Radiogenomics Framework for Non-Invasive Personalized Medicine

描述（由申请人提供）：肿瘤的分子谱目前用于确定预后和指导治疗。例如，EGFR基因突变的存在将最有可能导致抗EGFR治疗。最近发现一种影像表型可作为EGFR突变的生物标志物。这是一个新的新兴领域的可能性称为放射基因组学的先驱，定义为直接连接成像功能的基础分子特性。放射基因组学研究作为一个强大的新领域正在迅速获得认可，该领域具有几个有前途的应用，例如非侵入性分子病变评估。当可以识别出反映相关分子畸变（例如EGFR基因突变）的图像替代物时，它们可以在临床护理中容易地翻译。放射基因组学的附加值很容易转化，因为医学成像是肿瘤学常规管理的一部分。然而，这些早期应用程序并没有充分利用这些机会。首先，他们将相关性限制在少数手动注释的图像特征和预先选择的分子参数集。其次，最初的应用局限于单一的组学，集中在基因表达上，没有考虑到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，以确定非侵入性生物标志物，反映影响人类脑肿瘤治疗和临床结果的相关分子肿瘤特性。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，而是为放射科医生和临床医生提供新的生物标志物。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（例如，以大数据矩阵的形式），我们将识别预测临床结果的单变量和多变量图像特征。接下来，我们将使用复杂的方法与分子数据整合，以询问关于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谱，其中可以假设和验证分子特性和肿瘤异质性的图像特征。我们将有图像签名，是预后和图像签名反映了肿瘤的可操作的分子特性，如药物靶活性或药物签名。

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.