Statistical Methods For Quantitative Immunohistochemistry Biomarkers

定量免疫组织化学生物标志物的统计方法

• 批准号: 10331802
• 负责人: Inna Chervoneva
• 金额: $ 34.85万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331802
• 关键词: Algorithms; Antibodies; Biological Markers; Breast Cancer Patient; Cancer Patient; Cancerous; Cell physiology; Cells; Clinical Management; Clinical Research; Clinical Trials; Computer software; Data; Databases; Detection; Development; Disease; Elements; Evaluation; Fluorescence; Funding; Goals; Grant; Heterogeneity; Histologic; Histology; Image; Image Analysis; Immunofluorescence Immunologic; Immunohistochemistry; Individual; Institution; Malignant Neoplasms; Measures; Medical Oncologist; Methods; Modeling; Molecular Profiling; Pathologist; Pathology; Patient Recruitments; Patients; Pattern; Phosphorylation; Population; Post-Translational Protein Processing; Prediction of Response to Therapy; Process; Prognosis; Prognostic Marker; Proteins; Reader; Recurrence; Research Personnel; Risk; Sampling; Scanning; Signal Transduction; Slide; Software Tools; Solid; Stains; Statistical Methods; Surgeon; Systems Analysis; Techniques; Technology; Tissue Microarray; Tissues; Trees; Tumor Markers; Tumor Tissue; Validation; base; biomarker development; biomarker discovery; biomarker-driven; cancer biomarkers; cancer cell; cancer therapy; cell type; clinical practice; cohort; data resource; design; digital; digital imaging; drug response prediction; expectation; flexibility; image processing; improved; indexing; innovative technologies; interest; malignant breast neoplasm; method development; mortality; multidisciplinary; novel; novel therapeutics; optimal treatments; personalized medicine; predict clinical outcome; predicting response; prognostic; prognostic value; prognostication; protein biomarkers; protein expression; quantitative imaging; statistics; treatment response; tumor; user friendly software; user-friendly

PROJECT SUMMARY Immunohistochemistry (IHC) is widely used to classify cancer and guide clinical management of patients. A major power of IHC is the detection of markers in the spatially resolved context of tumor tissue, including cancerous and stromal elements, individual cell types, and subcellular compartments. Quantitative Immunofluorescence (QIF) is used increasingly for IHC quantification of proteins that may serve as prognostic biomarkers or targets for specific cancer therapies. A common application of QIF is simultaneous slide-based analysis of certain protein expression using tissue microarrays (TMAs), which incorporate tumor tissues from large cohorts of patients. This technology combined with machine readers allows for high-throughput molecular profiling of tumor tissues and rapid development and validation of new prognostic and predictive cancer biomarkers. The overall goal of our proposal is to develop an analytical framework of for discovery of new IHC biomarkers to improve prognostication and drug response prediction. The new statistical methods will be tailored to use the rich and untapped information on cell-level protein expression levels. Novel QIF IHC biomarkers will be optimized as flexible indices based either on distribution functions of cell-level protein expressions or on the spatial localization of the IHC signals viewed as marks of the spatial point pattern of cancer cells. The proposed studies will yield broadly applicable methods for development of new biomarkers in cancer. The improved biomarkers would support rational recruitment of patients at elevated risk of recurrence into biomarker-driven clinical trials, with a potential to have major near-term impact on clinical research and reduce mortality from cancer.

项目摘要 免疫组织化学（IHC）广泛用于对癌症进行分类和指导患者的临床管理。一个 IHC的主要力量是在肿瘤组织的空间解决上下文中检测标记物，包括 癌和基质元素，单个细胞类型和亚细胞室。定量 免疫荧光（QIF）越来越多地用于蛋白质的IHC定量，这些蛋白质可以用作预后 特定癌症疗法的生物标志物或靶标。 QIF的常见应用是同时基于幻灯片 使用组织微阵列（TMA）分析某些蛋白质表达 大量患者。这项技术与机器读取器相结合，可提供高通量分子 肿瘤组织的分析以及新的预后和预测性癌症的快速发展和验证 生物标志物。 我们建议的总体目标是开发一个分析框架，以发现新的IHC 生物标志物改善预后和药物反应预测。新的统计方法将是 量身定制，以使用有关细胞水平蛋白表达水平的丰富而未开发的信息。小说QIF IHC 生物标志物将被优化为基于细胞级蛋白的分布功能的灵活指数 表达式或在空间定位的IHC信号的空间定位上，被视为空间点模式的标记 癌细胞。拟议的研究将产生广泛适用的方法，用于开发新的生物标志物 癌症。改进的生物标志物将支持以较高的复发风险的理性招募患者 进入生物标志物驱动的临床试验，有可能对临床研究产生重大影响 降低癌症死亡率。