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.
| Original language | English |
|---|---|
| Pages (from-to) | 2688-2716 |
| Number of pages | 29 |
| Journal | Aging |
| Volume | 17 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 30 Oct 2025 |
Keywords
- high-throughput compound screening
- machine learning
- pro-senescence
- SAMP-Score
- Sen-Mark+
- senescence
- senescent marker positive cancer cells
ASJC Scopus subject areas
- Ageing
- Cell Biology
Access to Document
- Final published version
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.