Evidence map›Paper›PMID 42031621›Full record

ArticleEBioMedicine2026

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.

Stefan Hinz, Sturla M Grøndal, Masaru Miyano, Jennifer C Lopez, Kristen L Cotner, Taylor Thomsen, Chang Chen, Edward J Hester, Lisa D Yee, Victoria E Seewaldt and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Stefan HinzDepartment of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA. Electronic address: shinz@coh.org.
Sturla M GrøndalDepartment of Biomedicine & Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway.
Masaru MiyanoDepartment of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA.
Jennifer C LopezDepartment of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA.
Kristen L CotnerUC Berkeley-UC San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA, USA.
Taylor ThomsenUC Berkeley-UC San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA, USA.
Chang ChenDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Edward J HesterDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Lisa D YeeDepartment of Surgery, City of Hope Comprehensive Cancer Center, Duarte, CA, USA.
Victoria E SeewaldtDepartment of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA.
James B LorensDepartment of Biomedicine & Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway.
Lydia L SohnDepartment of Mechanical Engineering, University of California, Berkeley, CA, USA.
Mark A LaBargeDepartment of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA; Center for Cancer and Aging Research, City of Hope, Duarte, CA, USA. Electronic address: mlabarge@coh.org.

Funding

Mechanical Phenotyping of Random Periaerolar Fine Needle Aspiration-Collected Cells for Early Breast Cancer DetectionR01EB024989 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI Mark A LaBarge, Lydia L Sohn · 2017 to 2026
$4.8M
NIBIB NIH HHS R01 EB024989
6 · The paper itself

Abstract

backgroundEmerging evidence links cellular ageing and biophysical alterations with cancer susceptibility. Existing breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or family history. Risk is often underestimated or overestimated due to reliance on population-level data and absence of individualised tissue-based markers of breast cancer risk.

methodsWe profiled primary human mammary epithelial cells (HMECs) from women of varying ages and risk backgrounds using mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform that measures single-cell physical and mechanical properties. We developed a machine learning classifier, MechanoAge, to estimate chronological age based on mechanical phenotypes, and a biological age-based risk index, Mechano-RISQ. We further assessed cytoskeletal protein keratin 14 (KRT14) as a key mediator of underlying mechanical states through overexpression and knockdown experiments.

findingsEpithelial cells from normal tissue of young BRCA1/2 mutation carriers (n = 4), women with family history of breast cancer (n = 3), and tissue contralateral to a tumour-bearing breast (n = 9) exhibited elevated Mechano-RISQ scores, which reflects accelerated biological ageing compared to age-matched controls (n = 18). KRT14 overexpression induced a biologically aged phenotype in cells obtained from younger women, whereas knockdown partially reversed this state in cells from older women. CyTOF profiling and modelling showed KRT14 modulation impacted protein expression signatures associated with ageing and risk.

interpretationMechano-RISQ offers a proof of principle approach for identifying individuals at elevated risk of breast cancer, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of mammary epithelial cell ageing.

fundingNIH R01EB024989, R01CA237602, and P30CA033572, DOD BC181737, American Cancer Society-Fred Ross Desert Spirit Postdoctoral Fellowship (PF-21-184-01-CSM).

Indexed as

Breast NeoplasmsMachine LearningSingle-Cell AnalysisAdultDisease SusceptibilityEpithelial CellsFemaleHumansKeratin-14Middle AgedPhenotypeKeratin-14Breast cancer riskKeratin-14 remodellingMachine learningMicrofluidic node-pore sensingSingle-cell mechanical phenotyping

Identifiers

PMID42031621
PMCPMC13174242

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.