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Grant Details

Grant Number: 1U01CA209414-01A1 Interpret this number
Primary Investigator: Christiani, David
Organization: Harvard School Of Public Health
Project Title: The Boston Lung Cancer Survival Cohort
Fiscal Year: 2017
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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.

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Publications

Forward regression for Cox models with high-dimensional covariates.
Authors: Hong H.G. , Zheng Q. , Li Y. .
Source: Journal of multivariate analysis, 2019 Sep; 173, p. 268-290.
EPub date: 2019-03-05.
PMID: 31007300
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Serum Levels of 25-Hydroxyvitamin D at Diagnosis Are Not Associated with Overall Survival in Esophageal Adenocarcinoma.
Authors: Loehrer E. , Betensky R.A. , Giovannucci E. , Su L. , Shafer A. , Hollis B.W. , Christiani D.C. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019 Aug; 28(8), p. 1379-1387.
EPub date: 2019-06-11.
PMID: 31186263
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Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer.
Authors: Berker Y. , Vandergrift L.A. , Wagner I. , Su L. , Kurth J. , Schuler A. , Dinges S.S. , Habbel P. , Nowak J. , Mark E. , et al. .
Source: Scientific reports, 2019-07-16; 9(1), p. 10319.
EPub date: 2019-07-16.
PMID: 31311965
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Investigation of Leukocyte Telomere Length and Genetic Variants in Chromosome 5p15.33 as Prognostic Markers in Lung Cancer.
Authors: Kachuri L. , Helby J. , Bojesen S.E. , Christiani D.C. , Su L. , Wu X. , Tardón A. , Fernández-Tardón G. , Field J.K. , Davies M.P. , et al. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2019 Jul; 28(7), p. 1228-1237.
PMID: 31263055
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Potentially functional genetic variants in the TNF/TNFR signaling pathway genes predict survival of patients with non-small cell lung cancer in the PLCO cancer screening trial.
Authors: Guo Y. , Feng Y. , Liu H. , Luo S. , Clarke J.W. , Moorman P.G. , Su L. , Shen S. , Christiani D.C. , Wei Q. .
Source: Molecular carcinogenesis, 2019 Jul; 58(7), p. 1094-1104.
EPub date: 2019-04-15.
PMID: 30989732
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Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine.
Authors: Hong H.G. , Christiani D.C. , Li Y. .
Source: Precision clinical medicine, 2019 Jun; 2(2), p. 90-99.
EPub date: 2019-06-18.
PMID: 31355047
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Drawing inferences for high-dimensional linear models: A selection-assisted partial regression and smoothing approach.
Authors: Fei Z. , Zhu J. , Banerjee M. , Li Y. .
Source: Biometrics, 2019 Jun; 75(2), p. 551-561.
EPub date: 2019-03-29.
PMID: 30549000
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A modified partial likelihood score method for Cox regression with covariate error under the internal validation design.
Authors: Zucker D.M. , Zhou X. , Liao X. , Li Y. , Spiegelman D. .
Source: Biometrics, 2019 Jun; 75(2), p. 414-427.
EPub date: 2019-04-13.
PMID: 30525191
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SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis.
Authors: Zhang R. , Lai L. , Dong X. , He J. , You D. , Chen C. , Lin L. , Zhu Y. , Huang H. , Shen S. , et al. .
Source: Molecular oncology, 2019 May; 13(5), p. 1235-1248.
EPub date: 2019-04-17.
PMID: 30924596
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Multiclass linear discriminant analysis with ultrahigh-dimensional features.
Authors: Li Y. , Hong H.G. , Li Y. .
Source: Biometrics, 2019-04-22; , .
EPub date: 2019-04-22.
PMID: 31009070
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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, 2019-04-15; 144(8), p. 1867-1876.
EPub date: 2018-12-08.
PMID: 30259978
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Covariance-Insured Screening.
Authors: He K. , Kang J. , Hong H.G. , Zhu J. , Li Y. , Lin H. , Xu H. , Li Y. .
Source: Computational statistics & data analysis, 2019 Apr; 132, p. 100-114.
EPub date: 2018-09-22.
PMID: 30880853
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Incidental nonneoplastic parenchymal findings in patients undergoing lung resection for mass lesions.
Authors: Hung Y.P. , Hunninghake G.M. , Miller E.R. , Putman R. , Nishino M. , Araki T. , Hatabu H. , Sholl L.M. , Vivero M. .
Source: Human pathology, 2019 Apr; 86, p. 93-101.
EPub date: 2019-01-15.
PMID: 30658062
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Imaging of Cancer Immunotherapy: Current Approaches and Future Directions.
Authors: Nishino M. , Hatabu H. , Hodi F.S. .
Source: Radiology, 2019 Jan; 290(1), p. 9-22.
EPub date: 2018-11-20.
PMID: 30457485
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False discovery control for penalized variable selections with high-dimensional covariates.
Authors: He K. , Zhou X. , Jiang H. , Wen X. , Li Y. .
Source: Statistical applications in genetics and molecular biology, 2018-12-15; 17(6), .
EPub date: 2018-12-15.
PMID: 30864387
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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. , Li W. , et al. .
Source: International journal of cancer, 2018-11-15; 143(10), p. 2400-2408.
EPub date: 2018-09-21.
PMID: 29978465
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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; 12(1), p. 49.
EPub date: 2018-11-06.
PMID: 30400837
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PD-L1 expression, tumor mutational burden, and response to immunotherapy in patients with MET exon 14 altered lung cancers.
Authors: Sabari J.K. , Leonardi G.C. , Shu C.A. , Umeton R. , Montecalvo J. , Ni A. , Chen R. , Dienstag J. , Mrad C. , Bergagnini I. , et al. .
Source: Annals of oncology : official journal of the European Society for Medical Oncology, 2018-10-01; 29(10), p. 2085-2091.
PMID: 30165371
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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.
PMID: 29859855
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More accurate semiparametric regression in pharmacogenomics.
Authors: Rong Y. , Zhao S.D. , Zhu J. , Yuan W. , Cheng W. , Li Y. .
Source: Statistics and its interface, 2018; 11(4), p. 573-580.
EPub date: 2018-09-19.
PMID: 30815051
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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 12; 27(12), p. 1527-1535.
EPub date: 2018-09-05.
PMID: 30185536
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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.
PMID: 29683128
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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.
PMID: 29643546
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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.
PMID: 29120498
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A pairwise likelihood augmented Cox estimator for left-truncated data.
Authors: Wu F. , Kim S. , Qin J. , Saran R. , Li Y. .
Source: Biometrics, 2018 03; 74(1), p. 100-108.
EPub date: 2017-08-29.
PMID: 28853158
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