Genotype and Imaging Phenotype Biomarkers in Lung Cancer

描述(申请人提供)：基因组学的进步使我们认识到，肿瘤的特征是驱动疾病发展和进展的不同分子事件。但是，需要对异质肿瘤进行重复采样，而且化验成本相对较高，为监测这种疾病及其对治疗的反应提供了有限的机会。新的定量成像技术和新兴的“放射组学”领域提供了使用可在整个治疗过程中使用的非侵入性成像分析来搜索预测性生物标记物的机会。事实上，我们最近已经证明，放射性生物标记物在大量肺癌和头颈癌患者中有很强的预后表现，并与潜在的基因表达和体细胞突变模式有关。我们的变革性假设是，放射组学分析，无论是单独分析，还是结合从治疗前活检获得的基因组突变图谱数据，都可以提供肿瘤表型的详细特征。在这项提案中，我们将开发一个与公众共享的放射组学系统，开发一个专门用于分析放射和基因组数据的严格统计平台，并使用我们拥有无创CT(PET)成像数据和突变特征数据的肿瘤样本，将我们的开发应用于大量非小细胞肺癌(NSCLC)。我们还将探索量化肿瘤表型的放射影像特征是否与基因组突变特征有关，从而提供一种在整个治疗过程中非侵入性监测疾病分子状态的手段。这一建议利用了我们研究所的概况研究，这是一项全面的个性化癌症药物倡议，生成了大多数正在接受治疗的患者的突变数据。Profile使用对471个体细胞突变的分析测试推出，并于2013年扩展到外显子组测序。目前，每年约有12,000名患者登记在册。因此，在这个项目的时间段内，我们将获得4000名非小细胞肺癌患者的成像和基因组突变数据。我们还将利用现有的公共和私人数据库来验证我们发现的最相关的生物标记物。为了实现我们的目标，我们组建了一个跨学科的团队，其中包括成像、计算生物学、分子生物学、肿瘤学和生物信息学方面的专家。

DESCRIPTION (provided by applicant): Advances in genomics have led us to recognize that tumors are characterized by distinct molecular events that drive development and progression of disease. But the need for repeated sampling of heterogeneous tumors and the relatively high cost of the assays provides limited opportunities to monitor the disease and its response to treatment. New quantitative imaging techniques and the emerging field of "radiomics" provides opportunities to search for predictive biomarkers using non-invasive imaging assays that can be used throughout the course of treatment. Indeed, we have recently demonstrated that radiomic biomarkers have strong prognostic performance in large cohorts of lung and head and neck cancer patients, and are associated with the underlying gene-expression and somatic mutation patterns. Our transformative hypothesis is that radiomic analysis, either alone or in combination with genomic mutational profile data obtained from pre- treatment biopsies, can provide a detailed characterization of the tumor phenotype. In this proposal, we will develop a radiomics system that will be shared with the public, develop a rigorous statistics platform specific for analyzing radiomic and genomics data, and apply our developments on a large cohort of non-small cell lung cancer (NSCLC) using tumor samples for which we have both non-invasive CT(PET) imaging data and mutational profiling data. We will also explore whether the radiomic image features quantifying the tumor phenotype are related to genomic mutational profiles, providing a means to monitor non-invasively the molecular state of the disease throughout therapy. This proposal takes advantage of the Profile study at our institute, a comprehensive personalized cancer medicine initiative generating mutational data on the majority of patients undergoing therapy. Profile launched using an assay testing for 471 somatic mutations and expanded in 2013 to exome sequencing. Approximately 12,000 patients are currently enrolled in Profile each year. Therefore, within the time period of this project, we will have access to >4000 NSCLC patients with imaging and genomic mutation data. We will also leverage existing public and private databases to validate the most relevant biomarkers we discover. To achieve our goals we have assembled an interdisciplinary team including experts in imaging, computational biology, molecular biology, oncology, and bioinformatics.