Nasim Mavaddat

Nasim Mavaddat serves as a Senior Research Associate in the Department of Public Health and Primary Care at the University of Cambridge, with a dual affiliation to the Centre for Cancer Genetic Epidemiology (CCGE). Holding dual PhDs in cancer genetic epidemiology from Cambridge University (supervised by Prof Doug Easton and Prof Antonis Antoniou) and molecular immunology from Stanford University/University of Western Australia, she also qualified in Medicine from UWA. She has conducted research at both Oxford and Cambridge Universities and has lectured on the MPhil in Epidemiology program at Cambridge.

Her research focuses on genetic susceptibility to breast cancer, with particular expertise in developing polygenic risk scores for risk stratification and modeling cancer risk in BRCA1/BRCA2 mutation carriers. She has extensively investigated breast tumor pathology associated with genetic variants and studied risk factors including menopause and menarche. Her current work emphasizes modeling cancer risk in non-European populations, addressing critical gaps in genomic medicine. Her research directly contributes to the BOADICEA/CanRisk tool and CanRisk program, which have significant clinical applications for cancer risk assessment.

• Genetic susceptibility to breast cancer
• Polygenic risk scores development
• BRCA1/BRCA2 mutation carrier risk modeling
• Breast tumor pathology-genotype correlations
• Menarche/menopause and breast cancer risk
• Non-European population cancer risk modeling
• BOADICEA/CanRisk tool development

Her recent publications demonstrate a clear progression toward addressing health disparities in genomic medicine, with increasing focus on non-European populations and clinical implementation of risk prediction tools. The research shows strong translational potential, moving from basic genetic discovery to clinically applicable risk assessment frameworks.

Dr. Mavaddat's work represents a significant contribution to precision oncology, particularly in developing more equitable risk prediction models that can be applied across diverse populations. Her research bridges epidemiological methods with clinical applications, creating tools that directly impact cancer prevention strategies.