DEVELOPMENT OF AN AUTOMATED CARTRIDGE-BASED BREAST CANCER DETECTION ASSAY- AN ACADEMIC-INDUSTRIAL PARTNERSHIP

开发基于试剂盒的自动化乳腺癌检测方法——学术与工业合作

基本信息

批准号：
10417432
负责人：
SARASWATI SUKUMAR
金额：
$ 59.54万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-11 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10417432
关键词：
2019-nCoV Affect Africa Africa South of the Sahara Age Asia Benign Biological Assay Biopsy Body mass index Breast Breast Cancer Detection Breast Cancer Early Detection Cancer Detection Cessation of life Characteristics Clinic Clinical Clinical Research Collaborations Communicable Diseases Communities Core Biopsy DNA DNA Modification Process Developing Countries Development Diagnosis Diagnostic Disease Early Diagnosis Ensure Equilibrium Female Breast Carcinoma Fine needle aspiration biopsy Future Generations Genes Goals Gold HIV-1 Hour Industrialization Intervention Laboratories Legal patent Lesion Malignant - descriptor Malignant Neoplasms Mammary Neoplasms Manuals Mass in breast Measures Mediating Methods Methylation Modification Molecular Molecular Diagnosis Palpable Pathologist Pathology Patients Performance Persons Population Predictive Value Prospective Studies Publishing Race Reproducibility Resource-limited setting Sampling Savings Sensitivity and Specificity Slide South Africa Specificity Techniques Testing Time Tissues Training Tumor Subtype Underserved Population United States Validation Woman advanced disease base biomarker panel bisulfite breast cancer diagnosis breast lesion clinical practice commercialization cost detection assay diagnostic tool gene panel high risk imaging facilities improved industry partner innovation low and middle-income countries malignant breast neoplasm methylation biomarker mortality operation patient subsets prototype rapid test sample collection screening tumor tumor DNA underserved area

项目摘要

Abstract. In many low- and middle-income countries (LMICs) breast cancer is diagnosed at advanced stages. Many women present with a palpable breast mass, which is rare in communities where breast screening is available. In addition to limited imaging facilities, prolonged diagnostic delays (76-630 days) due in part to the extreme shortage of pathologists (as few as 1 per million/population) contribute to a 5-year mortality rate up to 4 times higher than that in the US. An innovative solution to this problem could be an affordable, easily deployable molecular test to identify and prioritize women likely to have a malignancy for expedited biopsy and pathology review. It is well established that early detection of breast cancer improves survival. With our industrial partner, Cepheid, we propose to build on our published breast cancer detection prototype to develop an affordable, <3- hour, automated breast cancer detection (aBCD) assay that analyzes a panel of hypermethylated genes in breast fine needle aspirates (FNAs).The proposed innovations will cut the assay time in half, and reduce costs by at least 3-fold to provide a single-cartridge assay for quick cancer detection. In Aim 1a, we will optimize the Offboard bisulfite-mediated DNA conversion method and test its efficiency in Patient Set 1 FNAs (N= 29 malignant, 25 benign). In Aim 1b we will select one optimal 5-marker panel and test its performance using first, the gold standard, FFPE samples (N= 30 malignant, 30 benign), and then, Patient Set 2 FNAs (N=35 malignant, 35 benign). In Aim 1c, we will perform technical validation of the aBCD assay. Intra-assay reproducibility will be assessed on multiple sample collections of Patient Set 3 FNAs (N=30 malignant, 30 benign). Inter-operator reproducibility will be determined using replicate FNA slides from Patient Set 2 (N= 35 malignant, 35 benign). The goal of Aim 2a is to perform clinical validation of the aBCD assay. We will first select a threshold in a Training set of FNAs: Patient Set 4 (N=100 malignant, 100 benign) to optimally balance sensitivity and specificity, and validate performance of the selected threshold in a Test set of FNAs: Patient Set 5 (N= 180 malignant, 180 benign). We will measure the accuracy (sensitivity, specificity, and positive- and negative-predictive value) of aBCD-based diagnosis to distinguish benign versus malignant lesions using histopathological diagnosis of the core biopsy as the gold standard. Lastly, in Aim 2b, to determine whether select patient characteristics alter the performance of the aBCD assay, we will test its clinical accuracy among specific patient subgroups based on age, race, BMI, and tumor characteristics (grade, stage, tumor subtype). All these steps are necessary to ensure an accurate and reliable test. This intervention is paradigm shifting, and could revolutionize the current detection of breast cancer in underserved regions of the world by expedited treatment and, in turn, saving thousands of lives yearly. This study will also facilitate further development of the aBCD assay toward commercialization, making it accessible globally.

抽象的。在许多低收入和中等收入国家（LMIC）中，乳腺癌在晚期阶段被诊断出。许多女性在乳房筛查的社区中很少见到乳房，这是很少见的可用的。除了有限的成像设施外，长时间的诊断延迟（76-630天）部分归因于非常短缺的病理学家（每百万/人口每百万人口1）导致5年死亡率至比美国高4倍。解决此问题的创新解决方案可能是一个负担得起的，易于部署的分子测试以识别和优先考虑可能患有快速活检和病理的妇女审查。众所周知，早期对乳腺癌的检测可改善生存率。与我们的工业合作伙伴一起 Cepheid，我们建议建立在我们已发表的乳腺癌检测原型上，以发展负担得起的，<3- 小时，自动化乳腺癌检测（ABCD）测定法，分析乳房中的一组高甲基化基因精细的针吸收（FNA）。拟议的创新将把测定时间切成两半，并降低成本至少3倍以提供单晶体测定法以快速癌症检测。在AIM 1A中，我们将优化 Bisulfite介导的DNA转换方法并测试其在患者组1 FNA中的效率（n = 29）恶性，25良性）。在AIM 1B中，我们将选择一个最佳的5标记面板，并使用第一，黄金标准，FFPE样品（n = 30个恶性，30良性），然后患者集2 FNA（n = 35个恶性肿瘤， 35良性）。在AIM 1C中，我们将对ABCD分析进行技术验证。测定可重复性将在多个患者组的样本收集中进行评估（n = 30恶性肿瘤，30良性）。操作员可重复性将使用患者组2的重复FNA载玻片（n = 35恶性肿瘤，35良性）确定。 AIM 2A的目的是对ABCD分析进行临床验证。我们将首先选择一个阈值 FNA的训练集：患者组4（n = 100个恶性，100良性），以最佳平衡灵敏度和特异性，并在FNA测试集中验证选定阈值的性能：患者组5（n = 180恶性肿瘤，180 良性）。我们将衡量的基于ABCD的诊断，使用组织病理学诊断来区分良性与恶性病变核心活检作为黄金标准。最后，在AIM 2B中，确定选择的患者特征是否改变了 ABCD分析的性能，我们将根据特定患者亚组测试其临床精度年龄，种族，BMI和肿瘤特征（级，阶段，肿瘤亚型）。所有这些步骤都是必要的精确可靠的测试。这种干预是范式的变化，可以彻底改变当前的检测通过加快治疗，在世界各地服务不足的地区的乳腺癌又节省了数千次乳腺癌每年生活。这项研究还将促进ABCD分析的进一步开发商业化，使其在全球范围内访问。