『Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers』のカバーアート

Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers

Using Machine Learning to Identify Senescence-Inducing Drugs for Resistant Cancers

無料で聴く

ポッドキャストの詳細を見る

このコンテンツについて

 Treating aggressive cancers that do not respond to standard therapies remains one of the most significant challenges in oncology. Among these are basal-like breast cancers (BLBC), which lack hormone receptors and HER2 amplification. This makes them unsuitable for many existing targeted treatments. As a result, therapeutic options are limited, and patient outcomes are often poor. One emerging strategy is to induce senescence, a state in which cancer cells permanently stop dividing but remain metabolically active. This approach aims to slow or stop tumor growth without killing the cells directly. Although promising, the clinical application of senescence-based therapies has been limited by several challenges. Senescence is typically identified using biomarkers such as p16, p21, and beta-galactosidase activity. However, these markers are often already present in aggressive cancers like BLBC (Sen‑Mark+ tumors), making it difficult to determine whether a treatment is truly inducing senescence or merely reflecting the tumor’s existing biology. Moreover, conventional screening methods may mistake reduced cell growth for senescence, cell death, or temporary growth arrest, leading to inaccurate assessments. This is especially problematic in large-scale drug screening, where thousands of compounds must be evaluated quickly and reliably. To overcome these issues, researchers from Queen Mary University of London and the University of Dundee have developed a new machine learning–based method to improve the detection of senescence in cancer cells. Their findings were recently published in Aging-US. The Study: Developing the SAMP-Score The study, titled “SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16-positive cancer cells,” was led by Ryan Wallis and corresponding author Cleo L. Bishop from Queen Mary University of London. This paper was featured on the cover of Aging-US Volume 17, Issue 11, and highlighted as our Editors’ Choice. Full blog - https://aging-us.org/2025/12/using-machine-learning-to-identify-senescence-inducing-drugs-for-resistant-cancers/ Paper DOI - https://doi.org/10.18632/aging.206333 Corresponding author - Cleo L. Bishop - c.l.bishop@qmul.ac.uk Abstract video - https://www.youtube.com/watch?v=qXI_KI3EgHE Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206333 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, SAMP-Score, senescence, senescent marker positive cancer cells, Sen-Mark+, machine learning, pro-senescence, high-throughput compound screening To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@Aging-US LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM
まだレビューはありません