MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.
[BACKGROUND] Emerging evidence links cellular ageing and biophysical alterations with cancer susceptibility.
- 표본수 (n) 4
APA
Hinz S, Grøndal SM, et al. (2026). MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.. EBioMedicine, 106241. https://doi.org/10.1016/j.ebiom.2026.106241
MLA
Hinz S, et al.. "MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.." EBioMedicine, 2026, pp. 106241.
PMID
42031621
Abstract
[BACKGROUND] Emerging 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.
[METHODS] We 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.
[FINDINGS] Epithelial 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.
[INTERPRETATION] Mechano-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.
[FUNDING] NIH R01EB024989, R01CA237602, and P30CA033572, DOD BC181737, American Cancer Society-Fred Ross Desert Spirit Postdoctoral Fellowship (PF-21-184-01-CSM).
[METHODS] We 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.
[FINDINGS] Epithelial 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.
[INTERPRETATION] Mechano-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.
[FUNDING] NIH R01EB024989, R01CA237602, and P30CA033572, DOD BC181737, American Cancer Society-Fred Ross Desert Spirit Postdoctoral Fellowship (PF-21-184-01-CSM).