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

项目摘要 早期发现乳腺癌，特别是那些高风险的乳腺癌，将大大改善预后。一 目前评估的早期乳腺癌检测方法是随机乳晕周围细针穿刺 （RPFNA）。RPFNA基于这样的假设，即可以检测到乳腺中广泛的细胞变化 通过随机组织取样。对所收集的上皮细胞进行细胞病理学以确定预处理。 癌症的变化;然而，这种分析只是半定量的。 我们建议开发一种即时（POC）、无标记的平台， 从RPFNA收集的上皮细胞，从而确定癌细胞和/或变化的存在 在细胞中，这将表明癌症的可能性。我们的平台将基于一种新的微流体 我们称之为“机械节点孔隙传感”（mechano-Node-Pore Sensing）。mechano-turbulence利用节点-孔隙传感器 与微流体收缩通道，同时测量单个细胞的直径， 压缩变形、横向变形和从变形中恢复。我们使用了这个多- 区分恶性与非恶性上皮细胞的机械表型的三维方法， 区分用细胞间隙干扰小分子处理或未处理的细胞， 正常原代人上皮细胞（HMEC）的亚系。重要的是，我们已经使用了机械抛光， 确定与实足年龄和恶性进展相关的机械表型。因此我们 假设机械-生物学及其机械表型细胞能力可能潜在地被 用于早期疾病检测。我们打算展示我们平台能力的全部潜力， 通过筛选去识别的RPFNA患者样本来区分正常细胞和转化细胞。 莉迪亚湖Sohn，加州大学伯克利分校机械工程教授， UCSF-加州大学伯克利分校生物工程联合研究生小组将与PI，Mark LaBarge说，她是希望之城的人口科学教授和乳腺癌生物学专家。 Sohn将领导该平台的开发，主要人员，Michael Lustig，副教授， 加州大学伯克利分校电气工程与计算机科学系。LaBarge将提供样本选择指导， 实验设计、数据分析和临床相关性。

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.