Research Paper|Volume 17, Issue 11|pp 2688—2716

SAMP-Score: a morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells

Ryan Wallis¹, Bethany K. Hughes¹, Madeleine Moore¹, Emily A. O’Sullivan¹, Luke C. McIlvenna¹, Luke Gammon¹, Anthony Hope², Fiona Bellany², Parul Dixit², Claire Mackenzie², Charlotte Green², David Gray², Cleo L. Bishop¹

¹Blizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK
²Drug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK

Received: June 10, 2025Accepted: October 3, 2025Published: October 30, 2025

https://doi.org/10.18632/aging.206333

Copyright: © 2025 Wallis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Senescence identification is rendered challenging due to a lack of universally available biomarkers. This represents a bottleneck in efforts to develop pro-senescence therapeutics – agents designed to induce the arrest of cellular proliferation associated with a senescence response in cancer cells for therapeutic gain. This is particularly true in contexts such as basal-like breast cancer (BLBC), which often express high levels of widely reported senescence hallmarks, which has led to the designation of these subtypes as senescence marker positive (Sen-Mark+). Unfortunately, these are often cancers with the most limited treatment options, where novel pro-senescence compounds would be of potential clinical utility.

Results: To address these challenges, we have developed SAMP-Score, a machine learning classification tool for identifying senescence induction in Sen-Mark+ cancers. This technique builds upon our previous observation that senescent cells develop distinct senescence-associated morphological profiles (SAMPs), which can be assessed readily in traditionally challenging contexts for senescence identification, including high-throughput screens.

Conclusions: Through application of SAMP-Score, we have identified QM5928, a novel pro-senescence compound, that is able to induce senescence in a variety of Sen-Mark+ cancers and has potential utility as a tool molecule to explore the mechanisms and pathways through which senescence induction occurs in these cells.