Project Summary Although aging, germline mutations, and family history of cancer are significant risks for breast cancer, it is still unclear why one person’s breast cells are more susceptible to this disease than another person’s. Aging changes cells and tissues such that they become more susceptible to cancer initiation. Our published data show that breast epithelial cells from young women who have high-risk germline mutations show accelerated aging with intermediate filament distribution, biological clock acceleration, and stromal immune cell milieu changes comparable to those of women who are 20–40 years older. In our currently funded R01EB024989, we discovered that the mechanical properties of normal breast epithelial cells, as measured by our mechano-Node Pore Sensing (mechano-NPS) platform, differ among younger and older women and that normal epithelial cells from genetically high-risk women who carry germline BRCA1, BRCA2, or PALB2 variants are mechanically “older” than their chronological age. We hypothesized that mechano-NPS can detect disease states based on the emergent mechanical properties that arise from the underlying molecular networks that define lineage and disease states. In this competitive renewal application, we extend this hypothesis to include detection of cancer susceptibility or risk, which is so far not detectable with genetic screening. We will innovate mechano-NPS and advance an in silico model of our device to increase the number of physical parameters it can measure, thereby providing a more complete portrait of single human mammary epithelial cells (HMECS) (Aim 1). We will build a machine learning cancer susceptibility detection system based on measuring mechanical properties of different primary HMEC (young, old, high-risk, family history of breast cancer, etc.) (Aim 2). Finally, we will dissect the molecular mechanisms of mechanical states measured by our advanced mechano-NPS platform (Aim 3). In the last funding period, we successfully designed, built, and validated the first-generation mechano-NPS platform at UC Berkeley and showed it to be portable and robust by building a second platform at City of Hope. The impact of our competitive renewal application will be far more reaching. Clinically useful genetic testing relies on a handful of known monogenic risk traits, but we hypothesize that emergent mechanical properties, measured from just a few hundred cells, are a characteristic of the biology that underlies cancer susceptible states, even those that are polygenic or epigenetic in nature and are passed within a family but that so far have defied definition.