Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer

描述(由申请者提供)：个性化医疗旨在通过承认生物多样性来为个人的需求量身定做医疗服务。鉴于各种高通量分子技术可以从组织和血液样本中表征个人的DNA、RNA和蛋白质，生产一组将决定个性化患者护理的生物标志物的前景正在推动生物技术的巨大进步。然而，这些方法的局限性包括需要侵入性活检，以及活检仅对一般不同类型病变的一小部分进行抽样。因此，活组织检查不能完全描述肿瘤的分子特征或其解剖、功能和生理特性，如大小、位置、形态、血管、扩散和灌流模式、氧合作用和代谢状态。鉴于这一内在的挑战，我们建议改变基于分子的个性化医学范式，从仅依赖于表征组织样本的范式，转变为包括甚至基于非侵入性医学成像检查中整个肿瘤及其周围的图像特征的表征的范式。为此，我们的总体目标是开发将成像和基因组数据整合在一起的工具和技术，从而能够绘制两者之间的关系图(“图像组学”图)。为了使我们的努力具有立竿见影的意义，我们将专注于一种疾病：非小细胞肺癌(NSCLC)，这是癌症死亡的主要原因，总的5年生存率为16%，在过去15年中没有明显变化。因此，(1)我们将开发、验证和公开可用的受控词汇和软件工具，用于建立带有载体的数据库，这些载体可以定量描述人体肿瘤在CT和PET图像中的特征。(2)我们将创建并公开一个新的多维数据库，该数据库将把CT和PET图像的这些特征与400名NSCLC患者切除的肿瘤的临床和基因表达微阵列数据相结合。(3)我们将通过(A)实施生物信息学方法，创建从CT和PET图像特征和临床数据到基因表达的关联图，以及(B)发现融合了成像、基因表达和其他临床数据的预后特征，从而展示综合成像/基因组/临床数据库的实用性。虽然我们的工具是专门为肺癌的CT和PET图像开发和验证的，但我们的工具将扩展到其他方式和疾病场景。每年可能影响数十万被诊断为肺癌的患者的特定结果将包括：(I)结合基因表达、成像特征和其他临床变量的新的多维预后标志，潜在地为理解非小细胞肺癌生物多样性及其临床管理带来新的见解，以及(Ii)仅从成像数据预测与临床相关的分子表型的能力，这最终可能有助于分子靶向治疗决策而不需要侵入性活组织检查。

DESCRIPTION (provided by applicant): Personalized medicine aims to tailor medical care to an individual's need through recognition of biological diversity. Given the variety of high-throughput molecular technologies that can characterize an individual's DNA, RNA and protein from samples of tissue and blood, the promise of producing a panel of biomarkers that will dictate individualized patient care is fueling tremendous advances in biotechnology. However, limitations to these approaches include the need for invasive biopsy, and the fact that biopsies only sample small portions of generally heterogeneous lesions. Biopsies therefore do not completely characterize the molecular profiles of tumors or their anatomical, functional and physiological properties, such as size, location, morphology, vascularity, diffusion and perfusion patterns, oxygenation, and metabolic state. In light of this intrinsic challenge, we propose to change the paradigm of molecularly-based personalized medicine from one relying on characterizing tissue samples alone, to one inclusive of, or even based on, characterization of image features of entire tumors and their surroundings in non-invasive medical imaging examinations. To this end, our over-arching goal is to develop tools and technologies that integrate imaging and genomic data, thereby allowing mapping of the relationships between the two ("image-omics" map). To focus and lend immediate significance to our efforts, we will concentrate on a single disease: non-small cell lung carcinoma (NSCLC), the leading cause of cancer death with an overall 5-year survival rate of 16% that has not changed appreciably over the past 15 years. Accordingly, (1) we will develop, validate and make publicly available, controlled vocabularies and software tools to be used in building databases with vectors that quantitatively describe features of human tumors in CT and PET images. (2) We will create and make publicly available a novel multidimensional database that integrates these features of CT and PET images with clinical and gene expression microarray data of excised tumors from 400 patients with NSCLC. (3) We will demonstrate the utility of the integrated imaging/genomic/clinical database, by (a) implementing bioinformatics approaches that create an association map from CT and PET image features and clinical data to gene expression, and (b) discovering prognostic signatures that incorporate imaging, gene expression and other clinical data. While specifically developed and validated for CT and PET images of lung cancer, our tools will be extensible to other modalities and disease scenarios. Specific outcomes, potentially impacting hundreds of thousands of patients diagnosed with lung cancer each year, will include (i) a new multidimensional prognostic signature that combines gene expression, imaging features and other clinical variables, potentially generating new insights into the understanding of NSCLC biologic diversity and its clinical management, and (ii) the ability to predict a clinically-relevant molecular phenotype from imaging data alone, which may eventually assist in molecularly- targeted therapeutic decisions without requiring invasive biopsies.