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Last Posted: Mar 28, 2023
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Towards the molecular era of discriminating multiple lung cancers
Z Wang, Ebiomedicine, March 21, 2023

This review summarizes recent advances in the molecular identification of multiple lung cancers and compares various methods based on somatic mutations, chromosome alterations, microRNAs, and tumor microenvironment markers. The paper also discusses current challenges at the forefront of genomics-based discrimination, including the selection of detection technology, application of next-generation sequencing, and intratumoral heterogeneity.

Keeping Quiet About Genetic Risk.
Susanna J Smith et al. Health affairs (Project Hope) 2023 3 (3) 443-447

Genetic information has many uses and implications: financial, psychological, clinical, and reproductive. Knowing my at-risk status, I want to protect my family financially. I want to ensure that I don’t pass on CADASIL. If I show up in an emergency room exhibiting stroke symptoms, I would actually prefer to have my CADASIL test results in a secure file so that health care providers can treat me appropriately. But I don’t want to spend fifteen, twenty, thirty years living with knowledge that my mind is about to fail or carrying the guilt of knowing I have been spared, waiting for other people in my family to falter.

A Polygenic Risk Score for Prostate Cancer Risk Prediction.
Kerry R Schaffer et al. JAMA internal medicine 2023 3

In this study, a prostate cancer polygenic risk score did not improve risk prediction of aggressive prostate cancer compared with a contemporary clinical risk predictor. Although the PRS269 improved model discrimination for all cancers, improvement was less than has been observed for other validated prostate cancer biomarker predictors such as the Prostate Health Index.

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes.
Muhammad Shoaib et al. Genetic epidemiology 2023 2

We evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC]?=?0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC?=?0.792) and separation in MGI (AUC?=?0.686).

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.