Biomarker Developmental Laboratory (BDL)

生物标志物发育实验室 (BDL)

• 批准号: 10674909
• 负责人: Steven J Skates
• 金额: $ 37.05万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10674909
• 关键词: Accounting; Adopted; Antibodies; Bar Codes; Binding; Biological; Biological Assay; Biological Markers; Biometry; Blood; Blood Tests; CA-125 Antigen; Cancer Etiology; Cervical; Clinical; Clinical Sensitivity; Collaborations; Custom; Cytology; DNA; Development; Early Detection Research Network; Early Diagnosis; Epigenetic Process; Epithelial Cells; Evaluable Disease; Event; Fractionation; Genes; Genome; Genomics; Hypermethylation; Irrigation; Laboratories; Letters; Liquid substance; Location; Malignant Neoplasms; Malignant neoplasm of fallopian tube; Malignant neoplasm of ovary; Mass Spectrum Analysis; Measures; Methylation; Morbidity - disease rate; Mutation; Oligonucleotides; Operative Surgical Procedures; Ovarian; Pap smear; Patients; Performance; Phase; Plasma; Procedures; Proteins; Proteome; Proteomics; Protocols documentation; Repetitive Sequence; Research Personnel; Sampling; Screening for Ovarian Cancer; Serum; Signal Transduction; Site; Slide; Specific qualifier value; Swab; Testing; Tissues; Training; Tumor Suppressor Genes; Uterus; Validation; Woman; analytical method; assay development; cancer biomarkers; cancer cell; candidate marker; cohort; constriction; detection assay; experience; genome wide methylation; genome-wide; genomic signature; improved; insight; machine learning method; mortality; performance tests; protein biomarkers; proteomic signature; screening; tumor

Project Summary/Abstract – Biomarker Developmental Laboratory The Biomarker Developmental Laboratory for the proposed BCC will develop and validate genomic and proteomic assays for the detection of ovarian cancer in uterine lavage samples. The genomic assays will analyze the cellular pellet from uterine lavage for genome wide hypomethylation. This genome-wide approach increases the clinical sensitivity of the biospecimen sampling. Machine learning methods will be applied to optimize the aggregation of the methylation signal across the genome accounting for the unknown fraction of sample derived from ovarian/fallopian tube cancer cells and the remaining fraction mostly comprised of uterine epithelial cells. Proteomic analysis of the uterine lavage fluid using proximity extension assays (PEAs) have a superior analytical sensitivity to mass spectrometry. Analysis of samples from a training cohort of cases and controls by PEA will identify upregulated proteins from the tumor and its microenvironment. Supervised biostatistical classifiers will define an optimal panel of up to 21 PEA biomarker proteins. The optimal panel will undergo Custom Panel development for absolute quantitation under the direction of the BRL (biomarker reference laboratory). The training samples will be re-assayed, and a classifier developed combining the custom panel proteins and the aggregated methylation signal to form a combined classifier. The combined classifier will be tested in uterine lavage samples from an independent validation cohort. If the classifier is validated, the classifier will be applied to the cervical swabs – same biospecimen as a Pap smear – to determine its performance in a readily accessible and patient acceptable sample.

项目概要/摘要-生物标志物开发实验室 拟议BCC的生物标志物开发实验室将开发和验证基因组和 蛋白质组学测定用于检测子宫灌洗样品中的卵巢癌。基因组分析将 分析来自子宫灌洗的细胞团的全基因组低甲基化。这种全基因组的方法 增加了生物样本取样的临床灵敏度。机器学习方法将应用于 优化整个基因组中甲基化信号的聚集，从而解释甲基化信号的未知部分。 样品来源于卵巢/输卵管癌细胞，剩余部分主要包含 子宫上皮细胞使用邻近延伸测定（PEAs）对子宫灌洗液进行蛋白质组学分析 对质谱法具有上级分析灵敏度。来自病例训练队列的样本分析 PEA的对照将鉴定来自肿瘤及其微环境的上调蛋白质。监督 生物统计分类器将定义最多21种PEA生物标志物蛋白的最佳组。最佳面板将 在BRL（生物标志物）的指导下进行定制样本组开发，以进行绝对定量 参考实验室）。训练样本将被重新分析，并且结合 定制组蛋白和聚集的甲基化信号以形成组合分类器。将合并的 将在来自独立验证组群的子宫灌洗样品中测试分类器。如果分类器是 经验证，分类器将应用于宫颈拭子-与巴氏涂片相同的生物标本-以 确定其在容易获得且患者可接受的样品中的性能。