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Yu C, Tang Y, Liu M, Xu X, Ge X, Ma H, Jin G, Shen H, Song C, Hu Z. The risk stratification and predictive performance of a new combined polygenic risk score for hepatocellular carcinoma. J Gastroenterol 2024:10.1007/s00535-024-02144-5. [PMID: 39126459 DOI: 10.1007/s00535-024-02144-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Recent genome-wide association studies (GWASs) in liver diseases have generated some polygenic risk scores (PRSs), but their predictive effectiveness on hepatocellular carcinoma (HCC) risk assessment remains unclear. METHODS Here, we constructed a novel combined polygenic risk score and evaluated its increment to the well-established risk model. We used 15 HCC-associated genetic loci from two PRSs and FinnGen GWAS data to calculate a PRS-combined score and to fit the related PRS model in the UK Biobank cohort (N = 436,162). The PRS-combined score was further assessed for risk stratification for HCC integrating with the recommended clinical risk scores. RESULTS The PRS-combined model achieved a better AUC (0.657) than that of PRS-HFC (0.637) and PRS-cirrhosis (0.645). The top 20% of the PRS-combined distribution had a 3.25 increased risk of HCC vs. the middle decile (45-55%). At the population level, the addition of PRS-combined to the CLivD score significantly increased the C-statistic (from 0.716 to 0.746) and provided a remarkable improvement in reclassification (NRI = 0.088) at the 10-year risk threshold of 0.2%. In clinic, additional assessment of PRS-combined would reclassify 34,647 intermediate-risk participants as high genetic risk, corresponding to an increase of 63.92% (62/97) of the HCC events classified at high risk using the Fibrosis-4 alone. CONCLUSIONS The PRS may enhance HCC risk prediction effectiveness in the general population and refine risk stratification of the conventional clinical indicator.
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Affiliation(s)
- Chengxiao Yu
- Health Management Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People's Republic of China
- Department of Health Management, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
| | - Yuchen Tang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
| | - Maojie Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
| | - Xin Xu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
| | - Xinyuan Ge
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, 100142, People's Republic of China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, 100142, People's Republic of China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China
- Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, 100142, People's Republic of China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China.
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China.
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, 211166, People's Republic of China.
- Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, 100142, People's Republic of China.
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Judd J, Spence JP, Pritchard JK, Kachuri L, Witte JS. Investigating the Role of Neighborhood Socioeconomic Status and Germline Genetics on Prostate Cancer Risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.31.24311312. [PMID: 39132496 PMCID: PMC11312637 DOI: 10.1101/2024.07.31.24311312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Background Genetic factors play an important role in prostate cancer (PCa) development with polygenic risk scores (PRS) predicting disease risk across genetic ancestries. However, there are few convincing modifiable factors for PCa and little is known about their potential interaction with genetic risk. We analyzed incident PCa cases (n=6,155) and controls (n=98,257) of European and African ancestry from the UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status (nSES)-and how it may interact with PRS-on PCa risk. Methods We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to understand the association of germline genetics with PCa in UKB. Using the English Indices of Deprivation, a set of validated metrics that quantify lack of resources within geographical areas, we performed logistic regression to investigate the main effects and interactions between nSES deprivation, PCa PRS, and PCa. Results The PCa PRS was strongly associated with PCa (OR=2.04; 95%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education (OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96; P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects showed little heterogeneity across nSES deprivation indices, except for the Townsend Index (P=0.03). Conclusions We reaffirmed genetics as a risk factor for PCa and identified nSES deprivation domains that influence PCa detection and are potentially correlated with environmental exposures that are a risk factor for PCa. These findings also suggest that nSES and genetic risk factors for PCa act independently.
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Mallabar-Rimmer B, Merriel SWD, Webster AP, Jackson L, Wood AR, Barclay M, Tyrrell J, Ruth KS, Thirlwell C, Oram R, Weedon MN, Bailey SER, Green HD. Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study. Eur J Hum Genet 2024:10.1038/s41431-024-01654-3. [PMID: 39090236 DOI: 10.1038/s41431-024-01654-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/15/2024] [Accepted: 06/17/2024] [Indexed: 08/04/2024] Open
Abstract
Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.
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Affiliation(s)
| | - Samuel W D Merriel
- Division of Population Health, Health Services Research & Primary Care, University of Manchester, Manchester, UK
| | - Amy P Webster
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Leigh Jackson
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Matthew Barclay
- Department of Behavioural Science & Health, Institute of Epidemiology & Health Care, University College London, London, UK
| | - Jessica Tyrrell
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Katherine S Ruth
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | | | - Richard Oram
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Sarah E R Bailey
- Department of Health and Community Sciences, University of Exeter, Exeter, UK
| | - Harry D Green
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
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Arena G, Landoulsi Z, Grossmann D, Payne T, Vitali A, Delcambre S, Baron A, Antony P, Boussaad I, Bobbili DR, Sreelatha AAK, Pavelka L, J Diederich N, Klein C, Seibler P, Glaab E, Foltynie T, Bandmann O, Sharma M, Krüger R, May P, Grünewald A. Polygenic Risk Scores Validated in Patient-Derived Cells Stratify for Mitochondrial Subtypes of Parkinson's Disease. Ann Neurol 2024; 96:133-149. [PMID: 38767023 DOI: 10.1002/ana.26949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 04/25/2024] [Accepted: 04/28/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE The aim of our study is to better understand the genetic architecture and pathological mechanisms underlying neurodegeneration in idiopathic Parkinson's disease (iPD). We hypothesized that a fraction of iPD patients may harbor a combination of common variants in nuclear-encoded mitochondrial genes ultimately resulting in neurodegeneration. METHODS We used mitochondria-specific polygenic risk scores (mitoPRSs) and created pathway-specific mitoPRSs using genotype data from different iPD case-control datasets worldwide, including the Luxembourg Parkinson's Study (412 iPD patients and 576 healthy controls) and COURAGE-PD cohorts (7,270 iPD cases and 6,819 healthy controls). Cellular models from individuals stratified according to the most significant mitoPRS were subsequently used to characterize different aspects of mitochondrial function. RESULTS Common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk in independent cohorts (Luxembourg Parkinson's Study odds ratio, OR = 1.31[1.14-1.50], p-value = 5.4e-04; COURAGE-PD OR = 1.23[1.18-1.27], p-value = 1.5e-29). Functional analyses in fibroblasts and induced pluripotent stem cells-derived neuronal progenitors revealed significant differences in mitochondrial respiration between iPD patients with high or low OXPHOS-PRS (p-values < 0.05). Clinically, iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients (false discovery rate [FDR]-adj p-value = 0.015), similar to prototypic monogenic forms of PD. Finally, iPD patients with high OXPHOS-PRS responded more effectively to treatment with mitochondrially active ursodeoxycholic acid. INTERPRETATION OXPHOS-PRS may provide a precision medicine tool to stratify iPD patients into a pathogenic subgroup genetically defined by specific mitochondrial impairment, making these individuals eligible for future intelligent clinical trial designs. ANN NEUROL 2024;96:133-149.
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Affiliation(s)
- Giuseppe Arena
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Zied Landoulsi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dajana Grossmann
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Translational Neurodegeneration Section "Albrecht-Kossel", Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Thomas Payne
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Armelle Vitali
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sylvie Delcambre
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexandre Baron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Paul Antony
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ibrahim Boussaad
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dheeraj Reddy Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ashwin Ashok Kumar Sreelatha
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Lukas Pavelka
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Nico J Diederich
- Department of Neurosciences, Centre Hospitalier de Luxembourg, Strassen, Luxembourg
| | - Christine Klein
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Philip Seibler
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, Institute of Neurology, University College London, London, UK
| | - Oliver Bandmann
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Rejko Krüger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier du Luxembourg, Luxembourg, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anne Grünewald
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
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Tian J, Zhang M, Zhang F, Gao K, Lu Z, Cai Y, Chen C, Ning C, Li Y, Qian S, Bai H, Liu Y, Zhang H, Chen S, Li X, Wei Y, Li B, Zhu Y, Yang J, Jin M, Miao X, Chen K. Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies. Genome Med 2024; 16:81. [PMID: 38872215 PMCID: PMC11170922 DOI: 10.1186/s13073-024-01355-y] [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: 11/17/2023] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Early detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population. METHODS To develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS148); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS183); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRSGenomewide). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants. RESULTS Three trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose-response effect of PRS183 on incident colorectal neoplasms. Incorporating PRS183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32). CONCLUSIONS Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.
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Affiliation(s)
- Jianbo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
| | - Ming Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Fuwei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Kai Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Can Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Caibo Ning
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Yanmin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Sangni Qian
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hao Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yizhuo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Heng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Shuoni Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Xiangpan Li
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Yongchang Wei
- Department of Gastrointestinal Oncology, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, China
| | - Jinhua Yang
- Jiashan Institute of Cancer Prevention and Treatment, Jiashan, China
| | - Mingjuan Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Research Center of Public Health, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
| | - Kun Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Colorectal Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Jung AW, Holm PC, Gaurav K, Hjaltelin JX, Placido D, Mortensen LH, Birney E, Brunak SR, Gerstung M. Multi-cancer risk stratification based on national health data: a retrospective modelling and validation study. Lancet Digit Health 2024; 6:e396-e406. [PMID: 38789140 DOI: 10.1016/s2589-7500(24)00062-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/19/2023] [Accepted: 03/13/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Health care is experiencing a drive towards digitisation, and many countries are implementing national health data resources. Although a range of cancer risk models exists, the utility on a population level for risk stratification across cancer types has not been fully explored. We aimed to close this gap by evaluating pan-cancer risk models built on electronic health records across the Danish population with validation in the UK Biobank. METHODS In this retrospective modelling and validation study, data for model development and internal validation were derived from the following Danish health registries: the Central Person Registry, the Danish National Patient Registry, the death registry, the cancer registry, and full-text medical records from secondary care records in the capital region. The development data included adults aged 16-86 years without previous malignant cancers in the time period from Jan 1, 1995, to Dec 31, 2014. The internal validation period was from Jan 1, 2015, to April 10, 2018, and the data included all adults without a previous indication of cancer aged 16-75 years on Dec 31, 2014. The external validation cohort from the UK Biobank included all adults without a previous indication of cancer aged 50-75 years. We used time-dependent Bayesian Cox hazard models built on the combined medical history of Danish individuals. A set of 1392 covariates from available clinical disease trajectories, text-mined basic health factors, and family histories were used to train predictive models of 20 major cancer types. The models were validated on cancer incidence between 2015 and 2018 across Denmark and on individuals in the UK Biobank. The primary outcomes were discrimination and calibration performance. FINDINGS From the Danish registries, we included 6 732 553 individuals covering 60 million hospital visits, 90 million diagnoses, and a total of 193 million life-years between Jan 1, 1978, and April 10, 2018. Danish registry data covering the period from Jan 1, 2015, to April 10, 2018, were used to internally validate risk models, containing a total of 4 248 491 individuals who remained at risk of a primary malignant cancer diagnosis and 67 401 cancer cases recorded. For the external validation, we evaluated the same time period in the UK Biobank covering 377 004 individuals with 11 486 cancer cases. The predictive performance of the models on Danish data showed good discrimination (concordance index 0·81 [SD 0·08], ranging from 0·66 [95% CI 0·65-0·67] for cervix uteri cancer to 0·91 [0·90-0·92] for liver cancer). Performance was similar on the UK Biobank in a direct transfer when controlling for shifts in the age distribution (concordance index 0·66 [SD 0·08], ranging from 0·55 [95% CI 0·44-0·66] for cervix uteri cancer to 0·78 [0·77-0·79] for lung cancer). Cancer risks were associated, in addition to heritable components, with a broad range of preceding diagnoses and health factors. The best overall performance was seen for cancers of the digestive system (oesophageal, stomach, colorectal, liver, and pancreatic) but also thyroid, kidney, and uterine cancers. INTERPRETATION Data available in national electronic health databases can be used to approximate cancer risk factors and enable risk predictions in most cancer types. Model predictions generalise between the Danish and UK health-care systems. With the emergence of multi-cancer early detection tests, electronic health record-based risk models could supplement screening efforts. FUNDING Novo Nordisk Foundation and the Danish Innovation Foundation.
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Affiliation(s)
- Alexander W Jung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK; University of Cambridge, Cambridge, UK
| | - Peter C Holm
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kumar Gaurav
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Laust Hvas Mortensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Statistics Denmark, Copenhagen, Denmark
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK
| | - S Ren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Hinxton, UK; Division of AI in Oncology, German Cancer Research Centre DKFZ, Heidelberg, Germany; Robert Bosch Center for Tumor Diseases, Stuttgart, Germany; Medical Faculty, Eberhard-Karls-University, Tübingen, Germany; University Hospital Tübingen, Tübingen, Germany.
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7
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Johansson M, Robbins HA. Harnessing population-wide health data to predict cancer risk. Lancet Digit Health 2024; 6:e377-e378. [PMID: 38789137 DOI: 10.1016/s2589-7500(24)00093-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024]
Affiliation(s)
- Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, 69366 Lyon CEDEX 07, France.
| | - Hilary A Robbins
- Genomic Epidemiology Branch, International Agency for Research on Cancer, 69366 Lyon CEDEX 07, France
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Vince RA, Sun H, Singhal U, Schumacher FR, Trapl E, Rose J, Cullen J, Zaorsky N, Shoag J, Hartman H, Jia AY, Spratt DE, Fritsche LG, Morgan TM. Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set. Eur Urol Oncol 2024:S2588-9311(24)00111-1. [PMID: 38734542 DOI: 10.1016/j.euo.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/26/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Polygenic risk scores (PRSs) have been developed to identify men with the highest risk of prostate cancer. Our aim was to compare the performance of 16 PRSs in identifying men at risk of developing prostate cancer and then to evaluate the performance of the top-performing PRSs in differentiating individuals at risk of aggressive prostate cancer. METHODS For this case-control study we downloaded 16 published PRSs from the Polygenic Score Catalog on May 28, 2021 and applied them to Michigan Genomics Initiative (MGI) patients. Cases were matched to the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry to obtain granular clinical and pathological data. MGI prospectively enrolls patients undergoing surgery at the University of Michigan, and MUSIC is a multi-institutional registry that prospectively tracks demographic, treatment, and clinical variables. The predictive performance of each PRS was evaluated using the area under the covariate-adjusted receiver operating characteristic curve (aAUC), and the association between PRS and disease aggressiveness according to prostate biopsy data was measured using logistic regression. KEY FINDINGS AND LIMITATIONS We included 18 050 patients in the analysis, of whom 15 310 were control subjects and 2740 were prostate cancer cases. The median age was 66.1 yr (interquartile range 59.9-71.6) for cases and 56.6 yr (interquartile range 42.6-66.7) for control subjects. The PRS performance in predicting the risk of developing prostate cancer according to aAUC ranged from 0.51 (95% confidence interval 0.51-0.53) to 0.67 (95% confidence interval 0.66-0.68). By contrast, there was no association between PRS and disease aggressiveness. CONCLUSIONS AND CLINICAL IMPLICATIONS Prostate cancer PRSs have modest real-world performance in identifying patients at higher risk of developing prostate cancer; however, they are limited in distinguishing patients with indolent versus aggressive disease. PATIENT SUMMARY Risk scores using data for multiple genes (called polygenic risk scores) can identify men at higher risk of developing prostate cancer. However, these scores need to be refined to be able to identify men with the highest risk for clinically significant prostate cancer.
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Affiliation(s)
- Randy A Vince
- Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA.
| | - Helen Sun
- Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Udit Singhal
- Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Erika Trapl
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Johnie Rose
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Jennifer Cullen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Nicholas Zaorsky
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Johnathan Shoag
- Department of Urology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Holly Hartman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Angela Y Jia
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Daniel E Spratt
- Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA
| | - Lars G Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Todd M Morgan
- Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
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Zhang X, He Y, Li X, Shraim R, Xu W, Wang L, Farrington SM, Campbell H, Timofeeva M, Zgaga L, Vaughan-Shaw P, Theodoratou E, Dunlop MG. Circulating 25-hydroxyvitamin D and survival outcomes of colorectal cancer: evidence from population-based prospective cohorts and Mendelian randomisation. Br J Cancer 2024; 130:1585-1591. [PMID: 38480934 PMCID: PMC11058806 DOI: 10.1038/s41416-024-02643-5] [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: 05/19/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND To investigate the association between circulating 25-hydroxyvitamin D (25-OHD) and colorectal cancer (CRC) survival outcomes. METHODS We conducted analyses among the Study of Colorectal Cancer in Scotland (SOCCS) and the UK Biobank (UKBB). Both cancer-specific survival (CSS) and overall survival (OS) outcomes were examined. The 25-OHD levels were categorised into three groups, and multi-variable Cox-proportional hazard models were applied to estimate hazard ratios (HRs). We performed individual-level Mendelian randomisation (MR) through the generated polygenic risk scores (PRS) of 25-OHD and summary-level MR using the inverse-variance weighted (IVW) method. RESULTS We observed significantly poorer CSS (HR = 0.65,95%CI = 0.55-0.76,P = 1.03 × 10-7) and OS (HR = 0.66,95%CI = 0.58-0.75,P = 8.15 × 10-11) in patients with the lowest compared to those with the highest 25-OHD after adjusting for covariates. These associations remained across patients with varied tumour sites and stages. However, we found no significant association between 25-OHD PRS and either CSS (HR = 0.98,95%CI = 0.80-1.19,P = 0.83) or OS (HR = 1.07,95%CI = 0.91-1.25,P = 0.42). Furthermore, we found no evidence for causal effects by conducting summary-level MR analysis for either CSS (IVW:HR = 1.04,95%CI = 0.85-1.28,P = 0.70) or OS (IVW:HR = 1.10,95%CI = 0.93-1.31,P = 0.25). CONCLUSION This study supports the observed association between lower circulating 25-OHD and poorer survival outcomes for CRC patients. Whilst the genotype-specific association between better outcomes and higher 25-OHD is intriguing, we found no support for causality using MR approaches.
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Affiliation(s)
- Xiaomeng Zhang
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Yazhou He
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xue Li
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- School of Public Health and the Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Rasha Shraim
- Department of Public Health and Primary Care, Institute of Population Health, Trinity College Dublin, Dublin, Republic of Ireland
| | - Wei Xu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Lijuan Wang
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Susan M Farrington
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Maria Timofeeva
- Danish Institute for Advanced Study (DIAS), Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Lina Zgaga
- Department of Public Health and Primary Care, Institute of Population Health, Trinity College Dublin, Dublin, Republic of Ireland
| | - Peter Vaughan-Shaw
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Malcolm G Dunlop
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
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10
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Fan Z, Zhang Y, Yao Q, Liu X, Duan H, Liu Y, Sheng C, Lyu Z, Yang L, Song F, Huang Y, Song F. Effects of joint screening for prostate, lung, colorectal, and ovarian cancer - results from a controlled trial. Front Oncol 2024; 14:1322044. [PMID: 38741776 PMCID: PMC11089133 DOI: 10.3389/fonc.2024.1322044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
Background Although screening is widely used to reduce cancer burden, untargeted cancers are frequently missed after single cancer screening. Joint cancer screening is presumed as a more effective strategy to reduce overall cancer burden. Methods Gender-specific screening effects on PLCO cancer incidence, PLCO cancer mortality, all-neoplasms mortality and all-cause mortality were evaluated, and meta-analyses based on gender-specific screening effects were conducted to achieve the pooled effects. The cut-off value of time-dependent receiver-operating-characteristic curve of 10-year combined PLCO cancer risk was used to reclassify participants into low- and high-risk subgroups. Further analyses were conducted to investigate screening effects stratified by risk groups and screening compliance. Results After a median follow-up of 10.48 years for incidence and 16.85 years for mortality, a total of 5,506 PLCO cancer cases, 1,845 PLCO cancer deaths, 3,970 all-neoplasms deaths, and 14,221 all-cause deaths were documented in the screening arm, while 6,261, 2,417, 5,091, and 18,516 outcome-specific events in the control arm. Joint cancer screening did not significantly reduce PLCO cancer incidence, but significantly reduced male-specific PLCO cancer mortality (hazard ratio and 95% confidence intervals [HR(95%CIs)]: 0.88(0.82, 0.95)) and pooled mortality [0.89(0.84, 0.95)]. More importantly, joint cancer screening significantly reduced both gender-specific all-neoplasm mortality [0.91(0.86, 0.96) for males, 0.91(0.85, 0.98) for females, and 0.91(0.87, 0.95) for meta-analyses] and all-cause mortality [0.90(0.88, 0.93) for male, 0.88(0.85, 0.92) for female, and 0.89(0.87, 0.91) for meta-analyses]. Further analyses showed decreased risks of all-neoplasm mortality was observed with good compliance [0.72(0.67, 0.77) for male and 0.72(0.65, 0.80) for female] and increased risks with poor compliance [1.61(1.40, 1.85) for male and 1.30(1.13, 1.40) for female]. Conclusion Joint cancer screening could be recommended as a potentially strategy to reduce the overall cancer burden. More compliance, more benefits. However, organizing a joint cancer screening not only requires more ingenious design, but also needs more attentions to the potential harms. Trial registration NCT00002540 (Prostate), NCT01696968 (Lung), NCT01696981 (Colorectal), NCT01696994 (Ovarian).
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Affiliation(s)
- Zeyu Fan
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yu Zhang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Qiaoling Yao
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xiaomin Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Hongyuan Duan
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ya Liu
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Chao Sheng
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Zhangyan Lyu
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Lei Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing, China
| | - Fangfang Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology, Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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11
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Zhang Y, Lindström S, Kraft P, Liu Y. Genetic Risk, Health-Associated Lifestyle, and Risk of Early-onset Total Cancer and Breast Cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.04.24305361. [PMID: 38633776 PMCID: PMC11023660 DOI: 10.1101/2024.04.04.24305361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Importance Early-onset cancer (diagnosed under 50 years of age) is associated with aggressive disease characteristics and its rising incidence is a global concern. The association between healthy lifestyle and early-onset cancer and whether it varies by common genetic variants is unknown. Objective To examine the associations between genetic risk, lifestyle, and risk of early-onset cancers. Design Setting and Participants We analyzed a prospective cohort of 66,308 white British participants who were under age 50 and free of cancer at baseline in the UK Biobank. Exposures Sex-specific composite total cancer polygenic risk scores (PRSs), a breast cancer-specific PRS, and sex-specific health-associated lifestyle scores (HLSs, which summarize smoking status, body mass index [males only], physical activity, alcohol consumption, and diet). Main Outcomes and Measures Hazard ratios (HRs) and 95% confidence intervals (CIs) for early-onset total and breast cancer. Results A total of 1,247 incident invasive early-onset cancer cases (female: 820, male: 427, breast: 386) were documented. In multivariable-adjusted analyses with 2-year latency, higher genetic risk (highest vs. lowest tertile of PRS) was associated with significantly increased risks of early-onset total cancer in females (HR, 95% CI: 1.85, 1.50-2.29) and males (1.94, 1.45-2.59) as well as early-onset breast cancer in females (3.06, 2.20-4.25). An unfavorable lifestyle (highest vs. lowest category of HLS) was associated with higher risk of total cancer and breast cancer in females across genetic risk categories; the association with total cancer was stronger in the highest genetic risk category than the lowest: HRs in females and men were 1.85 (1.02, 3.36), 3.27 (0.78, 13.72) in the highest genetic risk category and 1.15 (0.44, 2.98), 1.16 (0.39, 3.40) in the lowest. Conclusions and Relevance Both genetic and lifestyle factors were independently associated with early-onset total and breast cancer risk. Compared to those with low genetic risk, individuals with a high genetic risk may benefit more from adopting a healthy lifestyle in preventing early-onset cancer.
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Affiliation(s)
- Yin Zhang
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Peter Kraft
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yuxi Liu
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
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12
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Duncan MS, Diaz-Zabala H, Jaworski J, Tindle HA, Greevy RA, Lipworth L, Hung RJ, Freiberg MS, Aldrich MC. Interaction between Continuous Pack-Years Smoked and Polygenic Risk Score on Lung Cancer Risk: Prospective Results from the Framingham Heart Study. Cancer Epidemiol Biomarkers Prev 2024; 33:500-508. [PMID: 38227004 PMCID: PMC10988206 DOI: 10.1158/1055-9965.epi-23-0571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 10/13/2023] [Accepted: 01/11/2024] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Lung cancer risk attributable to smoking is dose dependent, yet few studies examining a polygenic risk score (PRS) by smoking interaction have included comprehensive lifetime pack-years smoked. METHODS We analyzed data from participants of European ancestry in the Framingham Heart Study Original (n = 454) and Offspring (n = 2,470) cohorts enrolled in 1954 and 1971, respectively, and followed through 2018. We built a PRS for lung cancer using participant genotyping data and genome-wide association study summary statistics from a recent study in the OncoArray Consortium. We used Cox proportional hazards regression models to assess risk and the interaction between pack-years smoked and genetic risk for lung cancer adjusting for European ancestry, age, sex, and education. RESULTS We observed a significant submultiplicative interaction between pack-years and PRS on lung cancer risk (P = 0.09). Thus, the relative risk associated with each additional 10 pack-years smoked decreased with increasing genetic risk (HR = 1.56 at one SD below mean PRS, HR = 1.48 at mean PRS, and HR = 1.40 at one SD above mean PRS). Similarly, lung cancer risk per SD increase in the PRS was highest among those who had never smoked (HR = 1.55) and decreased with heavier smoking (HR = 1.32 at 30 pack-years). CONCLUSIONS These results suggest the presence of a submultiplicative interaction between pack-years and genetics on lung cancer risk, consistent with recent findings. Both smoking and genetics were significantly associated with lung cancer risk. IMPACT These results underscore the contributions of genetics and smoking on lung cancer risk and highlight the negative impact of continued smoking regardless of genetic risk.
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Affiliation(s)
- Meredith S. Duncan
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Diaz-Zabala
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - James Jaworski
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hilary A. Tindle
- Geriatric Research Education and Clinical Centers (GRECC), Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
- Division of Internal Medicine, Vanderbilt University Medical Center, Nashville Tennessee
| | - Robert A. Greevy
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Loren Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Matthew S. Freiberg
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatric Research Education and Clinical Centers (GRECC), Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Melinda C. Aldrich
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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13
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Lee YC, Jung SH, Shivakumar M, Cha S, Park WY, Won HH, Eun YG, Biobank PM, Kim D. Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks. BMC Med 2024; 22:120. [PMID: 38486201 PMCID: PMC10941505 DOI: 10.1186/s12916-024-03305-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results. METHODS We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes. RESULTS We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption. CONCLUSIONS The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.
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Affiliation(s)
- Young Chan Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Soojin Cha
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Samsung Medical Center, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Young-Gyu Eun
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Penn Medicine Biobank
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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14
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Hu J, Ye Y, Zhou G, Zhao H. Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank. JNCI Cancer Spectr 2024; 8:pkae008. [PMID: 38366150 PMCID: PMC10919929 DOI: 10.1093/jncics/pkae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/04/2024] [Accepted: 02/08/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables. METHODS We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers. RESULTS Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group. CONCLUSION Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.
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Affiliation(s)
- Jiaqi Hu
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA
| | - Yixuan Ye
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Geyu Zhou
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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15
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LoPiccolo J, Gusev A, Christiani DC, Jänne PA. Lung cancer in patients who have never smoked - an emerging disease. Nat Rev Clin Oncol 2024; 21:121-146. [PMID: 38195910 PMCID: PMC11014425 DOI: 10.1038/s41571-023-00844-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/11/2024]
Abstract
Lung cancer is the most common cause of cancer-related deaths globally. Although smoking-related lung cancers continue to account for the majority of diagnoses, smoking rates have been decreasing for several decades. Lung cancer in individuals who have never smoked (LCINS) is estimated to be the fifth most common cause of cancer-related deaths worldwide in 2023, preferentially occurring in women and Asian populations. As smoking rates continue to decline, understanding the aetiology and features of this disease, which necessitate unique diagnostic and treatment paradigms, will be imperative. New data have provided important insights into the molecular and genomic characteristics of LCINS, which are distinct from those of smoking-associated lung cancers and directly affect treatment decisions and outcomes. Herein, we review the emerging data regarding the aetiology and features of LCINS, particularly the genetic and environmental underpinnings of this disease as well as their implications for treatment. In addition, we outline the unique diagnostic and therapeutic paradigms of LCINS and discuss future directions in identifying individuals at high risk of this disease for potential screening efforts.
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Affiliation(s)
- Jaclyn LoPiccolo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- The Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- The Eli and Edythe L. Broad Institute, Cambridge, MA, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | - Pasi A Jänne
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- The Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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16
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Rossi SH, Harrison H, Usher-Smith JA, Stewart GD. Risk-stratified screening for the early detection of kidney cancer. Surgeon 2024; 22:e69-e78. [PMID: 37993323 DOI: 10.1016/j.surge.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/22/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023]
Abstract
Earlier detection and screening for kidney cancer has been identified as a key research priority, however the low prevalence of the disease in unselected populations limits the cost-effectiveness of screening. Risk-stratified screening for kidney cancer may improve early detection by targeting high-risk individuals whilst limiting harms in low-risk individuals, potentially increasing the cost-effectiveness of screening. A number of models have been identified which estimate kidney cancer risk based on both phenotypic and genetic data, and while several of the former have been shown to identify individuals at high-risk of developing kidney cancer with reasonable accuracy, current evidence does not support including a genetic component. Combined screening for lung cancer and kidney cancer has been proposed, as the two malignancies share some common risk factors. A modelling study estimated that using lung cancer risk models (currently used for risk-stratified lung cancer screening) could capture 25% of patients with kidney cancer, which is only slightly lower than using the best performing kidney cancer-specific risk models based on phenotypic data (27%-33%). Additionally, risk-stratified screening for kidney cancer has been shown to be acceptable to the public. The following review summarises existing evidence regarding risk-stratified screening for kidney cancer, highlighting the risks and benefits, as well as exploring the management of potential harms and further research needs.
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Affiliation(s)
- Sabrina H Rossi
- Department of Surgery, University of Cambridge, Cambridge, UK.
| | - Hannah Harrison
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Juliet A Usher-Smith
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Grant D Stewart
- Department of Surgery, University of Cambridge, Cambridge, UK
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17
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Nakase T, Guerra G, Ostrom QT, Ge T, Melin B, Wrensch M, Wiencke JK, Jenkins RB, Eckel-Passow JE, Bondy ML, Francis SS, Kachuri L. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301112. [PMID: 38260701 PMCID: PMC10802631 DOI: 10.1101/2024.01.10.24301112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.
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Affiliation(s)
- Taishi Nakase
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Geno Guerra
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Robert B. Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Melissa L. Bondy
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Francis
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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18
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Yu EYW, Tang QY, Chen YT, Zhang YX, Dai YN, Wu YX, Li WC, Mehrkanoon S, Wang SZ, Zeegers MP, Wesselius A. Genome-wide exploration of genetic interactions for bladder cancer risk. Int J Cancer 2024; 154:81-93. [PMID: 37638657 DOI: 10.1002/ijc.34690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/14/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023]
Abstract
Although GWASs have been conducted to investigate genetic variation of bladder tumorigenesis, little is known about genetic interactions that may influence bladder cancer (BC) risk. By leveraging large-scale participants from UK Biobank, we established a discovery database with 4000 Caucasian participants (2000 cases vs 2000 non-cases), a database with 1648 Caucasian participants (824 cases vs 824 non-cases) and 856 non-Caucasian participants (428 cases vs 428 non-cases) as validation. We then performed a genome-wide SNP-SNP interaction investigation related to BC risk based a machine learning approach (ie, GenEpi). Moreover, we used the selected interactions to build a BC screening model with an integrated interaction-empowered polygenic risk score (iPRS) based on Cox proportional hazard model. With Bonferroni correction, we identified 10 statistically significant pairs of SNPs, which located in 17 chromosomes. Of these, four SNP-SNP interactions were found to be positively associated with BC risk among Caucasian participants (ORs 1.57-2.03), while six SNP-SNP interactions showed negatively associated with BC risk (ORs 0.54-0.65). Only four of the SNP-SNP interactions were consistently identified in non-Caucasian participants located in ST7L-ADSS2, FHIT-CHDH, LARP4B-LHPP and RBFOX3-MPRIP. In addition, the iPRS showed a HR of 1.81 (95% CI: 1.46-2.09) compared the highest tertile to the lowest tertile, with an enhanced AUC (0.91; 95% CI:0.85-0.97) than PRS (AUC: 0.86; 95% CI:0.76-0.95; P-DeLong test = 2.2 × 10-4 ). In summary, this study identified several important SNP-SNP interactions for BC risk, and developed an iPRS model for BC screening, which may help to identify the people at high-risk state of BC before early manifestation.
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Affiliation(s)
- Evan Yi-Wen Yu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, China
- Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Qiu-Yi Tang
- Medical School of Southeast University, Nanjing, China
| | - Ya-Ting Chen
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, China
- Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Yan-Xi Zhang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, China
- Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Ya-Nan Dai
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Yu-Xuan Wu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing, China
- Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Wen-Chao Li
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, China
| | - Siamak Mehrkanoon
- Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
| | - Shi-Zhi Wang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Maurice P Zeegers
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Anke Wesselius
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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20
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Wang JR, Zafereo ME, Wang W, Joshu C, Ray D. Association of Polygenic Score With Tumor Molecular Subtypes in Papillary Thyroid Carcinoma. J Clin Endocrinol Metab 2023; 109:e306-e313. [PMID: 37453101 DOI: 10.1210/clinem/dgad407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
CONTEXT Genome-wide association studies have identified germline variants associated with elevated PTC risk. It is also known that somatic driver mutations contribute to PTC development and as such PTCs can be further categorized into different molecular subtypes based on their somatic alterations. However, it remains unknown whether identified germline variants predictive of PTC risk are associated with specific molecular subtypes. OBJECTIVE The primary goal of the present study is to determine whether germline genetic risk, as assessed using a polygenic score (PGS) is associated with molecular subtypes of papillary thyroid carcinoma (PTC), defined based on tumor driver mutation status. METHODS This study was carried out using data from The Cancer Genome Atlas (TCGA) thyroid cancer study. A previously validated 10-single-nucleotide variation PGS for PTC derived from genome-wide association study hits was calculated to ascertain germline genetic risk. The primary molecular subtypes of interest were defined by tumor driver mutation status (BRAFV600E-mutated vs RAS-mutated vs "other"). We also explored associations between PGS and molecular subtypes defined by messenger RNA (mRNA) expression, microRNA expression, and DNA methylation patterns. Polytomous logistic regression analysis was used to assess the association between PGS and PTC molecular subtype with and without adjustment for clinical variables. Odds ratios (ORs) with their 95% CIs were estimated. RESULTS A total of 359 patients were included in the study. PGS was significantly associated specific tumor molecular subtypes defined by tumor driver mutation status. Increasing germline risk was associated with having a higher odd of BRAFV600E-mutated PTC compared to PTCs without driver mutations in the "other" category. No significant difference was detected in terms of PGS tumor categorization in the RAS subtype compared to BRAFV600E. In exploratory analyses, PGS was also associated with mRNA-, microRNA-, and DNA methylation-defined molecular subtypes, as defined by the TCGA PTC study. CONCLUSION PGS has molecular subtype-specific associations in PTC, which has implications for their use in risk prediction.
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Affiliation(s)
- Jennifer R Wang
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Mark E Zafereo
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77005, USA
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77005, USA
| | - Corinne Joshu
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
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21
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Ladabaum U, Ko CW. Colorectal Cancer Risk Prediction to Tailor Screening: Will We Embrace It or KISS It Goodbye? Clin Gastroenterol Hepatol 2023; 21:3236-3237. [PMID: 37100217 DOI: 10.1016/j.cgh.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 04/28/2023]
Affiliation(s)
- Uri Ladabaum
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Cynthia W Ko
- Division of Gastroenterology, University of Washington School of Medicine, Seattle, Washington
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22
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Ke TM, Lophatananon A, Muir KR. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank. Biomedicines 2023; 11:3206. [PMID: 38137427 PMCID: PMC10740416 DOI: 10.3390/biomedicines11123206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/24/2023] Open
Abstract
Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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23
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Trendowski MR, Lusk CM, Wenzlaff AS, Neslund-Dudas C, Gadgeel SM, Soubani AO, Schwartz AG. Assessing a Polygenic Risk Score for Lung Cancer Susceptibility in Non-Hispanic White and Black Populations. Cancer Epidemiol Biomarkers Prev 2023; 32:1558-1563. [PMID: 37578347 PMCID: PMC10841320 DOI: 10.1158/1055-9965.epi-23-0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 06/14/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black populations in model development. METHODS We used a previously published lung cancer PRS on the basis of 80 SNPs associated with lung cancer risk in the OncoArray cohort and validated in UK Biobank. The PRS was evaluated for association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of chronic obstructive pulmonary disease, and the top five principal components for genetic ancestry. RESULTS Among the 80 PRS SNPs included in the score, 14 were significantly associated with lung cancer risk (P < 0.05) in INHALE White participants, while there were no significant SNPs among INHALE Black participants. After adjusting for covariates, the PRS was significantly associated with risk in Whites (continuous score P = 0.007), but not in Blacks (continuous score P = 0.88). The PRS remained a statistically significant predictor of lung cancer risk in Whites ineligible for lung cancer screening under current U.S. Preventive Services Task Force guidelines (P = 0.02). CONCLUSIONS Using a previously validated PRS, we did find some predictive ability for lung cancer in INHALE White participants beyond traditional risk factors. However, this effect was not observed in Black participants, indicating the need to develop and validate ancestry-specific lung cancer risk models. IMPACT While a previously published lung cancer PRS was able to stratify White participants into different levels of risk, the model was not predictive in Blacks. Our findings highlight the need to develop and validate ancestry-specific lung cancer risk models.
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Affiliation(s)
- Matthew R. Trendowski
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine M. Lusk
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Angela S. Wenzlaff
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine Neslund-Dudas
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI, USA
- Henry Ford Cancer Institute, Henry Ford Health, Detroit, MI, USA
| | | | - Ayman O. Soubani
- Karmanos Cancer Institute, Detroit, MI, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Wayne State University School of Medicine, Detroit, MI, USA
| | - Ann G. Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
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24
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Wang L, Grimshaw AA, Mezzacappa C, Larki NR, Yang YX, Justice AC. Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review. Cancer Epidemiol Biomarkers Prev 2023; 32:1490-1497. [PMID: 37610426 PMCID: PMC10873036 DOI: 10.1158/1055-9965.epi-23-0468] [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: 05/02/2023] [Revised: 07/21/2023] [Accepted: 08/21/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data. METHODS We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors. RESULTS Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1). CONCLUSIONS While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination. IMPACT For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.
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Affiliation(s)
- Louise Wang
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Catherine Mezzacappa
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Navid Rahimi Larki
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of Digestive Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Yu-Xiao Yang
- Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA USA
| | - Amy C. Justice
- VA Connecticut Healthcare System, West Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- School of Public Health, Yale University, New Haven, CT, USA
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25
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Hoang T, Cho S, Choi JY, Kang D, Shin A. Assessments of dietary intake and polygenic risk score in associations with colorectal cancer risk: evidence from the UK Biobank. BMC Cancer 2023; 23:993. [PMID: 37853340 PMCID: PMC10583398 DOI: 10.1186/s12885-023-11482-1] [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: 08/03/2023] [Accepted: 10/05/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND This study aimed to explore the potential interaction between dietary intake and genetics on incident colorectal cancer (CRC) and whether adherence to healthy dietary habits could attenuate CRC risk in individuals at high genetic risk. METHODS We analyzed prospective cohort data of 374,004 participants who were free of any cancers at enrollment in UK Biobank. Dietary scores were created based on three dietary recommendations of the World Cancer Research Fund (WCRF) and the overall effects of 11 foods on CRC risks using the inverse-variance (IV) method. Genetic risk was assessed using a polygenic risk score (PRS) capturing overall CRC risk. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% CIs (confidence intervals) of associations. Interactions between dietary factors and the PRS were examined using a likelihood ratio test to compare models with and without the interaction term. RESULTS During a median follow-up of 12.4 years, 4,686 CRC cases were newly diagnosed. Both low adherence to the WCRF recommendations (HR = 1.12, 95% CI = 1.05-1.19) and high IV-weighted dietary scores (HR = 1.27, 95% CI = 1.18-1.37) were associated with CRC risks. The PRS of 98 genetic variants was associated with an increased CRC risk (HRT3vsT1 = 2.12, 95% CI = 1.97-2.29). Participants with both unfavorable dietary habits and a high PRS had a more than twofold increased risk of developing CRC; however, the interaction was not significant. Adherence to an overall healthy diet might attenuate CRC risks in those with high genetic risks (HR = 1.21, 95% CI = 1.08-1.35 for high vs. low IV-weighted dietary scores), while adherence to WCRF dietary recommendations showed marginal effects only (HR = 1.09, 95% CI = 1.00-1.19 for low vs. high WCRF dietary scores). CONCLUSION Dietary habits and the PRS were independently associated with CRC risks. Adherence to healthy dietary habits may exert beneficial effects on CRC risk reduction in individuals at high genetic risk.
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Affiliation(s)
- Tung Hoang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea
| | - Sooyoung Cho
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- BK21plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, Korea
- Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
- Institute of Environmental Medicine, Medical Research Center, Seoul National University, Seoul, Korea
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea.
- Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea.
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
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Dugué PA, Yu C, Hodge AM, Wong EM, Joo JE, Jung CH, Schmidt D, Makalic E, Buchanan DD, Severi G, English DR, Hopper JL, Milne RL, Giles GG, Southey MC. Reply to: Comments on "Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer". Int J Cancer 2023; 153:1545-1546. [PMID: 37387529 DOI: 10.1002/ijc.34644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023]
Affiliation(s)
- Pierre-Antoine Dugué
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Chenglong Yu
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - JiHoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
| | - Chol-Hee Jung
- Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
- Melbourne Bioinformatics, University of Melbourne, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Gianluca Severi
- Centre de Recherche en Epidémiologie et Santé des Populations (CESP, Inserm U1018), Facultés de Médecine Universités Paris-Saclay, UVSQ, Gustave Roussy, Villejuif, France
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Victoria, Australia
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He Q, Wu S, Zhou Y, Liu Y, Xia B, Li W, Zhao J, Mi N, Xie P, Qin X, Yuan J, Pan Y. Genetic factors, adherence to healthy lifestyle behaviors, and risk of bladder cancer. BMC Cancer 2023; 23:965. [PMID: 37828430 PMCID: PMC10568887 DOI: 10.1186/s12885-023-11455-4] [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: 04/28/2023] [Accepted: 09/27/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Genetic and lifestyle factors both contribute to the pathogenesis of bladder cancer, but the extent to which the increased genetic risk can be mitigated by adhering to a healthy lifestyle remains unclear. We aimed to investigate the association of combined lifestyle factors with bladder cancer risk within genetic risk groups. METHODS We conducted a prospective study of 375 998 unrelated participants of European ancestry with genotype and lifestyle data and free of cancer from the UK biobank. We generated a polygenic risk score (PRS) using 16 single nucleotide polymorphisms and a healthy lifestyle score based on body weight, smoking status, physical activity, and diet. Cox models were fitted to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of genetic and lifestyle factors on bladder cancer. RESULTS During a median follow-up of 11.8 years, 880 participants developed bladder cancer. Compared with those with low PRS, participants with intermediate and high PRS had a higher risk of bladder cancer (HR 1.29, 95% CI 1.07-1.56; HR 1.63, 95% CI 1.32-2.02, respectively). An optimal lifestyle was associated with an approximately 50% lower risk of bladder cancer than a poor lifestyle across all genetic strata. Participants with a high genetic risk and a poor lifestyle had 3.6-fold elevated risk of bladder cancer compared with those with a low genetic risk and an optimal lifestyle (HR 3.63, 95% CI 2.23 -5.91). CONCLUSIONS Adhering to a healthy lifestyle could substantially reduce the bladder cancer risk across all genetic strata, even for high-genetic risk individuals. For all populations, adopting an intermediate lifestyle is more beneficial than a poor one, and adhering to an optimal lifestyle is the ideal effective strategy for bladder cancer prevention.
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Affiliation(s)
- Qiangsheng He
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Siqing Wu
- School of Medicine, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China
| | - Ying Zhou
- Primary Care Office, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Yuchen Liu
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Bin Xia
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Wenjing Li
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Jinyu Zhao
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Ningning Mi
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu, China
| | - Peng Xie
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China
| | - Xiwen Qin
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
- School of Population and Global Health, Faculty of Medicine, Density and Health Sciences, University of Western Australia, Perth, AU-WA, Australia
| | - Jinqiu Yuan
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
- Clinical Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
| | - Yihang Pan
- Scientific Research Center, Big Data Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518107, Guangdong, China.
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28
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Im C, Sharafeldin N, Yuan Y, Wang Z, Sapkota Y, Lu Z, Spector LG, Howell RM, Arnold MA, Hudson MM, Ness KK, Robison LL, Bhatia S, Armstrong GT, Neglia JP, Yasui Y, Turcotte LM. Polygenic Risk and Chemotherapy-Related Subsequent Malignancies in Childhood Cancer Survivors: A Childhood Cancer Survivor Study and St Jude Lifetime Cohort Study Report. J Clin Oncol 2023; 41:4381-4393. [PMID: 37459583 PMCID: PMC10522108 DOI: 10.1200/jco.23.00428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/26/2023] [Accepted: 05/18/2023] [Indexed: 08/07/2023] Open
Abstract
PURPOSE Chemotherapeutic exposures are associated with subsequent malignant neoplasm (SMN) risk. The role of genetic susceptibility in chemotherapy-related SMNs should be defined as use of radiation therapy (RT) decreases. PATIENTS AND METHODS SMNs among long-term childhood cancer survivors of European (EUR; N = 9,895) and African (AFR; N = 718) genetic ancestry from the Childhood Cancer Survivor Study and St Jude Lifetime Cohort Study were evaluated. An externally validated 179-variant polygenic risk score (PRS) associated with pleiotropic adult cancer risk from the UK Biobank Study (N > 400,000) was computed for each survivor. SMN cumulative incidence comparing top and bottom PRS quintiles was estimated, along with hazard ratios (HRs) from proportional hazards models. RESULTS A total of 1,594 survivors developed SMNs, with basal cell carcinomas (n = 822), breast cancers (n = 235), and thyroid cancers (n = 221) being the most frequent. Although SMN risk associations with the PRS were extremely modest in RT-exposed EUR survivors (HR, 1.22; P = .048; n = 4,630), the increase in 30-year SMN cumulative incidence and HRs comparing top and bottom PRS quintiles was statistically significant among nonirradiated EUR survivors (n = 4,322) treated with alkylating agents (17% v 6%; HR, 2.46; P < .01), anthracyclines (20% v 8%; HR, 2.86; P < .001), epipodophyllotoxins (23% v 1%; HR, 12.20; P < .001), or platinums (46% v 7%; HR, 8.58; P < .01). This PRS also significantly modified epipodophyllotoxin-related SMN risk among nonirradiated AFR survivors (n = 414; P < .01). Improvements in prediction attributable to the PRS were greatest for epipodophyllotoxin-exposed (AUC, 0.71 v 0.63) and platinum-exposed (AUC,0.68 v 0.58) survivors. CONCLUSION A pleiotropic cancer PRS has strong potential for improving SMN clinical risk stratification among nonirradiated survivors treated with specific chemotherapies. A polygenic risk screening approach may be a valuable complement to an early screening strategy on the basis of treatments and rare cancer-susceptibility mutations.
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Affiliation(s)
- Cindy Im
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | - Noha Sharafeldin
- Hematology Oncology Division, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL
| | - Yan Yuan
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
| | - Zhaoming Wang
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
- Department of Computational Biology, St Jude Children's Research Hospital, Memphis, TN
| | - Yadav Sapkota
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
| | - Zhanni Lu
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | - Logan G. Spector
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | - Rebecca M. Howell
- Department of Radiation Physics, University of Texas at MD Anderson Cancer Center, Houston, TX
| | - Michael A. Arnold
- Department of Pathology and Laboratory Medicine, University of Colorado and Children's Hospital Colorado, Anschutz Medical Campus, Aurora, CO
| | - Melissa M. Hudson
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN
| | - Kirsten K. Ness
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN
| | - Joseph P. Neglia
- Department of Pediatrics, University of Minnesota, Minneapolis, MN
| | - Yutaka Yasui
- School of Public Health, University of Alberta, Edmonton, Alberta, Canada
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN
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29
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Abstract
Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.
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Affiliation(s)
- Xin Yang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Siddhartha Kar
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Early Cancer Institute, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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30
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Zhang Y, Yang H, Liu W, Song Q, Li Y, Zhang J, Zhou D, Li A. Comprehensive pan-cancer analysis of expression profiles and prognostic significance for NUMB and NUMBL in human tumors. Medicine (Baltimore) 2023; 102:e34717. [PMID: 37657045 PMCID: PMC10476719 DOI: 10.1097/md.0000000000034717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/21/2023] [Indexed: 09/03/2023] Open
Abstract
NUMB has been initially identified as a critical cell fate determinant that modulates cell differentiation via asymmetrical partitioning during mitosis, including tumor cells. However, it remains absent that a systematic assessment of the mechanisms underlying NUMB and its homologous protein NUMBLIKE (NUMBL) involvement in cancer. This study aimed to investigate the prognostic significance for NUMB and NUMBL in pan-cancer. In this study, using the online databases TIMER2.0, gene expression profiling interactive analysis, cBioPortal, the University of ALabama at Birmingham CANcer data analysis Portal, SearchTool for the Retrieval of Interacting Genes/Proteins, and R software, we focused on the relevance between NUMB/NUMBL and oncogenesis, progression, mutation, phosphorylation, function and prognosis. This study demonstrated that abnormal expression of NUMB and NUMBL were found to be significantly associated with clinicopathologic stages and the prognosis of survival. Besides, genetic alternations of NUMB and NUMBL focused on uterine corpus endometrial carcinoma, and higher genetic mutations of NUMBL were correlated with more prolonged overall survival and disease-free survival in different cancers. Moreover, S438 locus of NUMB peptide fragment was frequently phosphorylated in 4 cancer types and relevant to its phosphorylation sites. Furthermore, endocytosis processing and neurogenesis regulation were involved in the functional mechanisms of NUMB and NUMBL separately. Additionally, the pathway enrichment suggested that NUMB was implicated in Hippo, Neurotrophin, Thyroid hormone, and FoxO pathways, while MAPK, Hippo, Rap1, mTOR, and Notch pathways were related to the functions of NUMBL. This study highlights the predictive roles of NUMB and NUMBL in pan-cancer, suggesting NUMB and NUMBL might be served as potential biomarkers for diagnosis and prognosis in various malignant tumors.
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Affiliation(s)
- Yue Zhang
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Hebei Higher Education Institute Applied Technology Research Center on TCM Formula Preparation, Shijiazhuang, Hebei, China
- Hebei Key Laboratory of Chinese Medicine Research on Cardio-Cerebrovascular Disease, Shijiazhuang, Hebei, China
| | - Hongxia Yang
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Department of Clinical Foundation of Chinese Medicine, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, China
| | - Weizhe Liu
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Hebei Higher Education Institute Applied Technology Research Center on TCM Formula Preparation, Shijiazhuang, Hebei, China
| | - Qiuhang Song
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Hebei Key Laboratory of Chinese Medicine Research on Cardio-Cerebrovascular Disease, Shijiazhuang, Hebei, China
| | - Yunfeng Li
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Hebei Key Laboratory of Chinese Medicine Research on Cardio-Cerebrovascular Disease, Shijiazhuang, Hebei, China
| | - Juanjuan Zhang
- Hebei Key Laboratory of Chinese Medicine Research on Cardio-Cerebrovascular Disease, Shijiazhuang, Hebei, China
| | - Dingyan Zhou
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
| | - Aiying Li
- Department of Biochemistry and Molecular Biology, College of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, China
- Hebei Higher Education Institute Applied Technology Research Center on TCM Formula Preparation, Shijiazhuang, Hebei, China
- Hebei Key Laboratory of Chinese Medicine Research on Cardio-Cerebrovascular Disease, Shijiazhuang, Hebei, China
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31
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Romero-Garmendia I, Garcia-Etxebarria K. From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead. Genes (Basel) 2023; 14:1430. [PMID: 37510334 PMCID: PMC10379575 DOI: 10.3390/genes14071430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/05/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.
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Affiliation(s)
- Irati Romero-Garmendia
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (Universidad del País Vasco/Euskal Herriko Unibertsitatea), 48940 Leioa, Spain
| | - Koldo Garcia-Etxebarria
- Biodonostia, Gastrointestinal Genetics Group, 20014 San Sebastián, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 08036 Barcelona, Spain
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Ning ZK, Tian HK, Liu J, Hu CG, Liu ZT, Li H, Zong Z. Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer. Heliyon 2023; 9:e18242. [PMID: 37539127 PMCID: PMC10393628 DOI: 10.1016/j.heliyon.2023.e18242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 06/28/2023] [Accepted: 07/12/2023] [Indexed: 08/05/2023] Open
Abstract
Background RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients. Methods We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair. Results We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways. Conclusion Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.
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Affiliation(s)
- Zhi-kun Ning
- Department of Day Ward, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hua-kai Tian
- Department of General Surgery, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jiang Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ce-gui Hu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zi-tao Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hui Li
- Department of Rheumatology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhen Zong
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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33
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Farooqi R, Kooner JS, Zhang W. Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis. BMC Med Genomics 2023; 16:150. [PMID: 37386504 PMCID: PMC10311902 DOI: 10.1186/s12920-023-01584-x] [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: 11/30/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability. METHODS We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2). RESULTS Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups. CONCLUSIONS Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.
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Affiliation(s)
- Raabia Farooqi
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
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Dite GS, Spaeth E, Wong CK, Murphy NM, Allman R. Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model. GASTRO HEP ADVANCES 2023; 2:979-989. [PMID: 39130772 PMCID: PMC11308393 DOI: 10.1016/j.gastha.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/12/2023] [Indexed: 08/13/2024]
Abstract
Background and Aims Pancreatic cancer has the poorest 5-year survival rate of any major solid tumor, but when diagnosed at an early stage, survival rates improve. Population screening is impractical because pancreatic cancer is rare with a lifetime risk of 1.7%, but accurate risk stratification in the general population could enable health care providers to focus early detection strategies to at-risk individuals. Here, we validate a combined risk prediction model that integrates a polygenic risk score and a clinical risk model. Methods Using the UK Biobank, we conducted a prospective cohort study assessing 10-year pancreatic cancer risks based on a polygenic risk score, a clinical risk score, and a combined risk score. We assessed the association, discrimination, calibration, cumulative hazards, and standardized incidence ratios compared to population incidence rates for the risk scores. We also conducted net reclassification analyses. Results While all of the risk scores discriminated well between affected and unaffected participants, the combined risk score - with a Harrell's C-index of 0.714 (95% confidence interval [CI] = 0.698, 0.730) - discriminated better than both the polygenic risk score (P = .001) and the clinical risk score (P = .02). In terms of calibration, there was no problem with dispersion for the combined risk score (β = 0.952, 95% CI = 0.865-1.039, P = .3) and overall there was a small overestimation of risk (α = -0.089, 95% CI = -0.156 to -0.021, P = .009). Participants in the top decile of 10-year risk were at 1.413 (95% CI = 1.242-1.607) times population risk. Conclusion The combined risk score was able to identify individuals at substantially increased risk of pancreatic cancer and to whom targeted screening could be useful.
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Affiliation(s)
| | - Erika Spaeth
- Phenogen Sciences Inc, Charlotte, North Carolina
| | - Chi Kuen Wong
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | | | - Richard Allman
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
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Jee YH, Ho WK, Park S, Easton DF, Teo SH, Jung KJ, Kraft P. Polygenic risk scores for prediction of breast cancer in Korean women. Int J Epidemiol 2023; 52:796-805. [PMID: 36343017 PMCID: PMC10244045 DOI: 10.1093/ije/dyac206] [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: 11/21/2021] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Polygenic risk scores (PRSs) for breast cancer, developed using European and Asian genome-wide association studies (GWAS), have been shown to have good discrimination in Asian women. However, prospective calibration of absolute risk prediction models, based on a PRS or PRS combined with lifestyle, clinical and environmental factors, in Asian women is limited. METHODS We consider several PRSs trained using European and/or Asian GWAS. For each PRS, we evaluate the discrimination and calibration of three absolute risk models among 41 031 women from the Korean Cancer Prevention Study (KCPS)-II Biobank: (i) a model using incidence, mortality and risk factor distributions (reference inputs) among US women and European relative risks; (ii) a recalibrated model, using Korean reference but European relative risks; and (iii) a fully Korean-based model using Korean reference and relative risk estimates from KCPS. RESULTS All Asian and European PRS improved discrimination over lifestyle, clinical and environmental (Qx) factors in Korean women. US-based absolute risk models overestimated the risks for women aged ≥50 years, and this overestimation was larger for models that only included PRS (expected-to-observed ratio E/O = 1.2 for women <50, E/O = 2.7 for women ≥50). Recalibrated and Korean-based risk models had better calibration in the large, although the risk in the highest decile was consistently overestimated. Absolute risk projections suggest that risk-reducing lifestyle changes would lead to larger absolute risk reductions among women at higher PRS. CONCLUSIONS Absolute risk models incorporating PRS trained in European and Asian GWAS and population-appropriate average age-specific incidences may be useful for risk-stratified interventions in Korean women.
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Affiliation(s)
- Yon Ho Jee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Weang-Kee Ho
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
| | - Sohee Park
- Department of Biostatistics, Yonsei University Graduate School of Public Health, Seoul, Republic of Korea
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Sime Darby Medical Centre, Subang Jaya, Selangor, Malaysia
| | - Keum Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Republic of Korea
- Nuffield Department Population Health, University of Oxford, Oxford, UK
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Ali AT, Al-ani O, Al-ani F. Epidemiology and risk factors for ovarian cancer. PRZEGLAD MENOPAUZALNY = MENOPAUSE REVIEW 2023; 22:93-104. [PMID: 37674925 PMCID: PMC10477765 DOI: 10.5114/pm.2023.128661] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/01/2023] [Indexed: 09/08/2023]
Abstract
Ovarian cancer is a complex disease, mostly observed in postmenopausal women, and is associated with poor survival rates. It is the sixth most common cancer and the fifth most common cause of death due to cancer among women in developed countries. Thus, despite representing less than one third of all gynaecologic cancers, deaths due to ovarian cancer account for more than two thirds of deaths due to gynaecologic cancers. Its prevalence is higher in Western Europe and Northern America than Asia and Africa. In sub-Saharan Africa, there is a considerably lower prevalence of ovarian cancer than other parts of Africa. Ovarian cancer is multifaceted, involving many factors, complex biological processes and unpredictable consequences. Unlike other female cancers that have early warning symptoms, ovarian cancer's symptoms are non-specific. As a result, ovarian cancers are normally undetected until advanced stages (III or IV). The major risk factors for ovarian cancer include older age, genetics, family history, hormone replacement therapy, nulliparity, and dietary fat. Controversial factors include obesity, infertility, talc powder, radiation exposure, fertility medications and in vitro fertilization. The current review discusses the aetiology, epidemiology and risk factors for ovarian cancer. Nevertheless, identification of the main risk factors for ovarian cancer may increase the awareness among women of the general population. This should help to decrease the incidence rate of ovarian cancer and increase the five-year survival rate.
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Affiliation(s)
- Aus Tariq Ali
- Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa
| | - Osamah Al-ani
- Faculty of Medicine, Odessa National Medical University, Odessa, Ukraine
| | - Faisal Al-ani
- Faculty of Medicine, Odessa National Medical University, Odessa, Ukraine
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Huntley C, Torr B, Sud A, Rowlands CF, Way R, Snape K, Hanson H, Swanton C, Broggio J, Lucassen A, McCartney M, Houlston RS, Hingorani AD, Jones ME, Turnbull C. Utility of polygenic risk scores in UK cancer screening: a modelling analysis. Lancet Oncol 2023; 24:658-668. [PMID: 37178708 DOI: 10.1016/s1470-2045(23)00156-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND It is proposed that, through restriction to individuals delineated as high risk, polygenic risk scores (PRSs) might enable more efficient targeting of existing cancer screening programmes and enable extension into new age ranges and disease types. To address this proposition, we present an overview of the performance of PRS tools (ie, models and sets of single nucleotide polymorphisms) alongside harms and benefits of PRS-stratified cancer screening for eight example cancers (breast, prostate, colorectal, pancreas, ovary, kidney, lung, and testicular cancer). METHODS For this modelling analysis, we used age-stratified cancer incidences for the UK population from the National Cancer Registration Dataset (2016-18) and published estimates of the area under the receiver operating characteristic curve for current, future, and optimised PRS for each of the eight cancer types. For each of five PRS-defined high-risk quantiles (ie, the top 50%, 20%, 10%, 5%, and 1%) and according to each of the three PRS tools (ie, current, future, and optimised) for the eight cancers, we calculated the relative proportion of cancers arising, the odds ratios of a cancer arising compared with the UK population average, and the lifetime cancer risk. We examined maximal attainable rates of cancer detection by age stratum from combining PRS-based stratification with cancer screening tools and modelled the maximal impact on cancer-specific survival of hypothetical new UK programmes of PRS-stratified screening. FINDINGS The PRS-defined high-risk quintile (20%) of the population was estimated to capture 37% of breast cancer cases, 46% of prostate cancer cases, 34% of colorectal cancer cases, 29% of pancreatic cancer cases, 26% of ovarian cancer cases, 22% of renal cancer cases, 26% of lung cancer cases, and 47% of testicular cancer cases. Extending UK screening programmes to a PRS-defined high-risk quintile including people aged 40-49 years for breast cancer, 50-59 years for colorectal cancer, and 60-69 years for prostate cancer has the potential to avert, respectively, a maximum of 102, 188, and 158 deaths annually. Unstratified screening of the full population aged 48-49 years for breast cancer, 58-59 years for colorectal cancer, and 68-69 years for prostate cancer would use equivalent resources and avert, respectively, an estimated maximum of 80, 155, and 95 deaths annually. These maximal modelled numbers will be substantially attenuated by incomplete population uptake of PRS profiling and cancer screening, interval cancers, non-European ancestry, and other factors. INTERPRETATION Under favourable assumptions, our modelling suggests modest potential efficiency gain in cancer case detection and deaths averted for hypothetical new PRS-stratified screening programmes for breast, prostate, and colorectal cancer. Restriction of screening to high-risk quantiles means many or most incident cancers will arise in those assigned as being low-risk. To quantify real-world clinical impact, costs, and harms, UK-specific cluster-randomised trials are required. FUNDING The Wellcome Trust.
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Affiliation(s)
- Catherine Huntley
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; National Cancer Registration and Analysis Service, National Health Service (NHS) England, London, UK
| | - Bethany Torr
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; Haemato-oncology Unit, The Royal Marsden Hospital NHS Foundation Trust, Sutton, UK
| | - Charlie F Rowlands
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Rosalind Way
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Katie Snape
- St Georges University of London, London, UK; South West Thames Regional Genetics Service, St George's Hospital, London, UK
| | - Helen Hanson
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; South West Thames Regional Genetics Service, St George's Hospital, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Evolution and Genome Instability Laboratory, University College London Cancer Institute, London, UK
| | - John Broggio
- National Cancer Registration and Analysis Service, National Health Service (NHS) England, London, UK
| | - Anneke Lucassen
- Wellcome Centre for Human Genetics and Centre for Personalised Medicine, University of Oxford, Oxford, UK
| | - Margaret McCartney
- Population and Behavioural Science Division, School of Medicine, University of St Andrews, St Andrews, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, UK
| | - Aroon D Hingorani
- University College London British Heart Foundation Research Accelerator Centre, London, UK; Health Data Research UK, London, UK; National Institute of Health Research Biomedical Research Centre and Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK; National Cancer Registration and Analysis Service, National Health Service (NHS) England, London, UK; Cancer Genetics Unit, The Royal Marsden NHS Foundation Trust, London, UK.
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Song H, Jung YS, Tran TXM, Moon CM, Park B. Increased risk of pancreatic, thyroid, prostate and breast cancers in men with a family history of breast cancer: A population-based study. Int J Cancer 2023. [PMID: 37248785 DOI: 10.1002/ijc.34573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/18/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023]
Abstract
The association between a family history of breast cancer (FHBC) in female first-degree relatives (FDRs) and cancer risk in men has not been evaluated. This study aimed to compare the risks of overall and site-specific cancers in men with and without FHBC. A population-based study was conducted with 3 329 106 men aged ≥40 years who underwent national cancer screening between 2013 and 2014. Men with and without FHBC in their female FDRs were age-matched in a 1:4 ratio. Men without FHBC were defined as those without a family history of any cancer type in their FDRs. Data from 69 124 men with FHBC and 276 496 men without FHBC were analyzed. The mean follow-up period was 4.7 ± 0.9 years. Men with an FHBC in any FDR (mother or sister) had a higher risk of pancreatic, thyroid, prostate and breast cancers than those without an FHBC (adjusted hazard ratios [aHRs] (95% confidence interval [CI]): 1.35 (1.07-1.70), 1.33 (1.12-1.56), 1.28 (1.13-1.44) and 3.03 (1.130-8.17), respectively). Although an FHBC in any one of the FDRs was not associated with overall cancer risk, FHBC in both mother and sibling was a significant risk factor for overall cancer (aHR: 1.69, 95% CI:1.11-2.57) and increased the risk of thyroid cancer by 3.41-fold (95% CI: 1.10-10.61). FHBC in the mother or sister was a significant risk factor for pancreatic, thyroid, prostate and breast cancers in men; therefore, men with FHBC may require more careful BRCA1/2 mutation-related cancer surveillance.
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Affiliation(s)
- Huiyeon Song
- Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea
| | - Yoon Suk Jung
- Division of Gastroenterology, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Chang Mo Moon
- Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
- Inflammation-Cancer Microenvironment Research Center, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea
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Ahern J, Thompson W, Fan CC, Loughnan R. Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study ®. Behav Genet 2023; 53:292-309. [PMID: 37017779 PMCID: PMC10655749 DOI: 10.1007/s10519-023-10139-w] [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: 12/01/2022] [Accepted: 02/28/2023] [Indexed: 04/06/2023]
Abstract
Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.
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Affiliation(s)
- Jonathan Ahern
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA.
| | - Wesley Thompson
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA, 92161, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92037, USA
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA
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Jin G, Ma H, Shen H, Hu Z. Polygenic score: An anchor holding the whole life course. Chin Med J (Engl) 2023; 136:883-885. [PMID: 37026867 PMCID: PMC10278760 DOI: 10.1097/cm9.0000000000002648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Indexed: 04/08/2023] Open
Affiliation(s)
- Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211112, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211112, China
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu 211112, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu 215000, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211112, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211112, China
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu 211112, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu 215000, China
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Xiao B, Velez Edwards DR, Lucas A, Drivas T, Gray K, Keating B, Weng C, Jarvik GP, Hakonarson H, Kottyan L, Elhadad N, Wei W, Luo Y, Kim D, Ritchie M, Verma SS. Inference of Causal Relationships Between Genetic Risk Factors for Cardiometabolic Phenotypes and Female-Specific Health Conditions. J Am Heart Assoc 2023; 12:e026561. [PMID: 36846987 PMCID: PMC10111435 DOI: 10.1161/jaha.121.026561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 11/11/2022] [Indexed: 03/01/2023]
Abstract
Background Cardiometabolic diseases are highly comorbid, but their relationship with female-specific or overwhelmingly female-predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross-trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score-based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross-trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female-specific health conditions.
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Affiliation(s)
- Brenda Xiao
- Graduate Program in Genomics and Computational BiologyUniversity of PennsylvaniaPhiladelphiaPA
| | - Digna R. Velez Edwards
- Division of Quantitative Sciences, Department of Obstetrics and GynecologyVanderbilt University Medical CenterNashvilleTN
| | - Anastasia Lucas
- Department of GeneticsUniversity of PennsylvaniaPhiladelphiaPA
| | - Theodore Drivas
- Department of GeneticsUniversity of PennsylvaniaPhiladelphiaPA
| | - Kathryn Gray
- Department of Obstetrics and GynecologyBrigham and Women’s HospitalBostonMA
| | - Brendan Keating
- Department of SurgeryUniversity of PennsylvaniaPhiladelphiaPA
| | - Chunhua Weng
- Department of Biomedical InformaticsColumbia UniversityNew YorkNY
| | - Gail P. Jarvik
- Departments of Medicine (Medical Genetics) and Genome SciencesUniversity of Washington Medical CenterSeattleWA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaPA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology and Division of Allergy & Immunology, Cincinnati Children’s Hospital Medical Center, Department of PediatricsUniversity of CincinnatiOH
| | - Noemie Elhadad
- Department of Biomedical InformaticsColumbia UniversityNew YorkNY
| | - Wei‐Qi Wei
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTN
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of MedicineNorthwestern UniversityEvanstonIL
| | - Dokyoon Kim
- Department of Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaPA
| | - Marylyn Ritchie
- Department of GeneticsUniversity of PennsylvaniaPhiladelphiaPA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPA
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Su YR, Sakoda LC, Jeon J, Thomas M, Lin Y, Schneider JL, Udaltsova N, Lee JK, Lansdorp-Vogelaar I, Peterse EF, Zauber AG, Zheng J, Zheng Y, Hauser E, Baron JA, Barry EL, Bishop DT, Brenner H, Buchanan DD, Burnett-Hartman A, Campbell PT, Casey G, Castellví-Bel S, Chan AT, Chang-Claude J, Figueiredo JC, Gallinger SJ, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampe J, Hampel H, Harrison TA, Hoffmeister M, Hua X, Huyghe JR, Jenkins MA, Keku TO, Le Marchand L, Li L, Lindblom A, Moreno V, Newcomb PA, Pharoah PDP, Platz EA, Potter JD, Qu C, Rennert G, Schoen RE, Slattery ML, Song M, van Duijnhoven FJB, Van Guelpen B, Vodicka P, Wolk A, Woods MO, Wu AH, Hayes RB, Peters U, Corley DA, Hsu L. Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort. Cancer Epidemiol Biomarkers Prev 2023; 32:353-362. [PMID: 36622766 PMCID: PMC9992158 DOI: 10.1158/1055-9965.epi-22-0817] [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: 07/28/2022] [Revised: 10/18/2022] [Accepted: 01/04/2023] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance. METHODS The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group). RESULTS In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity. CONCLUSIONS The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort. IMPACT The proposed model has potential utility in risk-stratified colorectal cancer prevention.
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Affiliation(s)
- Yu-Ru Su
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jennifer L Schneider
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Natalia Udaltsova
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Jeffrey K Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA
| | - Iris Lansdorp-Vogelaar
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Elisabeth F.P. Peterse
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jiayin Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Yingye Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Elizabeth Hauser
- VA Cooperative Studies Program Epidemiology Center, Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - John A Baron
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Elizabeth L Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010 Australia
| | | | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Steven J Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Stephen B Gruber
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Xinwei Hua
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center and Technion-Israel Institute of Technology, Haifa, Israel
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Fränzel JB van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Biomedical Center, Faculty of Medicine Pilsen, Charles University, Prague, Czech Republic
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John’s, Canada
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, New York, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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Hassanin E, Spier I, Bobbili DR, Aldisi R, Klinkhammer H, David F, Dueñas N, Hüneburg R, Perne C, Brunet J, Capella G, Nöthen MM, Forstner AJ, Mayr A, Krawitz P, May P, Aretz S, Maj C. Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence. BMC Med Genomics 2023; 16:42. [PMID: 36872334 PMCID: PMC9987090 DOI: 10.1186/s12920-023-01469-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/21/2023] [Indexed: 03/07/2023] Open
Abstract
BACKGROUND AND AIMS Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification. METHODS To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20-80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively. RESULTS Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704). CONCLUSION The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.
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Affiliation(s)
- Emadeldin Hassanin
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Isabel Spier
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany.,European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands
| | - Dheeraj R Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Rana Aldisi
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany.,Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Friederike David
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Nuria Dueñas
- Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
| | - Robert Hüneburg
- National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany.,Department of Internal Medicine I, University Hospital Bonn, Bonn, Germany
| | - Claudia Perne
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany
| | - Joan Brunet
- European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.,Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain.,Hereditary Cancer Program, Catalan Institute of Oncology-IDBIGI, 17007, Girona, Spain
| | - Gabriel Capella
- European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.,Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
| | - Markus M Nöthen
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,Centre for Human Genetics, University of Marburg, Marburg, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Andreas Mayr
- Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Stefan Aretz
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany. .,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany. .,European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
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Xin J, Jiang X, Li H, Chen S, Zhang Z, Wang M, Gu D, Du M, Christiani DC. Prognostic evaluation of polygenic risk score underlying pan-cancer analysis: evidence from two large-scale cohorts. EBioMedicine 2023; 89:104454. [PMID: 36739632 PMCID: PMC9931923 DOI: 10.1016/j.ebiom.2023.104454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/07/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Polygenic risk score (PRS) has been demonstrated to be effective in identifying individuals at high risk of developing cancer, but its prognostic value remains unclear. METHODS We constructed site-specific PRSs by aggregating the risk effect of independent variants derived from previous genome-wide association studies (GWASs) across 17 cancer types. The Cox proportional hazards model was used to evaluate the association of each PRS with cancer survival, leveraging data from two prospective European cohorts, namely the UK Biobank involving 19,628 incident cases and The Cancer Genome Atlas involving 7079 prevalent cases. The combined PRS (CPRS), determined by merging site-specific PRSs, was further used to assess the prognostic effect of PRS on overall cancer in a sex-specific manner. FINDINGS We discovered that the cancer risk-related PRS was associated with neither overall survival (OS) nor cancer-specific survival (CSS) of each site-specific cancer with an underlying false discovery rate (FDR) P > 0.05, as evidenced by consistent findings from the two cohorts. Furthermore, the fixed-effect meta-analysis of the two cohorts provided no evidence to support for an association between CPRS and overall cancer survival in both males [OS: hazard ratio (HR)meta = 1.00, Pmeta = 0.760; CSS: HRmeta = 1.01, Pmeta = 0.447] and females (OS: HRmeta = 0.97, Pmeta = 0.067; CSS: HRmeta = 0.96, Pmeta = 0.054). Similar results were observed across multiple sensitivity analyses. INTERPRETATION Our findings indicate that the risk-specific PRS might not be a clinically useful tool in cancer prognosis prediction and further studies focusing on the development of polygenic prognostic score are warranted. FUNDING This project was funded by the National Natural Science Foundation of China (82173601 and 82073631), and Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).
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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
| | - Xia Jiang
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huiqin 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 Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Silu Chen
- 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, Center for Global Health, School of Public Health, 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, Center for Global Health, School of Public 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, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
| | - Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA.
| | - David C Christiani
- Departments of Environmental Health and Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
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Pagadala MS, Lynch J, Karunamuni R, Alba PR, Lee KM, Agiri FY, Anglin T, Carter H, Gaziano JM, Jasuja GK, Deka R, Rose BS, Panizzon MS, Hauger RL, Seibert TM. Polygenic risk of any, metastatic, and fatal prostate cancer in the Million Veteran Program. J Natl Cancer Inst 2023; 115:190-199. [PMID: 36305680 PMCID: PMC9905969 DOI: 10.1093/jnci/djac199] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/13/2022] [Accepted: 10/26/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Genetic scores may provide an objective measure of prostate cancer risk and thus inform screening decisions. We evaluated whether a polygenic hazard score based on 290 genetic variants (PHS290) is associated with prostate cancer risk in a diverse population, including Black men, who have higher average risk of prostate cancer death but are often treated as a homogeneously high-risk group. METHODS This was a retrospective analysis of the Million Veteran Program, a national, population-based cohort study of US military veterans conducted 2011-2021. Cox proportional hazards analyses tested for association of genetic and other risk factors (including self-reported race and ethnicity and family history) with age at death from prostate cancer, age at diagnosis of metastatic (nodal or distant) prostate cancer, and age at diagnosis of any prostate cancer. RESULTS A total of 590 750 male participants were included. Median age at last follow-up was 69 years. PHS290 was associated with fatal prostate cancer in the full cohort and for each racial and ethnic group (P < .001). Comparing men in the highest 20% of PHS290 with those in the lowest 20% (based on percentiles from an independent training cohort), the hazard ratio for fatal prostate cancer was 4.42 (95% confidence interval = 3.91 to 5.02). When accounting for guideline-recommended risk factors (family history, race, and ethnicity), PHS290 remained a strong independent predictor of any, metastatic, and fatal prostate cancer. CONCLUSIONS PHS290 stratified US veterans of diverse ancestry for lifetime risk of prostate cancer, including metastatic and fatal cancer. Predicting genetic risk of lethal prostate cancer with PHS290 might inform individualized decisions about prostate cancer screening.
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Affiliation(s)
- Meghana S Pagadala
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA
- Biomedical Science Program, University of California San Diego, La Jolla, CA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System (VINCI), Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Roshan Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Patrick R Alba
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System (VINCI), Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kyung Min Lee
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System (VINCI), Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Fatai Y Agiri
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System (VINCI), Salt Lake City, UT, USA
| | - Tori Anglin
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System (VINCI), Salt Lake City, UT, USA
- Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Guneet Kaur Jasuja
- Center for Healthcare Organization and Implementation Research (CHOIR), VA Bedford Healthcare System, Bedford, MA, USA
- Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Urology, University of California San Diego, La Jolla, CA, USA
| | - Rishi Deka
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Brent S Rose
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Urology, University of California San Diego, La Jolla, CA, USA
| | - Matthew S Panizzon
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Center for Behavioral Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Richard L Hauger
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Center for Behavioral Genetics of Aging, University of California San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA, USA
| | - Tyler M Seibert
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
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Conroy MC, Lacey B, Bešević J, Omiyale W, Feng Q, Effingham M, Sellers J, Sheard S, Pancholi M, Gregory G, Busby J, Collins R, Allen NE. UK Biobank: a globally important resource for cancer research. Br J Cancer 2023; 128:519-527. [PMID: 36402876 PMCID: PMC9938115 DOI: 10.1038/s41416-022-02053-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/21/2022] Open
Abstract
UK Biobank is a large-scale prospective study with deep phenotyping and genomic data. Its open-access policy allows researchers worldwide, from academia or industry, to perform health research in the public interest. Between 2006 and 2010, the study recruited 502,000 adults aged 40-69 years from the general population of the United Kingdom. At enrolment, participants provided information on a wide range of factors, physical measurements were taken, and biological samples (blood, urine and saliva) were collected for long-term storage. Participants have now been followed up for over a decade with more than 52,000 incident cancer cases recorded. The study continues to be enhanced with repeat assessments, web-based questionnaires, multi-modal imaging, and conversion of the stored biological samples to genomic and other '-omic' data. The study has already demonstrated its value in enabling research into the determinants of cancer, and future planned enhancements will make the resource even more valuable to cancer researchers. Over 26,000 researchers worldwide are currently using the data, performing a wide range of cancer research. UK Biobank is uniquely placed to transform our understanding of the causes of cancer development and progression, and drive improvements in cancer treatment and prevention over the coming decades.
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Affiliation(s)
- Megan C Conroy
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK.
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Jelena Bešević
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Wemimo Omiyale
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Qi Feng
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | | | | | | | | | | | - John Busby
- UK Biobank, Stockport, Greater Manchester, UK
| | - Rory Collins
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- UK Biobank, Stockport, Greater Manchester, UK
| | - Naomi E Allen
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
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He S, Sun D, Li H, Cao M, Yu X, Lei L, Peng J, Li J, Li N, Chen W. Real-World Practice of Gastric Cancer Prevention and Screening Calls for Practical Prediction Models. Clin Transl Gastroenterol 2023; 14:e00546. [PMID: 36413795 PMCID: PMC9944379 DOI: 10.14309/ctg.0000000000000546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/11/2022] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Some gastric cancer prediction models have been published. Still, the value of these models for application in real-world practice remains unclear. We aim to summarize and appraise modeling studies for gastric cancer risk prediction and identify potential barriers to real-world use. METHODS This systematic review included studies that developed or validated gastric cancer prediction models in the general population. RESULTS A total of 4,223 studies were screened. We included 18 development studies for diagnostic models, 10 for prognostic models, and 1 external validation study. Diagnostic models commonly included biomarkers, such as Helicobacter pylori infection indicator, pepsinogen, hormone, and microRNA. Age, sex, smoking, body mass index, and family history of gastric cancer were frequently used in prognostic models. Most of the models were not validated. Only 25% of models evaluated the calibration. All studies had a high risk of bias, but over half had acceptable applicability. Besides, most studies failed to clearly report the application scenarios of prediction models. DISCUSSION Most gastric cancer prediction models showed common shortcomings in methods, validation, and reports. Model developers should further minimize the risk of bias, improve models' applicability, and report targeting application scenarios to promote real-world use.
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Affiliation(s)
- Siyi He
- 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Dianqin Sun
- 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - He 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Maomao Cao
- 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Xinyang Yu
- 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Lin Lei
- Department of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province, China
| | - Ji Peng
- Department of Cancer Prevention and Control, Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province, 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
| | - Wanqing 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/Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Beijing, China
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48
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Byrne S, Boyle T, Ahmed M, Lee SH, Benyamin B, Hyppönen E. Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types. Int J Epidemiol 2023:6990971. [PMID: 36651198 DOI: 10.1093/ije/dyac238] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 12/20/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank. METHODS In 2006-2010, participants aged 37-73 years had their lifestyle assessed and were followed up for incident cancers until 2015-2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer (n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined ('overall cancer'), and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed. RESULTS There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 (0.87, 0.90)] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (P < 0.0003 for all). CONCLUSIONS The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention.
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Affiliation(s)
- Stephanie Byrne
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Terry Boyle
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Muktar Ahmed
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
- Department of Epidemiology, Faculty of Public Health, Jimma University Institute of Health, Jimma, Ethiopia
| | - Sang Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
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49
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Namba S, Saito Y, Kogure Y, Masuda T, Bondy ML, Gharahkhani P, Gockel I, Heider D, Hillmer A, Jankowski J, MacGregor S, Maj C, Melin B, Ostrom QT, Palles C, Schumacher J, Tomlinson I, Whiteman DC, Okada Y, Kataoka K. Common Germline Risk Variants Impact Somatic Alterations and Clinical Features across Cancers. Cancer Res 2023; 83:20-27. [PMID: 36286845 PMCID: PMC9811159 DOI: 10.1158/0008-5472.can-22-1492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/20/2022] [Accepted: 10/21/2022] [Indexed: 02/03/2023]
Abstract
Aggregation of genome-wide common risk variants, such as polygenic risk score (PRS), can measure genetic susceptibility to cancer. A better understanding of how common germline variants associate with somatic alterations and clinical features could facilitate personalized cancer prevention and early detection. We constructed PRSs from 14 genome-wide association studies (median n = 64,905) for 12 cancer types by multiple methods and calibrated them using the UK Biobank resources (n = 335,048). Meta-analyses across cancer types in The Cancer Genome Atlas (n = 7,965) revealed that higher PRS values were associated with earlier cancer onset and lower burden of somatic alterations, including total mutations, chromosome/arm somatic copy-number alterations (SCNA), and focal SCNAs. This contrasts with rare germline pathogenic variants (e.g., BRCA1/2 variants), showing heterogeneous associations with somatic alterations. Our results suggest that common germline cancer risk variants allow early tumor development before the accumulation of many somatic alterations characteristic of later stages of carcinogenesis. SIGNIFICANCE Meta-analyses across cancers show that common germline risk variants affect not only cancer predisposition but the age of cancer onset and burden of somatic alterations, including total mutations and copy-number alterations.
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Affiliation(s)
- Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuki Saito
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan
| | - Yasunori Kogure
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
| | - Tatsuo Masuda
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan
- StemRIM Institute of Regeneration-Inducing Medicine, Osaka University, Osaka, Japan
| | - Melissa L. Bondy
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Puya Gharahkhani
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Axel Hillmer
- Institute of Pathology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Janusz Jankowski
- Office of Vice President Research and Innovation, Laucala Bay Campus, University of South Pacific, Suva, Fiji
- Institute for Clinical Trials, University College London, Holborn, London
| | - Stuart MacGregor
- Statistical Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina
- The Preston Robert Tisch Brain Tumor Center, Duke University School of Medicine, Durham, North Carolina
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
| | - Claire Palles
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | | | - Ian Tomlinson
- Edinburgh Cancer Research Centre, IGMM, University of Edinburgh, Crewe Road, Edinburgh, United Kingdom
| | - David C. Whiteman
- Cancer Control, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
| | - Keisuke Kataoka
- Division of Molecular Oncology, National Cancer Center Research Institute, Tokyo, Japan
- Division of Hematology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
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50
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Blout Zawatsky CL, Bick D, Bier L, Funke B, Lebo M, Lewis KL, Orlova E, Qian E, Ryan L, Schwartz MLB, Soper ER. Elective genomic testing: Practice resource of the National Society of Genetic Counselors. J Genet Couns 2023; 32:281-299. [PMID: 36597794 DOI: 10.1002/jgc4.1654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/24/2022] [Accepted: 10/28/2022] [Indexed: 01/05/2023]
Abstract
Genetic counseling for patients who are pursuing genetic testing in the absence of a medical indication, referred to as elective genomic testing (EGT), is becoming more common. This type of testing has the potential to detect genetic conditions before there is a significant health impact permitting earlier management and/or treatment. Pre- and post-test counseling for EGT is similar to indication-based genetic testing. Both require a complete family and medical history when ordering a test or interpreting a result. However, EGT counseling has some special considerations including greater uncertainties around penetrance and clinical utility and a lack of published guidelines. While certain considerations in the selection of a high-quality genetic testing laboratory are universal, there are some considerations that are unique to the selection of a laboratory performing EGT. This practice resource intends to provide guidance for genetic counselors and other healthcare providers caring for adults seeking pre- or post-test counseling for EGT. Genetic counselors and other genetics trained healthcare providers are the ideal medical professionals to supply accurate information to individuals seeking counseling about EGT enabling them to make informed decisions about testing and follow-up.
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Affiliation(s)
- Carrie L Blout Zawatsky
- Genomes2People, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Ariadne Labs, Boston, Massachusetts, USA.,The MGH Institute of Health Professions, Boston, Massachusetts, USA
| | | | - Louise Bier
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Matthew Lebo
- Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Pathology, Harvard Medical School, Cambridge, Massachusetts, USA.,Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Boston, Massachusetts, USA
| | - Katie L Lewis
- Center for Precision Health Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Ekaterina Orlova
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Emily Qian
- Department of Genetics, Yale University, New Haven, Connecticut, USA
| | | | - Marci L B Schwartz
- Cardiac Genome Clinic, Ted Rogers Centre for Heart Research, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Emily R Soper
- The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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