Lung cancer heterogeneity and its impact on drug resistance.

肺癌异质性及其对耐药性的影响。

• 批准号: 7785211
• 负责人: STEVEN J ALTSCHULER
• 金额: $ 50.1万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7785211
• 关键词: Asses; Biochemical; Biological; Biological Assay; Biological Models; Cancer Patient; Cancer cell line; Cell Fraction; Cell Line; Cells; Clinical; Collection; Complex; Computing Methodologies; Databases; Development; Diagnosis; Disease; Disease Progression; Drug resistance; Drug usage; E-Cadherin; Fluorescence; Formalin; Freezing; Genetic; Goals; Heterogeneity; Image; Immunofluorescence Immunologic; In Vitro; Label; Lead; Malignant Neoplasms; Malignant neoplasm of lung; Methodology; Methods; Modeling; Mus; Non-Small-Cell Lung Carcinoma; Outcome; Paclitaxel; Paraffin Embedding; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Primary Neoplasm; Research; Resources; Sampling; Series; Signal Transduction; Techniques; Testing; Therapeutic; Tissue Banking; Tissue Banks; Tissues; Translating; Tumorigenicity; Work; Xenograft procedure; base; cancer cell; cancer type; chemotherapy; clinically relevant; drug sensitivity; effective therapy; fluorescence imaging; human disease; image processing; in vivo; molecular marker; neoplastic cell; outcome forecast; public health relevance; response; tissue culture; tissue/cell culture; tool; treatment planning; treatment response; tumor

DESCRIPTION (provided by applicant): Phenotypic heterogeneity among cancer cells, observed even within single tumors, presents enormous challenges for developing "optimal" targeted treatment plans. In practical terms, heterogeneity can translate into varying degrees of tumorigenicity and drug response among tumor cells. Our hypothesis is that the characterization of a small number of subpopulations and their responses to drugs will lead to significant improvements in the diagnosis, prognosis, and treatment of lung cancer. Thus, it is our long-term goal to identify clinically important tumor phenotypes that are predictive of therapeutic outcome. To identify cellular subpopulations, we make use of high-content image-based platform for obtaining large number of immuno-fluorescence images of cancer cells exposed to varying drug treatments. Image processing tools are used to extract quantitative and multi-dimensional single-cell phenotypes. Subsequent analytical techniques are applied to determine the most informative cellular descriptions, to identify phenotypically distinct subpopulations, and to correlate with single-cell drug responses. This image-based approach does not require genetic or biochemical manipulation and can translate directly to disease-relevant primary cell samples. Taken together, this approach will initiate the development of databases for correlating quantitative descriptions of tumor heterogeneity to drug sensitivity and therapeutic outcome. In this study we develop our methodology on a progression of model systems, starting from cell lines, xenografts and finally moving to tissue sections of primary patient tumor samples. The proposed research has three goals. The first aim develops and optimizes experimental assays to capture signaling heterogeneity from models of non small cell lung cancer. The second aim develops and optimizes computational methodology to test whether patterns of signaling heterogeneity correlate with drug sensitivities. The final aim tests the feasibility of translating image-based assays to frozen and formalin fixed, paraffin embedding (FFPE) primary patient samples. PUBLIC HEALTH RELEVANCE: In this proposal, we will study non small cell lung cancer heterogeneity and its implications for chemotherapy. We will develop experimental and computational approaches to capture and characterize lung cancer heterogeneity, identify their correlation to drug responses, and test the feasibility of applying our approach to clinically relevant samples. Ultimately we aim to provide a deeper understanding of mechanisms involved in the progression of lung cancer, the identification of new targets, and the potential for more effective therapies for many types of cancer.

描述（由申请人提供）：即使在单个肿瘤中也观察到癌细胞之间的表型异质性，这对制定“最佳”靶向治疗计划提出了巨大挑战。实际上，异质性可以转化为肿瘤细胞之间不同程度的致瘤性和药物反应。我们的假设是，少数亚群的特征及其对药物的反应将导致肺癌的诊断、预后和治疗的显著改善。因此，我们的长期目标是确定临床重要的肿瘤表型，预测治疗结果。为了识别细胞亚群，我们利用高含量的基于图像的平台获得暴露于不同药物治疗的癌细胞的大量免疫荧光图像。图像处理工具用于提取定量和多维单细胞表型。随后的分析技术应用于确定最具信息量的细胞描述，识别表型不同的亚群，并与单细胞药物反应相关联。这种基于图像的方法不需要基因或生化操作，可以直接转化为与疾病相关的原代细胞样本。总之，这种方法将启动数据库的发展，将肿瘤异质性的定量描述与药物敏感性和治疗结果相关联。在这项研究中，我们在模型系统的进展上开发了我们的方法，从细胞系，异种移植物开始，最后移动到原发患者肿瘤样本的组织切片。拟议的研究有三个目标。第一个目标是开发和优化实验分析，以捕获非小细胞肺癌模型的信号异质性。第二个目标是开发和优化计算方法，以测试信号异质性模式是否与药物敏感性相关。最终目的是测试将基于图像的检测方法应用于冷冻和福尔马林固定石蜡包埋（FFPE）原始患者样本的可行性。