships at Mayo Clinic and Stanford, specializing clinically in liver cancer treatment.

Seminars

Thursday 29th January 2026
Modeling Tumor Heterogeneity to Capture Variable Patient Responses in Oncology
11:30 am
  • Evaluating inter-patient variability by leveraging diverse preclinical models to predict the range of clinical responses and identify potential non-responders
  • Incorporating prior treatment history and resistance mechanisms in models to better reflect real-world patient populations and improve translational relevance
  • Designing models that address intra-tumor heterogeneity to ensure reproducible and clinically meaningful preclinical efficacy data
Wednesday 28th January 2026
Fireside Chat: Exploring the Utility of GEMM Models to Balance Translational Insights & Practical Limitations
9:30 am
  • Leveraging Genetically Engineered Mouse Models (GEMMs) to replicate complex genetic drivers of oncogenesis and provide a more physiologically relevant tumor microenvironment for therapeutic evaluation
  • Navigating challenges of cost, breeding complexity, and experimental timelines to evaluate feasibility and scalability across diverse oncology research programs
  • Comparing translational predictability of GEMMs vs syngeneic, PDX, and humanized models in preclinical oncology research
Renu Dhanasekaran, Professor, Stanford University - 10th Tumor Models Summit San Francisco 2026