|Grant Number:||7U19CA148127-03 Interpret this number|
|Primary Investigator:||Amos, Christopher|
|Project Title:||Transdisciplinary Research in Cancer of the Lung (TRICL)|
Lung cancer is the leading cause of cancer-related mortality in the U.S. and the world. The genetic factors that commonly influence lung cancer susceptibility have not yet been investigated thoroughly, but eight genome-wide association studies have been performed. In area 1 of our response, we propose to integrate data from these eight studies that comprise over 13,000 lung cancer cases and 25,000 controls to increase power to detect genetic factors influencing all types of lung cancer and to allow us to analyze specific subsets, such as cases with early onset, specific histological sets, gender-defined groups and never smokers and the extended sample size will allow us to study gene-environment interactions. Existing genome-wide association studies have not analyzed data from nonCaucasian ethnic backgrounds and we therefore propose characterizing and fine mapping genetic factoid In 1500 African-American and 1500 Asian case-control pairs along with an additional 3000 Caucasian case-control pairs. We will then replicate our findings in a broader collection of an additional 12,000 case-control pairs. In area 2, we will evaluate genes in specific loci including nicotinic acetycholine receptor subunits CHRNA5 and CHRNA3 for the effiects that identified SNPs from area 1 have upon these genes functions. We will also study several other loci that have been identified through existing GWAS as strongly associated with lung cancer risk, including PSMA4, hTERT, CLPT1 ML, BAT3, and hMSHS. For each of these genes, we will study modulation of these genes using cellular models relevant to their known functions. In area 3, we propose epidemiological characterization of genetic and environmental risk factors for lung cancer. We will characterize mechanisms by which variation in the loci identified in area 1 influence risk, in conjunction with smoking and other exposures. We will also evaluate the calibration of existing risk prediction models for lung cancer and then develop new models based upon genetic and environmental data from this initiative. The overarching goal of our proposal is the identification of individuals at high risk for lung cancer development for whom screening and early detection would be most beneficial in reducing the burden of lung cancer.
SNP characteristics predict replication success in association studies.
Authors: Gorlov IP, Moore JH, Peng B, Jin JL, Gorlova OY, Amos CI
Source: Hum Genet, 2014 Dec;133(12), p. 1477-86.
EPub date: 2014 Oct 2.
Bayesian variable selection for hierarchical gene-environment and gene-gene interactions.
Authors: Liu C, Ma J, Amos CI
Source: Hum Genet, 2014 Aug 26;null, p. null.
EPub date: 2014 Aug 26.
A review of the application of inflammatory biomarkers in epidemiologic cancer research.
Authors: Brenner DR, Scherer D, Muir K, Schildkraut J, Boffetta P, Spitz MR, Le Marchand L, Chan AT, Goode EL, Ulrich CM, Hung RJ
Source: Cancer Epidemiol Biomarkers Prev, 2014 Sep;23(9), p. 1729-51.
EPub date: 2014 Jun 24.
Scientific overview: 2013 BBC plenary symposium on tobacco addiction.
Authors: De Biasi M, McLaughlin I, Perez EE, Crooks PA, Dwoskin LP, Bardo MT, Pentel PR, Hatsukami D
Source: Drug Alcohol Depend, 2014 Aug 1;141, p. 107-17.
EPub date: 2014 Jun 2.
Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer.
Authors: Wang Y, McKay JD, Rafnar T, Wang Z, Timofeeva MN, Broderick P, Zong X, Laplana M, Wei Y, Han Y, Lloyd A, Delahaye-Sourdeix M, Chubb D, Gaborieau V, Wheeler W, Chatterjee N, Thorleifsson G, Sulem P, Liu G, Kaaks R, Henrion M, Kinnersley B, Vallée M, LeCalvez-Kelm F, Stevens VL, Gapstur SM, Chen WV, Zaridze D, Szeszenia-Dabrowska N, Lissowska J, Rudnai P, Fabianova E, Mates D, Bencko V, Foretova L, Janout V, Krokan HE, Gabrielsen ME, Skorpen F, Vatten L, Njølstad I, Chen C, Goodman G, Benhamou S, Vooder T, Välk K, Nelis M, Metspalu A, Lener M, Lubi?ski J, Johansson M, Vineis P, Agudo A, Clavel-Chapelon F, Bueno-de-Mesquita HB, Trichopoulos D, Khaw KT, Johansson M, Weiderpass E, Tjønneland A, Riboli E, Lathrop M, Scelo G, Albanes D, Caporaso NE, Ye Y, Gu J, Wu X, Spitz MR, Dienemann H, Rosenberger A, Su L, Matakidou A, Eisen T, Stefansson K, Risch A, Chanock SJ, Christiani DC, Hung RJ, Brennan P, Landi MT, Houlston RS, Amos CI
Source: Nat Genet, 2014 Jul;46(7), p. 736-41.
EPub date: 2014 Jun 1.
The ?3?4* nicotinic ACh receptor subtype mediates physical dependence to morphine: mouse and human studies.
Authors: Muldoon PP, Jackson KJ, Perez E, Harenza JL, Molas S, Rais B, Anwar H, Zaveri NT, Maldonado R, Maskos U, McIntosh JM, Dierssen M, Miles MF, Chen X, De Biasi M, Damaj MI
Source: Br J Pharmacol, 2014 Aug;171(16), p. 3845-57.
A genome-wide gene-environment interaction analysis for tobacco smoke and lung cancer susceptibility.
Authors: Zhang R, Chu M, Zhao Y, Wu C, Guo H, Shi Y, Dai J, Wei Y, Jin G, Ma H, Dong J, Yi H, Bai J, Gong J, Sun C, Zhu M, Wu T, Hu Z, Lin D, Shen H, Chen F
Source: Carcinogenesis, 2014 Jul;35(7), p. 1528-35.
EPub date: 2014 Mar 22.
How to get the most from microarray data: advice from reverse genomics.
Authors: Gorlov IP, Yang JY, Byun J, Logothetis C, Gorlova OY, Do KA, Amos C
Source: BMC Genomics, 2014 Mar 21;15, p. 223.
EPub date: 2014 Mar 21.
Nicotine enhances excitability of medial habenular neurons via facilitation of neurokinin signaling.
Authors: Dao DQ, Perez EE, Teng Y, Dani JA, De Biasi M
Source: J Neurosci, 2014 Mar 19;34(12), p. 4273-84.
Exposure to secondhand tobacco smoke and lung cancer by histological type: a pooled analysis of the International Lung Cancer Consortium (ILCCO).
Authors: Kim CH, Lee YC, Hung RJ, McNallan SR, Cote ML, Lim WY, Chang SC, Kim JH, Ugolini D, Chen Y, Liloglou T, Andrew AS, Onega T, Duell EJ, Field JK, Lazarus P, Le Marchand L, Neri M, Vineis P, Kiyohara C, Hong YC, Morgenstern H, Matsuo K, Tajima K, Christiani DC, McLaughlin JR, Bencko V, Holcatova I, Boffetta P, Brennan P, Fabianova E, Foretova L, Janout V, Lissowska J, Mates D, Rudnai P, Szeszenia-Dabrowska N, Mukeria A, Zaridze D, Seow A, Schwartz AG, Yang P, Zhang ZF
Source: Int J Cancer, 2014 Oct 15;135(8), p. 1918-30.
EPub date: 2014 Mar 25.
Characterizing the genetic basis of methylome diversity in histologically normal human lung tissue.
Authors: Shi J, Marconett CN, Duan J, Hyland PL, Li P, Wang Z, Wheeler W, Zhou B, Campan M, Lee DS, Huang J, Zhou W, Triche T, Amundadottir L, Warner A, Hutchinson A, Chen PH, Chung BS, Pesatori AC, Consonni D, Bertazzi PA, Bergen AW, Freedman M, Siegmund KD, Berman BP, Borok Z, Chatterjee N, Tucker MA, Caporaso NE, Chanock SJ, Laird-Offringa IA, Landi MT
Source: Nat Commun, 2014 Feb 27;5, p. 3365.
EPub date: 2014 Feb 27.
A network-based kernel machine test for the identification of risk pathways in genome-wide association studies.
Authors: Freytag S, Manitz J, Schlather M, Kneib T, Amos CI, Risch A, Chang-Claude J, Heinrich J, Bickeböller H
Source: Hum Hered, 2013;76(2), p. 64-75.
EPub date: 2014 Jan 14.
Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data.
Authors: Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen G, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D
Source: Carcinogenesis, 2014 May;35(5), p. 1039-45.
EPub date: 2014 Jan 13.