Predicting Adjuvant Chemotherapy Response in Lung Cancer

项目简介：肺癌是美国癌症死亡的主要原因。辅助化疗越来越多地被用作非小细胞肺癌（NSCLC）切除术患者的标准治疗。然而，这种治疗也伴随着严重的副作用。德克萨斯大学肺癌研究卓越专业项目（UT SPORE）收集了大量的药物敏感性数据，以及临床、流行病学和全基因组分子谱数据，以开发个性化的癌症治疗方法。然而，如何将这些海量数据整合转化为科学知识和临床应用，已经成为当前癌症研究的瓶颈。本研究旨在解决这一问题，建立肺癌辅助化疗反应的综合预测模型。我们将利用现有的临床前、临床和流行病学数据来开发一个综合的预测模型。我们将与UT SPORE在肺癌领域合作，收集独立患者队列的新数据，以验证该模型。本研究的具体目的是：(1)开发和比较个体分子谱数据集的预测特征，包括mRNA表达、蛋白质表达、拷贝数变异和种系多态性数据。(2)结合预测分子特征和临床信息，建立辅助化疗应答的综合预测模型。(3)采用独立的患者队列对综合预测模型进行验证和表征。本项目汇集了一支在定量研究、临床研究、转化研究、病理学和遗传流行病学等方面优势互补的优秀研究团队，致力于提高肺癌治疗水平。如果成功实施，该项目将对肺癌临床实践和转化癌症研究产生重大影响。

DESCRIPTION (provided by applicant): Project Summary: Lung Cancer is the leading cause of death from cancer in the United States. Adjuvant chemotherapy is increasingly used as the standard of care for patients with resected Non-Small-Cell Lung Cancer (NSCLC). However, such treatment is also associated with serious adverse effects. A large amount of drug sensitivity data, as well as clinical, epidemiology and genome-wide molecular profiling data have been collected by The University of Texas Specialized Program in Research Excellence (UT SPORE) in Lung Cancer to develop personalized cancer treatments. However, the integration and translation of these massive data to scientific knowledge and clinical usage has become a bottleneck of current cancer research. This study aims at tackling this problem and building a comprehensive prediction model of response to adjuvant chemotherapy in lung cancer. We will use the existing preclinical, clinical and epidemiology data to develop a comprehensive prediction model. We will collaborate with UT SPORE in Lung Cancer to collect new data on an independent patient cohort to validate the model. The specific aims of this study are: (1) To develop and compare predictive signatures from individual molecular profiling datasets including mRNA expression, protein expression, copy number variation and germline polymorphism data. (2) To build a comprehensive prediction model of response to adjuvant chemotherapy by integrating predictive molecular signatures and clinical information. (3) To validate and characterize the comprehensive prediction model using an independent patient cohort. This project assembles an outstanding research team with complementary expertise in quantitative research, clinical research, translational research, pathology and genetic epidemiology, and is dedicated to improving lung cancer treatments. If implemented successfully, this project will have substantial impact on lung cancer clinical practice and translational cancer research.