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

在手机上进行乳腺肿瘤的护理点细胞和分子病理学

• 批准号: 10358633
• 负责人: Ashutosh Chilkoti
• 金额: $ 60.29万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10358633
• 关键词: Academic Medical Centers; Address; Africa; Antibodies; Automobile Driving; Biological Assay; Biological Markers; Breast; Breast Cancer Cell; Breast Cancer Patient; Breast Cancer Treatment; Breast biopsy; Cancer Etiology; Caring; Cells; Cellular Morphology; Cellular Phone; Cessation of life; Clinical; Clinical Research; Clinical Trials; Computer software; Core Biopsy; Country; Cytology; Cytopathology; Data; Development; Device or Instrument Development; Devices; Diagnosis; Diagnostic; Epidermal Growth Factor Receptor; Estrogen Receptors; Evaluation; Fine needle aspiration biopsy; Gold; Health Personnel; Health Services Accessibility; Histology; Histopathology; Human; Image; Imaging Device; Immunodiagnostics; Immunohistochemistry; Infrastructure; Intervention; Life; Malignant Neoplasms; Mammary Neoplasms; Measures; Medical center; Methods; Modification; Molecular; Molecular Profiling; Mus; Needles; North Carolina; Operative Surgical Procedures; Outcome; Pathologic; Pathological Staging; Pathologist; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Performance; Persons; Phase; Pilot Projects; Population; Progesterone Receptors; Prognosis; Research; Research Personnel; Resource-limited setting; Resources; Sampling; Savings; Sensitivity and Specificity; Services; Specimen; Tanzania; Technology; Telemedicine; Testing; Time; Training; Training and Infrastructure; Translating; Translations; Tumor Markers; Tumor Subtype; Universities; Validation; Visit; Woman; accurate diagnosis; algorithm training; base; breast cancer diagnosis; breast cancer survival; breast pathology; cancer cell; cancer diagnosis; cancer subtypes; cellular imaging; cellular pathology; clinical investigation; clinically relevant; cloud platform; cost; data repository; disorder subtype; improved; improved outcome; industry partner; innovation; machine learning algorithm; malignant breast neoplasm; mobile computing; molecular marker; molecular pathology; mortality; point of care; point of care testing; pre-clinical; preclinical study; protein biomarkers; prototype; rapid diagnosis; response; smartphone Application; subtype-specific therapies; treatment planning; tumor; usability; user-friendly; virtual; wireless

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.

抽象的 乳腺癌（BC）是女性中最常见的癌症，是癌症死亡的主要原因 全球妇女，每年有160万例新病例和50万公元前的死亡。在低 - 资源环境（LRS）占了新病例的一半，以及卑诗省全球的大多数死亡。第一个 开始为BC开始挽救生命治疗的关键步骤是癌症的准确，及时的病理证实 诊断，这项任务在许多LR中仍然具有挑战性。传统病理评估涉及处理 通过细胞块方法进行外科手术切除的标本：（1）细胞组织病理学，鉴定出异常 细胞形态指示恶性肿瘤，（2）鉴定肿瘤生物标志物的分子病理学 特异性雌激素受体（ER），孕酮受体（PR），人表皮生长因子受体-2 （HER2）和扩散制造商KI67。使用这些标记的乳腺癌亚型对于 确定预后以及选择亚型特异性疗法。不幸的是，基于组织学 病理服务需要强大的病理基础设施和受过训练的病理学家，从而限制了对这些的机会 许多LRS的服务。例如，坦桑尼亚只有15位训练有素的病理学家，一个超过55个国家 百万人。因此，迫切需要新方法准确诊断癌症以及 分析分子生物标志物的表达水平以进行肿瘤亚型。技术驱动的解决方案 自动化细胞病理学的用户干预最少，几乎没有基础设施要求 极大地影响了LRS乳腺癌的治疗。由此需要的动机，目的 提案是最终确定ePiview-d4护理测试（POCT）的开发，以分析两个细胞 和针样标本的乳腺癌的分子特征。景观组件 设备启用易于访问的，低成本的基于智能手机的明亮菲尔德蜂窝蜂窝图像。 乳房活检无需病理学家评估。同时，设备的D4 POCT组件 图像从乳房FNA定量ER/PR/PR/PR/HER2/KI67水平的护理抗体微阵列 裂解物具有皮摩尔敏感性在30分钟内的裂解液，从而消除了需要额外访问的需求 可以启动治疗计划。 Epiview-D4将使细胞病理学和 使用机器学习算法集成到智能手机应用程序中的乳腺癌的分子谱。 在此提案中，我们将对ML算法进行最终设备开发和培训，然后进行临床前 Epiview-D4 POCT的验证和临床调查，首先在杜克大学医学中心，然后 在乞力马扎罗基督教医疗中心的预期LRS中。这项技术的影响在于它的潜力 通过实现快速准确的诊断和 乳腺癌的亚型，从而及时驾驶适当的乳腺癌患者治疗 因此，在LRS中，每年有数十万名BC的女性改善结果。