Abstract IA25: Implementation of risk prediction to improve health: The promises and challenges of precision

Abstract

Advances in the understanding of risk factors have transformed oncology, providing leads for prevention and, in particular, screening. Part of this transformation has been fueled by studies of molecular changes and genetic mutations that are inherited or that precede cancer by years, potentially leading the way to a new era of precision screening and prevention. However, these new leads bring important challenges that demand caution. As Sean Carroll has pointed out, it turns out that life from the molecular scale all the way up to the ecological scale is usually governed by longer chains of interactions than we first imagine, with more links in between (Serengeti Rules: The Quest to Discover How Life Works and Why It Matters, 2016). Rather than screening for existing asymptomatic disease, we have entered the era of screening for risk factors, or even screening for risk factors for risk factors (disease risk predisposition). This is a double-edged endeavor, because genes may influence fate, but not in a linear or straightforward manner. Therefore, prediction of outcome is far less precise than measurement of the predisposing genetic and molecular alterations. Many people may be labeled as carriers of risk factors that will never develop the clinical forms of the disease predicted by the genetic changes, a form of genetic overdiagnosis. So the new era of risk prediction can bring both benefits and harms. A critical tool in determining the balance of benefits and harms of increasingly sensitive omics technologies is the use of a formal analytic framework, to be discussed in the presentation. Citation Format: Barnett S. Kramer. Implementation of risk prediction to improve health: The promises and challenges of precision. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA25.