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      Whole-genome sequencing of East Asian lung cancers reveals new germline pathogenic variants

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      Cancer Cell
      Elsevier BV

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          Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes

          Summary While several lung cancer susceptibility loci have been identified, much of lung cancer heritability remains unexplained. Here, 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated GWAS analysis of lung cancer on 29,266 patients and 56,450 controls. We identified 18 susceptibility loci achieving genome wide significance, including 10 novel loci. The novel loci highlighted the striking heterogeneity in genetic susceptibility across lung cancer histological subtypes, with four loci associated with lung cancer overall and six with lung adenocarcinoma. Gene expression quantitative trait analysis (eQTL) in 1,425 normal lung tissues highlighted RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes, OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer.
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            Higher Lung Cancer Incidence in Young Women Than Young Men in the United States

            Previous studies showed a higher incidence of lung cancer among young women than among young men in the United States. Whether this pattern has continued in contemporary birth cohorts and, if so, whether it can be fully explained by sex differences in smoking behaviors are unknown.
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              Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations

              Genetic variation plays an important role in the development of non-small cell lung cancer (NSCLC). However, major genetic factors for lung cancer have not been fully identified, especially in Chinese populations, which deters us from using a polygenic risk score (PRS) to identify sub-populations at high-risk of lung cancer for prevention. To systematically identify genetic variants for NSCLC risk, we newly genotyped 19,546 samples and conducted a meta-analysis of genome-wide association studies (GWASs) of 27,120 cases and 27,355 controls. We then built a PRS for Chinese populations and evaluated its utility and effectiveness in predicting high-risk populations of lung cancer in an independent prospective cohort of 95,408 individuals from China Kadoorie Biobank (CKB). We identified 19 susceptibility loci to be significantly associated with NSCLC risk at 5·0×10 -8 , including six novel ones. When applied to the CKB cohort, the PRS of the risk loci successfully predicted lung cancer incidence in a dose-response manner ( P trend =2·02×10 -9 ). Specially, we observed apparently separate predictive morbidity curves for low, intermediate, and high genetic risk populations respectively, and PRS was an independent effective risk stratification indicator beyond age and pack-years during a 10-year follow-up time of the cohort. Based on the systematic identification of the risk loci for NSCLC, we have proved for the first time that GWAS-derived PRS can be effectively used in screening for high-risk populations of lung cancer, potentially leading to a feasible PRS-based lung cancer screening program for individualized prevention in Chinese populations. National Natural Science Foundation of China, the Priority Academic Program for the Development of Jiangsu Higher Education Institutions, National Key R&D Program of China, and China’s Thousand Talents Program.
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                Author and article information

                Journal
                Cancer Cell
                Cancer Cell
                Elsevier BV
                15356108
                October 2022
                October 2022
                : 40
                : 10
                : 1081-1083
                Article
                10.1016/j.ccell.2022.09.006
                45a9965c-b987-45b0-b73c-a53a96a0b125
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://www.elsevier.com/open-access/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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