预测肺癌辅助化疗反应-猫眼课题宝

预测肺癌辅助化疗反应

批准号：

8617729

负责人：

Yang Xie

金额：

$32.72万

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

项目状态：

已结题

起止时间：

2010-09-01 至 2016-02-29

来源：

https://reporter.nih.gov/project-details/8617729

关键词：

Accounting Adjuvant Chemotherapy Adopted Adverse effects Area Under Curve Cancer Center Cancer Patient Cause of Death Cessation of life Chemotherapy-Oncologic Procedure Clinical Clinical Data Clinical Research Copy Number Polymorphism DNA copy number Data Data Analyses Data Set Decision Making Dimensions Disease-Free Survival Epidemiology Evaluation Excision Genes Genetic Polymorphism Goals Histology Individual Joints Knowledge Lead Literature Lung Malignant Neoplasms Malignant neoplasm of lung Medical Oncology Medicine Messenger RNA Methodology Methods Modeling Molecular Profiling Non-Small-Cell Lung Carcinoma Outcome Pathological Staging Pathology Patients Performance Receiver Operating Characteristics Recurrence Research Resected Resistance Risk Sampling Selection for Treatments Staging Subgroup Survival Rate Texas Time Tissue Banking Tissue Banks Translational Research Translations Tumor-Derived United States Universities Weight Woman anticancer research base cancer therapy chemotherapy clinical decision-making clinical epidemiology clinical practice cohort drug sensitivity epidemiologic data follow-up genetic epidemiology genome-wide improved mRNA Expression men novel pre-clinical programs protein expression public health relevance randomized trial response standard of care

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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.

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