Evidence map›Paper›PMID 41182172›Full record

ArticleAging2025

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

Ryan Wallis, Bethany K Hughes, Madeleine Moore, Emily A O'Sullivan, Luke C McIlvenna, Luke Gammon, Anthony Hope, Fiona Bellany, Parul Dixit, Claire Mackenzie and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ryan WallisBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Bethany K HughesBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Madeleine MooreBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Emily A O'SullivanBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Luke C McIlvennaBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Luke GammonBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.
Anthony HopeDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Fiona BellanyDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Parul DixitDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Claire MackenzieDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Charlotte GreenDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
David GrayDrug Discovery Unit, School of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Cleo L BishopBlizard Institute, Faculty of Medicine and Dentistry, Queen Mary University of London, London E1 2AT, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSenescence 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.

resultsTo 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.

conclusionsThrough 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.

Indexed as

Breast NeoplasmsCellular SenescenceCyclin-Dependent Kinase Inhibitor p16Machine LearningCell Line, TumorFemaleHumansCyclin-Dependent Kinase Inhibitor p16high-throughput compound screeningmachine learningpro-senescenceSAMP-Scoresenescencesenescent marker positive cancer cellsSen-Mark+

Identifiers

PMID41182172
PMCPMC12822720

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.