Molecular and Therapeutic Basis of Morphometric Aberrations in Brain Tumors

脑肿瘤形态畸变的分子和治疗基础

基本信息

批准号：
9059039
负责人：
Hang Chang
金额：
$ 39.71万
依托单位：
UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-05-01 至 2020-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9059039
关键词：
Address Adult Algorithmic Software Atlases Bayesian Modeling Bioinformatics Biological Biology Brain Neoplasms California Cells Cellular Morphology Classification Clinical Computer Analysis Computing Methodologies DNA Methylation Data Data Set Databases Descriptor Environment Gene Expression Generations Glioblastoma Glioma Goals Health Hematoxylin and Eosin Staining Method Heterogeneity Histology Histopathology Human Image Immunohistochemistry Knowledge Light Link Malignant Neoplasms Methods Modeling Molecular Molecular Profiling Molecular Target Morphology Multivariate Analysis Necrosis Oligodendroglioma-Astrocytoma Outcome Paraffin Embedding Pathologist Pathology Patients Preparation Primary Brain Neoplasms Process Property Publishing Quality Control Research Research Infrastructure Research Personnel Resources Sampling Slide Staining method Stains Systems Analysis Technology The Cancer Genome Atlas Therapeutic Tissues Tumor Bank Tumor Subtype Tumor-Associated Process Universities Validation Variant base bioimaging biological heterogeneity cohort complement system computer framework genome-wide genomic biomarker genomic data genomic signature innovation insight medical schools molecular scale molecular subtypes novel therapeutics open source outcome prediction predictive modeling prognostic repository sample fixation tool tumor tumor heterogeneity

项目摘要

DESCRIPTION (provided by applicant): Adult primary brain tumors such as gliomas are characterized by enormous cellular diversity captured by grading. For gliomas, tumor grade is based on the region with the highest level of aberrant histopathology. Recently, a large number of subtypes have been characterized based on morphological variants and their molecular characterization showing an enormous heterogeneity that is only being discovered. This poses an enormous challenge in interpreting these subtypes and understanding their clinical and molecular associations. We propose on capturing cellular profiles in a systematic way using computational analysis of whole slide images in terms of their composition and cellular content. Current methods do not address intrinsic batch effects, biological heterogeneity that is present in a large cohort, classification of aberrant cellular morphologies (e.g., astrocytoma, oligodendroglioma), and the need to for high throughput processing of vast amount of image-based data. We propose to advance the field by addressing these issues and building a knowledge repository of brain tumor. Additionally, due to the availability of large-scale molecular data, we will build molecular signatures for prognostic morphometric features and subtypes. In this manner computational image-based modeling and representation of tumor histology can provide new avenues for hypothesis generation through molecular association. The end result will be an atlas where molecular correlates of prognostic morphometric subtypes and brain tumors can be identified. Morphometric subtypes will be validated through an independent cohort, and molecular predictors of morphometric subtypes will validated through immunohistochemistry from the sample bank at the Stanford University Medical School and University of California.

描述（由应用提供）：成年原发性脑肿瘤（如神经胶质瘤）的特征是通过分级捕获的巨大细胞多样性。对于神经胶质瘤，肿瘤等级基于具有最高水平的组织病理学水平。最近，已经根据形态变异的变异及其分子表征表征了许多亚型，显示出巨大的异质性。这在解释这些亚型并了解它们的临床和分子关联方面是一个巨大的挑战。我们建议使用整个幻灯片图像的计算分析以其组成和细胞含量来以系统的方式捕获细胞谱。当前的方法不涉及在大型队列中提出的固有批次效应，生物异质性，对异常细胞形态的分类（例如星形胶质细胞瘤，少突胶质瘤）以及大量基于图像数据的大量处理的需求。我们建议通过解决这些问题并建立脑肿瘤知识存储库来发展领域。另外，由于大规模分子的可用性数据，我们将建立用于预后形态计量特征和亚型的分子特征。以这种方式，基于计算图像的建模和肿瘤组织学的表示可以通过分子关联为假设产生的新途径。最终结果将是一个地图集，可以鉴定出预后形态计量学和脑肿瘤的分子相关性。形态计量亚型将通过独立队列进行验证，形态计量学亚型的分子预测指标将通过斯坦福大学医学院和加利福尼亚大学的样本库进行免疫组织化学验证。