BU researchers determine obesity’s role in triple-negative breast cancer
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Sep-2025 13:11 ET (24-Sep-2025 17:11 GMT/UTC)
Current cancer screening methods are limited in scope, often detecting only a few cancer types with low positive predictive value and suboptimal patient adherence. In recent years, liquid biopsy-based multi-cancer early detection (MCED) has emerged as a promising approach to revolutionize cancer control. Despite several MCED tests reaching clinical trial phases and seeking regulatory approval, none have yet been approved for clinical use, highlighting uncertainties regarding their efficacy and applicability. This review comprehensively examines the advancements in MCED technologies and offers insights into the selection of cancer types for inclusion in MCED panels. Researchers explore the clinical development pathway for MCED, from biomarker discovery and analytical validation to large-scale randomized controlled trials, emphasizing the importance of selecting appropriate endpoints such as reducing late-stage cancer incidence or cancer-specific mortality. Key challenges, including achieving optimal sensitivity for early-stage cancers, minimizing false positives and negatives, and ensuring equitable access to MCED tests, are also addressed. Finally, they evaluate the added value and health economic benefits of integrating MCED into established healthcare systems through widespread implementation. By providing a thorough analysis of these aspects, this review aims to advance the field of cancer screening and guide future research and development efforts.
A new study by researchers at the Icahn School of Medicine at Mount Sinai, Memorial Sloan Kettering Cancer Center, and collaborators, suggests that artificial intelligence (AI) could significantly improve how doctors determine the best treatment for cancer patients—by enhancing how tumor samples are analyzed in the lab. The findings, published in the July 9 online edition of Nature Medicine, showed that AI can accurately predict genetic mutations from routine pathology slides—potentially reducing the need for rapid genetic testing in certain cases.
A new study, led by experts at the University of Nottingham, provides the strongest evidence to date that cancer is extremely rare in turtles, a finding that could offer valuable clues for preventing or fighting cancer in humans.
While previous research had hinted that cancer might be uncommon in turtles, the new analysis, published in BioScience, shows that only about 1% of individuals are affected, far less than in mammals or birds. The study was led by Dr Ylenia Chiari from the School of Life Sciences at the University of Nottingham, alongside Dr Scott Glaberman from the University of Birmingham, in collaboration with a team of researchers from zoos across the US, UK, and Europe.
The team analysed medical records and necropsies (autopsies) from hundreds of zoo turtles, including individuals from Chester Zoo in the UK.
A new study from University of Michigan Rogel Cancer Center researchers identifies a cellular signature that explains why about one-third of prostate cancers respond especially poorly to treatment.
Keck Medicine of USC is participating in a national, multisite clinical trial examining if a genetically engineered herpes simplex virus, when combined with immunotherapy, reduces or eliminates melanoma tumors.
Impact Factor Update: With MicroRNA’s addition to the Impact Factor list, 62 Bentham Science journals now hold this prestigious distinction. Current Neuropharmacology continues to lead the list with a 2024 impact factor of 5.3, followed by Recent Patents on Anti-Cancer Drug Discovery (4.1) and Current Medicinal Chemistry (3.5). Bentham Science is committed to publishing impactful research in the years to come.
A new, low-cost biosensing technology that could make rapid at-home tests up to 100 times more sensitive to viruses like COVID-19. The diagnostic could expand rapid screening to other life-threatening conditions like prostate cancer and sepsis, as well. Created by researchers at the University of California, Berkeley, the test combines a natural evaporation process called the “coffee-ring effect” with plasmonics and AI to detect biomarkers of disease with remarkable precision in just minutes.