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Hsieh AR, Luo YL, Bao BY, Chou TC. Comparative analysis of genetic risk scores for predicting biochemical recurrence in prostate cancer patients after radical prostatectomy. BMC Urol 2024; 24:136. [PMID: 38956663 PMCID: PMC11218119 DOI: 10.1186/s12894-024-01524-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/25/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk. METHODS As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS). RESULTS AND CONCLUSIONS We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10-6) in our study.
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Affiliation(s)
- Ai-Ru Hsieh
- Department of Statistics, Tamkang University, New Taipei City, 251301, Taiwan.
| | - Yi-Ling Luo
- Department of Public Health, College of Public Health, China Medical University, Taichung, 40402, Taiwan
| | - Bo-Ying Bao
- School of Pharmacy, China Medical University, Taichung, 406040, Taiwan
- Department of Nursing, Asia University, Taichung, 41354, Taiwan
| | - Tzu-Chieh Chou
- Department of Public Health, College of Public Health, China Medical University, Taichung, 40402, Taiwan
- Department of Health Risk Management, College of Public Health, China Medical University, Taichung, 40402, Taiwan
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陈 睿, 王 静, 王 硕, 唐 思, 索 晨. [Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:892-898. [PMID: 37866943 PMCID: PMC10579072 DOI: 10.12182/20230960209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Indexed: 10/24/2023]
Abstract
Objective To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer. Methods The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated. Results The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population. Conclusion We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.
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Affiliation(s)
- 睿琳 陈
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 静茹 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 硕 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 思琦 唐
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 晨 索
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
- 上海市重大传染病和生物安全研究院 (上海 200032)Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
- 复旦大学泰州健康科学研究院 (泰州 225316)Fudan University Taizhou Institute of Health Sciences, Taizhou 225316, China
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Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H, Kang R, Zhang L, Liu S, Sun X, Zhang S. Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study. JMIR Public Health Surveill 2023; 9:e41640. [PMID: 36607729 PMCID: PMC9862335 DOI: 10.2196/41640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. RESULTS In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. CONCLUSIONS We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.
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Affiliation(s)
- Lanwei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Liyang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Huifang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Ruihua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Luyao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shuzheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Xibin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shaokai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
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4
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Byun J, Han Y, Li Y, Xia J, Long E, Choi J, Xiao X, Zhu M, Zhou W, Sun R, Bossé Y, Song Z, Schwartz A, Lusk C, Rafnar T, Stefansson K, Zhang T, Zhao W, Pettit RW, Liu Y, Li X, Zhou H, Walsh KM, Gorlov I, Gorlova O, Zhu D, Rosenberg SM, Pinney S, Bailey-Wilson JE, Mandal D, de Andrade M, Gaba C, Willey JC, You M, Anderson M, Wiencke JK, Albanes D, Lam S, Tardon A, Chen C, Goodman G, Bojeson S, Brenner H, Landi MT, Chanock SJ, Johansson M, Muley T, Risch A, Wichmann HE, Bickeböller H, Christiani DC, Rennert G, Arnold S, Field JK, Shete S, Le Marchand L, Melander O, Brunnstrom H, Liu G, Andrew AS, Kiemeney LA, Shen H, Zienolddiny S, Grankvist K, Johansson M, Caporaso N, Cox A, Hong YC, Yuan JM, Lazarus P, Schabath MB, Aldrich MC, Patel A, Lan Q, Rothman N, Taylor F, Kachuri L, Witte JS, Sakoda LC, Spitz M, Brennan P, Lin X, McKay J, Hung RJ, Amos CI. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer. Nat Genet 2022; 54:1167-1177. [PMID: 35915169 PMCID: PMC9373844 DOI: 10.1038/s41588-022-01115-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/27/2022] [Indexed: 02/03/2023]
Abstract
To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.
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Affiliation(s)
- Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Jun Xia
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | - Wen Zhou
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Department of Molecular Medicine, Laval University, Quebec City, Quebec, Canada
| | - Zhuoyi Song
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ann Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine Lusk
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | | | | | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zhao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rowland W Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Xihao Li
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Kyle M Walsh
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Olga Gorlova
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Dakai Zhu
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Susan M Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Susan Pinney
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | | | - Colette Gaba
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - James C Willey
- The University of Toledo College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Ming You
- Center for Cancer Prevention, Houston Methodist Research Institute, Houston, TX, USA
| | | | - John K Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephan Lam
- Department of Integrative Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Stig Bojeson
- Department of Clinical Biochemistry, Herlev Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Thomas Muley
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Angela Risch
- Division of Cancer Epigenomics, DKFZ - German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, Salzburg, Austria
- Cancer Cluster Salzburg, Salzburg, Austria
| | | | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | - David C Christiani
- Department of Epidemiology, Harvard T.H.Chan School of Public Health, Boston, MA, USA
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Susanne Arnold
- University of Kentucky, Markey Cancer Center, Lexington, KY, USA
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
| | - Sanjay Shete
- Department of Biostatistics, University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | | | | | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Angeline S Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, NH, USA
| | | | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P. R. China
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Mikael Johansson
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jian-Min Yuan
- UPMC Hillman Cancer Center and Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Melinda C Aldrich
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alpa Patel
- American Cancer Society, Atlanta, GA, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fiona Taylor
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Margaret Spitz
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Xihong Lin
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - James McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
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5
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol 2022; 11:766939. [PMID: 35059311 PMCID: PMC8764453 DOI: 10.3389/fonc.2021.766939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China. Methods A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk. Conclusions We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Shao-Kai Zhang,
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6
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. Lung Cancer 2021; 163:27-34. [PMID: 34894456 DOI: 10.1016/j.lungcan.2021.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. MATERIALS AND METHODS Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. RESULTS By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. CONCLUSIONS We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China.
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7
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Sun P, Lu Q, Li Z, Qin N, Jiang Y, Ma H, Jin G, Yu H, Dai J. Assessment of prognostic prediction models for gastric cancer using genomic and transcriptomic profiles. Meta Gene 2021. [DOI: 10.1016/j.mgene.2021.100890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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8
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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9
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Lyu Z, Li N, Chen S, Wang G, Tan F, Feng X, Li X, Wen Y, Yang Z, Wang Y, Li J, Chen H, Lin C, Ren J, Shi J, Wu S, Dai M, He J. Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population. Cancer Med 2020; 9:3983-3994. [PMID: 32253829 PMCID: PMC7286442 DOI: 10.1002/cam4.3025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/20/2020] [Accepted: 03/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population. METHODS During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability. RESULTS A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (PHL = .689) and all subgroups. CONCLUSIONS We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.
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Affiliation(s)
- Zhangyan Lyu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuohua Chen
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Gang Wang
- Health Department of Kailuan (Group), Tangshan, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoshuang Feng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Wen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuoyu Yang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yalong Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongda Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunqing Lin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiansong Ren
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jufang Shi
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shouling Wu
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Min Dai
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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10
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Yu C, Qin N, Pu Z, Song C, Wang C, Chen J, Dai J, Ma H, Jiang T, Jiang Y. Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer. Breast Cancer Res Treat 2019; 175:691-699. [PMID: 30868394 DOI: 10.1007/s10549-019-05177-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 02/19/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer. METHODS Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set. RESULTS Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively. CONCLUSIONS Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.
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Affiliation(s)
- Chengxiao Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Zhening Pu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.,Center of Clinical Research, Wuxi Institute of Translational Medicine, Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214000, China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.,Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China
| | - Jiaping Chen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China. .,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Yue Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China. .,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
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11
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Xin J, Chu H, Ben S, Ge Y, Shao W, Zhao Y, Wei Y, Ma G, Li S, Gu D, Zhang Z, Du M, Wang M. Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction. Gene 2018; 673:174-180. [PMID: 29908285 DOI: 10.1016/j.gene.2018.06.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/25/2018] [Accepted: 06/12/2018] [Indexed: 12/29/2022]
Abstract
Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.
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Affiliation(s)
- Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haiyan Chu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuqiu Ge
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Shao
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Gaoxiang Ma
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, The Affiliated Nanjing Hospital of Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
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12
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Snetselaar R, van Oosterhout MFM, Grutters JC, van Moorsel CHM. Telomerase Reverse Transcriptase Polymorphism rs2736100: A Balancing Act between Cancer and Non-Cancer Disease, a Meta-Analysis. Front Med (Lausanne) 2018. [PMID: 29536006 PMCID: PMC5835035 DOI: 10.3389/fmed.2018.00041] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The enzyme telomerase reverse transcriptase (TERT) is essential for telomere maintenance. In replicating cells, maintenance of telomere length is important for the preservation of vital genetic information and prevention of genomic instability. A common genetic variant in TERT, rs2736100 C/A, is associated with both telomere length and multiple diseases. Carriage of the C allele is associated with longer telomere length, while carriage of the A allele is associated with shorter telomere length. Furthermore, some diseases have a positive association with the C and some with the A allele. In this study, meta-analyses were performed for two groups of diseases, cancerous diseases, e.g., lung cancer and non-cancerous diseases, e.g., pulmonary fibrosis, using data from genome-wide association studies and case-control studies. In the meta-analysis it was found that cancer positively associated with the C allele (pooled OR 1.16 [95% CI 1.09–1.23]) and non-cancerous diseases negatively associated with the C allele (pooled OR 0.81 [95% CI 0.65–0.99]). This observation illustrates that the ambiguous role of telomere maintenance in disease hinges, at least in part, on a single locus in telomerase genes. The dual role of this single nucleotide polymorphism also emphasizes that therapeutic agents aimed at influencing telomere maintenance should be used with caution.
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Affiliation(s)
- Reinier Snetselaar
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands
| | - Matthijs F M van Oosterhout
- Interstitial Lung Diseases Center of Excellence, Department of Pathology, St Antonius Hospital, Nieuwegein, Netherlands
| | - Jan C Grutters
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands.,Division of Heart and Lung, University Medical Center Utrecht, Utrecht, Netherlands
| | - Coline H M van Moorsel
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands.,Division of Heart and Lung, University Medical Center Utrecht, Utrecht, Netherlands
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13
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Charvat H, Sasazuki S, Shimazu T, Budhathoki S, Inoue M, Iwasaki M, Sawada N, Yamaji T, Tsugane S. Development of a risk prediction model for lung cancer: The Japan Public Health Center-based Prospective Study. Cancer Sci 2018; 109:854-862. [PMID: 29345859 PMCID: PMC5834815 DOI: 10.1111/cas.13509] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 12/28/2017] [Accepted: 01/08/2018] [Indexed: 02/02/2023] Open
Abstract
Although the impact of tobacco consumption on the occurrence of lung cancer is well‐established, risk estimation could be improved by risk prediction models that consider various smoking habits, such as quantity, duration, and time since quitting. We constructed a risk prediction model using a population of 59 161 individuals from the Japan Public Health Center (JPHC) Study Cohort II. A parametric survival model was used to assess the impact of age, gender, and smoking‐related factors (cumulative smoking intensity measured in pack‐years, age at initiation, and time since cessation). Ten‐year cumulative probability of lung cancer occurrence estimates were calculated with consideration of the competing risk of death from other causes. Finally, the model was externally validated using 47 501 individuals from JPHC Study Cohort I. A total of 1210 cases of lung cancer occurred during 986 408 person‐years of follow‐up. We found a dose‐dependent effect of tobacco consumption with hazard ratios for current smokers ranging from 3.78 (2.00‐7.16) for cumulative consumption ≤15 pack‐years to 15.80 (9.67‐25.79) for >75 pack‐years. Risk decreased with time since cessation. Ten‐year cumulative probability of lung cancer occurrence estimates ranged from 0.04% to 11.14% in men and 0.07% to 6.55% in women. The model showed good predictive performance regarding discrimination (cross‐validated c‐index = 0.793) and calibration (cross‐validated χ2 = 6.60; P‐value = .58). The model still showed good discrimination in the external validation population (c‐index = 0.772). In conclusion, we developed a prediction model to estimate the probability of developing lung cancer based on age, gender, and tobacco consumption. This model appears useful in encouraging high‐risk individuals to quit smoking and undergo increased surveillance.
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Affiliation(s)
- Hadrien Charvat
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Shizuka Sasazuki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Taichi Shimazu
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Sanjeev Budhathoki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Manami Inoue
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Motoki Iwasaki
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Norie Sawada
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Taiki Yamaji
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
| | - Shoichiro Tsugane
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan
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Sakoda LC, Henderson LM, Caverly TJ, Wernli KJ, Katki HA. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. CURR EPIDEMIOL REP 2017. [PMID: 29531893 DOI: 10.1007/s40471-017-0126-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.
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Affiliation(s)
- Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Tanner J Caverly
- Center for Clinical Management Research, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD USA
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Lung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility Regions. J Thorac Oncol 2016; 10:1538-45. [PMID: 26352532 DOI: 10.1097/jto.0000000000000666] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We evaluated the lung cancer-predictive performance of single-nucleotide polymorphisms (SNPs) in these regions. METHODS Lung cancer cases (N = 778) and controls (N = 1166) were genotyped for 77 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used stepwise logistic regression and decision-tree analyses. In a subset nested in the Pittsburgh Lung Screening Study, change in area under the receiver operator characteristic curve and net reclassification improvement were used to compare predictions made by risk factor models with and without genetic variables. RESULTS Variable selection and model development kept two SNPs in each of three GWAS regions, rs2736100 and rs7727912 in 5p15.33, rs805297 and rs1802127 in 6p21.33, and rs8034191 and rs12440014 in 15q25.1. The ratio of cases to controls was three times higher among subjects with a high-risk genotype in every one as opposed to none of the three GWAS regions (odds ratio, 3.14; 95% confidence interval, 2.02-4.88; adjusted for sex, age, and pack-years). Adding a three-level classified count of GWAS regions with high-risk genotypes to an age and smoking risk factor-only model improved lung cancer prediction by a small amount: area under the receiver operator characteristic curve, 0.725 versus 0.717 (p = 0.056); overall net reclassification improvement was 0.052 across low-, intermediate-, and high- 6-year lung cancer risk categories (<3.0%, 3.0%-4.9%, ≥ 5.0%). CONCLUSION Specifying genotypes for SNPs in three GWAS-identified susceptibility regions improved lung cancer prediction, but probably by an extent too small to affect disease control practice.
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Cheng Y, Jiang T, Zhu M, Li Z, Zhang J, Wang Y, Geng L, Liu J, Shen W, Wang C, Hu Z, Jin G, Ma H, Shen H, Dai J. Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations. Oncotarget 2016; 8:53959-53967. [PMID: 28903315 PMCID: PMC5589554 DOI: 10.18632/oncotarget.10403] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 06/04/2016] [Indexed: 11/25/2022] Open
Abstract
In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.
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Affiliation(s)
- Yang Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhihua Li
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jiahui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Liguo Geng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jia Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Wei Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Cheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
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17
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Qian DC, Han Y, Byun J, Shin HR, Hung RJ, McLaughlin JR, Landi MT, Seminara D, Amos CI. A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations. Cancer Epidemiol Biomarkers Prev 2016; 25:1208-15. [PMID: 27222311 DOI: 10.1158/1055-9965.epi-15-1318] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 05/13/2016] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways. METHODS For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant SNP variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC). RESULTS Compared with typical binomial prediction models that have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001). CONCLUSIONS Our biologically informed approach demonstrated a larger increase in AUC over nongenetic counterpart models relative to previous approaches that incorporate variants. IMPACT This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference. Cancer Epidemiol Biomarkers Prev; 25(8); 1208-15. ©2016 AACR.
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Affiliation(s)
- David C Qian
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Younghun Han
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Jinyoung Byun
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Hae Ri Shin
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada
| | - John R McLaughlin
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | | | | | - Christopher I Amos
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire.
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18
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Moorthy B, Chu C, Carlin DJ. Polycyclic aromatic hydrocarbons: from metabolism to lung cancer. Toxicol Sci 2016; 145:5-15. [PMID: 25911656 DOI: 10.1093/toxsci/kfv040] [Citation(s) in RCA: 430] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Excessive exposure to polycyclic aromatic hydrocarbons (PAHs) often results in lung cancer, a disease with the highest cancer mortality in the United States. After entry into the lung, PAHs induce phase I metabolic enzymes such as cytochrome P450 (CYP) monooxygenases, i.e. CYP1A1/2 and 1B1, and phase II enzymes such as glutathione S-transferases, UDP glucuronyl transferases, NADPH quinone oxidoreductases (NQOs), aldo-keto reductases (AKRs), and epoxide hydrolases (EHs), via the aryl hydrocarbon receptor (AhR)-dependent and independent pathways. Humans can also be exposed to PAHs through diet, via consumption of charcoal broiled foods. Metabolism of PAHs through the CYP1A1/1B1/EH pathway, CYP peroxidase pathway, and AKR pathway leads to the formation of the active carcinogens diol-epoxides, radical cations, and o-quinones. These reactive metabolites produce DNA adducts, resulting in DNA mutations, alteration of gene expression profiles, and tumorigenesis. Mutations in xenobiotic metabolic enzymes, as well as polymorphisms of tumor suppressor genes (e.g. p53) and/or genes involved in gene expression (e.g. X-ray repair cross-complementing proteins), are associated with lung cancer susceptibility in human populations from different ethnicities, gender, and age groups. Although various metabolic activation/inactivation pathways, AhR signaling, and genetic susceptibilities contribute to lung cancer, the precise points at which PAHs induce tumor initiation remain unknown. The goal of this review is to provide a current state-of-the-science of the mechanisms of human lung carcinogenesis mediated by PAHs, the experimental approaches used to study this complex class of compounds, and future directions for research of these compounds.
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Affiliation(s)
- Bhagavatula Moorthy
- *Department of Pediatrics, Baylor College of Medicine, Houston, Texas and Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709
| | - Chun Chu
- *Department of Pediatrics, Baylor College of Medicine, Houston, Texas and Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709
| | - Danielle J Carlin
- *Department of Pediatrics, Baylor College of Medicine, Houston, Texas and Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709
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CPEB4 and IRF4 expression in peripheral mononuclear cells are potential prognostic factors for advanced lung cancer. J Formos Med Assoc 2016; 116:114-122. [PMID: 27113098 DOI: 10.1016/j.jfma.2016.01.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 08/25/2015] [Accepted: 01/20/2016] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND/PURPOSE Lung cancer is a heterogeneous disease with varied outcomes. Molecular markers are eagerly investigated to predict a patient's treatment response or outcome. Previous studies used frozen biopsy tissues to identify crucial genes as prognostic markers. We explored the prognostic value of peripheral blood (PB) molecular signatures in patients with advanced non-small cell lung cancer (NSCLC). METHODS Peripheral blood mononuclear cell (PBMC) fractions from patients with advanced NSCLC were applied for RNA extraction, cDNA synthesis, and real-time polymerase chain reaction (PCR) for the expression profiling of eight genes: DUSP6, MMD, CPEB4, RNF4, STAT2, NF1, IRF4, and ZNF264. Proportional hazard (PH) models were constructed to evaluate the association of the eight expressing genes and multiple clinical factors [e.g., sex, smoking status, and Charlson comorbidity index (CCI)] with overall survival. RESULTS One hundred and forty-one patients with advanced NSCLC were enrolled. They included 109 (77.30%) patients with adenocarcinoma, 12 (8.51%) patients with squamous cell carcinoma, and 20 (14.18%) patients with other pathological lung cancer types. A PH model containing two significant survival-associated genes, CPEB4 and IRF4, could help in predicting the overall survival of patients with advanced stage NSCLC [hazard ratio (HR) = 0.48, p < 0.0001). Adding multiple clinical factors further improved the prediction power of prognosis (HR = 0.33; p < 0.0001). CONCLUSION Molecular signatures in PB can stratify the prognosis in patients with advanced NSCLC. Further prospective, interventional clinical trials should be performed to test if gene profiling also predicts resistance to chemotherapy.
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20
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David SP, Wang A, Kapphahn K, Hedlin H, Desai M, Henderson M, Yang L, Walsh KM, Schwartz AG, Wiencke JK, Spitz MR, Wenzlaff AS, Wrensch MR, Eaton CB, Furberg H, Mark Brown W, Goldstein BA, Assimes T, Tang H, Kooperberg CL, Quesenberry CP, Tindle H, Patel MI, Amos CI, Bergen AW, Swan GE, Stefanick ML. Gene by Environment Investigation of Incident Lung Cancer Risk in African-Americans. EBioMedicine 2016; 4:153-61. [PMID: 26981579 PMCID: PMC4776066 DOI: 10.1016/j.ebiom.2016.01.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/22/2015] [Accepted: 01/05/2016] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Genome-wide association studies have identified polymorphisms linked to both smoking exposure and risk of lung cancer. The degree to which lung cancer risk is driven by increased smoking, genetics, or gene-environment interactions is not well understood. METHODS We analyzed associations between 28 single nucleotide polymorphisms (SNPs) previously associated with smoking quantity and lung cancer in 7156 African-American females in the Women's Health Initiative (WHI), then analyzed main effects of top nominally significant SNPs and interactions between SNPs, cigarettes per day (CPD) and pack-years for lung cancer in an independent, multi-center case-control study of African-American females and males (1078 lung cancer cases and 822 controls). FINDINGS Nine nominally significant SNPs for CPD in WHI were associated with incident lung cancer (corrected p-values from 0.027 to 6.09 × 10(-5)). CPD was found to be a nominally significant effect modifier between SNP and lung cancer for six SNPs, including CHRNA5 rs2036527[A](betaSNP*CPD = - 0.017, p = 0.0061, corrected p = 0.054), which was associated with CPD in a previous genome-wide meta-analysis of African-Americans. INTERPRETATION These results suggest that chromosome 15q25.1 variants are robustly associated with CPD and lung cancer in African-Americans and that the allelic dose effect of these polymorphisms on lung cancer risk is most pronounced in lighter smokers.
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Affiliation(s)
- Sean P. David
- Division of General Medical Disciplines, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Ange Wang
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Kristopher Kapphahn
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Haley Hedlin
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Manisha Desai
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Henderson
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Lingyao Yang
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Kyle M. Walsh
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- Program in Cancer Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States
| | - Ann G. Schwartz
- Department of Oncology, Karmanos Cancer Institute, Wayne State University, Detroit, MI, United States
| | - John K. Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Margaret R. Spitz
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, United States
| | - Angela S. Wenzlaff
- Department of Oncology, Karmanos Cancer Institute, Wayne State University, Detroit, MI, United States
| | - Margaret R. Wrensch
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Charles B. Eaton
- Center for Primary Care and Prevention, Department of Family Medicine, Warren Alpert Medical School of Brown University, Pawtucket, RI, United States
| | - Helena Furberg
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - W. Mark Brown
- Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, United States
| | - Benjamin A. Goldstein
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, United States
| | - Themistocles Assimes
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, United States
| | - Charles L. Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | - Hilary Tindle
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, United States
| | - Manali I. Patel
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Christopher I. Amos
- Departments of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
- Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | | | - Gary E. Swan
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Marcia L. Stefanick
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
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Schwartz AG, Cote ML. Epidemiology of Lung Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 893:21-41. [PMID: 26667337 DOI: 10.1007/978-3-319-24223-1_2] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Lung cancer continues to be one of the most common causes of cancer death despite understanding the major cause of the disease: cigarette smoking. Smoking increases lung cancer risk 5- to 10-fold with a clear dose-response relationship. Exposure to environmental tobacco smoke among nonsmokers increases lung cancer risk about 20%. Risks for marijuana and hookah use, and the new e-cigarettes, are yet to be consistently defined and will be important areas for continued research as use of these products increases. Other known environmental risk factors include exposures to radon, asbestos, diesel, and ionizing radiation. Host factors have also been associated with lung cancer risk, including family history of lung cancer, history of chronic obstructive pulmonary disease and infections. Studies to identify genes associated with lung cancer susceptibility have consistently identified chromosomal regions on 15q25, 6p21 and 5p15 associated with lung cancer risk. Risk prediction models for lung cancer typically include age, sex, cigarette smoking intensity and/or duration, medical history, and occupational exposures, however there is not yet a risk prediction model currently recommended for general use. As lung cancer screening becomes more widespread, a validated model will be needed to better define risk groups to inform screening guidelines.
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Affiliation(s)
- Ann G Schwartz
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA.
| | - Michele L Cote
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA
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22
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Gray EP, Teare MD, Stevens J, Archer R. Risk Prediction Models for Lung Cancer: A Systematic Review. Clin Lung Cancer 2015; 17:95-106. [PMID: 26712102 DOI: 10.1016/j.cllc.2015.11.007] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 11/09/2015] [Accepted: 11/12/2015] [Indexed: 11/25/2022]
Abstract
Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds.
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Affiliation(s)
- Eoin P Gray
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
| | - M Dawn Teare
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - John Stevens
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Rachel Archer
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
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Xu ZW, Wang GN, Dong ZZ, Li TH, Cao C, Jin YH. CHRNA5 rs16969968 Polymorphism Association with Risk of Lung Cancer - Evidence from 17,962 Lung Cancer Cases and 77,216 Control Subjects. Asian Pac J Cancer Prev 2015; 16:6685-90. [DOI: 10.7314/apjcp.2015.16.15.6685] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Meza R, Meernik C, Jeon J, Cote ML. Lung cancer incidence trends by gender, race and histology in the United States, 1973-2010. PLoS One 2015; 10:e0121323. [PMID: 25822850 PMCID: PMC4379166 DOI: 10.1371/journal.pone.0121323] [Citation(s) in RCA: 245] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Accepted: 01/30/2015] [Indexed: 02/07/2023] Open
Abstract
Background Lung cancer (LC) incidence in the United States (US) continues to decrease but with significant differences by histology, gender and race. Whereas squamous, large and small cell carcinoma rates have been decreasing since the mid-80s, adenocarcinoma rates remain stable in males and continue to increase in females, with large racial disparities. We analyzed LC incidence trends by histology in the US with an emphasis on gender and racial differences. Methods LC incidence rates from 1973–2010 were obtained from the SEER cancer registry. Age-adjusted incidence trends of five major histological types by gender and race were evaluated using joinpoint regression. Trends of LC histology and stage distributions from 2005–2010 were analyzed. Results US LC incidence varies by histology. Squamous, large and small cell carcinoma rates continue to decrease for all gender/race combinations, whereas adenocarcinoma rates remain relatively constant in males and increasing in females. An apparent recent increase in the incidence of squamous cell carcinoma and adenocarcinoma since 2005 can be explained by a concomitant decrease in the number of cases classified as other non-small cell carcinoma. Black males continue to be disproportionally affected by squamous LCs, and blacks continue to be diagnosed with more advanced cancers than whites. Conclusions LC incidence by histology continues to change over time. Additional variations are expected as screening becomes disseminated. It is important to continue to monitor LC rates to evaluate the impact of screening on current trends, assess the continuing benefits of tobacco control, and focus efforts on reducing racial disparities.
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Affiliation(s)
- Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Clare Meernik
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States of America
| | - Jihyoun Jeon
- Program in Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America
| | - Michele L Cote
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States of America
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Wang X, Oldani MJ, Zhao X, Huang X, Qian D. A review of cancer risk prediction models with genetic variants. Cancer Inform 2014; 13:19-28. [PMID: 25288876 PMCID: PMC4179686 DOI: 10.4137/cin.s13788] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Revised: 06/30/2014] [Accepted: 07/01/2014] [Indexed: 12/31/2022] Open
Abstract
Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.
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Affiliation(s)
- Xuexia Wang
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Michael J Oldani
- Criminology and Anthropology Department, University of Wisconsin-Whitewater, Whitewater, WI, USA
| | - Xingwang Zhao
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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Establishment and characterization of a lung cancer cell line, SMC-L001, from a lung adenocarcinoma. In Vitro Cell Dev Biol Anim 2014; 50:519-26. [PMID: 24569940 DOI: 10.1007/s11626-014-9736-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Accepted: 01/21/2014] [Indexed: 01/22/2023]
Abstract
Lung cancer cell lines are a valuable tool for elucidating lung tumorigenesis and developing novel therapies. However, the majority of cell lines currently available were established from tumors in patients of Caucasian origin, limiting our ability to investigate how cancers in patients of different ethnicities differ from one another in terms of tumor biology and drug responses. In this study, we established a human non-small cell lung carcinoma cell line, SMC-L001, and characterized its genome and tumorigenic potential. SMC-L001 cells were isolated from a Korean lung adenocarcinoma patient (male, pStage IIb) and were propagated in culture. SMC-L001 cells were adherent. DNA fingerprinting analysis indicated that the SMC-L001 cell line originated from parental tumor tissue. Comparison of the genomic profile of the SMC-L001 cell line and the original tumor revealed an identical profile with 739 mutations in 46 cancer-related genes, including mutations in TP53 and KRAS. Furthermore, SMC-L001 cells were highly tumorigenic, as evidenced by the induction of solid tumors in immunodeficient mice. In summary, we established a new lung cancer cell line with point mutations in TP53 and KRAS from a Korean lung adenocarcinoma patient that will be useful for investigating ethnic differences in lung cancer biology and drug response.
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Nie W, Zang Y, Chen J, Xiu Q. TERT rs2736100 polymorphism contributes to lung cancer risk: a meta-analysis including 49,869 cases and 73,464 controls. Tumour Biol 2014; 35:5569-74. [PMID: 24535778 DOI: 10.1007/s13277-014-1734-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2013] [Accepted: 02/05/2014] [Indexed: 11/27/2022] Open
Abstract
Some studies investigated the association of TERT rs2736100 polymorphism with lung cancer (LC). But the results were not consistent. We performed a meta-analysis to examine the association between rs2736100 and LC. Databases including PubMed, EMBASE, Wanfang, and China National Knowledge Infrastructure (CNKI) were searched. Data were extracted, and pooled odds ratios (ORs) with 95 % confidence intervals (CIs) were calculated. A total of 19 studies including 49,869 cases and 73,464 controls were involved in this meta-analysis. Overall, a significant association between TERT rs2736100 polymorphism and LC risk was observed (OR=1.23, 95 % CI 1.18-1.28, P<0.00001). This polymorphism was also significantly associated with LC risk in Asians (OR=1.27, 95 % CI 1.22-1.33, P<0.00001), Caucasians (OR=1.14, 95 % CI 1.10-1.18, P<0.00001), female patients (OR=1.37, 95 % CI 1.24-1.51, P<0.00001), male patients (OR=1.23, 95 % CI 1.15-1.31, P<0.00001), adenocarcinoma patients (OR=1.35, 95 % CI 1.28-1.41, P<0.00001), squamous cell carcinoma patients (OR=1.13, 95 % CI 1.04-1.21, P=0.002), small cell lung cancer patients (OR=1.09, 95 % CI 1.03-1.16, P=0.004), current smokers (OR=1.22, 95 % CI 1.17-1.27, P<0.00001), former smokers (OR=1.14, 95 % CI 1.08-1.21, P<0.0001), and never smokers (OR=1.37, 95 % CI 1.31-1.43, P<0.00001), respectively. This meta-analysis suggested that TERT rs2736100 polymorphism was a risk factor for LC.
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Affiliation(s)
- Wei Nie
- Department of Respiratory Medicine, Shanghai Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai, 200003, China
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Abstract
Deaths from lung cancer exceed those from any other type of malignancy, with 1·5 million deaths in 2010. Prevention and smoking cessation are still the main methods to reduce the death toll. The US National Lung Screening Trial, which compared CT screening with chest radiograph, yielded a mortality advantage of 20% to participants in the CT group. International debate is ongoing about whether sufficient evidence exists to implement CT screening programmes. When questions about effectiveness and cost-effectiveness have been answered, which will await publication of the largest European trial, NELSON, and pooled analysis of European CT screening trials, we discuss the main topics that will need consideration. These unresolved issues are risk prediction models to identify patients for CT screening; radiological protocols that use volumetric analysis for indeterminate nodules; options for surgical resection of CT-identified nodules; screening interval; and duration of screening. We suggest that a demonstration project of biennial screening over a 4-year period should be undertaken.
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Affiliation(s)
- John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, The University of Liverpool Cancer Research Centre, Liverpool, UK.
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Brothers JF, Hijazi K, Mascaux C, El-Zein RA, Spitz MR, Spira A. Bridging the clinical gaps: genetic, epigenetic and transcriptomic biomarkers for the early detection of lung cancer in the post-National Lung Screening Trial era. BMC Med 2013; 11:168. [PMID: 23870182 PMCID: PMC3717087 DOI: 10.1186/1741-7015-11-168] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Accepted: 06/20/2013] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the leading cause of cancer death worldwide in part due to our inability to identify which smokers are at highest risk and the lack of effective tools to detect the disease at its earliest and potentially curable stage. Recent results from the National Lung Screening Trial have shown that annual screening of high-risk smokers with low-dose helical computed tomography of the chest can reduce lung cancer mortality. However, molecular biomarkers are needed to identify which current and former smokers would benefit most from annual computed tomography scan screening in order to reduce the costs and morbidity associated with this procedure. Additionally, there is an urgent clinical need to develop biomarkers that can distinguish benign from malignant lesions found on computed tomography of the chest given its very high false positive rate. This review highlights recent genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the early detection of lung cancer both in the diagnostic and screening setting.
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