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Developing approaches for modeling genomic and proteomic profiles in lung cancer

开发肺癌基因组和蛋白质组图谱建模方法

基本信息

批准号：
8103076
负责人：
Pamela Markell McMahon
金额：
$ 23.18万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-01 至 2013-03-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8103076
关键词：
Ablation Address Adherence Advisory Committees Age Algorithms American Award Benign Biological Markers Cancer Burden Cancer Intervention Characteristics Clinical Complex Data Development Disease Doctor of Philosophy Effectiveness Eligibility Determination Future Gender General Hospitals Genomics Goals Growth and Development function Guidelines Image Imaging technology Individual Institutes Investments Lesion Malignant Neoplasms Malignant neoplasm of lung Mass Screening Massachusetts Measurable Mentors Mentorship Methods Modeling Molecular Profiling Natural History Neoplasm Metastasis Outcome Patients Phase Policies Population Population Dynamics Positioning Attribute Proteomics Race Relative (related person)Research Research Personnel Risk Role Screening for cancer Screening procedure Simulate Site Smoking Smoking History Smoking Status Source Specific qualifier value Spiral Computed Tomography Staging Structure Technology Assessment Time Workplace base cell type cohort cost effective cost effectiveness design experience follow-up improved interest lung cancer screening meetings models and simulation mortality novel programs statistics tool

项目摘要

DESCRIPTION (provided by applicant): Despite recent decreases in smoking rates, lung cancer claimed more than 163,000 American lives in 2005. Interest in possible lung cancer screening programs is intense; several large ongoing trials are evaluating imaging technologies to detect early-stage lung cancer. In addition, public and private investment in genomic and proteomic research may add biomarkers to the list of tools for lung cancer screening. Decisions about the appropriate roles of imaging and biomarkers in lung cancer screening programs can be informed by modeling, a formal, transparent way to integrate available data. In the proposed independent phase, I will incorporate genomic and proteomic profiles into the Lung Cancer Policy Model, a comprehensive microsimulation model of lung cancer designed to evaluate the cost, effectiveness, and cost-effectiveness of screening programs. I am an outcomes researcher with experience in developing complex disease simulation models, including the natural history model at the core of the Lung Cancer Policy Model. My long term research agenda is to develop methods and approaches to making disease simulation models more robust and useful to decision makers. Specifically, future approaches for cancer screening, treatment, and surveillance promise to be increasingly tailored to the individual patient. Modeling cancer interventions will require much more detail on individual characteristics and clinical algorithms than is now typical in disease simulation models. The site of the proposed study, the Institute for Technology Assessment (ITA) at Massachusetts General Hospital, offers a rich combination of facilities, expertise, and mentorship. The proposed Sponsor, G. Scott Gazelle, MD MPH PhD, became my primary mentor when I joined the ITA in 1998 as a research assistant, and recently chaired my doctoral dissertation committee. We have established a comfortable and productive work environment and have developed a mentoring plan that will allow me to complete my transition to an independent researcher role. Protected time for coursework, meetings with an Advisory Committee of clinical and modeling experts, and completion of ongoing analyses involving the Lung Cancer Policy Model will position me appropriately to undertake the proposed research and apply for an independent research award.

描述（由申请人提供）：尽管最近吸烟率有所下降，但肺癌在2005年夺去了超过16.3万美国人的生命。人们对可能的肺癌筛查项目非常感兴趣；几项正在进行的大型试验正在评估检测早期肺癌的成像技术。此外，对基因组和蛋白质组学研究的公共和私人投资可能会将生物标志物添加到肺癌筛查工具列表中。通过建模，一种整合现有数据的正式、透明的方式，可以决定成像和生物标志物在肺癌筛查项目中的适当作用。在拟议的独立阶段，我将把基因组和蛋白质组学资料纳入肺癌政策模型，这是一个全面的肺癌微观模拟模型，旨在评估筛查项目的成本、有效性和成本效益。