Hinz Stefan, Grøndal Sturla M, Miyano Masaru, Lopez Jennifer C, Cotner Kristen L, Thomsen Taylor, Chen Chang, Hester Edward J, Yee Lisa D, Seewaldt Victoria E, Lorens James B, Sohn Lydia L, LaBarge Mark A
Department of Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA.
Department of Biomedicine & Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway.
bioRxiv. 2025 Aug 12:2025.08.08.668946. doi: 10.1101/2025.08.08.668946.
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 neglect of cellular aging and mechanobiological alterations.
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 captures single-cell mechanical properties. Using machine learning, we developed a classifier, MechanoAge, to predict age-related mechanical phenotypes and introduce a novel index, mechano-RISQ, to quantify deviations linked to breast cancer risk. We further assessed the cytoskeletal protein keratin 14 (KRT14) as a molecular mediator of these mechanical states through overexpression and knockdown experiments.
Cells from younger women carrying BRCA1/2 mutations or with a family history of breast cancer exhibited accelerated mechanical aging compared to age-matched controls. Elevated mechano-RISQ scores reflected an increased proportion of cells with "older" mechanical profiles. KRT14 overexpression induced an aged mechanical phenotype in younger cells, while knockdown partially reversed this state in older cells. CyTOF profiling and modeling showed KRT14 modulation impacted protein expression signatures associated with aging and risk, particularly in luminal cells.
Mechanical properties of breast epithelial cells reflect biologic aging and cancer susceptibility. Mechano-RISQ offers a new approach for identifying individuals at elevated risk, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of epithelial aging.
现有的乳腺癌风险模型无法充分识别处于潜在风险的个体，尤其是在没有已知基因突变或家族病史的女性中。由于依赖人群水平的数据以及忽视细胞衰老和机械生物学改变，风险常常被低估或高估。
我们使用机械节点孔传感技术（mechano-NPS）对来自不同年龄和风险背景女性的原代人乳腺上皮细胞（HMECs）进行了分析，mechano-NPS是一种能够捕获单细胞机械特性的高通量微流控平台。通过机器学习，我们开发了一个分类器MechanoAge来预测与年龄相关的机械表型，并引入了一个新的指标mechano-RISQ来量化与乳腺癌风险相关的偏差。我们还通过过表达和敲低实验进一步评估了细胞骨架蛋白角蛋白14（KRT14）作为这些机械状态的分子介质。
与年龄匹配的对照组相比，携带BRCA1/2突变或有乳腺癌家族病史的年轻女性的细胞表现出加速的机械衰老。升高的mechano-RISQ分数反映了具有“更老”机械特征的细胞比例增加。KRT14过表达在年轻细胞中诱导出衰老的机械表型，而敲低则在老年细胞中部分逆转了这种状态。质谱流式细胞术分析和建模表明，KRT14的调节影响了与衰老和风险相关的蛋白质表达特征，尤其是在管腔细胞中。
乳腺上皮细胞的机械特性反映了生物衰老和癌症易感性。Mechano-RISQ为识别高风险个体提供了一种新方法，特别是在平均风险人群中，并且通过纳入上皮衰老的生物物理测量可能补充现有的风险模型。
