Skip to main content
Grant Details

Grant Number: 5U01CA209414-02 Interpret this number
Primary Investigator: Christiani, David
Organization: Harvard School Of Public Health
Project Title: The Boston Lung Cancer Survival Cohort
Fiscal Year: 2018
Back to top


Abstract

ABSTRACT Lung cancer is a heterogeneous disease and the leading cancer-related killer in the United States. Understanding the molecular causes of this heterogeneity is the focus of current basic and translational research. The Boston Lung Cancer Study (BLCS) is cancer epidemiology cohort of over 11,000 lung cancer cases enrolled at Massachusetts General Hospital and the Dana-Farber Cancer Institute from 1992-present. This is the first and most comprehensive survivor cohort with the longest follow-up period, and will grow to over 14,000 by the end of this proposed cycle with additional recruitment to enhance the samples with oncogenic driver mutation status. By identifying the relevant patients and providing critical archived tumor tissues, through collaboration within the Dana- Farber/Harvard Cancer Center (DF/HCC) Lung Cancer Program, the BLCS supported the first discovery of the association between EGFR mutations and response to therapy with EGFR-TKIs, initiating the era of targeted therapy in lung cancer. We are now applying for support of the existing cohort infrastructure and resume the recruitment of new cases through a cooperative agreement. The overarching aims are 3-fold: 1) To maintain the lung cancer core cohort and maximize its potential for future scientific needs for lung cancer survival research; 2) To enable cutting- edge research questions by epidemiologic methods development methodologic, and piloting approaches that will turn into productive multidisciplinary project grants aimed at studying various aspects of survival, as well as treatment toxicity; and 3) To leverage the DF/HCC lung program resources for creative multidisciplinary collaborations focused on treatment outcomes. The rationale for recruiting new cases into the large cohort include: 1) facilitating the evaluation between new treatments and traditional therapy and the study design of new clinical investigation in an ever-changing therapeutic environment; 2) collecting more cases with rare oncogenic driver mutations; 3) including patients with accurate histology, smoking histories, and mutational load analyses treated with immunotherapy; 4) collecting repeated biological samples suitable for development of prognostic/predictive biomarkers; and 5) allowing better assessment of the less-studied small-cell lung cancer (SCLC). This application will also support us to establish an innovative COPD phenotyping database via automated image analysis of high- resolution computed tomography, as well as a radiomics data base, as well as to develop a new method of analyzing linked genetic epidemiologic data and information from electronic medical records (EMR) with support from an ongoing R35 award (Dr. Xihong Lin). The BLCS cohort is one of the few survival cohorts contributing substantial data on survival status, with tumor mutation data and tissue available. The combination of biomarker data, tumor molecular characterization, CT imaging and traditional epidemiologic risk factor data will allow powerful translational research and provide unique opportunities to further explore predictors of survival and treatment outcomes. We aim to maintain the quality of the BLCS follow-up and associated data as well as to develop approaches that will provide novel opportunities to investigators.

Back to top


Publications

Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes.
Authors: Hong H.G. , Chen X. , Christiani D.C. , Li Y. .
Source: Biometrics, 2018 06; 74(2), p. 421-429.
EPub date: 2017-11-09 00:00:00.0.
PMID: 29120498
Related Citations

Bayesian variable selection for parametric survival model with applications to cancer omics data.
Authors: Duan W. , Zhang R. , Zhao Y. , Shen S. , Wei Y. , Chen F. , Christiani D.C. .
Source: Human Genomics, 2018-11-06 00:00:00.0; 12(1), p. 49.
EPub date: 2018-11-06 00:00:00.0.
PMID: 30400837
Related Citations

Potentially functional genetic variants in the complement-related immunity gene-set are associated with non-small cell lung cancer survival.
Authors: Qian D. , Liu H. , Wang X. , Ge J. , Luo S. , Patz E.F. , Moorman P.G. , Su L. , Shen S. , Christiani D.C. , et al. .
Source: International Journal Of Cancer, 2018-09-27 00:00:00.0; , .
EPub date: 2018-09-27 00:00:00.0.
PMID: 30259978
Related Citations

DNA Methylation of LRRC3B: A Biomarker for Survival of Early-Stage Non-Small Cell Lung Cancer Patients.
Authors: Guo Y. , Zhang R. , Shen S. , Wei Y. , Salama S.M. , Fleischer T. , Bjaanæs M.M. , Karlsson A. , Planck M. , Su L. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2018-09-05 00:00:00.0; , .
EPub date: 2018-09-05 00:00:00.0.
PMID: 30185536
Related Citations

Genetic variant of IRAK2 in the toll-like receptor signaling pathway and survival of non-small cell lung cancer.
Authors: Xu Y. , Liu H. , Liu S. , Wang Y. , Xie J. , Stinchcombe T.E. , Su L. , Zhang R. , Christiani D.C. , Wei L. , et al. .
Source: International Journal Of Cancer, 2018-07-06 00:00:00.0; , .
EPub date: 2018-07-06 00:00:00.0.
PMID: 29978465
Related Citations

Multi-Omics Analysis Reveals a HIF Network and Hub Gene EPAS1 Associated with Lung Adenocarcinoma.
Authors: Wang Z. , Wei Y. , Zhang R. , Su L. , Gogarten S.M. , Liu G. , Brennan P. , Field J.K. , McKay J.D. , Lissowska J. , et al. .
Source: Ebiomedicine, 2018 Jun; 32, p. 93-101.
EPub date: 2018-05-31 00:00:00.0.
PMID: 29859855
Related Citations

Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review.
Authors: Grace H.H. , Li Y. .
Source: Applied Mathematics : A Journal Of Chinese Universities, 2017 Dec; 32(4), p. 379-396.
EPub date: 2017-12-29 00:00:00.0.
PMID: 29683128
Related Citations

Partition-based ultrahigh-dimensional variable screening.
Authors: Kang J. , Hong H.G. , Li Y.I. .
Source: Biometrika, 2017 Nov; 104(4), p. 785-800.
EPub date: 2017-10-09 00:00:00.0.
PMID: 29643546
Related Citations




Back to Top