Pedram Razavi MD, PhD
Director, Liquid Biopsy and Cancer Genomics, MSK Biomarker Development Program Memorial Sloan Kettering Cancer Center
Dr. Pedram Razavi is a medical oncologist and physician-scientist at Memorial Sloan Kettering Cancer Center (MSK), specializing in precision oncology for breast cancer. He serves as Director of the Breast Cancer Translational Program and Molecular Tumor Board, and Director of Liquid Biopsy and Cancer Genomics for the MSK Biomarker Development Program. His research focuses on integrative clinicogenomic approaches and circulating tumor biomarkers to characterize breast cancer at both systemic and molecular levels and to expand the clinical utility of liquid biopsy technologies.
Dr. Razavi earned his MD from Tehran University of Medical Sciences and his MPH and PhD in cancer epidemiology from the University of Southern California. He completed a postdoctoral fellowship at the Channing Laboratory, an internal medicine residency at USC, and a medical oncology fellowship at MSK, where he also conducted postdoctoral research in cancer genomics in the lab of Dr. José Baselga.
Seminars
- We performed MRD monitoring using Myriad’s Precise MRD and show that this ultrasensitive approach is a promising tool for monitoring disease burden and treatment response in the setting of CDK4/6 inhibitor therapy for metastatic breast cancer
- The Precise MRD assay enabled 100% pre-CDK4/6i detection and identified 50/180 (28%) on-treatment samples with tumor fraction in the 1-100 parts per million range
- An early decrease in ctDNA levels was significantly associated with longer progression-free survival, and our results also demonstrate that ctDNA can distinguish between molecularly stable disease and molecular CR among patients with radiographic CR, highlighting its potential as a biomarker to guide treatment strategies in those with outstanding clinical responses
- The evolving role of clinical-genomic integration in metastatic breast cancer management
- Machine learning-driven risk stratification: uncovering patterns in patient outcomes
- Understanding how genomic and clinical variables together enhance predictive accuracy
- Implications for treatment optimization and patient monitoring
