P-1: Molecular Signatures for Individualizing Lung Cancer Therapy

来自患者的非小细胞肺癌(NSCLC)对药物的敏感性或耐药性存在很大差异 化疗和靶向药物。我们假设这些差异将反映在肿瘤的mrna中。 和蛋白质签名，这些签名可以用来提高疗效 接受治疗的机会。最终目标是开发和使用这些签名来确定最佳可用的治疗方法 对那个人来说。然而，为了实现这一目标，临床前模型迫切需要 开发这样的签名并测试新的疗法。该项目建议使用一大块非小细胞肺癌电池面板 系统地测量临床前治疗反应表型的品系和异种移植物，定义相关的 这些反应的mRNA和蛋白质生物标记物签名，然后在其他细胞系中验证这些签名， 异种移植和病人肿瘤标本。我们还将确定患者的mRNA和蛋白质生物标记物 样本并在临床前模型中进行测试，最终产生有效的生物标记物进行响应 患者的预测和经过验证的临床前模型。具体目标是：目标1)量化衡量 一组人NSCLC细胞系和异种移植瘤(-100)的药物敏感性/耐药表型 (~50)，包括从患者肿瘤直接制成的异种移植而不需要干预培养，并在 体外药物反应表型与原位(肺)移植的药物反应表型；目的2)鉴定微阵列 基于mRNA和反相蛋白阵列(RPPA)的治疗反应的表达特征 NSCLC系列中的药物；使用这些签名，我们将在新的NSCLC测试集中预测药物反应 细胞系和异种移植，并进行一项“临床前试验”，比较标准的非选择方法 治疗(NST)与签名选择疗法(SST)；目的3)验证可用的签名 临床注解的非小细胞肺癌标本对标准药物和靶向药物的反应(约100例冷冻和 200个福尔马林固定的石蜡包埋标本)。我们还将独立开发响应签名 直接从患者样本中获取，并使用临床前模型和其他患者测试这些预测能力 标本。最后，我们将测试其他调查人员报告的签名，并将信息最丰富的 和我们自己的。从所有这些我们将开发新的方法来选择最好的可用的治疗方法 个体患者，建立系统测试新药的临床前人类肿瘤模型系统， 并开发签名以指导其最有效的使用。这个项目汇集了一个多学科的 由基础和临床研究人员组成的团队，有相当多的初步数据，并有临床注释的肿瘤 供研究的标本。它利用病理学、生物统计学和生物信息学的核心并进行交互 与该孢子中的多个其他项目以及多个孢子间的合作成核。

Non-small cell lung cancers (NSCLCs) from patients exhibit large differences in sensitivity or resistance to chemotherapy and targeted drugs. We hypothesize that these differences will be reflected in tumor mRNA and protein signatures prior to treatment, and that these signatures can be used to improve the effectiveness of therapy. The eventual goal to develop and use such signatures to determine the best available treatment for that individual. To move towards this goal, however, there is a critical need for preclinical models to develop such signatures and test new therapies. This project proposes to use a large panel of NSCLC cell lines and xenografts to systematically measure preclinical therapy response phenotypes, define associated mRNA and protein biomarker signatures of these responses, and then validate these in other cell lines, xenografts, and patient tumor specimens. We will also identify mRNA and protein biomarkers in patient specimens and test them in the preclinical models, eventually resulting in validated biomarkers for response prediction in patients and validated preclinical models. Specific Aims are: Aim 1) To measure quantitative drug sensitivity/resistance phenotypes in a large panel (-100) of human NSCLC cell lines and xenografts (~50), including xenografts made directly from patient tumors without intervening culture, and compare in vitro drug response phenotypes with those of orthotopic (lung) xenografts; Aim 2) To identify microarray mRNA and reverse phase protein array (RPPA)-based expression signatures of response to therapeutic agents in NSCLC lines; using these signatures we will predict drug responses in a new "test" set of NSCLC cell lines and xenografts, and conduct a "preclinical trial" comparing the approach of standard non-selected therapy (NST) versus signature-selected therapy (SST); Aim 3) To validate these signatures on available NSCLC specimens clinically annotated as to response to standard and targeted agents (~100 frozen and 200 formalin-fixed paraffin embedded specimens). We will also independently develop response signatures directly from patient samples and test these for predictive ability using preclinical models and other patient specimens. Finally, we will test signatures reported by other investigators and integrate the most informative with our own. From all of this we will develop new methods to select the best available therapies for individual patients, establish a preclinical human tumor model system for systematically testing new drugs, and develop signatures to guide their most efficient use. This project has assembled a multidisciplinary team of basic and clinical investigators, has considerable preliminary data, and clinically annotated tumor specimens for study. It makes use of the Pathology, Biostatistics, and Bioinformatics Cores and interacts with multiple other projects in this SPORE as well as nucleating multiple inter-SPORE collaborations.