Integrated Molecular, Cellular, and Imaging Characterization of NLST detected lung cancer

NLST 检测肺癌的综合分子、细胞和成像特征

基本信息

  • 批准号:
    10415430
  • 负责人:
  • 金额:
    $ 15.6万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-22 至 2022-08-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY/ABSTRACT The landmark NLST demonstrated a 20% mortality reduction in lung cancer in individuals who underwent low dose computed tomography (LDCT) screening relative to plain chest radiography. The results of the NLST have resulted in a sea change in US health policy, such that third party payers and Medicare now provide LDCT screening as a preventive service benefit in eligible, high risk smokers. Understanding the factors underlying tumor indolence or aggression that result in heterogeneous clinical outcomes, may facilitate clinical decision making in the context of lung cancer screening and thereby greatly increase its effectiveness. We hypothesize that the mutational landscape of screen-detected lung cancers is an important contributor to their indolence or aggressiveness. To address this hypothesis we will take advantage of the comprehensively annotated NLST biorepository. Whole exome sequencing (WES) of the previously collected NLST samples will be performed to determine the genomic features that distinguish between screen-detected indolent and aggressive lung tumors that result in heterogeneous clinical outcomes. Samples from 110 patients with aggressive cancers and 494 with indolent cancers along with matching reference tissues will be assessed. By utilizing WES, we will be able to identify the impact of genes not previously associated with lung cancer as well as novel alleles of genes with known roles in lung cancer. This will be the first comprehensive genomic characterization of aggressive and indolent lung cancers diagnosed in the course of a lung cancer screening trial. The detailed genomic and imaging-based characterization of screen-detected tumors will ultimately impact clinical management of those cancers. Through separate funding, we will also integrate the genomic data obtained through this research with the tumor immune microenvironment (as characterized by multiplex immunofluorescence analysis of the same specimens) and CT imaging features to build integrated, multiparametric models of tumor biology that can be used to predict the biological behavior of lung cancers in the screening setting.
项目总结/文摘

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(2)
Drug Development for Metastasis Prevention.
预防转移的药物开发。
  • DOI:
    10.1615/critrevoncog.v20.i5-6.150
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Fontebasso Y;Dubinett SM
  • 通讯作者:
    Dubinett SM
CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA.
  • DOI:
    10.1186/s13059-017-1191-5
  • 发表时间:
    2017-03-24
  • 期刊:
  • 影响因子:
    12.3
  • 作者:
    Kang S;Li Q;Chen Q;Zhou Y;Park S;Lee G;Grimes B;Krysan K;Yu M;Wang W;Alber F;Sun F;Dubinett SM;Li W;Zhou XJ
  • 通讯作者:
    Zhou XJ
Immunosurveillance and Regression in the Context of Squamous Pulmonary Premalignancy.
  • DOI:
    10.1158/2159-8290.cd-20-1087
  • 发表时间:
    2020-10
  • 期刊:
  • 影响因子:
    28.2
  • 作者:
    Krysan K;Tran LM;Dubinett SM
  • 通讯作者:
    Dubinett SM
Pathologic and gene expression comparison of CT- screen detected and routinely detected stage I/0 lung adenocarcinoma in NCCN risk-matched cohorts.
  • DOI:
    10.1016/j.ctarc.2021.100486
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Burks EJ;Zhang J;Sullivan TB;Shi X;Sands JM;Regis SM;McKee BJ;McKee AB;Zhang S;Liu H;Liu G;Spira A;Beane J;Lenburg ME;Rieger-Christ KM
  • 通讯作者:
    Rieger-Christ KM
Understanding the mechanisms of immune-evasion by lung cancer in the context of chronic inflammation in emphysema.
了解肺气肿慢性炎症背景下肺癌免疫逃避的机制。
  • DOI:
    10.21037/jtd.2019.01.22
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Salehi-Rad,Ramin;Dubinett,StevenM
  • 通讯作者:
    Dubinett,StevenM
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DENISE R. ABERLE其他文献

DENISE R. ABERLE的其他文献

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{{ truncateString('DENISE R. ABERLE', 18)}}的其他基金

Individually-tailored clinical decision support for management of indeterminate pulmonary nodules
针对不确定肺结节管理的个性化临床决策支持
  • 批准号:
    10307996
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
EFIRM-Liquid Biopsy (eLB): Ultrasensitive ctDNA and miRNA Detection for Early Assessment of Lung Cancer
EFIRM-液体活检 (eLB):用于肺癌早期评估的超灵敏 ctDNA 和 miRNA 检测
  • 批准号:
    10225427
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
EFIRM-Liquid Biopsy (eLB): Ultrasensitive ctDNA and miRNA Detection for Early Assessment of Lung Cancer
EFIRM-液体活检 (eLB):用于肺癌早期评估的超灵敏 ctDNA 和 miRNA 检测
  • 批准号:
    9982813
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
EFIRM Liquid Biopsy Research Laboratory: Early Lung Cancer Assessment
EFIRM 液体活检研究实验室:早期肺癌评估
  • 批准号:
    10763321
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
EFIRM-Liquid Biopsy (eLB): Ultrasensitive ctDNA and miRNA Detection for Early Assessment of Lung Cancer
EFIRM-液体活检 (eLB):用于肺癌早期评估的超灵敏 ctDNA 和 miRNA 检测
  • 批准号:
    10456340
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
Individually-tailored clinical decision support for management of indeterminate pulmonary nodules
针对不确定肺结节管理的个性化临床决策支持
  • 批准号:
    10055957
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
Individually-tailored clinical decision support for management of indeterminate pulmonary nodules
针对不确定肺结节管理的个性化临床决策支持
  • 批准号:
    10539247
  • 财政年份:
    2018
  • 资助金额:
    $ 15.6万
  • 项目类别:
Molecular and Imaging Biomarkers for Early Lung Cancer Detection in the Setting of Indeterminate Pulmonary Nodules
不确定肺结节中早期肺癌检测的分子和影像生物标志物
  • 批准号:
    10231155
  • 财政年份:
    2016
  • 资助金额:
    $ 15.6万
  • 项目类别:
Molecular and Imaging Biomarkers for Early Lung Cancer Detection in the Setting of Indeterminate Pulmonary Nodules
不确定肺结节中早期肺癌检测的分子和影像生物标志物
  • 批准号:
    10018815
  • 财政年份:
    2016
  • 资助金额:
    $ 15.6万
  • 项目类别:
The Boston University-UCLA Lung Cancer Biomarker Development Lab
波士顿大学-加州大学洛杉矶分校肺癌生物标志物开发实验室
  • 批准号:
    9277841
  • 财政年份:
    2016
  • 资助金额:
    $ 15.6万
  • 项目类别:
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