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，大量的脑组织学切片可以对患者进行分层，以提高诊断和治疗水平。 疗法然而，处理大量组织学切片需要先进的算法和软件 用于可视化和数据分析的基础架构。我们将开发和验证一个计算平台， 用于精准医疗的脑肿瘤分层。该平台将介绍一大批 组织切片，通过计算形态学亚型。随后，形态学亚型将 用于丰富基因组信息，以揭示形态学增强的基因组子集（MEGS），并 鉴定表观遗传调控基因。将通过分析整个载玻片图像来计算形态学指数 (WSI)H&E染色的肿瘤切片，这将使核方面的多参数表示， 组织病理学的形状、类型、组织和异常区域。然而，H&E的稳健处理 染色的WSIs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，以消除批次效应和量化肿瘤中的畸变 （ii）研究基因组畸变是否可以使用H&E染色进行分类， 部分;和（iii）表示表征肿瘤异质性的度量。在目标2中，形态学亚型 并鉴定预测结果的肿瘤异质性指数。这些形态计量学 然后将使用亚型和肿瘤异质性指数来富集基因组和表观遗传特征， 使用免疫组织化学对独立样品进行验证。目标1-2的计算部分将 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 作者：Kitware，Inc.最终产品将是根据预先计算的图谱对新患者进行分层。 预测结果的形态学亚型，用已发表的基因组亚型标记病理， 并为改进靶向治疗产生新的假设。