Mechanical Phenotyping of Random Periaerolar Fine Needle Aspiration-Collected Cells for Early Breast Cancer Detection

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Project Summary Early detection of breast cancer, especially those with high risk, would greatly improve outcomes. One method under current evaluation for early breast-cancer detection is random periareolar fine needle aspiration (RPFNA). RPFNA is based on the assumption that widespread cellular changes in the breast can be detected by random-tissue sampling. Cytopathology is performed on the collected epithelial cells to determine pre- cancerous changes; however, this analysis is only semi-quantitative. We propose to develop a point-of-care (POC), label-free platform to mechanically phenotype epithelial cells collected from RPFNAs to thus determine the presence of cancer cells and/or changes in cells that would indicate the likelihood of cancer. Our platform will be based on a novel microfluidic method we call “mechano-Node-Pore Sensing” (mechano-NPS). Mechano-NPS utilizes a node-pore sensor with a microfluidic contraction channel to measure simultaneously a single cell’s diameter, resistance to compressive deformation, transverse deformation, and recovery from deformation. We have used this multi- dimensional method of mechanical phenotyping to differentiate malignant vs. non-malignant epithelial cells, distinguish cells treated or untreated with cytoskeletal-perturbing small molecules, and discriminate between sub-lineages of normal primary human epithelial cells (HMECs). Importantly, we have used mechano-NPS to identify mechanical phenotypes that correlate with chronological age and malignant progression. Thus, we hypothesize that mechano-NPS and its ability to mechanically phenotype cells could potentially be used for early disease detection. We intend to demonstrate the full potential of our platform ability to distinguish normal cells from transformed ones by screening de-identified RPFNA patient samples. PI Lydia L. Sohn, Professor of Mechanical Engineering at UC Berkeley and Core Member of the UCSF-UC Berkeley Joint Graduate Group in Bioengineering will lead this NIH R01 project with PI, Mark LaBarge, who is a Professor of Population Science and an expert in breast-cancer biology at City of Hope. Sohn will lead the development of the platform with Key Personnel, Michael Lustig, Associate Professor of Electrical Engineering & Computer Sciences at UC Berkeley. LaBarge will provide guidance on sample choice, experimental design, data analysis, and clinical relevance.