Genotype and Imaging Phenotype Biomarkers in Lung Cancer

肺癌的基因型和影像表型生物标志物

• 批准号: 8799943
• 负责人: Hugo Aerts
• 金额: $ 66.76万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-09 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8799943
• 关键词: Affect; Aftercare; Bioinformatics; Biological Assay; Biological Markers; Biopsy; Cancer Etiology; Cancer Patient; Cessation of life; Clinical; Clinical Data; Clinical Trials; Communities; Computational Biology; Dana-Farber Cancer Institute; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Disease; Disease Management; Disease Progression; Enrollment; Epidermal Growth Factor Receptor; Event; Gene Expression; Genetic Programming; Genomics; Genotype; Goals; Image; Image Analysis; Imaging Techniques; Imaging technology; Institutes; KRAS2 gene; Lung; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Medical Imaging; Medicine; Methods; Modeling; Molecular; Molecular Biology; Molecular Profiling; Monitor; Motion; Mutation; Needs Assessment; Non-Small-Cell Lung Carcinoma; Oncogenes; Outcome; Patients; Pattern; Performance; Phase; Phenotype; Positron-Emission Tomography; Resistance; Sampling; Slice; Somatic Mutation; Source; Subgroup; System; Testing; Time; Treatment outcome; United States; Validation; base; cancer care; cancer imaging; cohort; cost; data integration; exome sequencing; head and neck cancer patient; image archival system; image reconstruction; improved; insight; neoplastic cell; non-invasive imaging; oncology; personalized medicine; prognostic; prospective; public health relevance; quantitative imaging; response; stability testing; statistics; technology development; treatment response; tumor

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.

描述（由申请人提供）：基因组学的进展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，需要重复对异质肿瘤进行采样，并且分析的成本相对较高，这为监测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“放射线学”的新兴领域为使用非侵入性成像测定法搜索预测性生物标志物提供了机会，可以在整个治疗过程中使用。实际上，我们最近证明了放射线生物标志物在大量肺和头颈癌患者中具有强烈的预后性能，并且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，单独或与从治疗前活检获得的基因组突变谱数据结合使用的放射素分析可以提供肿瘤表型的详细表征。在该提案中，我们将开发一种将与公众共享的放射系统系统，开发一个针对分析放射组和基因组数据的严格统计平台，并将我们的发展应用于大量非小细胞肺癌（NSCLC），使用肿瘤样本，为此我们既有非发明的CT（PET）Impational Impational Profication Proficition Profitication Profitical Profitical Profitical Profitical Proficity profitication数据。我们还将探讨量化肿瘤表型的放射线图像特征是否与基因组突变谱有关，从而提供了一种在整个治疗过程中非侵入性地监测该疾病的分子状态的方法。该建议利用了我们研究所的个人资料研究，这是一项全面的个性化癌症医学计划，生成了大多数接受治疗的患者的突变数据。使用471个体细胞突变的测定测试启动的配置文件，并于2013年扩展到外显子组测序。目前每年大约有12,000名患者入学。因此，在该项目的期间，我们将可以访问具有成像和基因组突变数据的4000名NSCLC患者。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志物。为了实现我们的目标，我们组建了一个跨学科团队，包括成像，计算生物学，分子生物学，肿瘤学和生物信息学专家。