Point-of-care cellular and molecular pathology of breast tumors on a cell phone

摘要 乳腺癌(BC)是女性最常见的癌症，也是#年癌症死亡的主要原因。 世界各地的妇女，每年有160万新病例和50万人死亡。确诊为低血压病的患者 资源设置(LRS)占全球新增病例的一半，占BC死亡的大部分。第一 癌症的准确和及时的病理确认是开始挽救生命的BC治疗的关键一步 诊断，这在许多LRS中仍然是一项具有挑战性的任务。传统的病理评估涉及处理 手术切除的标本用细胞阻滞法：(1)确定异常的细胞组织病理学 指示恶性肿瘤的细胞形态，以及(2)识别肿瘤生物标记物的分子病理学， 特别是雌激素受体(ER)、孕激素受体(PR)、人表皮生长因子受体-2 (HER2)，以及扩散制造者Ki67。使用这些标记物的乳腺癌亚型对于 决定预后，以及选择特定亚型的治疗方法。不幸的是，基于组织学的 病理服务需要强大的病理基础设施和训练有素的病理学家，限制了对这些服务的访问 许多LRS中的服务。例如，在一个人口超过55人的国家坦桑尼亚，只有15名训练有素的病理学家 百万人。因此，迫切需要新的方法来准确诊断癌症，以及 分析肿瘤亚型分子生物标志物的表达水平。技术驱动的解决方案，可以 自动化细胞病理，只需最少的用户干预，几乎不需要任何基础设施 极大地影响了LRS中乳腺癌的管理。在这种需求的驱使下，这一目标 建议最终完成EpiView-D4护理点测试(POCT)的开发，以分析细胞 以及针吸标本中乳腺癌的分子特征。的EpiView组件 该设备实现了基于智能手机的易于获取、低成本的细针抽吸物的Brightfield细胞成像 乳房活组织检查无需病理学家的评估。同时，设备的D4 POCT组件 用于定量检测乳腺FNA中ER/PR/Her2/Ki67水平的照料点抗体微阵列 在护理点30分钟内具有皮摩尔敏感性的裂解液，消除了之前额外检查的需要 可以启动治疗计划。EpiView-D4将能够自动读取细胞病理学和 乳腺癌的分子图谱，使用集成到智能手机应用程序中的机器学习算法。 在这份计划中，我们将进行ML算法的最终设备开发和培训，然后是临床前 Epiview-D4 POCT的验证和临床研究，首先是在杜克大学医学中心，然后是 在乞力马扎罗基督教医疗中心预定的LRS中。这项技术的影响在于它有可能 通过实现快速、准确的诊断和 乳腺癌的亚型，从而推动乳腺癌患者的及时和适当的治疗 因此，LRS每年都会改善数十万患有BC的妇女的结果。

ABSTRACT Breast cancer (BC) is the most common cancer among women and is the leading cause of cancer death in women worldwide, with 1.6 million new cases and 500,000 BC deaths annually. Patients diagnosed in low- resource settings (LRS) account for half of new cases, and the majority of deaths from BC worldwide. The first critical step to starting life-saving treatment for BC is the accurate and timely pathologic confirmation of a cancer diagnosis, a task which remains challenging in many LRS. Traditional pathology assessment involves processing surgically excised specimens with cell-block methods for: (1) cellular histopathology, which identifies abnormal cellular morphologies indicative of malignancy, and (2) molecular pathology, which identifies tumor biomarkers, specifically estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and the proliferation maker Ki67. Breast cancer subtyping using these markers is essential for determining prognosis, as well as for selecting subtype-specific therapies. Unfortunately, histology-based pathology services require a strong pathology infrastructure and trained pathologists, limiting access to these services in many LRS. For example, there are only 15 trained pathologists in Tanzania, a country of over 55 million people. There is hence an urgent need for new methods to accurately diagnose cancer, as well as to analyze expression levels of molecular biomarkers for tumor subtyping. A technology driven solution that could automate cellular pathology with minimal user-intervention and virtually no infrastructure requirements could thus enormously impact the management of breast cancer in LRS. Motivated by this need, the objective of this proposal is to finalize the development of the EpiView-D4 point-of-care test (POCT) to analyze both the cellular and molecular features of breast cancer from needle aspiration specimens. The EpiView component of the device enables easily accessible, low-cost, smart-phone based brightfield cellular imaging of fine needle aspirate breast biopsies without the need for pathologist assessment. In parallel, the D4 POCT component of the device images a point-of-care antibody microarray for the quantification of ER/PR/Her2/Ki67 levels from breast FNA lysate with picomolar sensitivity within 30 minutes at point-of-care, eliminating the need for additional visits before a treatment plan can be initiated. The EpiView-D4 will enable automated readout of both cytopathology and the molecular profiles of breast cancer, using machine learning algorithms integrated into a smartphone application. In this proposal, we will conduct final device development and training of ML algorithms, followed by pre-clinical validation and clinical investigation of the Epiview-D4 POCT, first at Duke University Medical Center, and then in the intended LRS of Kilimanjaro Christian Medical Center. The impact of this technology lies in its potential to dramatically improve breast cancer management worldwide by enabling rapid and accurate diagnosis and subtyping of breast cancers, thereby driving timely and appropriate treatment for breast cancer patients and hence improving the outcomes for hundreds of thousands of women with BC annually in LRS.