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Are polygenic risk scores ready for the cancer clinic?—a perspective

To realize the goals of precision medicine in complex disease, discriminative clinical risk models are needed. One approach that has been proposed is polygenic risk scores (PRSs). PRSs incorporate information about inherited genetic risk for cancer, specifically those genetic variants that are commo...

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Detalles Bibliográficos
Autores principales: Klein, Robert J., Gümüş, Zeynep H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186162/
https://www.ncbi.nlm.nih.gov/pubmed/35693291
http://dx.doi.org/10.21037/tlcr-21-698
Descripción
Sumario:To realize the goals of precision medicine in complex disease, discriminative clinical risk models are needed. One approach that has been proposed is polygenic risk scores (PRSs). PRSs incorporate information about inherited genetic risk for cancer, specifically those genetic variants that are common in the population. While PRSs are clearly associated with risk of cancer, there is an on-going debate on whether integrating PRSs into clinical practice have utility. Here, we present this important discussion to the cancer clinic. We argue that in cancer, the clinical utility of PRSs will depend on their actionability, or how such a score may guide clinical practice. In turn, the actionability depends on several factors. First, actionability depends on the discriminative power of the score, or how well it predicts who is at risk of the disease. Second, it depends on their comparative performance with respect to existing practice, as a score with good discriminative power will not be useful if there are better predictors used in the clinic. Finally, for a PRS to be useful there must also be available preventive actions. We discuss the strengths and challenges of utilizing a PRS in the context of each of these criteria, and provide insights on what is needed towards moving forward in translating PRSs into the cancer clinic. We further argue that in future studies, beyond predicting cancer risk, similarly developed PRS models may be of utility in predicting prognosis or treatment resistance.