Radiogenomics Framework for Non-Invasive Personalized Medicine

非侵入性个性化医疗的放射基因组学框架

基本信息

批准号：
8837360
负责人：
Olivier Gevaert
金额：
$ 51.55万
依托单位：
STANFORD UNIVERSITY
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：
2015-02-15 至 2019-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8837360
关键词：
Accounting Adult Algorithms Archives Belief Bioinformatics Biological Markers Biopsy Brain Neoplasms Cancer Patient Clinical Clinical Treatment Communities Comorbidity DNA DNA Methylation DNA Sequence Alteration DNA copy number Data Development Disease Drug Targeting EGFR gene Edema Environment Epidermal Growth Factor Receptor Gene Expression Gene Expression Profile Genes Genomics Glioblastoma Glioma Goals Heterogeneity High-Throughput Nucleotide Sequencing Human Image Individual Institution Internet Investigation Lead Learning Lesion Link Location MGMT gene Magnetic Resonance Imaging Malignant Neoplasms Maps Medical Imaging Methods Methylation Molecular Molecular Profiling Mutation Necrosis Needle biopsy procedure Oncogenes Operative Surgical Procedures Outcome Patient Care Patients Pattern Pharmaceutical Preparations Phenotype Property RNA Radiogenomics Research Resources Sampling Technology The Cancer Genome Atlas Three-Dimensional Image Tissues Translating Tumor Tissue anticancer research base c-erbB-1 Proto-Oncogenes cancer imaging cancer site clinical care clinically relevant cohort empowered imaging biomarker in vivo innovation malignant breast neoplasm oncology operation outcome forecast personalized medicine prognostic programs protein profiling public health relevance quantitative imaging radiologist response tumor

项目摘要

DESCRIPTION (provided by applicant): Molecular profiles of tumors are nowadays used to determine prognosis and to guide therapy. For example the presence of a mutation in the EGFR gene will most likely lead to anti-EGFR therapy. Recently an image phenotype was discovered that acts as a biomarker of EGFR mutation. This is a precursor of the possibilities of a new emerging field called radiogenomics defined as directly linking imaging features to underlying molecular properties. Research in radiogenomics is rapidly gaining recognition as a powerful new field that has several promising applications, such as non-invasive molecular lesion assessment. When image surrogates can be identified that mirror relevant molecular aberrations (e.g. a mutation in the EGFR gene) they can be readily translated in clinical care. The value added by radiogenomics can be readily translated, as medical imaging is part of routine management in oncology. However, these early applications have not taken full advantage of the opportunities. First, they limit the correlation to either a handful of manually annotated image features and a pre-selected set of molecular parameters. Secondly, the initial applications are limited to a single omics by focusing on gene expression, without taking into account DNA mutations, DNA copy number changes or DNA methylation changes. We will develop a radiogenomics framework to identify non-invasive biomarkers mirroring relevant molecular tumor properties that impact treatment and clinical outcome of human brain tumors. Our objective is not to mimic a radiologist's expertise through computational means, but to empower radiologists and clinicians with new biomarkers. We will offer innovative new algorithms to represent medical images. Once such a representation is computed (e.g., in the form of a large data matrix), we will identify univariate and multivariate image signatures predictive of clinical outcome. Next, we will use sophisticated methods for integration with molecular data to interrogate different views of the data with respect to a clinically relevant outcome. The end result is a radiogenomics map where image signatures of molecular properties and tumor heterogeneity can be hypothesized and validated. We will have image signatures that are prognostic and image signatures reflecting actionable molecular properties of a tumor such as drug target activity or drug signatures.

描述（由申请人提供）：如今，肿瘤的分子特征是确定预后和指导治疗的。例如，EGFR基因中存在突变很可能会导致抗EGFR治疗。最近，发现了一个图像表型，该表型充当EGFR突变的生物标志物。这是称为放射基因组学的新出现领域的可能性的前体，该领域被定义为将成像特征与基础分子特性联系起来。放射基因组学的研究正在迅速获得一个有力的新领域，该领域具有多种有希望的应用，例如非侵入性分子病变评估。当可以鉴定出图像替代物可以镜像相关的分子像差（例如，EGFR基因中的突变）可以在临床护理中很容易翻译。放射基因组学添加的值可以很容易地翻译，因为医学成像是肿瘤学常规管理的一部分。但是，这些早期申请并未充分利用机会。首先，它们将相关性限制为少数手动注释的图像特征和一组预选的分子参数。其次，初始应用仅限于单个OMIC，通过关注基因表达，而无需考虑DNA突变，DNA拷贝数变化或DNA甲基化变化。我们将开发一个放射基因组学框架，以识别反映相关分子肿瘤特性的非侵入性生物标志物，这些特性会影响人脑肿瘤的治疗和临床结果。我们的目标不是通过计算手段模仿放射科医生的专业知识，而是要增强放射科医生和临床医生的能力。我们将提供创新的新算法来表示医学图像。一旦计算出这样的表示形式（例如，以大数据矩阵的形式），我们将确定单变量和多变量图像特征，可预测临床结果的预测。接下来，我们将使用复杂的方法与分子数据集成，以询问与临床相关结果的数据的不同观点。最终结果是放射基因组学图，其中可以假设和验证分子特性和肿瘤异质性的图像特征。我们将拥有预后的图像特征和图像特征，以反映肿瘤的可行分子特性，例如药物靶点活动或药物特征。