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: 10586029
• 负责人: Ashutosh Chilkoti
• 金额: $ 58.5万
• 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/10586029
• 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; Correlation Studies; Country; Cytology; Cytopathology; Data; Development; Device or Instrument Development; Devices; Diagnosis; Diagnostic; Epidermal Growth Factor Receptor; Estrogen Receptors; Evaluation; Fine needle aspiration biopsy; Health Personnel; Health Services Accessibility; Histology; Histopathology; Human; Image; Imaging Device; Immunodiagnostics; Immunohistochemistry; Infrastructure; Intervention; Investigation; 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; Proliferating; Research; Research Personnel; Resource-limited setting; Resources; Sampling; Selection for Treatments; Sensitivity and Specificity; Services; Specimen; Tanzania; Technology; Telemedicine; Testing; Training; Training and Infrastructure; Translating; Translations; Tumor Markers; Tumor Subtype; Universities; Validation; Visit; Woman; accurate diagnosis; algorithm training; aspirate; 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; smartphone based device; subtype-specific therapies; treatment planning; tumor; usability; user-friendly; virtual; wireless transmission

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.

摘要 乳腺癌（BC）是女性中最常见的癌症，也是女性癌症死亡的主要原因。 每年有160万新病例和50万BC死亡。诊断为低- 资源环境（LRS）占全球新病例的一半，以及BC死亡的大多数。第一 对BC开始挽救生命的治疗的关键步骤是准确和及时的病理学确认癌症 诊断，这在许多LRS中仍然是一项具有挑战性的任务。传统的病理学评估涉及处理 采用细胞块法手术切除的标本，用于：（1）细胞组织病理学，用于识别异常 指示恶性肿瘤的细胞形态，和（2）分子病理学，其鉴定肿瘤生物标志物， 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 （HER 2）和增殖标志物Ki 67。使用这些标记物进行乳腺癌亚型分型对于 确定预后，以及用于选择亚型特异性治疗。不幸的是，基于组织学的 病理学服务需要强大的病理学基础设施和训练有素的病理学家， 在许多LRS服务。例如，坦桑尼亚只有15名训练有素的病理学家，而该国人口超过55 万人.因此，迫切需要新的方法来准确地诊断癌症，以及 分析肿瘤亚型的分子生物标志物的表达水平。技术驱动的解决方案， 因此，具有最少用户干预和几乎没有基础设施要求的自动化细胞病理学可以 极大地影响了LRS中乳腺癌的管理。出于这一需要， 该提案是完成EpiView-D4即时检测（POCT）的开发，以分析细胞 和乳腺癌的分子特征。的EpiView组件 该设备可对细针抽吸物进行方便、低成本、基于智能手机的明视野细胞成像 无需病理学家评估的乳房活检。同时，器械的D4 POCT组件 对用于定量来自乳腺FNA的ER/PR/Her 2/Ki 67水平的即时抗体微阵列进行成像 在护理点30分钟内检测出具有皮摩尔灵敏度的裂解物，无需在 可以启动治疗计划。EpiView-D4将能够自动读出细胞病理学结果和 乳腺癌的分子概况，使用集成到智能手机应用程序中的机器学习算法。 在本提案中，我们将进行最终设备开发和ML算法培训，然后进行临床前 Epiview-D4 POCT的验证和临床研究，首先在杜克大学医学中心，然后 在预期的LRS的基利曼哈罗基督教医疗中心。这项技术的影响在于它的潜力， 通过实现快速准确的诊断，显著改善全球乳腺癌管理， 对乳腺癌进行分型，从而为乳腺癌患者提供及时和适当的治疗， 从而改善了每年在LRS的数十万BC妇女的结果。