Lung cancer heterogeneity and its impact on drug resistance.

肺癌异质性及其对耐药性的影响。

基本信息

批准号：
8267104
负责人：
STEVEN J ALTSCHULER
金额：
$ 45.38万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
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/8267104
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

项目摘要

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）：癌细胞之间的表型异质性，即使在单个肿瘤中也观察到，为制定“最佳”靶向治疗计划面临着巨大的挑战。实际上，肿瘤细胞之间的异质性可以转化为不同程度的肿瘤性和药物反应。我们的假设是，少数亚群的表征及其对药物的反应将导致诊断，预后和肺癌治疗的显着改善。因此，我们的长期目标是确定可以预测治疗结果的临床上重要的肿瘤表型。为了鉴定细胞亚群，我们利用基于图像的高含量平台来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。随后的分析技术用于确定最有用的细胞描述，以识别表型不同的亚群，并与单细胞药物反应相关。这种基于图像的方法不需要遗传或生化操纵，并且可以直接转化为疾病与疾病相关的原代细胞样品。综上所述，这种方法将启动数据库的发展，以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，我们开发了关于模型系统进展的方法，从细胞系，异种移植开始，最后移至原发性肿瘤样品的组织切片。拟议的研究有三个目标。第一个目标开发并优化了实验测定，以捕获非小细胞肺癌模型的信号异质性。第二个目标开发并优化了计算方法，以测试信号异质性模式是否与药物敏感性相关。最终目的测试了将基于图像的测定法转换为冷冻和福尔马林固定的石蜡嵌入（FFPE）的主要患者样品的可行性。公共卫生相关性：在此提案中，我们将研究非小细胞肺癌异质性及其对化学疗法的影响。我们将开发实验和计算方法来捕获和表征肺癌异质性，确定其与药物反应的相关性，并测试将我们的方法应用于临床相关样本的可行性。最终，我们旨在更深入地了解肺癌进展，新靶标的鉴定以及对许多类型癌症进行更有效疗法的潜力。