Current Status
Not Enrolled
Get Started

What is the current status of research on polygenic scores (PGS)?

What are the main opportunities and limitations of PGS for cancer prevention?

How could PGS be clinically applied for cancer prevention in the future?

2 March 2022, 17:00 CET

Register Now!

Join us for the live event, ask your questions, and share your comments directly with our speakers.

Share this learning opportunity


Dr James McKay

Deputy Branch Head, Genomic Epidemiology

International Agency for Research on Cancer, Lyon, France



Dr Nilanjan Chatterjee

Bloomberg Distinguished Professor of Biostatistics and Genetic Epidemiology

Johns Hopkins University, Baltimore, United States

Genome-wide association studies of increasing sample size and diverse ancestry are now leading to polygenic scores (PGS) with significant potential for risk-stratification across cancers.

In this talk, I will provide a brief overview of emerging opportunities as well as some limitations of the use of PGS for developing risk-stratified approaches to cancer prevention. I will describe results from a recent study on the validation of a breast cancer risk prediction model that integrates PGS with other established risk factors and provide an assessment of potential clinical utility of the model for breast cancer prevention.  


Dr Linda Kachuri

Postdoctoral Scholar | incoming Assistant Professor in the Department of Epidemiology & Population Health

University of California San Francisco | Stanford University, United States

Prostate-specific antigen (PSA) screening for prostate cancer is widely used, but remains controversial due to issues with sensitivity and specificity. PSA is highly heritable, therefore one avenue for improving its diagnostic accuracy is to account for variation in PSA that is due to genetics and does not reflect prostate cancer. In this talk I will present findings from the largest genome-wide association study (GWAS) of PSA levels and demonstrate how using a polygenic score (PGS) to correct PSA values can improve clinical utility by reducing overdiagnosis and unnecessary testing, as well as increasing detection of aggressive disease.


And other not-to-be-missed resources on polygenic scores

VIDEO: Polygenic risk scores improve cancer risk prediction and stratification (4 minutes)

Hurson A N et al. for the B-CAST Risk Modelling Group (2021). Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int J of Epidemiol.50(6): 1897-1911. 

Kachuri L (2021). Polygenic risk scores improve cancer risk prediction and stratification, Behind the Paper, Nature Portfolio Cancer Community.

Kachuri L et al. (2020). Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction. Nature Communications.11(1): 6084.    

Wang T et al. (2021). Commentary: Polygenic risk for breast cancer: in search for potential clinical utility. Int J of Epidemiol.50(6): 1911-1913.          

Developed with the support of and in collaboration with the European Society for Medical Oncology (ESMO).

Scroll to Top