Abstract

Abstract Age is the major risk factor in most carcinomas, yet, little is known about the specific reasons aging increases cancer susceptibility. In the mammary gland, luminal epithelial cells rank high as the putative breast cancer cell of origin. Dysregulation of keratin intermediate filament proteins exemplifies a hallmark age-dependent change in luminal cells, which implicates mechanical states unique to cancer susceptible cells. We implemented mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell analysis that simultaneously measures cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We demonstrated that the epithelial lineages, chronological ages, and stages of cancer progression of primary human mammary epithelial cells (HMEC) exhibited discrete mechanical phenotypes. We trained a machine learning model that accurately predicted the chronological age of average risk HMEC cells based exclusively on mechanical properties. Application of the model to cells from women who are germline carriers of high-risk cancer-causing mutations showed that they are mechanically old irrespective of their chronological age, suggesting that mechanical states could be a window into detection and prevention of cancer susceptible states. Indeed, this mechano-age model detected high-risk women with >90% accuracy. Mass spectrometry and cell-based functional assays in mammary epithelia revealed that cytoskeleton related proteins keratin 14 (KRT14) and pseudopodium enriched atypical kinase 1 (PEAK1) were key drivers of age-dependent mechanical signatures. Pharmacological and gene silencing approaches that targeted KRT14 and PEAK1 modulated the mechanical age of HMEC and, in the case of PEAK1 modulation, ablated luminal epithelial cells in an age- and lineage- dependent manner. We define an intersection between mechanical phenotypes and novel age-dependent changes in cytoskeleton-related proteins that we hypothesize can be exploited to assess an individual’s breast cancer susceptibility and provide new targets for cancer-prevention strategies. Citation Format: Stefan Hinz, Masaru Miyano, Antigoni Manousopoulou, Rosalyn W. Sayaman, Kristina Y. Aguilera, Michael E. Todhunter, Jennifer C. Lopez, Leo D. Wang, Lydia L. Sohn, Mark A. LaBarge. Aging-dependent emergent mechanical properties of single epithelial cells exploited for detection of breast cancer susceptibility [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr A016.

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