Stratifying brain tumors by structural subtyping and heterogeneity

通过结构亚型和异质性对脑肿瘤进行分层

• 批准号: 9813397
• 负责人: Bahram A. Parvin
• 金额: $ 42.96万
• 依托单位: UNIVERSITY OF NEVADA RENO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-05 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9813397
• 关键词: Adult; Algorithmic Software; Algorithms; Atlases; Biological Markers; Brain; Brain Neoplasms; Clinical; Clinical Data; Computational Technique; Computer software; Computing Methodologies; Data Analytics; Diagnostic; Epigenetic Process; Genes; Genomics; Glioblastoma; Glioma; Goals; Heterogeneity; Histology; Histopathology; Image; Imagery; Imaging Device; Immunohistochemistry; Infrastructure; Inherited; Label; Left; Literature; Methods; Molecular; Molecular Abnormality; Molecular Profiling; Morphology; Necrosis; Nuclear; Outcome; Pathology; Patient-Focused Outcomes; Patients; Preparation; Process; Publishing; Sampling; Shapes; Stains; Stratification; Structure; Techniques; Technology; The Cancer Genome Atlas; Tissue Banks; Translations; Tumor Subtype; Validation; Variant; Virulent; base; biological heterogeneity; clinical application; cohort; computational platform; cost; genomic aberrations; genomic data; genomic signature; imaging biomarker; improved; indexing; novel; open source; outcome prediction; patient population; patient stratification; precision medicine; predicting response; response; survival prediction; targeted treatment; treatment planning; tumor; tumor heterogeneity; whole slide imaging

Proposal Summary We hypothesize that large cohorts of brain histology sections can stratify patients for improved diagnostic and therapy. However, processing a large cohort of histology sections requires advanced algorithm and software infrastructure for visualization and data analytic. We will develop and validate a computational platform for stratifying brain tumors for the applications of precision medicine. The platform will profile a large cohort of histology sections, of the brain by computing morphometric subtypes. Subsequently, morphometric subtypes will be used to enrich genomic information to reveal morphometrically enhanced genomic subsets (MEGS), and to identify epigenetically regulated genes. Morphometric indices will be computed by profiling whole slide images (WSI) of H&E stained tumor sections, which will enable multi-parametric representation in terms of nuclear- shape, -types, -organization, and aberrant regions of histopathology. However, robust processing of the H&E stained WSIs is not without challenges and suffers from batch effects, biological heterogeneity, and complexities associated with aberrant histopathology. The proposal has two aims and deliverables. In Aim 1, we will (i) develop and refine computational methods for eliminating the batch effects and quantifying aberrations in tumor signature at multiple levels; (ii) investigate whether genomic aberrations can be classified using H&E stained sections; and (iii) represent metrics for characterizing tumor heterogeneity. In Aim 2, morphometric subtypes and tumor heterogeneity indices that are predictive of the outcome will be identified. These morphometric subtypes and tumor heterogeneity indices will then be used to enrich genomic and epigenetic signatures for validation on independent samples using immunohistochemistry. Computational components of Aims 1-2 will be integrated with the open source software platform that is being developed for managing and visualizing histology sections by Kitware, Inc. The final product will be to stratify a new patient against an atlas of precomputed morphometric subtypes that are predictive of the outcome, label the pathology with a published genomic subtype, and to generate new hypothesis for improved targetted therapy.

提案摘要 我们假设大量脑组织学切片可以对患者进行分层，以改善诊断和 治疗。然而，处理大量组织学切片需要先进的算法和软件 可视化和数据分析的基础设施。我们将开发并验证一个计算平台 对脑肿瘤进行分层以用于精准医学的应用。该平台将描述一大批 通过计算形态测量亚型来绘制大脑的组织学切片。随后，形态测量亚型将 用于丰富基因组信息以揭示形态增强的基因组子集（MEGS），并 识别表观遗传调控基因。形态指数将通过分析整个幻灯片图像来计算 (WSI) H&E 染色的肿瘤切片，这将能够实现核参数的多参数表示 形状、类型、组织和组织病理学的异常区域。然而，H&E 的稳健处理 染色的 WSI 并非没有挑战，并且受到批次效应、生物异质性和复杂性的影响 与异常的组织病理学有关。该提案有两个目标和可交付成果。在目标 1 中，我们将 (i) 开发和完善计算方法，以消除批次效应并量化肿瘤中的畸变 多层次签名； (ii) 研究是否可以使用 H&E 染色对基因组畸变进行分类 部分； (iii) 代表表征肿瘤异质性的指标。在目标 2 中，形态测量亚型 并且将确定预测结果的肿瘤异质性指数。这些形态测量 然后，亚型和肿瘤异质性指数将用于丰富基因组和表观遗传特征 使用免疫组织化学对独立样本进行验证。目标 1-2 的计算组件将是 与正在开发的用于管理和可视化组织学的开源软件平台集成 Kitware, Inc. 提供的部分。最终产品将根据预先计算的图谱对新患者进行分层 预测结果的形态测量亚型，用已发表的基因组亚型标记病理学， 并产生改进靶向治疗的新假设。