Lung Cancer Screening via Ultrasensitive and Cost-efficient Analysis of Tumor DNA Signatures in Blood

通过对血液中肿瘤 DNA 特征进行超灵敏且经济高效的分析来筛查肺癌

• 批准号: 10227085
• 负责人: Abhijit Patel
• 金额: $ 96.71万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-06 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10227085
• 关键词: Address; Age; Algorithms; Alleles; Area; Back; Biological; Biological Assay; Biological Markers; Biometry; Blood; Blood Circulation; Blood Tests; Clinical; Colonoscopy; Cost Effectiveness Analysis; DNA; DNA Sequence Alteration; Data; Detection; Development; Diagnosis; Disease; Economic Factors; Excision; Fatality rate; Frequencies; Genomic Segment; Hematopoiesis; Individual; Lesion; Libraries; Lung; Lung Neoplasms; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Mammography; Medicine; Methods; Morphologic artifacts; Mutation; Nucleic Acid Biochemistry; Operative Surgical Procedures; Pap smear; Patients; Performance; Plasma; Population; Predictive Value; Probability; Research; Rice; Sampling; Screening for cancer; Sensitivity and Specificity; Serum Proteins; Skates; Smoker; Somatic Mutation; Specificity; Specimen; Survival Rate; Technology; Temperature; Testing; Thermodynamics; Tissues; Training; Tube; Tumor stage; Tumor-Derived; Universities; Variant; Work; X-Ray Computed Tomography; base; biochemical tools; biomarker development; cancer biomarkers; cancer survival; cancer type; cell free DNA; cost; cost efficient; design; exome sequencing; genetic signature; high risk population; improved; innovation; lung basal segment; lung cancer screening; machine learning algorithm; multidisciplinary; mutant; neoplastic; next generation sequencing; premalignant; preservation; prospective; protein biomarkers; purge; repository; screening; success; survival outcome; tool; tumor; tumor DNA; variant of unknown significance

PROJECT SUMMARY Substantial improvements in cancer survival rates could be achieved by developing better tools to detect occult malignancies at an earlier, more curable stage. Unfortunately, efforts to identify serum protein biomarkers that are sufficiently cancer-specific to be used for screening have found little success. Here, we pursue an alternative strategy based on detection of exceptionally tumor-specific mutant DNA fragments in the circulation of patients with early-stage lung cancer. Because these tumor-derived DNA fragments harbor genetic signatures that would be uncommon in healthy individuals, they hold great promise for screening applications where a high frequency of false-positive results would be unacceptable. However, it is a formidable challenge to create an assay that is able to detect trace quantities of mutant DNA released into the bloodstream from a small, early-stage tumor, without knowing the tumor’s mutation profile beforehand. Additional challenges are posed by economic factors as well as the presence of low-level mutations in the healthy population. To address these challenges, we have assembled a multidisciplinary team with highly complementary expertise from Microsoft Research and from Yale, Rice, and Harvard Universities. In this proposal, we describe innovative solutions in which we apply tools of biochemistry, thermodynamics, machine learning, and biostatistics to develop and validate an ultrasensitive, cost-efficient assay for detecting rare mutant DNA fragments in blood as markers of early-stage lung cancer.

项目摘要 通过开发更好的工具来检测癌症的隐匿性， 在更早更容易治愈的阶段治疗恶性肿瘤。不幸的是，鉴定血清蛋白生物标志物的努力， 用于筛查的足够癌症特异性的方法几乎没有成功。在这里，我们追求一个 基于检测循环中异常肿瘤特异性突变DNA片段的替代策略 早期肺癌的患者。因为这些肿瘤衍生的DNA片段含有基因， 这些签名在健康个体中并不常见，但它们对筛选应用程序有很大的希望。 其中高频率的假阳性结果是不可接受的。然而，这是一个艰巨的挑战 创建一种检测方法，能够检测从血液中释放到血液中的微量突变DNA。 小的，早期的肿瘤，事先不知道肿瘤的突变谱。其他挑战包括 经济因素以及健康人群中低水平突变的存在造成的。到 为了应对这些挑战，我们组建了一支具有高度互补专业知识的多学科团队 来自微软研究院，耶鲁大学，莱斯大学和哈佛大学。在本建议中，我们描述 创新的解决方案，我们应用生物化学，热力学，机器学习和 生物统计学，以开发和验证一种用于检测罕见突变DNA的超灵敏、具有成本效益的检测方法 血液中的碎片作为早期肺癌的标志。