Optimization and Validation of an indicator cell assay for blood-based diagnosis of lung cancer

用于肺癌血液诊断的指示细胞测定的优化和验证

• 批准号: 9762864
• 负责人: Jennifer Joy Smith
• 金额: $ 50.92万
• 依托单位: PRECYTE, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762864
• 关键词: Alzheimer disease detection; Alzheimer' s Disease; Amyotrophic Lateral Sclerosis; Benign; Biological Assay; Biological Markers; Biopsy; Biosensor; Blood; Blood specimen; Cells; Cellular Assay; Clinical; Cultured Cells; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Early Diagnosis; Ensure; Gene Expression; Genes; Genetic Transcription; Goals; Human; Image; Imaging Device; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measures; Methods; Nodule; Non-Small-Cell Lung Carcinoma; Output; Patients; Pattern; Performance; Phase; Positioning Attribute; Predictive Value; Prevalence; Probability; Risk; Sampling; Serum; Signal Transduction; Site; Source; Specificity; Standardization; Stratification; Technology; Test Result; Testing; Time; Tissue-Specific Gene Expression; Training; Validation; Variant; Visual; Work; base; blind; cell type; cohort; cost effective; diagnostic accuracy; economic impact; improved; mouse model; nano-string; noninvasive diagnosis; novel; outcome forecast; research clinical testing; response; tool

Project Summary. The Indicator Cell Assay Platform (iCAP) is an inexpensive blood-based assay that can be used for early detection of disease, disease stage stratification, prognosis and response to therapy for a variety of diseases. The iCAP uses cultured, standardized cells as biosensors, capitalizing on the ability of cells to respond to disease signals present in serum with exquisite sensitivity, as opposed to traditional assays that rely on direct detection of molecules in blood. Developing the iCAP involves exposing cultured cells to serum from normal or diseased subjects, measuring a global differential response pattern, and using it to train a reliable disease classifier based on the expression of a small number of genes. Deploying the iCAP involves measuring only expression of classifier genes using cost-effective tools. We have demonstrated the iCAP by pre- symptomatic detection of disease in an amyotrophic lateral sclerosis mouse model, and early detection of Alzheimer's disease in humans, which we are currently validating. Here, we are developing an iCAP for diagnosis of lung cancer (LC). Blood biomarkers of LC are critically needed for use in combination with existing imaging tools to improve diagnostic accuracy. Our goal is to develop an iCAP for use on patients who have indeterminate pulmonary nodules (IPNs) identified by imaging that cannot be confidently classified as malignant or benign from the data. For clinical utility, the iCAP needs to distinguish malignant from benign nodules with 1) High sensitivity and negative predictive value (NPV) to minimize the number of patients with malignant tumors that have negative test results, and 2) A specificity that will provide economic impact and actionable results for patients correctly identified with benign nodules. We have demonstrated proof of concept for the LC iCAP and achieved 96% NPV, 92% sensitivity and 52% specificity in distinguishing non-small cell lung cancer from benign nodules (with independent samples). Potential for clinical utility is high with low risk of missing malignant tumors (8% FNR), actionable results for 52% of patients with benign nodules, and performance that is better or similar to other assays on the market and in development. For Phase II, we propose to optimize and validate the assay with larger cohorts from independent sites to position us to commercialize the assay as a clinical test. We aim to: 1) Optimize experimental, technical, and computational parameters of the iCAP, 2) Train and test an improved iCAP classifier using optimal conditions, and 3) Validate the classifier with blind independent samples. Our goal is to achieve clinical utility and greater performance than competing tests in development with ≥95% sensitivity and >60% specificity, with >90% NPV. Our ultimate goal is to develop a test that can be offered to patients at the time of finding an IPN by imaging. Our simple blood-based assay will give patients a probability of disease using a continuous variable. We will develop a visual depiction of the data that patients and doctors can use to assess risk and decide treatment. With our collaborator, Dr. Massion, we will work to refine the best clinical approach.

项目摘要。指示细胞分析平台（iCAP）是一种廉价的基于血液的分析方法， 用于疾病的早期发现、疾病分期分层、预后判断及对多种治疗的反应 疾病。iCAP使用培养的标准化细胞作为生物传感器，利用细胞的能力， 与传统的检测方法不同， 直接检测血液中的分子开发iCAP涉及将培养的细胞暴露于来自 正常或患病的受试者，测量全球差异反应模式，并使用它来训练可靠的 基于少量基因表达的疾病分类器。部署iCAP涉及测量 仅表达分类器基因，使用具有成本效益的工具。我们已经通过预- 肌萎缩侧索硬化小鼠模型中疾病的症状检测，以及 我们目前正在验证。 在这里，我们正在开发用于诊断肺癌（LC）的iCAP。LC的血液生物标志物至关重要 需要与现有的成像工具结合使用，以提高诊断准确性。我们的目标是开发 iCAP用于通过成像识别的不确定肺结节（IPN）患者， 从数据中可以确信地将其分类为恶性或良性。对于临床实用性，iCAP需要区分 1）高灵敏度和阴性预测值（NPV），以最大限度地减少恶性结节， 具有阴性测试结果的恶性肿瘤患者的数量，以及2）将提供 对正确识别为良性结节的患者的经济影响和可操作的结果。我们有 证明了LC iCAP的概念验证，并实现了96%的NPV，92%的灵敏度和52%的特异性， 区分非小细胞肺癌与良性结节（独立样本）。潜在的临床 实用性高，恶性肿瘤缺失风险低（8% FNR），52%的患者可采取行动， 良性结节，并且性能优于或类似于市场上和开发中的其他测定。 对于II期，我们建议使用来自独立研究中心的较大队列优化和验证试验 使我们能够将其商业化作为临床试验。我们的目标是：1）优化实验，技术和 2）使用最优条件训练和测试改进的iCAP分类器， 3）用盲独立样本对分类器进行分类。我们的目标是实现临床实用性和更大的 性能优于开发中的竞争测试，灵敏度≥95%，特异性>60%，NPV>90%。 我们的最终目标是开发一种可以在通过成像发现IPN时提供给患者的测试。我们 简单的基于血液的测定将使用连续变量给患者提供疾病的概率。我们将开发 患者和医生可以用来评估风险和决定治疗的数据的可视化描述。与我们 合作者Massion博士，我们将努力完善最佳临床方法。