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Last Posted: Aug 27, 2024
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Harnessing cancer genomes for precision oncology

From the abstract: "Precision oncology has just received a boost: a report on the prevalence of mutations in cancer driver genes based on whole genome sequencing of 10,000 clinical cases. The challenge ahead lies in how to explore the data to accelerate new discoveries in cancer biology while advancing precision oncology. "

The State of State Biomarker Testing Insurance Coverage Laws

From the article:" There has been a recent push for legislation mandating coverage of “biomarker testing” according to evidence that receipt of such testing can depend on race and ethnicity, age, geography, or site of care. A specific type of biomarker testing for cancer subtypes (ie, “tumor profiling”) is becoming particularly important because many newer cancer therapies target specific mutations (eg, EGFR in non–small cell lung cancer, KRAS in colorectal cancer). Although use of biomarker testing is increasing, barriers to access persist. Cost and lack of insurance coverage are common barriers to testing because insurance coverage varies across commercial payers, Medicare, and Medicaid."

Clinical Value of Molecular Targets and FDA-Approved Genome-Targeted Cancer Therapies.
Ariadna Tibau et al. JAMA Oncol 2024 4

From the abstract: "What is the validity of the molecular targets and clinical benefits of US Food and Drug Administration–approved genome-targeted cancer drugs based on the results of pivotal clinical trials? In this cohort study, 50 molecular-targeted drugs covering 84 indications were identified. Using an international grading system to evaluate molecular targetability strength (European Society for Medical Oncology Scale for Clinical Actionability of Molecular Targets) and a scale to assess clinical benefit in genome-targeted cancer therapies (European Society for Medical Oncology Magnitude of Clinical Benefit Scale), 24 indications (29%) supported high-benefit genomic-based cancer treatments. The therapeutic benefit grading frameworks used in this study can help stakeholders identify therapies with the greatest clinical potential. "

Deep learning in cancer genomics and histopathology
M Unger et al, Genome Medicine, March 27, 2024

From the abstract: " Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. "

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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.

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