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

用于链接和挖掘非小细胞肺癌图像和基因组数据的工具

• 批准号: 8153431
• 负责人: SANDY A. NAPEL
• 金额: $ 64.73万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8153431
• 关键词: Adenocarcinoma; Biodiversity; Bioinformatics; Biological; Biological Markers; Biology; Biopsy; Biotechnology; Blood; Cancer Etiology; Caring; Cessation of life; Chest; Clinical; Clinical Data; Clinical Management; Computer software; Controlled Vocabulary; DNA; Data; Databases; Development; Diagnosis; Diffusion; Disease; Gene Expression; Genomics; Goals; Health; Human; Image; Individual; Knowledge; Lesion; Light; Link; Location; Lung Neoplasms; Magnetic Resonance Imaging; Malignant neoplasm of lung; Maps; Medical; Medical Imaging; Medicine; Metabolic; Mining; Modality; Molecular; Molecular Profiling; Morphology; Non-Small-Cell Lung Carcinoma; Outcome; Patient Care; Patients; Pattern; Perfusion; Physiological; Positron-Emission Tomography; Probability; Property; Proteins; Public Domains; RNA; Sampling; Software Tools; Squamous cell carcinoma; Survival Rate; Technology; Tissue Sample; Tissues; Validation; base; bioimaging; clinically relevant; improved; insight; molecular phenotype; novel; prognostic; radiologist; therapeutic target; tool; tumor; vector

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.

描述（由申请人提供）：个性化医学旨在通过识别生物多样性来量身定制医疗服务。鉴于可以从组织和血液样本中表征个体的DNA，RNA和蛋白质的高通量分子技术，因此产生一组生物标志物的承诺将决定个性化的患者护理，这助长了生物技术学的巨大进步。但是，这些方法的局限性包括对侵入性活检的需求，以及活检仅采样通常异质性病变的一小部分。因此，活检并未完全表征肿瘤的分子谱或其解剖学，功能和生理特性，例如大小，位置，形态，血管，血管性，扩散和灌注模式，氧合和代谢状态。鉴于这一内在挑战，我们建议将基于分子的个性化药物的范式从单独表征组织样本的表征，甚至是基于非侵入性医学成像检查中的整个肿瘤及其周围环境的图像特征的表征。为此，我们的整理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（“图像派”地图）。为了集中精力并赋予我们的努力，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5年生存率为16％，在过去的15年中并未发生明显变化。因此，（1）我们将开发，验证和公开可用，受控的词汇和软件工具，用于构建具有矢量的数据库，以定量描述CT和PET图像中人类肿瘤的特征。 （2）我们将创建并公开提供一个新型的多维数据库，该数据库将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集成在一起。 （3）我们将通过（a）实施生物信息学方法来证明综合成像/基因组/临床数据库的实用性，这些方法从CT和PET图像特征和临床数据创建关联图到基因表达，以及（b）发现包含成像，基因表达和其他临床数据的预后签名。虽然专门为CT和肺癌的PET图像进行了专门开发和验证，但我们的工具将适用于其他方式和疾病情景。特定结果，每年可能影响数十万诊断患有肺癌的患者，将包括（i）一种新的多维预后签名，结合了基因表达，成像特征和其他临床变量，可能会产生新的见解，从而使NSCLC生物多样性及其临床管理的理解能够从临床上进行临床数据，以及II ii）的能力，以及II I IMPEATIST，以及II I IMPEATIST，以及II I IMPEATIST，II IMPEATISTIONTIRATION，II）在临床上进行了临床的临床水平。协助分子靶向的治疗决定，而无需进行入侵活检。 公共卫生相关性：该项目与人类健康具有重要意义。非小细胞肺癌中的演示项目有望提供改进的预后特征，将CT和PET图像与基因组组织谱和其他现有临床数据相结合的CT和PET图像的良好和可重复的医学特征表征。从长远来看，我们为整合医学成像和基因组数据而开发的工具有可能提高我们对疾病生物学的了解，并通过减少活检并更快地融合到最佳管理/治疗方法来改善患者护理。