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

描述（由申请人提供）：个性化医疗旨在通过认识生物多样性，根据个人需要量身定制医疗服务。鉴于各种各样的高通量分子技术可以从组织和血液样本中表征个体的DNA、RNA和蛋白质，生产一组生物标记物的前景将决定个性化的病人护理，这推动了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，以及活检只能对一般异质性病变的一小部分进行取样。因此，活组织检查不能完全表征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、血管分布、扩散和灌注模式、氧合和代谢状态。鉴于这一内在挑战，我们建议改变基于分子的个性化医学范式，从仅依赖于组织样本的特征，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环境的图像特征的特征。为此，我们的首要目标是开发整合成像和基因组数据的工具和技术，从而允许绘制两者之间的关系（“图像组学”地图）。为了使我们的努力具有直接意义，我们将把重点放在单一疾病上：非小细胞肺癌（NSCLC），这是癌症死亡的主要原因，其总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、验证并提供公开可用的受控词汇表和软件工具，用于构建具有定量描述CT和PET图像中人类肿瘤特征的矢量的数据库。(2)我们将创建并公开一个新的多维数据库，该数据库将来自400名非小细胞肺癌患者的CT和PET图像的这些特征与临床和基因表达微阵列数据相结合。(3)我们将展示综合成像/基因组/临床数据库的效用，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)发现结合成像、基因表达和其他临床数据的预后特征。虽然专门为肺癌的CT和PET图像开发和验证，但我们的工具将扩展到其他模式和疾病场景。具体结果，每年可能影响数十万被诊断为肺癌的患者，将包括(i)结合基因表达，影像学特征和其他临床变量的新的多维预后特征，可能为理解非小细胞肺癌生物多样性及其临床管理提供新的见解，以及（ii）仅从影像学数据预测临床相关分子表型的能力。这可能最终有助于分子靶向治疗决策，而不需要侵入性活检。

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. PUBLIC HEALTH RELEVANCE: This project has major relevance for human health. The demonstration project in non-small cell lung cancer promises to provide an improved prognostic signature that integrates well-annotated and reproducible medical feature characterizations of CT and PET images with genomic tissue profiles and other existing clinical data. Over the long term, tools we develop for the integration of medical imaging and genomic data have the potential to improve our knowledge of the biology of the disease, and to improve patient care by generating fewer biopsies and converging more rapidly to optimal management/treatment.