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，也是女性癌症死亡的主要原因。 全球每年有 160 万新发病例和 50 万 BC 死亡。诊断为低度血症的患者 资源环境 (LRS) 占全球 BC 新增病例的一半和死亡人数的大部分。第一个 开始 BC 挽救生命治疗的关键一步是准确、及时地对癌症进行病理学确认 诊断，对于许多 LRS 来说仍然是一项具有挑战性的任务。传统的病理评估涉及处理 使用细胞块方法手术切除的标本用于：（1）细胞组织病理学，识别异常 指示恶性肿瘤的细胞形态，以及（2）分子病理学，可识别肿瘤生物标志物， 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 (HER2) 和增殖标记 Ki67。使用这些标记物进行乳腺癌亚型分析至关重要 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训练有素的病理学家，限制了对这些服务的获取 许多 LRS 中的服务。例如，坦桑尼亚只有 15 名经过培训的病理学家，而该国拥有超过 55 名病理学家。 万人。因此，迫切需要新的方法来准确诊断癌症，以及 分析肿瘤亚型的分子生物标志物的表达水平。技术驱动的解决方案可以 以最少的用户干预实现细胞病理学的自动化，并且几乎不需要基础设施 极大地影响了 LRS 中乳腺癌的治疗。在这种需求的推动下，本次活动的目的 该提案的目的是完成 EpiView-D4 即时检测 (POCT) 的开发，以分析细胞 以及针吸标本中乳腺癌的分子特征。 EpiView 组件 该设备可轻松获取、低成本、基于智能手机的细针抽吸明场细胞成像 乳房活检，无需病理学家评估。同时，设备的 D4 POCT 组件 对床旁抗体微阵列进行成像，用于定量乳腺 FNA 中的 ER/PR/Her2/Ki67 水平 在护理点 30 分钟内即可获得具有皮摩尔灵敏度的裂解液，无需在检测前进行额外就诊 可以启动治疗计划。 EpiView-D4 将能够自动读出细胞病理学和 使用集成到智能手机应用程序中的机器学习算法来了解乳腺癌的分子特征。 在这个提案中，我们将进行最终的设备开发和机器学习算法的训练，然后是临床前 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、准确的诊断和治疗，显着改善全球乳腺癌管理 乳腺癌亚型，从而推动乳腺癌患者及时、适当的治疗 因此，每年 LRS 可以改善数十万患有 BC 的女性的治疗结果。