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Kim NY, Lee H, Kim S, Kim YJ, Lee H, Lee J, Kwak SH, Lee S. The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population. Sci Rep 2024; 14:5749. [PMID: 38459065 PMCID: PMC10923897 DOI: 10.1038/s41598-024-55313-0] [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: 05/30/2023] [Accepted: 02/22/2024] [Indexed: 03/10/2024] Open
Abstract
The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E-02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β -cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel's C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.
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Affiliation(s)
- Na Yeon Kim
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Haekyung Lee
- Division of Nephrology, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, South Korea
| | - Sehee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul, South Korea
| | - Ye-Jee Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Seoul, South Korea
| | - Hyunsuk Lee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Junhyeong Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea.
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Wei X, Sun D, Gao J, Zhang J, Zhu M, Yu C, Ma Z, Fu Y, Ji C, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Jin G, Chen Z, Hu Z, Li L, Shen H, Lv J, Ma H. Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study. Int J Cancer 2024; 154:807-815. [PMID: 37846649 DOI: 10.1002/ijc.34765] [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: 06/02/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 10/18/2023]
Abstract
The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.
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Affiliation(s)
- Xiaoxia Wei
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jiaxin Gao
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Zhimin Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yating Fu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Ji
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Grassmann F, Mälarstig A, Dahl L, Bendes A, Dale M, Thomas CE, Gabrielsson M, Hedman ÅK, Eriksson M, Margolin S, Huang TH, Ulfstedt M, Forsberg S, Eriksson P, Johansson M, Hall P, Schwenk JM, Czene K. The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk. Br J Cancer 2024; 130:620-627. [PMID: 38135714 PMCID: PMC10876928 DOI: 10.1038/s41416-023-02541-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/22/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVE Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk. SUBJECTS In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort. RESULTS In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy. CONCLUSIONS Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.
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Affiliation(s)
- Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Institute for Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany.
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - Leo Dahl
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Annika Bendes
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Matilda Dale
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Cecilia Engel Thomas
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Marike Gabrielsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Åsa K Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Pfizer Worldwide Research, Development and Medical, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Tzu-Hsuan Huang
- Cancer Immunology Discovery, Pfizer Inc., San Diego, CA, USA
| | | | | | - Per Eriksson
- Olink Proteomics, Uppsala Science Park, Uppsala, Sweden
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Solna, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Gibson TM, Karyadi DM, Hartley SW, Arnold MA, Berrington de Gonzalez A, Conces MR, Howell RM, Kapoor V, Leisenring WM, Neglia JP, Sampson JN, Turcotte LM, Chanock SJ, Armstrong GT, Morton LM. Polygenic risk scores, radiation treatment exposures and subsequent cancer risk in childhood cancer survivors. Nat Med 2024; 30:690-698. [PMID: 38454124 PMCID: PMC11029534 DOI: 10.1038/s41591-024-02837-7] [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: 04/27/2023] [Accepted: 01/26/2024] [Indexed: 03/09/2024]
Abstract
Survivors of childhood cancer are at increased risk for subsequent cancers attributable to the late effects of radiotherapy and other treatment exposures; thus, further understanding of the impact of genetic predisposition on risk is needed. Combining genotype data for 11,220 5-year survivors from the Childhood Cancer Survivor Study and the St Jude Lifetime Cohort, we found that cancer-specific polygenic risk scores (PRSs) derived from general population, genome-wide association study, cancer loci identified survivors of European ancestry at increased risk of subsequent basal cell carcinoma (odds ratio per s.d. of the PRS: OR = 1.37, 95% confidence interval (CI) = 1.29-1.46), female breast cancer (OR = 1.42, 95% CI = 1.27-1.58), thyroid cancer (OR = 1.48, 95% CI = 1.31-1.67), squamous cell carcinoma (OR = 1.20, 95% CI = 1.00-1.44) and melanoma (OR = 1.60, 95% CI = 1.31-1.96); however, the association for colorectal cancer was not significant (OR = 1.19, 95% CI = 0.94-1.52). An investigation of joint associations between PRSs and radiotherapy found more than additive increased risks of basal cell carcinoma, and breast and thyroid cancers. For survivors with radiotherapy exposure, the cumulative incidence of subsequent cancer by age 50 years was increased for those with high versus low PRS. These findings suggest a degree of shared genetic etiology for these malignancy types in the general population and survivors, which remains evident in the context of strong radiotherapy-related risk.
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Affiliation(s)
- Todd M Gibson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Danielle M Karyadi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen W Hartley
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael A Arnold
- Department of Pathology, Children's Hospital of Colorado, University of Colorado, Denver, CO, USA
| | | | - Miriam R Conces
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH, USA
| | - Rebecca M Howell
- Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vidushi Kapoor
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wendy M Leisenring
- Cancer Prevention and Clinical Statistics Programs, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Joseph P Neglia
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lucie M Turcotte
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gregory T Armstrong
- Department of Epidemiology and Cancer Control, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Lindsay M Morton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Zhu P, Zhang Y, Chen Q, Qiu W, Chen M, Xue L, Lin M, Yang H. The interaction of diet, alcohol, genetic predisposition, and the risk of breast cancer: a cohort study from the UK Biobank. Eur J Nutr 2024; 63:343-356. [PMID: 37914956 PMCID: PMC10899287 DOI: 10.1007/s00394-023-03269-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 10/06/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND Dietary factors have consistently been associated with breast cancer risk. However, there is limited evidence regarding their associations in women with different genetic susceptibility to breast cancer, and their interaction with alcohol consumption is also not well understood. METHODS We analyzed data from 261,853 female participants in the UK Biobank. Multivariable adjusted Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between dietary factors and breast cancer risk. Additionally, we assessed the interaction of dietary factors with alcohol consumption and polygenic risk score (PRS) for breast cancer. RESULTS A moderately higher risk of breast cancer was associated with the consumption of processed meat (HR = 1.10, 95% CI 1.03, 1.18, p-trend = 0.016). Higher intake of raw vegetables and fresh fruits, and adherence to a healthy dietary pattern were inversely associated with breast cancer risk [HR (95% CI):0.93 (0.88-0.99), 0.87 (0.81, 0.93) and 0.93 (0.86-1.00), p for trend: 0.025, < 0.001, and 0.041, respectively]. Furthermore, a borderline significant interaction was found between alcohol consumption and the intake of processed meat with regard to breast cancer risk (P for interaction = 0.065). No multiplicative interaction was observed between dietary factors and PRS. CONCLUSION Processed meat was positively associated with breast cancer risk, and vegetables, fruits, and healthy dietary patterns were negatively associated with breast cancer risk. We found no strong interaction of dietary factors with alcohol consumption and genetic predisposition for risk of breast cancer.
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Affiliation(s)
- Pingxiu Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China
| | - Yanyu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China
| | - Qianni Chen
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Wenji Qiu
- School of Health Management, Fujian Medical University, Fuzhou, 350122, China
| | - Minhui Chen
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Lihua Xue
- Department of Ultrasonography, Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China
| | - Moufeng Lin
- No. 5 Hospital of Fuqing City, Fuzhou, 350319, China
| | - Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou, 350122, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77, Stockholm, Sweden.
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Taris N, Luporsi E, Osada M, Thiblet M, Mathelin C. [News in breast oncology genetics for female and male population]. GYNECOLOGIE, OBSTETRIQUE, FERTILITE & SENOLOGIE 2024; 52:149-157. [PMID: 38190969 DOI: 10.1016/j.gofs.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 01/10/2024]
Abstract
OBJECTIVES Breast oncology genetics emerged almost 30 years ago with the discovery of the BRCA1 and BRCA2 genes. The evolution of analytical practices has progressively allowed access to tests whose results now have a considerable impact on the management of both female and male breast cancers. The Sénologie commission of the Collège national des gynécologues et obstétriciens français (CNGOF) asked five specialists in breast surgery, oncology and oncological genetics to draw up a summary of the oncogenetic testing criteria used and the clinical implications for the female and male population of the test results, with or without an identified causal variant. In the case of proven genetic risk, surveillance, risk-reduction strategies, and the specificities of surgical and medical management (with PARP inhibitors in particular) were updated. METHODS This summary was based on national and international guidelines on the monitoring and therapeutic management of genetic risk, and a recent review of the literature covering the last five years. RESULTS Despite successive technical developments, the probability of identifying a causal variant in a situation suggestive of a predisposition to breast and ovarian cancer remains around 10% in France. The risk of breast cancer in women with a causal variant of the BRCA1, BRCA2, PALB2, TP53, CDH1 and PTEN genes is estimated at between 35% and 85% at age 70. The presence of a causal variant in one of these genes is the subject of different recommendations for men and women, concerning both surveillance, the age of onset and imaging modalities of which vary according to the genes involved, and risk-reduction surgery, which is possible for women as soon as their risk level exceeds 30% and remains exceptionally indicated for men. In the case of breast cancer, PARP inhibitors are a promising new class of treatment for BRCA germline mutations. CONCLUSION A discipline resolutely focused on understanding molecular mechanisms, screening and preventive medicine/surgery, oncology genetics is currently also involved in new medical/surgical approaches, the long-term benefits/risks of which will need to be monitored.
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Affiliation(s)
- Nicolas Taris
- Unité de génétique oncologique, ICANS, avenue Albert-Calmette, 67200 Strasbourg, France.
| | - Elisabeth Luporsi
- Service de génétique, hôpital Femme-Mère-Enfant, CHR de Metz-Thionville, Site de Mercy, 1, allée du Château, 57085 Metz cedex, France.
| | - Marine Osada
- Service de chirurgie, ICANS, avenue Albert-Calmette, 67200 Strasbourg, France; CHRU, avenue Molière, 67200 Strasbourg, France.
| | - Marie Thiblet
- Service de chirurgie, ICANS, avenue Albert-Calmette, 67200 Strasbourg, France; CHRU, avenue Molière, 67200 Strasbourg, France.
| | - Carole Mathelin
- Service de chirurgie, ICANS, avenue Albert-Calmette, 67200 Strasbourg, France; CHRU, avenue Molière, 67200 Strasbourg, France.
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Laplana M, Lopez-Ortega R, Fibla J. Polygenic risk score comparator (PRScomp): Test population vs. worldwide populations. Int J Med Inform 2024; 183:105333. [PMID: 38184939 DOI: 10.1016/j.ijmedinf.2023.105333] [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: 06/14/2023] [Revised: 12/19/2023] [Accepted: 12/28/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND Polygenic risk scores (PRS) are a powerful tool for predicting an individual's genetic risk for complex diseases. METHODS We have developed a web service (PRScomp) as a user-friendly tool to evaluate PRS of the user own population and compare it with worldwide populations. RESULTS A disease/trait database has been constructed from GWAS Catalog summary statistics. Genotype data of test population is uploaded and merged with the reference dataset (1000 Genome Project and Human Genome Diversity Project) to obtain a file including the common SNPs. The user can select a disease/trait from the database and a curated set of risk markers is used to calculate summatory PRS. Distribution of z-scored PRS values is presented in publication-ready plots and text files that can be downloaded. DISCUSSION PRScomp can be useful for public health decision-making by identifying population-specific genetic risk factors and informing the development of targeted interventions for at-risk populations.
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Affiliation(s)
- Marina Laplana
- Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, IRBLleida, Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain.
| | - Ricard Lopez-Ortega
- Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, IRBLleida, Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain; Unitat de Citogenètica i Genètica Mèdica, Hospital Universitari Arnau de Vilanova, IRBLleida, Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain
| | - Joan Fibla
- Departament de Ciències Mèdiques Bàsiques, Universitat de Lleida, IRBLleida, Av. Alcalde Rovira Roure, 80, 25198 Lleida, Spain.
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Yiangou K, Mavaddat N, Dennis J, Zanti M, Wang Q, Bolla MK, Abubakar M, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baten A, Behrens S, Bermisheva M, de Gonzalez AB, Białkowska K, Boddicker N, Bodelon C, Bogdanova NV, Bojesen SE, Brantley KD, Brauch H, Brenner H, Camp NJ, Canzian F, Castelao JE, Cessna MH, Chang-Claude J, Chenevix-Trench G, Chung WK, Colonna SV, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dunning AM, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Flyger H, Fritschi L, Gago-Dominguez M, Gentry-Maharaj A, González-Neira A, Guénel P, Hahnen E, Haiman CA, Hamann U, Hartikainen JM, Ho V, Hodge J, Hollestelle A, Honisch E, Hooning MJ, Hoppe R, Hopper JL, Howell S, Howell A, Jakovchevska S, Jakubowska A, Jernström H, Johnson N, Kaaks R, Khusnutdinova EK, Kitahara CM, Koutros S, Kristensen VN, Lacey JV, Lambrechts D, Lejbkowicz F, Lindblom A, Lush M, Mannermaa A, Mavroudis D, Menon U, Murphy RA, Nevanlinna H, Obi N, Offit K, Park-Simon TW, Patel AV, Peng C, Peterlongo P, Pita G, Plaseska-Karanfilska D, Pylkäs K, Radice P, Rashid MU, Rennert G, Roberts E, Rodriguez J, Romero A, Rosenberg EH, Saloustros E, Sandler DP, Sawyer EJ, Schmutzler RK, Scott CG, Shu XO, Southey MC, Stone J, Taylor JA, Teras LR, van de Beek I, Willett W, Winqvist R, Zheng W, Vachon CM, Schmidt MK, Hall P, MacInnis RJ, Milne RL, Pharoah PD, Simard J, Antoniou AC, Easton DF, Michailidou K. Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302043. [PMID: 38410445 PMCID: PMC10896416 DOI: 10.1101/2024.02.12.24302043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.
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Affiliation(s)
- Kristia Yiangou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Maria Zanti
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Mustapha Abubakar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Thomas U. Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Irene L. Andrulis
- Fred A, Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada, M5G 1X5
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, M5S 1A8
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA, 92617
| | - Natalia N. Antonenkova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Kristan J. Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, ON, Canada, K7L 3N6
| | | | - Adinda Baten
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium, 3000
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- St Petersburg State University, St, Petersburg, Russia, 199034
| | | | - Katarzyna Białkowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
| | - Nicholas Boddicker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Clara Bodelon
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Natalia V. Bogdanova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany, 30625
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 2200
| | - Kristen D. Brantley
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Hiltrud Brauch
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany, 72074
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany, 72074
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany, 69120
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Nicola J. Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jose E. Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) Foundation, Complejo Hospitalario Universitario de Santiago, SERGAS, Vigo, Spain, 36312
| | - Melissa H. Cessna
- Department of Pathology, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
- Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Georgia Chenevix-Trench
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 4006
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA, 10032
| | - NBCS Collaborators
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Research, Vestre Viken Hospital, Drammen, Norway, 3019
- Section for Breast- and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Ullevål, Oslo, Norway, 0450
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, 0379
- Department of Pathology, Akershus University Hospital, Lørenskog, Norway, 1478
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway, 1478
- Oslo Breast Cancer Research Consortium, Oslo University Hospital, Oslo, Norway, 0379
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - Sarah V. Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Angela Cox
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Simon S. Cross
- Division of Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA, 19111
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Diana M. Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK, SO17 1BJ
| | - A. Heather Eliassen
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany, 04107
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany, 04103
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - D. Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany, 91054
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia, 6102
| | - Manuela Gago-Dominguez
- Cancer Genetics and Epidemiology Group, Genomic Medicine Group, Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain, 15706
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
- Spanish Network on Rare Diseases (CIBERER)
| | - Pascal Guénel
- Team ‘Exposome and Heredity’, CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France, 94805
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 90033
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jaana M. Hartikainen
- Cancer RC, University of Eastern Finland, Kuopio, Finland, 70210
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
| | - Vikki Ho
- Health Innovation and Evaluation Hub, Université de Montréal Hospital Research Centre (CRCHUM), Montréal, Québec, Canada
| | - James Hodge
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Antoinette Hollestelle
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Ellen Honisch
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 40225
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Reiner Hoppe
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- University of Tübingen, Tübingen, Germany, 72074
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
| | - Sacha Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, UK
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK, M13 9PL
| | - ABCTB Investigators
- Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia, 2145
| | - kConFab Investigators
- Research Department, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, 3000
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia, 3000
| | - Simona Jakovchevska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland, 171-252
| | - Helena Jernström
- Oncology, Clinical Sciences in Lund, Lund University, Lund, Sweden, 221 85
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Elza K. Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- Department of Genetics and Fundamental Medicine, Ufa University of Science and Technology, Ufa, Russia, 450076
| | - Cari M. Kitahara
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA, 20892
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Vessela N. Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - James V. Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA, 91010
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA, 91010
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium, 3000
- VIB Center for Cancer Biology, VIB, Leuven, Belgium, 3001
| | | | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, 171 76
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden, 171 76
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland, 70210
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece, 711 10
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
| | - Rachel A. Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada, V5Z 1L3
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 00290
| | - Nadia Obi
- Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
| | | | - Alpa V. Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Cheng Peng
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy, 20139
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Paolo Radice
- Unit of Predictice Medicine, Molecular Bases of Genetic Risk, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy, 20133
| | - Muhammad U. Rashid
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, Pakistan, 54000
| | - Gad Rennert
- Technion, Faculty of Medicine and Association for Promotion of Research in Precision Medicine, Haifa, Israel
| | - Eleanor Roberts
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 28222
| | - Efraim H. Rosenberg
- Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | | | - Dale P. Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Elinor J. Sawyer
- School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy’s Campus, King’s College London, London, UK
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50931
| | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia, 6000
| | - Jack A. Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Irma van de Beek
- Department of Clinical Genetics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | - Walter Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Celine M. Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands, 1066 CX
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
- Department of Oncology, Södersjukhuset, Stockholm, Sweden, 118 83
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Paul D.P. Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA, 90069
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval Research Center, Québec City, Québec, Canada, G1V 4G2
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
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59
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Barili V, Ambrosini E, Bortesi B, Minari R, De Sensi E, Cannizzaro IR, Taiani A, Michiara M, Sikokis A, Boggiani D, Tommasi C, Serra O, Bonatti F, Adorni A, Luberto A, Caggiati P, Martorana D, Uliana V, Percesepe A, Musolino A, Pellegrino B. Genetic Basis of Breast and Ovarian Cancer: Approaches and Lessons Learnt from Three Decades of Inherited Predisposition Testing. Genes (Basel) 2024; 15:219. [PMID: 38397209 PMCID: PMC10888198 DOI: 10.3390/genes15020219] [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: 12/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Germline variants occurring in BRCA1 and BRCA2 give rise to hereditary breast and ovarian cancer (HBOC) syndrome, predisposing to breast, ovarian, fallopian tube, and peritoneal cancers marked by elevated incidences of genomic aberrations that correspond to poor prognoses. These genes are in fact involved in genetic integrity, particularly in the process of homologous recombination (HR) DNA repair, a high-fidelity repair system for mending DNA double-strand breaks. In addition to its implication in HBOC pathogenesis, the impairment of HR has become a prime target for therapeutic intervention utilizing poly (ADP-ribose) polymerase (PARP) inhibitors. In the present review, we introduce the molecular roles of HR orchestrated by BRCA1 and BRCA2 within the framework of sensitivity to PARP inhibitors. We examine the genetic architecture underneath breast and ovarian cancer ranging from high- and mid- to low-penetrant predisposing genes and taking into account both germline and somatic variations. Finally, we consider higher levels of complexity of the genomic landscape such as polygenic risk scores and other approaches aiming to optimize therapeutic and preventive strategies for breast and ovarian cancer.
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Affiliation(s)
- Valeria Barili
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Enrico Ambrosini
- Medical Genetics, University Hospital of Parma, 43126 Parma, Italy
| | - Beatrice Bortesi
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Roberta Minari
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Erika De Sensi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | | | - Antonietta Taiani
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Maria Michiara
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Angelica Sikokis
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Daniela Boggiani
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Chiara Tommasi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Olga Serra
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Francesco Bonatti
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Alessia Adorni
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Anita Luberto
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | | | - Davide Martorana
- Medical Genetics, University Hospital of Parma, 43126 Parma, Italy
| | - Vera Uliana
- Medical Genetics, University Hospital of Parma, 43126 Parma, Italy
| | - Antonio Percesepe
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
- Medical Genetics, University Hospital of Parma, 43126 Parma, Italy
| | - Antonino Musolino
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
| | - Benedetta Pellegrino
- Medical Oncology Unit, University Hospital of Parma, 43126 Parma, Italy
- Breast Unit, University Hospital of Parma, 43126 Parma, Italy
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Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
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Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
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Stan DL, Kim JO, Schaid DJ, Carlson EE, Kim CA, Sinnwell JP, Couch FJ, Vachon CM, Cooke AL, Goldenberg BA, Pruthi S. Breast Cancer Polygenic-Risk Score Influence on Risk-Reducing Endocrine Therapy Use: Genetic Risk Estimate (GENRE) Trial 1-Year and 2-Year Follow-Up. Cancer Prev Res (Phila) 2024; 17:77-84. [PMID: 38154464 DOI: 10.1158/1940-6207.capr-23-0256] [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: 07/13/2023] [Revised: 10/26/2023] [Accepted: 12/21/2023] [Indexed: 12/30/2023]
Abstract
Refinement of breast cancer risk estimates with a polygenic-risk score (PRS) may improve uptake of risk-reducing endocrine therapy (ET). A previous clinical trial assessed the influence of adding a PRS to traditional risk estimates on ET use. We stratified participants according to PRS-refined breast cancer risk and evaluated ET use and ET-related quality of life (QOL) at 1-year (previously reported) and 2-year follow-ups. Of 151 participants, 58 (38.4%) initiated ET, and 22 (14.6%) discontinued ET by 2 years; 42 (27.8%) and 36 (23.8%) participants were using ET at 1- and 2-year follow-ups, respectively. At the 2-year follow-up, 39% of participants with a lifetime breast cancer risk of 40.1% to 100.0%, 18% with a 20.1% to 40.0% risk, and 16% with a 0.0% to 20.0% risk were taking ET (overall P = 0.01). Moreover, 40% of participants whose breast cancer risk increased by 10% or greater with addition of the PRS to a traditional breast cancer-risk model were taking ET versus 0% whose risk decreased by 10% or greater (P = 0.004). QOL was similar for participants taking or not taking ET at 1- and 2-year follow-ups, although most who discontinued ET did so because of adverse effects. However, these QOL results may have been skewed by the long interval between QOL surveys and lack of baseline QOL data. PRS-informed breast cancer prevention counseling has a lasting, but waning, effect over time. Additional follow-up studies are needed to address the effect of PRS on ET adherence, ET-related QOL, supplemental breast cancer screening, and other risk-reducing behaviors. PREVENTION RELEVANCE Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.
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Affiliation(s)
- Daniela L Stan
- Breast Diagnostic Clinic, Mayo Clinic, Rochester, Minnesota
- Mayo Clinic Cancer Center, Mayo Clinic, Rochester, Minnesota
| | - Julian O Kim
- Department of Radiation Oncology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Erin E Carlson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Christina A Kim
- Department of Medical Oncology and Hematology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Fergus J Couch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota
| | - Celine M Vachon
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Andrew L Cooke
- Department of Radiation Oncology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Benjamin A Goldenberg
- Department of Medical Oncology and Hematology, Max Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Sandhya Pruthi
- Breast Diagnostic Clinic, Mayo Clinic, Rochester, Minnesota
- Mayo Clinic Cancer Center, Mayo Clinic, Rochester, Minnesota
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Schreurs MAC, Ramón Y Cajal T, Adank MA, Collée JM, Hollestelle A, van Rooij J, Schmidt MK, Hooning MJ. The benefit of adding polygenic risk scores, lifestyle factors, and breast density to family history and genetic status for breast cancer risk and surveillance classification of unaffected women from germline CHEK2 c.1100delC families. Breast 2024; 73:103611. [PMID: 38039887 PMCID: PMC10730863 DOI: 10.1016/j.breast.2023.103611] [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: 09/25/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023] Open
Abstract
To determine the changes in surveillance category by adding a polygenic risk score based on 311 breast cancer (BC)-associated variants (PRS311), questionnaire-based risk factors and breast density on personalized BC risk in unaffected women from Dutch CHEK2 c.1100delC families. In total, 117 unaffected women (58 heterozygotes and 59 non-carriers) from CHEK2 families were included. Blood-derived DNA samples were genotyped with the GSAMDv3-array to determine PRS311. Lifetime BC risk was calculated in CanRisk, which uses data from the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Women, were categorized into three surveillance groups. The surveillance advice was reclassified in 37.9 % of heterozygotes and 32.2 % of non-carriers after adding PRS311. Including questionnaire-based risk factors resulted in an additional change in 20.0 % of heterozygotes and 13.2 % of non-carriers; and a subanalysis showed that adding breast density on top shifted another 17.9 % of heterozygotes and 33.3 % of non-carriers. Overall, the majority of heterozygotes were reclassified to a less intensive surveillance, while non-carriers would require intensified surveillance. The addition of PRS311, questionnaire-based risk factors and breast density to family history resulted in a more personalized BC surveillance advice in CHEK2-families, which may lead to more efficient use of surveillance.
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Affiliation(s)
- Maartje A C Schreurs
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Teresa Ramón Y Cajal
- Familial Cancer Clinic, Medical Oncology Service, Hospital Sant Pau, Barcelona, Spain
| | - Muriel A Adank
- Department of Clinical Genetics, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | | | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
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Elam KK, Bountress KE, Ha T, Shaw DS, Wilson MN, Aliev F, Dick DM, Lemery-Chalfant K. Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples. Dev Psychopathol 2024; 36:82-91. [PMID: 35983793 PMCID: PMC9938843 DOI: 10.1017/s0954579422000980] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Externalizing behavior in early adolescence is associated with alcohol use in adolescence and early adulthood and these behaviors often emerge as part of a developmental sequence. This pattern can be the result of heterotypic continuity, in which different behaviors emerge over time based on an underlying shared etiology. In particular, there is largely a shared genetic etiology underlying externalizing and substance use behaviors. We examined whether polygenic risk for alcohol use disorder predicted (1) externalizing behavior in early adolescence and alcohol use in adolescence in the Early Steps Multisite sample and (2) externalizing behavior in adolescence and alcohol use in early adulthood in the Project Alliance 1 (PAL1) sample. We examined associations separately for African Americans and European Americans. When examining European Americans in the Early Steps sample, greater polygenic risk was associated with externalizing behavior in early adolescence. In European Americans in PAL1, we found greater polygenic risk was associated with alcohol use in early adulthood. Effects were largely absent in African Americans in both samples. Results imply that genetic predisposition for alcohol use disorder may increase risk for externalizing and alcohol use as these behaviors emerge developmentally.
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Affiliation(s)
- Kit K. Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7 St., Suite 116, Bloomington, IN 47405
| | - Kaitlin E. Bountress
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - Thao Ha
- Department of Psychology, Arizona State University
| | | | | | - Fazil Aliev
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
| | - Danielle M. Dick
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
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Bolze A, Cirulli ET, Hajek C, Schnell Blitstein JM, Grzymski JJ. The Potential of Genetics in Identifying Women at Lower Risk of Breast Cancer. JAMA Oncol 2024; 10:236-239. [PMID: 38153744 PMCID: PMC10870185 DOI: 10.1001/jamaoncol.2023.5468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/13/2023] [Indexed: 12/29/2023]
Abstract
Importance Genetic information is not being used to identify women at lower risk of breast cancer or other diseases in clinical practice. With the new US Preventive Services Task Force guidelines lowering the age for mammogram screening for all, there is a potential benefit in identifying women at lower risk of disease who may defer the start of mammographic screening. This genetic risk-based approach would help mitigate overscreening, associated costs, and anxiety. Objective To assess breast cancer incidence and age of onset among women at low genetic risk compared with women at average risk and evaluate the potential to delay mammography on the basis of genetic risk stratification. Design, Setting, and Participants This retrospective case-control study included 25 591 women from the Healthy Nevada Project sequenced by Helix between 2018 and 2022. Data extracted from electronic health records at the end of 2022 (mean length of electronic health record available was 12 years) were used for the analysis in 2023. Main Outcomes and Measures Breast cancer diagnosis was identified from electronic health records. Classification to the low-risk genetic group required (1) the absence of pathogenic variants or a variant of uncertain significance in BRCA1, BRCA2, PALB2, ATM, or CHEK2, and (2) a low polygenic risk score (bottom 10%) using a 313-single-nucleotide variant model. Results Of 25 591 women in the study (mean [SD] age was 53.8 [16.9] years), 2338 women (9.1%) were classified as having low risk for breast cancer; 410 women (1.6%) were classified as high risk; and 22 843 women (89.3%) as average risk. There was a significant reduction in breast cancer diagnosis among the low-risk group (hazard ratio, 0.39; 95% CI, 0.29-0.52; P < .001). By 45 years of age, 0.69% of women in the average-risk group were diagnosed with breast cancer, whereas women in the low-risk group reached this rate at 51 years. By 50 years of age, 1.41% of those in the average-risk group were diagnosed with breast cancer, whereas those in the low-risk group reached this rate at age 58 years. These findings suggest that deferring mammogram screening by 5 to 10 years for women at low risk of breast cancer aligns with new draft recommendations. Conclusions and Relevance The findings of this retrospective case-control study underscore the value of genetics in individualizing the onset of breast cancer screening. Improving breast cancer risk stratification by implementing both high-risk and low-risk strategies in screening can refine preventive measures and optimize health care resource allocation.
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Valentini V, Bucalo A, Conti G, Celli L, Porzio V, Capalbo C, Silvestri V, Ottini L. Gender-Specific Genetic Predisposition to Breast Cancer: BRCA Genes and Beyond. Cancers (Basel) 2024; 16:579. [PMID: 38339330 PMCID: PMC10854694 DOI: 10.3390/cancers16030579] [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: 12/21/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Among neoplastic diseases, breast cancer (BC) is one of the most influenced by gender. Despite common misconceptions associating BC as a women-only disease, BC can also occur in men. Additionally, transgender individuals may also experience BC. Genetic risk factors play a relevant role in BC predisposition, with important implications in precision prevention and treatment. The genetic architecture of BC susceptibility is similar in women and men, with high-, moderate-, and low-penetrance risk variants; however, some sex-specific features have emerged. Inherited high-penetrance pathogenic variants (PVs) in BRCA1 and BRCA2 genes are the strongest BC genetic risk factor. BRCA1 and BRCA2 PVs are more commonly associated with increased risk of female and male BC, respectively. Notably, BRCA-associated BCs are characterized by sex-specific pathologic features. Recently, next-generation sequencing technologies have helped to provide more insights on the role of moderate-penetrance BC risk variants, particularly in PALB2, CHEK2, and ATM genes, while international collaborative genome-wide association studies have contributed evidence on common low-penetrance BC risk variants, on their combined effect in polygenic models, and on their role as risk modulators in BRCA1/2 PV carriers. Overall, all these studies suggested that the genetic basis of male BC, although similar, may differ from female BC. Evaluating the genetic component of male BC as a distinct entity from female BC is the first step to improve both personalized risk assessment and therapeutic choices of patients of both sexes in order to reach gender equality in BC care. In this review, we summarize the latest research in the field of BC genetic predisposition with a particular focus on similarities and differences in male and female BC, and we also discuss the implications, challenges, and open issues that surround the establishment of a gender-oriented clinical management for BC.
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Affiliation(s)
- Virginia Valentini
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Agostino Bucalo
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Giulia Conti
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Ludovica Celli
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Virginia Porzio
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Carlo Capalbo
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
- Medical Oncology Unit, Sant’Andrea University Hospital, 00189 Rome, Italy
| | - Valentina Silvestri
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
| | - Laura Ottini
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy; (V.V.); (A.B.); (G.C.); (L.C.); (V.P.); (C.C.); (V.S.)
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Lauschke VM, Zhou Y, Ingelman-Sundberg M. Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward. Annu Rev Pharmacol Toxicol 2024; 64:33-51. [PMID: 37506333 DOI: 10.1146/annurev-pharmtox-051921-091209] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.
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Affiliation(s)
- Volker M Lauschke
- Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden;
- Tübingen University, Tübingen, Germany
| | - Yitian Zhou
- Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden;
- Tübingen University, Tübingen, Germany
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Sun X, Reiner AS, Tran AP, Watt GP, Oh JH, Mellemkjær L, Lynch CF, Knight JA, John EM, Malone KE, Liang X, Woods M, Derkach A, Concannon P, Bernstein JL, Shu X. A genome-wide association study of contralateral breast cancer in the Women's Environmental Cancer and Radiation Epidemiology Study. Breast Cancer Res 2024; 26:16. [PMID: 38263039 PMCID: PMC10807183 DOI: 10.1186/s13058-024-01765-1] [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: 09/30/2023] [Accepted: 01/06/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Contralateral breast cancer (CBC) is the most common second primary cancer diagnosed in breast cancer survivors, yet the understanding of the genetic susceptibility of CBC, particularly with respect to common variants, remains incomplete. This study aimed to investigate the genetic basis of CBC to better understand this malignancy. FINDINGS We performed a genome-wide association analysis in the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study of women with first breast cancer diagnosed at age < 55 years including 1161 with CBC who served as cases and 1668 with unilateral breast cancer (UBC) who served as controls. We observed two loci (rs59657211, 9q32, SLC31A2/FAM225A and rs3815096, 6p22.1, TRIM31) with suggestive genome-wide significant associations (P < 1 × 10-6). We also found an increased risk of CBC associated with a breast cancer-specific polygenic risk score (PRS) comprised of 239 known breast cancer susceptibility single nucleotide polymorphisms (SNPs) (rate ratio per 1-SD change: 1.25; 95% confidence interval 1.14-1.36, P < 0.0001). The protective effect of chemotherapy on CBC risk was statistically significant only among patients with an elevated PRS (Pheterogeneity = 0.04). The AUC that included the PRS and known breast cancer risk factors was significantly elevated. CONCLUSIONS The present GWAS identified two previously unreported loci with suggestive genome-wide significance. We also confirm that an elevated risk of CBC is associated with a comprehensive breast cancer susceptibility PRS that is independent of known breast cancer risk factors. These findings advance our understanding of genetic risk factors involved in CBC etiology.
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Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
- Department of Epidemiology, Zhejiang Chinese Medical University, Zhejiang, China
| | - Anne S Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Anh Phong Tran
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gordon P Watt
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lene Mellemkjær
- Diet, Cancer and Health, Danish Cancer Institute, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Charles F Lynch
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA, 52242, USA
| | - Julia A Knight
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Esther M John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kathleen E Malone
- Epidemiology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Xiaolin Liang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Meghan Woods
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Andriy Derkach
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Patrick Concannon
- Department of Pathology, Immunology and Laboratory Medicine, Genetics Institute, University of Florida, Gainesville, FL, USA
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA
| | - Xiang Shu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10017, USA.
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Baliakas P, Munters AR, Kämpe A, Tesi B, Bondeson ML, Ladenvall C, Eriksson D. Integrating a Polygenic Risk Score into a clinical setting would impact risk predictions in familial breast cancer. J Med Genet 2024; 61:150-154. [PMID: 37580114 PMCID: PMC10850617 DOI: 10.1136/jmg-2023-109311] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/28/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND Low-impact genetic variants identified in population-based genetic studies are not routinely measured as part of clinical genetic testing in familial breast cancer (BC). We studied the consequences of integrating an established Polygenic Risk Score (PRS) (BCAC 313, PRS313) into clinical sequencing of women with familial BC in Sweden. METHODS We developed an add-on sequencing panel to capture 313 risk variants in addition to the clinical screening of hereditary BC genes. Index patients with no pathogenic variant from 87 families, and 1000 population controls, were included in comparative PRS calculations. Including detailed family history, sequencing results and tumour pathology information, we used BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.6 to estimate contralateral and lifetime risks without and with PRS313. RESULTS Women with BC but no pathogenic variants in hereditary BC genes have a higher PRS313 compared with population controls (mean+0.78 SD, p<3e-9). Implementing PRS313 in the clinical risk estimation before their BC diagnosis would have changed the recommended follow-up in 24%-45% of women. CONCLUSIONS Our results show the potential impact of incorporating PRS313 directly in the clinical genomic investigation of women with familial BC.
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Affiliation(s)
- Panagiotis Baliakas
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Arielle R Munters
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Anders Kämpe
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Bianca Tesi
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska Institutet, Stockholm, Sweden
| | - Marie-Louise Bondeson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Claes Ladenvall
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Daniel Eriksson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Department of Clinical Genetics, Akademiska Sjukhuset, Uppsala, Sweden
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Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [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: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
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Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
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Infante M, Arranz-Ledo M, Lastra E, Olaverri A, Ferreira R, Orozco M, Hernández L, Martínez N, Durán M. Profiling of the genetic features of patients with breast, ovarian, colorectal and extracolonic cancers: Association to CHEK2 and PALB2 germline mutations. Clin Chim Acta 2024; 552:117695. [PMID: 38061684 DOI: 10.1016/j.cca.2023.117695] [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: 09/13/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND AND AIMS Cancer predisposition goes beyond BRCA and DNA Mismatch Repair (MMR) genes since multi-gene panel testing has become the routine diagnostic tool for hereditary cancer suspicion (HCS) cases. CHEK2 and PALB2 are some of the foremost-mutated non-BRCA/MMR actionable genes in families with a significant familial aggregation. Therefore, the purpose of this work is to unravel which tumours other than breast, ovary or colorectal display the patients. MATERIALS AND METHODS We have analysed 528 probands that meet the inclusion criteria for Hereditary Breast and Ovarian Cancer and Lynch Syndrome established by our Hereditary Cancer Regional Program with a customized 35 genes-panel by using Ion Torrent™ Technology. RESULTS We have identified pathogenic variants (PVs) in 61 families (1.55%), of which more than half (31 probands) harboured PVs in CHEK2 and PALB2 genes. Ours results reveal that not only were PVs CHEK2 and PALB2 carriers more likely to have family history of cancer not limited to breast, ovarian or colorectal cancers, but also they are prone to other extracolonic cancers, noteworthy endometrial and gastric cancers. CONCLUSIONS Multigene panel testing improves the chance of finding PVs in actionable genes in families with HCS. In addition, the coexistence of variants should be recorded to implement a polygenic risk algorithm that might explain the missing heritability in the aforementioned families.
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Affiliation(s)
- Mar Infante
- Cancer Genetics Group, Unit of Excellence Institute of Biomedicine and Molecular Genetics , University of Valladolid-Spanish National Research Council (IBGM, UVa-CSIC), C/ Sanz y Forés 3, 47003 Valladolid, Spain.
| | - Mónica Arranz-Ledo
- Cancer Genetics Group, Unit of Excellence Institute of Biomedicine and Molecular Genetics , University of Valladolid-Spanish National Research Council (IBGM, UVa-CSIC), C/ Sanz y Forés 3, 47003 Valladolid, Spain
| | - Enrique Lastra
- Unit of Genetic Counseling in Cancer, Burgos University Hospital, Burgos, Spain
| | - Amaya Olaverri
- Unit of Genetic Counseling in Cancer, Rio Hortega University Hospital, Valladolid, Spain
| | - Raquel Ferreira
- Unit of Genetic Counseling in Cancer, Rio Hortega University Hospital, Valladolid, Spain
| | - Marta Orozco
- Unit of Genetic Counseling in Cancer, Rio Hortega University Hospital, Valladolid, Spain
| | - Lara Hernández
- Cancer Genetics Group, Unit of Excellence Institute of Biomedicine and Molecular Genetics , University of Valladolid-Spanish National Research Council (IBGM, UVa-CSIC), C/ Sanz y Forés 3, 47003 Valladolid, Spain
| | - Noemí Martínez
- Cancer Genetics Group, Unit of Excellence Institute of Biomedicine and Molecular Genetics , University of Valladolid-Spanish National Research Council (IBGM, UVa-CSIC), C/ Sanz y Forés 3, 47003 Valladolid, Spain
| | - Mercedes Durán
- Cancer Genetics Group, Unit of Excellence Institute of Biomedicine and Molecular Genetics , University of Valladolid-Spanish National Research Council (IBGM, UVa-CSIC), C/ Sanz y Forés 3, 47003 Valladolid, Spain
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Cardone KM, Dudek S, Keat K, Bradford Y, Cindi Z, Daar ES, Gulick R, Riddler SA, Lennox JL, Sinxadi P, Haas DW, Ritchie MD. Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:594-610. [PMID: 38160309 PMCID: PMC10764076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ∼26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ∼7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ∼6-10% of variability in multivariate models (including age, sex, and PCs) but only ∼1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.
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Affiliation(s)
- Kathleen M Cardone
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
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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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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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Wang YC, He JL, Tsai CL, Tzeng HE, Chang WS, Pan SH, Chen LH, Su CH, Lin JC, Hung CC, Bau DT, Tsai CW. The Contribution of Tissue Inhibitor of Metalloproteinase-2 Genotypes to Breast Cancer Risk in Taiwan. Life (Basel) 2023; 14:9. [PMID: 38276258 PMCID: PMC10817502 DOI: 10.3390/life14010009] [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: 11/28/2023] [Revised: 12/16/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Tissue inhibitor of metalloproteinase-2 (TIMP-2) is an endogenous inhibitor of matrix metalloproteinase-2 and is highly expressed in breast cancer (BC) cases at diagnosis. However, the genetic investigations for the association of TIMP-2 genotypes with BC risk are rather limited. In this study, contribution of TIMP-2 rs8179090, rs4789936, rs2009196 and rs7342880 genotypes to BC risk was examined among Taiwan's BC population. TIMP-2 genotypic profiles were revealed among 1232 BC cases and 1232 controls about their contribution to BC using a PCR-based RFLP methodology. The TIMP-2 rs8179090 homozygous variant CC genotype was significantly higher in BC cases than controls (odds ratio (OR) = 2.76, 95% confidence interval (95%CI) = 1.78-4.28, p = 0.0001). Allelic analysis showed that C allele carriers have increased risk for BC (OR = 1.39, 95%CI = 1.20-1.62, p = 0.0001). Genotypic together with allelic analysis showed that TIMP-2 rs4789936, rs2009196 or rs7342880 were not associated with BC risk. Stratification analysis showed that TIMP-2 rs8179090 genotypes were significantly associated with BC risk among younger (≤55) aged women, not among those of an elder (>55) age. Last, rs8179090 genotypes were also associated with triple negative BC. This study sheds light into the etiology of BC in Taiwanese women. Rs8179090 may be incorporated into polygenic risk scores and risk prediction models, which could aid in stratifying individuals for targeted breast cancer screening.
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Affiliation(s)
- Yun-Chi Wang
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Jie-Long He
- Department of Post-Baccalaureate Veterinary Medicine, Asia University, Taichung 413305, Taiwan
| | - Chung-Lin Tsai
- Division of Cardiac and Vascular Surgery, Cardiovascular Center, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Huey-En Tzeng
- Division of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, and Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan
| | - Wen-Shin Chang
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Shih-Han Pan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Li-Hsiou Chen
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Chen-Hsien Su
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Jiunn-Cherng Lin
- Division of Cardiology, Department of Internal Medicine, Taichung Veterans General Hospital, Chiayi Branch, Chiayi 60090, Taiwan
| | - Chih-Chiang Hung
- Division of Breast Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Da-Tian Bau
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413305, Taiwan
| | - Chia-Wen Tsai
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 404333, Taiwan
- Terry Fox Cancer Research Laboratory, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk alters the penetrance of monogenic kidney disease. Nat Commun 2023; 14:8318. [PMID: 38097619 PMCID: PMC10721887 DOI: 10.1038/s41467-023-43878-9] [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: 07/11/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Jordan G Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Lucotte EA, Asgari Y, Sugier PE, Karimi M, Domenighetti C, Lesueur F, Boland-Augé A, Ostroumova E, de Vathaire F, Zidane M, Guénel P, Deleuze JF, Boutron-Ruault MC, Severi G, Liquet B, Truong T. Investigation of common genetic risk factors between thyroid traits and breast cancer. Hum Mol Genet 2023; 33:38-47. [PMID: 37740403 PMCID: PMC10729861 DOI: 10.1093/hmg/ddad159] [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: 06/16/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023] Open
Abstract
Breast cancer (BC) risk is suspected to be linked to thyroid disorders, however observational studies exploring the association between BC and thyroid disorders gave conflicting results. We proposed an alternative approach by investigating the shared genetic risk factors between BC and several thyroid traits. We report a positive genetic correlation between BC and thyroxine (FT4) levels (corr = 0.13, p-value = 2.0 × 10-4) and a negative genetic correlation between BC and thyroid-stimulating hormone (TSH) levels (corr = -0.09, p-value = 0.03). These associations are more striking when restricting the analysis to estrogen receptor-positive BC. Moreover, the polygenic risk scores (PRS) for FT4 and hyperthyroidism are positively associated to BC risk (OR = 1.07, 95%CI: 1.00-1.13, p-value = 2.8 × 10-2 and OR = 1.04, 95%CI: 1.00-1.08, p-value = 3.8 × 10-2, respectively), while the PRS for TSH is inversely associated to BC risk (OR = 0.93, 95%CI: 0.89-0.97, p-value = 2.0 × 10-3). Using the PLACO method, we detected 49 loci associated to both BC and thyroid traits (p-value < 5 × 10-8), in the vicinity of 130 genes. An additional colocalization and gene-set enrichment analyses showed a convincing causal role for a known pleiotropic locus at 2q35 and revealed an additional one at 8q22.1 associated to both BC and thyroid cancer. We also found two new pleiotropic loci at 14q32.33 and 17q21.31 that were associated to both TSH levels and BC risk. Enrichment analyses and evidence of regulatory signals also highlighted brain tissues and immune system as candidates for obtaining associations between BC and TSH levels. Overall, our study sheds light on the complex interplay between BC and thyroid traits and provides evidence of shared genetic risk between those conditions.
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Affiliation(s)
- Elise A Lucotte
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
| | - Yazdan Asgari
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
| | - Pierre-Emmanuel Sugier
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64013 Pau, France
| | - Mojgan Karimi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
| | - Cloé Domenighetti
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
| | - Fabienne Lesueur
- Inserm, U900, Institut Curie, PSL University, Mines ParisTech, 75006 Paris, France
| | - Anne Boland-Augé
- National Centre of Human Genomics Research, François Jacob Institute of Biology, Commissariat à l’Energie Atomique, Paris-Saclay University, 91000 Evry, France
| | | | - Florent de Vathaire
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team of Epidemiology of radiations, 94807 Villejuif, France
| | - Monia Zidane
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team of Epidemiology of radiations, 94807 Villejuif, France
| | - Pascal Guénel
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
| | - Jean-François Deleuze
- National Centre of Human Genomics Research, François Jacob Institute of Biology, Commissariat à l’Energie Atomique, Paris-Saclay University, 91000 Evry, France
| | | | - Gianluca Severi
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
- Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50121 Florence, Italy
| | - Benoît Liquet
- Laboratoire de Mathématiques et de leurs Applications de Pau, Université de Pau et des Pays de l’Adour, UMR CNRS 5142, E2S-UPPA, 64013 Pau, France
- School of Mathematical and Physical Sciences, Macquarie University, 2109 Sydney, Australia
| | - Thérèse Truong
- Paris-Saclay University, UVSQ, Gustave Roussy, Inserm, CESP, Team “Exposome and Heredity”, 94807 Villejuif, France
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77
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Yang Z, Zhang Y, Song M, Huang X, Lin Y, Yang H. The interaction between systemic inflammatory markers and polygenic risk score in breast cancer risk: A cohort study in the UK Biobank. Cancer Epidemiol 2023; 87:102490. [PMID: 37976632 DOI: 10.1016/j.canep.2023.102490] [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: 09/09/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Systemic inflammatory markers have been widely used in cancer prognosis prediction recently. However, there is limited knowledge regarding their impact on breast cancer risk and their interaction with polygenic risk scores. METHODS A cohort study of 202,403 female participants from the UK Biobank were analyzed to estimate the hazard ratio (HR) for the incidence and mortality of breast cancer based on inflammatory markers using Cox regression models. Additionally, we stratified the analysis by polygenic risk scores (PRS) for breast cancer, and examined the interaction between these markers and PRS through likelihood ratio tests and relative excess risk due to interaction (RERI). RESULTS Women in the highest tertile of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and C-reactive protein (CRP) showed an increased risk of breast cancer [HR (95 %CI) = 1.10 (1.02-1.18), 1.09 (1.01-1.17) and 1.15 (1.05-1.25), respectively], as compared to those in the lowest tertile. Regarding breast cancer mortality, only NLR and CRP exhibited consistent results in the univariate model [HR (95 %CI) = 1.25 (0.99-1.58) and 1.39 (1.10-1.77), respectively]. When stratified by PRS, stronger associations between inflammatory markers and breast cancer risk were observed in the high PRS group. Furthermore, there was a significant additive interaction between CRP and PRS [RERI (95 % CI) = 0.30 (0.06-0.53)]. CONCLUSION NLR and CRP are associated with breast cancer risk and mortality, and the effect of CRP is influenced by PRS. Systematic inflammatory markers, together with PRS, might be applied in combined screening for breast cancer.
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Affiliation(s)
- Zixuan Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122 China
| | - Yanyu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122 China
| | - Mengjie Song
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122 China
| | - Xiaoxi Huang
- Department of Breast, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, China
| | - Yuxiang Lin
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China; Breast Cancer Institute, Fujian Medical University, Fuzhou 350001, China.
| | - Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122 China; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177 Sweden.
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78
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Oblak T, Škerl P, Narang BJ, Blagus R, Krajc M, Novaković S, Žgajnar J. Breast cancer risk prediction using Tyrer-Cuzick algorithm with an 18-SNPs polygenic risk score in a European population with below-average breast cancer incidence. Breast 2023; 72:103590. [PMID: 37857130 PMCID: PMC10587756 DOI: 10.1016/j.breast.2023.103590] [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: 06/21/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/21/2023] Open
Abstract
GOALS To determine whether an 18 single nucleotide polymorphisms (SNPs) polygenic risk score (PRS18) improves breast cancer (BC) risk prediction for women at above-average risk of BC, aged 40-49, in a Central European population with BC incidence below EU average. METHODS 502 women aged 40-49 years at the time of BC diagnosis completed a questionnaire on BC risk factors (as per Tyrer-Cuzick algorithm) with data known at age 40 and before BC diagnosis. Blood samples were collected for DNA isolation. 250 DNA samples from healthy women aged 50 served as a control cohort. 18 BC-associated SNPs were genotyped in both groups and PRS18 was calculated. The predictive power of PRS18 to detect BC was evaluated using a ROC curve. 10-year BC risk was calculated using the Tyrer-Cuzick algorithm adapted to the Slovenian incidence rate (S-IBIS): first based on questionnaire-based risk factors and, second, including PRS18. RESULTS The AUC for PRS18 was 0.613 (95 % CI 0.570-0.657). 83.3 % of women were classified at above-average risk for BC with S-IBIS without PRS18 and 80.7 % when PRS18 was included. CONCLUSION BC risk prediction models and SNPs panels should not be automatically used in clinical practice in different populations without prior population-based validation. In our population the addition of an 18SNPs PRS to questionnaire-based risk factors in the Tyrer-Cuzick algorithm in general did not improve BC risk stratification, however, some improvements were observed at higher BC risk scores and could be valuable in distinguishing women at intermediate and high risk of BC.
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Affiliation(s)
- Tjaša Oblak
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia; Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Petra Škerl
- Department of Molecular Diagnostics, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Benjamin J Narang
- Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia; Department of Automatics, Jožef Stefan Institute, Biocybernetics and Robotics, Jamova cesta 39, Ljubljana, Slovenia; Faculty of Sport, University of Ljubljana, Gortanova 22, Ljubljana, Slovenia.
| | - Rok Blagus
- Institute for Biostatistics and Medical Informatics, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia; Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000, Koper, Slovenia.
| | - Mateja Krajc
- Cancer Genetics Clinic, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Srdjan Novaković
- Department of Molecular Diagnostics, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
| | - Janez Žgajnar
- Department of Surgical Oncology, Institute of Oncology Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
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79
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Dix-Peek T, Dickens C, Augustine TN, Phakathi BP, Van Den Berg EJ, Joffe M, Ayeni OA, Cubasch H, Nietz S, Mathew CG, Hayat M, Neugut AI, Jacobson JS, Ruff P, Duarte RA. FGFR2 genetic variants in women with breast cancer. Mol Med Rep 2023; 28:226. [PMID: 37830168 PMCID: PMC10619128 DOI: 10.3892/mmr.2023.13113] [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: 12/09/2022] [Accepted: 05/11/2023] [Indexed: 10/14/2023] Open
Abstract
Black African populations are more genetically diverse than others, but genetic variants have been studied primarily in European populations. The present study examined the association of four single nucleotide polymorphisms (SNPs) of the fibroblast growth factor receptor 2, associated with breast cancer in non‑African populations, with breast cancer in Black, southern African women. Genomic DNA was extracted from whole blood samples of 1,001 patients with breast cancer and 1,006 controls (without breast cancer), and the rs2981582, rs35054928, rs2981578, and rs11200014 polymorphisms were analyzed using allele‑specific Kompetitive allele‑specific PCR™, and the χ2 or Fisher's exact tests were used to compare the genotype frequencies. There was no association between those SNPs and breast cancer in the studied cohort, although an association was identified between the C/C homozygote genotype for rs2981578 and invasive lobular carcinoma. These results show that genetic biomarkers of breast cancer risk in European populations are not necessarily associated with risk in sub‑Saharan African populations. African populations are more heterogenous than other populations, and the information from this population can help focus genetic risks of cancer in this understudied population.
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Affiliation(s)
- Thérèse Dix-Peek
- Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Caroline Dickens
- Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Tanya N. Augustine
- School of Anatomical Sciences, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Boitumelo P. Phakathi
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of Kwa-Zulu Natal, Durban 4001, South Africa
| | - Eunice J. Van Den Berg
- Department of Histopathology, National Health Laboratory Services, Chris Hani Baragwanath Hospital, Johannesburg 1864, South Africa
- Department of Anatomical Pathology, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Maureen Joffe
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- South African Medical Research Council Common Epithelial Cancer Research Centre, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd., Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Oluwatosin A. Ayeni
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- South African Medical Research Council Common Epithelial Cancer Research Centre, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd., Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Division of Radiation Oncology, Department of Radiation Sciences, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Herbert Cubasch
- South African Medical Research Council Common Epithelial Cancer Research Centre, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd., Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Batho Pele Breast Unit, Chris Hani Baragwanath Academic Hospital, Soweto 1860, South Africa
- Department of Surgery, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Sarah Nietz
- South African Medical Research Council Common Epithelial Cancer Research Centre, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Department of Surgery, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Christopher G. Mathew
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, United Kingdom
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Mahtaab Hayat
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Alfred I. Neugut
- Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, New York 10032, United States of America
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York 10032, United States of America
| | - Judith S. Jacobson
- Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, New York 10032, United States of America
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York 10032, United States of America
| | - Paul Ruff
- Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- South African Medical Research Council Common Epithelial Cancer Research Centre, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
- Non-Communicable Diseases Research Division, Wits Health Consortium (PTY) Ltd., Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
| | - Raquel A.B. Duarte
- Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of The Witwatersrand, Johannesburg 2193, South Africa
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Fritsche LG, Nam K, Du J, Kundu R, Salvatore M, Shi X, Lee S, Burgess S, Mukherjee B. Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks. PLoS Genet 2023; 19:e1010907. [PMID: 38113267 PMCID: PMC10763941 DOI: 10.1371/journal.pgen.1010907] [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: 08/09/2023] [Revised: 01/03/2024] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
OBJECTIVE To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity. METHODS Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis. RESULTS The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection. CONCLUSION By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.
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Affiliation(s)
- Lars G. Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Jiacong Du
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Ritoban Kundu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Xu Shi
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
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81
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Shanazarov N, Zhapparov Y, Kumisbekova R, Turzhanova D, Zulkhash N. Association of Gene Polymorphisms with Breast Cancer Risk in the Kazakh Population. Asian Pac J Cancer Prev 2023; 24:4195-4207. [PMID: 38156855 PMCID: PMC10909110 DOI: 10.31557/apjcp.2023.24.12.4195] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024] Open
Abstract
OBJECTIVE The research aim is analyzing and identify reliable genetic markers of breast cancer risk in the Kazakh population. METHODS The databases were analyzed with the selection of polymorphisms associated with the development of breast cancer and further genotypic study of a group of women with a confirmed diagnosis of breast adenocarcinoma (group No. 1) and a group of relatively healthy women (group No. 2). RESULT The research presents the results of a study on the frequency of certain single-nucleotide polymorphisms in patients with breast cancer in the Republic of Kazakhstan. The frequency of single-nucleotide polymorphisms rs4646, rs1065852, rs4244285, rs67376798, rs6504950, rs2229774, rs1800056, rs16942, rs4987047 is statistically significant compared to the control group of patients. These polymorphisms in the Kazakh population have a direct association with an increased risk of breast cancer in women and may be used as cancer indicators during the genetic screening of patients with a complicated family history. Single-nucleotide polymorphisms such as rs55886062, rs3918290, rs12721655, rs4987117, rs2229774, rs11203289, rs137852576, rs11571833, rs80359062 and rs11571746 were found in more than 40. Zero percent of patients with breast cancer may be used as markers for detecting patients at increased risk of breast malignancy in the Kazakh population without a history of poor family history. CONCLUSION The usage of the data obtained in a set of state programs for early screening of patients will improve the rates of early breast tumor detection, form groups of patients with a high risk of disease development and improve the quality and expectancy of life.
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Affiliation(s)
- Nasrulla Shanazarov
- Department of Strategic Development, Science and Education, Medical Centre Hospital of President’s Affairs Administration of the Republic of Kazakhstan, Astana, Republic of Kazakhstan.
- Center for Photodynamic Therapy, Medical Centre Hospital of President’s Affairs Administration of the Republic of Kazakhstan, Astana, Republic of Kazakhstan.
| | - Yerbol Zhapparov
- Clinical and Diagnostic Department, “UMIT” International Oncological Tomotherapy Center, Astana, Republic of Kazakhstan.
| | - Raushan Kumisbekova
- Department of Chemotherapy, Multidisciplinary Medical Center of the Akimat of Astana, Astana, Republic of Kazakhstan.
| | - Dinara Turzhanova
- Department of Radiology named after Academician Zh.Kh. Khamzabaev, Astana Medical University, Astana, Republic of Kazakhstan.
| | - Nargiz Zulkhash
- Department of Public Health, Astana Medical University, Astana Medical University, Astana, Republic of Kazakhstan.
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82
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Long L, He H, Shen Q, Peng H, Zhou X, Wang H, Zhang S, Qin S, Lu Z, Zhu Y, Tian J, Chang J, Miao X, Shen N, Zhong R. Birthweight, genetic risk, and gastrointestinal cancer incidence: a prospective cohort study. Ann Med 2023; 55:62-71. [PMID: 36503347 PMCID: PMC9754019 DOI: 10.1080/07853890.2022.2146743] [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] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce. The study aimed to prospectively assess the interactions and joint effects of birthweight and genetic risk levels on gastrointestinal cancer incidence in adulthood. METHODS A total of 254,997 participants were included in the UK Biobank study. We used multivariate restricted cubic splines and Cox regression models to estimate the hazard ratios (HRs) and 95% confidential intervals (CI) for the association between birthweight and gastrointestinal cancer risk, then constructed a polygenic risk score (PRS) to assess its interaction and joint effect with birthweight on the development of gastrointestinal cancer. RESULTS We documented 2512 incident cases during a median follow-up of 8.88 years. Compare with participants reporting a normal birthweight (2.5-4.5 kg), multivariable-adjusted HR of gastrointestinal cancer incidence for participants with high birthweight (≥4.5 kg) was 1.17 (95%CI: 1.01-1.36). Such association was remarkably observed in pancreatic cancer, with an HR of 1.82 (95%CI: 1.26-2.64). No statistically significant association was observed between low birth weight and gastrointestinal cancers. Participants with high birthweight and high PRS had the highest risk of gastrointestinal cancer (HR: 2.95, 95%CI: 2.19-3.96). CONCLUSION Our findings highlight that high birthweight is associated with a higher incidence of gastrointestinal cancer, especially for pancreatic cancer. Benefits would be obtained from birthweight control, particularly for individuals with a high genetic risk.KEY MESSAGESThe epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce.This cohort study of 254,997 adults in the United Kingdom found an association of high birthweight with the incidence of gastrointestinal cancer, especially for pancreatic cancer, and also found that participants with high birthweight and high polygenic risk score had the highest risk of gastrointestinal cancer.Our data suggests a possible effect of in utero or early life exposures on adulthood gastrointestinal cancer, especially for those with a high genetic risk.
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Affiliation(s)
- Lu Long
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Heng He
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qian Shen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongxia Peng
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaorui Zhou
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Haoxue Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shanshan Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shifan Qin
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- School of Public Health, Wuhan University, Wuhan, China
| | - Jianbo Tian
- School of Public Health, Wuhan University, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Miao
- School of Public Health, Wuhan University, Wuhan, China
| | - Na Shen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, China
- Na Shen Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, 430030, China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- CONTACT Rong Zhong Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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83
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Levi H, Carmi S, Rosset S, Yerushalmi R, Zick A, Yablonski-Peretz T, Wang Q, Bolla MK, Dennis J, Michailidou K, Lush M, Ahearn T, Andrulis IL, Anton-Culver H, Antoniou AC, Arndt V, Augustinsson A, Auvinen P, Beane Freeman L, Beckmann M, Behrens S, Bermisheva M, Bodelon C, Bogdanova NV, Bojesen SE, Brenner H, Byers H, Camp N, Castelao J, Chang-Claude J, Chirlaque MD, Chung W, Clarke C, Collee MJ, Colonna S, Couch F, Cox A, Cross SS, Czene K, Daly M, Devilee P, Dork T, Dossus L, Eccles DM, Eliassen AH, Eriksson M, Evans G, Fasching P, Fletcher O, Flyger H, Fritschi L, Gabrielson M, Gago-Dominguez M, García-Closas M, Garcia-Saenz JA, Genkinger J, Giles GG, Goldberg M, Guénel P, Hall P, Hamann U, He W, Hillemanns P, Hollestelle A, Hoppe R, Hopper J, Jakovchevska S, Jakubowska A, Jernström H, John E, Johnson N, Jones M, Vijai J, Kaaks R, Khusnutdinova E, Kitahara C, Koutros S, Kristensen V, Kurian AW, Lacey J, Lambrechts D, Le Marchand L, Lejbkowicz F, Lindblom A, Loibl S, Lori A, Lubinski J, Mannermaa A, Manoochehri M, Mavroudis D, Menon U, Mulligan A, Murphy R, Nevelsteen I, Newman WG, Obi N, O'Brien K, Offit K, Olshan A, Plaseska-Karanfilska D, Olson J, Panico S, Park-Simon TW, Patel A, Peterlongo P, Rack B, Radice P, Rennert G, Rhenius V, Romero A, Saloustros E, Sandler D, Schmidt MK, Schwentner L, Shah M, Sharma P, Simard J, Southey M, Stone J, Tapper WJ, Taylor J, Teras L, Toland AE, Troester M, Truong T, van der Kolk LE, Weinberg C, Wendt C, Yang XR, Zheng W, Ziogas A, Dunning AM, Pharoah P, Easton DF, Ben-Sachar S, Elefant N, Shamir R, Elkon R. Evaluation of European-based polygenic risk score for breast cancer in Ashkenazi Jewish women in Israel. J Med Genet 2023; 60:1186-1197. [PMID: 37451831 PMCID: PMC10715538 DOI: 10.1136/jmg-2023-109185] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/28/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. METHODS We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. RESULTS In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). CONCLUSIONS Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.
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Grants
- R01 CA176785 NCI NIH HHS
- NU58DP006344 NCCDPHP CDC HHS
- R37 CA070867 NCI NIH HHS
- HHSN261201800015I NCI NIH HHS
- R01 CA064277 NCI NIH HHS
- P50 CA116201 NCI NIH HHS
- G1000143 Medical Research Council
- P30 CA062203 NCI NIH HHS
- HHSN261201800015C NCI NIH HHS
- R01 CA047305 NCI NIH HHS
- HHSN261201800009I NCI NIH HHS
- R01 CA163353 NCI NIH HHS
- UM1 CA164917 NCI NIH HHS
- U01 CA199277 NCI NIH HHS
- U01 CA179715 NCI NIH HHS
- HHSN261201800032C NCI NIH HHS
- U54 CA156733 NCI NIH HHS
- HHSN261201800009C NCI NIH HHS
- Z01 CP010119 Intramural NIH HHS
- UM1 CA164973 NCI NIH HHS
- P01 CA087969 NCI NIH HHS
- UM1 CA164920 NCI NIH HHS
- NU58DP006320 CDC HHS
- UM1 CA176726 NCI NIH HHS
- R01 CA092447 NCI NIH HHS
- Z01 ES049030 Intramural NIH HHS
- R01 CA058860 NCI NIH HHS
- K07 CA092044 NCI NIH HHS
- HHSN261201800016C NCI NIH HHS
- P50 CA058223 NCI NIH HHS
- R01 CA100374 NCI NIH HHS
- P30 CA008748 NCI NIH HHS
- R01 CA128978 NCI NIH HHS
- R01 CA047147 NCI NIH HHS
- U19 CA148537 NCI NIH HHS
- R01 CA116167 NCI NIH HHS
- R01 CA148667 NCI NIH HHS
- R01 CA063464 NCI NIH HHS
- HHSN261201800016I NCI NIH HHS
- UM1 CA186107 NCI NIH HHS
- P30 CA023100 NCI NIH HHS
- U01 CA063464 NCI NIH HHS
- R01 CA077398 NCI NIH HHS
- R01 CA054281 NCI NIH HHS
- R01 CA132839 NCI NIH HHS
- P30 CA068485 NCI NIH HHS
- U01 CA058860 NCI NIH HHS
- U01 CA164920 NCI NIH HHS
- R35 CA253187 NCI NIH HHS
- 14136 Cancer Research UK
- U19 CA148112 NCI NIH HHS
- HHSN261201800032I NCI NIH HHS
- U01 CA098758 NCI NIH HHS
- Z01 ES044005 Intramural NIH HHS
- U19 CA148065 NCI NIH HHS
- P30 CA033572 NCI NIH HHS
- R01 CA069664 NCI NIH HHS
- Wellcome Trust
- 001 World Health Organization
- Z01 ES049033 Intramural NIH HHS
- R01 CA192393 NCI NIH HHS
- U01 CA164973 NCI NIH HHS
- R37 CA054281 NCI NIH HHS
- Consellería de Industria Programa Sectorial de Investigación Aplicada
- Statistics Netherlands
- South Eastern Norway Health Authority
- Lower Saxonian Cancer Society
- Lise Boserup Fund
- Heidelberger Zentrum für Personalisierte Onkologie Deutsches Krebsforschungszentrum In Der Helmholtz-Gemeinschaft
- Lon V. Smith Foundation
- Scottish Funding Council
- Komen Foundation
- Claudia von Schilling Foundation for Breast Cancer Research
- Russian Foundation for Basic Research
- Ligue Contre le Cancer
- Sigrid Juselius Foundation
- Kuopion Yliopistollinen Sairaala
- Sheffield Experimental Cancer Medicine Centre
- Stockholm läns landsting
- Department of Health and Human Services (USA)
- Department of Defence (USA)
- Stichting Tegen Kanker
- David F. and Margaret T. Grohne Family Foundation
- Sundhed og Sygdom, Det Frie Forskningsråd
- Stavros Niarchos Foundation
- Post-Cancer GWAS initiative
- Institute of the Ruhr University Bochum
- Instituto de Salud Carlos III
- Institute of Cancer Research
- Public Health Institute
- Fondation du cancer du sein du Québec
- Institut National de la Santé et de la Recherche Médicale
- Pink Ribbon
- Institute for Prevention and Occupational Medicine
- K.G. Jebsen Centre for Breast Cancer Research
- Research Centre for Genetic Engineering and Biotechnology
- Center of Excellence (Finland)
- Robert and Kate Niehaus Clinical Cancer Genetics Initiative
- Rudolf Bartling Foundation
- Center for Disease Control and Prevention (USA)
- Karolinska Institutet
- Norges Forskningsråd
- Robert Bosch Stiftung
- Intramural Research Funds of the National Cancer Institute (USA)
- Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC
- Intramural Research Program of the Division of Cancer Epidemiology and Genetics
- Centre International de Recherche sur le Cancer
- Queensland Cancer Fund
- Red Temática de Investigación Cooperativa en Cáncer
- Intramural Research Program of the National Institutes of Health
- National Health Service (UK)
- Ministerie van Volksgezondheid, Welzijn en Sport
- National cancer institute (USA)
- KWF Kankerbestrijding
- Märit and Hans Rausings Initiative Against Breast Cancer
- Associazione Italiana per la Ricerca sul Cancro
- Fundación Científica Asociación Española Contra el Cáncer
- ERC advanced grant
- Australian National Health and Medical Research Council
- Agence Nationale de la Recherche
- Dutch Prevention Funds,
- Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail
- American Cancer Society
- Dutch Zorg Onderzoek
- Alexander von Humboldt-Stiftung
- Ministerio de Economia y Competitividad (Spain)
- Ministère du Développement Économique, de l’Innovation et de l’Exportation
- Susan G. Komen for the Cure
- Minister of Science and Higher Education
- Medical Research Council UK
- Ministry of Science and Higher Education of the Russian Federation
- Ministry of Science and Higher Education (Sweden)
- Against Breast Cancer
- Mutuelle Générale de l’Education Nationale
- Academy of Finland
- Deutsche Krebshilfe e.V.
- Dietmar-Hopp Foundation,
- Division of Cancer Prevention, National Cancer Institute
- Deutsche Krebshilfe
- World Cancer Research Fund
- Genome Québec
- National Cancer Institute’s Surveillance, Epidemiology and End Results Program
- Breast Cancer Campaign
- National Cancer Research Network
- Berta Kamprad Foundation FBKS
- Bert von Kantzows foundation
- Biomedical Research Centre at Guy’s and St Thomas
- Genome Canada
- Freistaat Sachsen
- Biobanking and Biomolecular Resources Research Infrastructure
- Friends of Hannover Medical School
- Breast Cancer Research Foundation
- California Department of Public Health
- Government of Russian Federation
- Deutsche Forschungsgemeinschaft
- National Institute for Health and Care Research
- National Health and Medical Research Council (Australia)
- German Federal Ministry of Research and Education
- National Institute of Environmental Health Sciences
- Breast Cancer Now
- Seventh Framework Programme
- Transcan
- Centrum för idrottsforskning
- UK National Institute for Health Research Biomedical Research Centre
- University of Crete
- National Breast Cancer Foundation (Finland)
- European Regional Development Fund
- National Breast Cancer Foundation (Australia)
- United States Army Medical Research and Materiel Command
- EU Horizon 2020 Research and Innovation Programme
- Directorate-General XII, Science, Research, and Development
- Baden Württemberg Ministry of Science, Research and Arts
- VicHealth
- Fondo de Investigación Sanitario
- Victorian Breast Cancer Research Consortium.
- Finnish Cancer Foundation
- University of Southern California San Francisco
- Fomento de la Investigación Clínica Independiente
- the Cancer Biology Research Center (CBRC), Djerassi Oncology Center
- Bundesministerium für Bildung und Forschung
- Cancerfonden
- Tel Aviv University Center for AI and Data Science
- University of Oulu
- National Breast Cancer Foundation (JS)
- Safra Center for Bioinformatics
- Fondation de France, Institut National du Cancer
- Israeli Science Foundation
- University of Utah
- National Cancer Center Research and Development Fund (Japan)
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Oak Foundation
- Health Research Fund (FIS)
- Ontario Familial Breast Cancer Registry
- New South Wales Cancer Council
- North Carolina University Cancer Research Fund
- Kreftforeningen
- Northern California Breast Cancer Family Registry
- Institut Gustave Roussy
- Huntsman Cancer Institute, University of Utah
- Ovarian Cancer Research Fund
- NIHR Oxford Biomedical Research Centre
- Hellenic Health Foundation
- Oulun Yliopistollinen Sairaala
- Helmholtz Society
- Herlev and Gentofte Hospital
- PSRSIIRI-701
- Helsinki University Hospital Research Fund
- Cancer Council Victoria
- National Research Council (Italy)
- Cancer Council Tasmania
- Cancer Council Western Australia
- Hamburger Krebsgesellschaft
- Gustav V Jubilee foundation
- National Program of Cancer Registries
- Canadian Cancer Society
- Cancer Council South Australia
- Canadian Institutes of Health Research
- Cancer Council NSW
- Guy's & St. Thomas' NHS Foundation Trust
- Netherlands Organisation of Scientific Research
- Cancer Institute NSW
- National Institutes of Health (USA)
- National Research Foundation of Korea
- Syöpäsäätiö
- Cancer Foundation of Western Australia
- Netherlands Cancer Registry (NKR),
- Cancer Fund of North Savo
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Affiliation(s)
- Hagai Levi
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Department of Human Molecular Genetics and Biochemistry, Tel Aviv University, Tel Aviv, Israel
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Saharon Rosset
- Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel
| | - Rinat Yerushalmi
- Institute of Oncology, Davidoff Cancer Center, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Aviad Zick
- Department of oncology, Hadassah Medical Center, Jerusalem, Israel
- Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tamar Yablonski-Peretz
- Department of oncology, Hadassah Medical Center, Jerusalem, Israel
- Hebrew University of Jerusalem, Jerusalem, Israel
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Annelie Augustinsson
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Päivi Auvinen
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
- Department of Oncology, Cancer Center, Kuopio University Hospital, Kuopio, Finland
| | - Laura Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Matthias Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
| | - Clara Bodelon
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Helen Byers
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Nicola Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt lake city, UT, USA
| | - Jose Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Wendy Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Christine Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia
| | - Margriet J Collee
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Sarah Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt lake city, UT, USA
| | - Fergus Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Angela Cox
- Department of Oncology and Metabolism, Sheffield Institute for Nucleic Acids (SInFoNiA), University of Sheffield, Sheffield, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, Netherlands
- Department of Human Genetics, Leiden University Medical, Leiden, Netherlands
| | - Thilo Dork
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
| | - Laure Dossus
- Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Diana M Eccles
- Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gareth Evans
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Peter Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, International Cancer Genetics and Epidemiology Group, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jeanine Genkinger
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, New York, New York, USA
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Mark Goldberg
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montreal, QU, Canada
| | - Pascal Guénel
- Team 'Exposome and Heredity', CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Hillemanns
- Gynaecology Research Unit, Hannover Medical School, Hamburg, Germany
| | | | - Reiner Hoppe
- Dr Margarete Fischer Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tubingen, Germany
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Simona Jakovchevska
- Research Centre for Genetic Engineering and Biotechnology 'Georgi D. Efremov', Skopje, North Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Helena Jernström
- Oncology, Department of Clinical Sciences in Lund, Lund University, Lund, Sweden
| | - Esther John
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Michael Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK
| | - Joseph Vijai
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Cari Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Vessela Kristensen
- Institute of Clinical Medicine, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Allison W Kurian
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - James Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Flavio Lejbkowicz
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Annika Lindblom
- Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | | | - Adriana Lori
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Jan Lubinski
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College, London, UK
| | - AnnaMarie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | - Rachel Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - Ines Nevelsteen
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - William G Newman
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Nadia Obi
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Katie O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Ken Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Olshan
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Janet Olson
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Salvatore Panico
- Dipertimento Di Medicina Clinca e Chirurgia, Federico II University, Naples, Italy
| | | | - Alpa Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Brigitte Rack
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Gad Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | - Valerie Rhenius
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Atocha Romero
- Laboratorio de Oncología Molecular, Hospital Clínico San Carlos, Madrid, Spain
| | | | - Dale Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Lukas Schwentner
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Priyanka Sharma
- Department of Internal Medicine, Division of Medical Oncology, University of Kansas Medical Center, Westwood, KS, USA
| | - Jacques Simard
- Genomics Center, Molecular Medicine, Université Laval, Quebec, Quebec, Canada
| | - Melissa Southey
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - William J Tapper
- Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Jack Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Lauren Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Amanda E Toland
- Department of Cancer Biology and Genetics, The Ohio State University, Columbus, OH, USA
| | - Melissa Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Team 'Exposome and Heredity', CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France
| | | | - Clarice Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Camilla Wendt
- Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden
| | - Xiaohong Rose Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Argyrios Ziogas
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul Pharoah
- 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
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA
| | - 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
| | - Shay Ben-Sachar
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel
| | - Naama Elefant
- Clalit Research Institute, Clalit Health Services, Ramat Gan, Israel
- Department of Genetics, Hadassah Medical Center, Jerusalem, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Tel Aviv University, Tel Aviv, Israel
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84
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [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: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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85
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Zhai S, Mehrotra DV, Shen J. Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities. Brief Bioinform 2023; 25:bbad470. [PMID: 38152980 PMCID: PMC10782924 DOI: 10.1093/bib/bbad470] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.
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Affiliation(s)
- Song Zhai
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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86
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Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel) 2023; 15:5380. [PMID: 38001640 PMCID: PMC10670420 DOI: 10.3390/cancers15225380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/26/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 6BT, UK;
| | - Cornelia D. Kazemali
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Jacques Simard
- Endocrinology and Nephology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada;
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
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87
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Mao X, He W, Eriksson M, Lindström LS, Holowko N, Bajalica-Lagercrantz S, Hammarström M, Grassmann F, Humphreys K, Easton D, Hall P, Czene K. Prediction of breast cancer risk for sisters of women attending screening. J Natl Cancer Inst 2023; 115:1310-1317. [PMID: 37243694 PMCID: PMC10637039 DOI: 10.1093/jnci/djad101] [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: 01/09/2023] [Revised: 04/17/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters. METHODS We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively. RESULTS A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively. CONCLUSION A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Research Institute, The Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Svetlana Bajalica-Lagercrantz
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Mattias Hammarström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute for Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Douglas Easton
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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88
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Mundt E, Mabey B, Rainville I, Ricker C, Singh N, Gardiner A, Manley S, Slavin T. Breast and colorectal cancer risks among over 6,000 CHEK2 pathogenic variant carriers: A comparison of missense versus truncating variants. Cancer Genet 2023; 278-279:84-90. [PMID: 37839337 DOI: 10.1016/j.cancergen.2023.10.002] [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: 07/21/2023] [Revised: 09/20/2023] [Accepted: 10/08/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND AND AIMS Heterozygous truncating pathogenic variants (PVs) in CHEK2 confer a 1.5 to 3-fold increased risk for breast cancer and may elevate colorectal cancer risks. Less is known regarding missense variants. Here we compared the cancer associations with truncating and missense PVs in CHEK2 across breast and colorectal cancer. METHODS This was a retrospective analysis of 705,797 patients who received single laboratory multigene panel testing between 2013 and 2020. Multivariable logistic regression models determined cancer risk associated with CHEK2 variants as odds ratios (ORs) and 95% confidence intervals (CIs) after adjusting for age at diagnosis, cancer history, and ancestry. Breast and colorectal cancer analyses were performed using 6255 CHEK2 PVs, including truncating PVs (N = 4505) and missense PVs (N = 1750). RESULTS CHEK2 PVs were associated with an increased risk of ductal invasive breast cancer (p < 0.001) and ductal carcinoma in situ (DCIS) (p < 0.001), with no statistically significant differences when truncating PVs (p < 0.001) and missense PVs (p < 0.001) were evaluated separately. All CHEK2 variants assessed conferred little to no risk of colorectal cancer. CONCLUSIONS In our large cohort, CHEK2 truncating and missense PVs conferred similar risks for breast cancer and did not seem to elevate risk for colorectal cancer.
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Affiliation(s)
- Erin Mundt
- Myriad Genetics Laboratories, Inc., Salt Lake City, UT, United States of America.
| | - Brent Mabey
- Myriad Genetics, Inc., Salt Lake City, UT, United States of America
| | - Irene Rainville
- Myriad Genetics Laboratories, Inc., Salt Lake City, UT, United States of America
| | - Charite Ricker
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, United States of America
| | - Nanda Singh
- Myriad Genetics Laboratories, Inc., Salt Lake City, UT, United States of America
| | - Anna Gardiner
- Myriad Genetics, Inc., Salt Lake City, UT, United States of America
| | - Susan Manley
- Myriad Genetics, Inc., Salt Lake City, UT, United States of America
| | - Thomas Slavin
- Myriad Genetics, Inc., Salt Lake City, UT, United States of America
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89
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Yasui Y, Letsou W, Wang F, Im C, Sapkota Y, Wang Z, Salehabadi SM, Baedke JL, Moon WJ, Liu Q, Robison LL, Martinez JM. Inference on the Genetic Architecture of Breast Cancer Risk. Cancer Epidemiol Biomarkers Prev 2023; 32:1518-1523. [PMID: 36652676 PMCID: PMC10352461 DOI: 10.1158/1055-9965.epi-22-1073] [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: 10/07/2022] [Revised: 10/26/2022] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND What are the major determinants of women's breast cancer risk? Rare mutations such as those in the BRCA1/2 genes, polygenic scores of common alleles identified by genome-wide association studies, or nongenetic factors? METHODS The population-based Nordic Twin Study of Cancer, with 3,933 breast cancer cases among 21,054 monozygotic (MZ) and 30,939 dizygotic (DZ) female twin pairs, provides three key clues to this question: (i) the average lifetime risk, approximately 8%, does not differ by twin zygosity; (ii) the mean time interval between diagnoses when both twins develop disease (i.e., disease concordance) also does not differ by zygosity; but, (iii) conditioning on one twin having developed disease, the incidence rate in the co-twin is approximately 1% per year if the pair is MZ and 0.5% per year if DZ. RESULTS Assuming that nongenetic risk factors are shared similarly between twins regardless of zygosity, we can draw two conclusions from (i) to (iii). CONCLUSIONS First, (i) and (iii) imply that the chief determinant of risk is in the germline DNA, because the conditional incidence rate is several-fold higher than the average risk (8% lifetime) in MZ twins but only half as much in DZ twins. Second, the seeming inconsistency between the two-fold conditional incidence rate (iii) and the equality of the mean inter-twin disease intervals in disease concordance (ii) can be resolved if the risk factors in the germline DNA are rare variants, not common variants. IMPACT This paper details simple deductive reasoning for these conclusions and draws a critical inference regarding breast cancer etiology. See related In the Spotlight, p. 1477.
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Affiliation(s)
- Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
| | - William Letsou
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Fan Wang
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Cindy Im
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
| | - Yadav Sapkota
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Zhaoming Wang
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | | | - Jessica L. Baedke
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Won Jong Moon
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Qi Liu
- School of Public Health, University of Alberta, Edmonton, Alberta T6G 1C9, Canada
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA
| | - Jose Miguel Martinez
- Department of Statistics and Operations Research, The Polytechnic University of Catalonia, 08028 Barcelona, Spain
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90
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Vassy JL, Kerman BJ, Harris EJ, Lemke AA, Clayman ML, Antwi AA, MacIsaac K, Yi T, Brunette CA. Perceived benefits and barriers to implementing precision preventive care: Results of a national physician survey. Eur J Hum Genet 2023; 31:1309-1316. [PMID: 36807341 PMCID: PMC10620193 DOI: 10.1038/s41431-023-01318-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Polygenic risk scores (PRS) may improve risk-stratification in preventive care. Their clinical implementation will depend on primary care physicians' (PCPs) uptake. We surveyed PCPs in a national physician database about the perceived clinical utility, benefits, and barriers to the use of PRS in preventive care. Among 367 respondents (participation rate 96.3%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p < 0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and insurance discrimination concerns (22%) as extreme barriers. Latent class analysis identified 3 subclasses of respondents. Skeptics (n = 83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n = 134, 36.5%) and enthusiasts (n = 150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS. Overall results suggest that PCPs generally endorse using PRS to guide medical decision-making about preventive care, and barriers identified suggest interventions to address their needs and concerns.
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Affiliation(s)
- Jason L Vassy
- Harvard Medical School, Boston, MA, USA.
- Veterans Affairs Boston Healthcare System, Boston, MA, USA.
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Precision Population Health, Ariadne Labs, Boston, MA, USA.
| | - Benjamin J Kerman
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth J Harris
- Harvard Medical School, Boston, MA, USA
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Amy A Lemke
- Norton Children's Research Institute, Affiliated with the University of Louisville School of Medicine, Louisville, KY, USA
| | - Marla L Clayman
- UMass Chan Medical School, Department of Population and Quantitative Health Sciences, Worcester, MA, USA
- Edith Nourse Rogers Memorial Veterans' Hospital, Bedford, MA, USA
| | - Ashley A Antwi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Katharine MacIsaac
- Harvard Medical School, Boston, MA, USA
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Thomas Yi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
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91
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Huang S, Xu JT, Yang M. Review: Predictive approaches to breast cancer risk. Heliyon 2023; 9:e21344. [PMID: 38034632 PMCID: PMC10685136 DOI: 10.1016/j.heliyon.2023.e21344] [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: 10/05/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023] Open
Abstract
Despite the deployment of specific breast cancer screening strategies, breast cancer incidence rates have escalated significantly over recent decades. In a bid to reverse this trend, scientists have engaged in extensive epidemiological research into breast cancer prevalence, identifying numerous individual risk factors and promoting population-wide health education. Coupled with advances in genetic testing, risk prediction models based on breast cancer genes have been developed, albeit with inherent limitations. In the new millennium, the emergence of artificial intelligence (AI) as a dominant technological force suggests that breast cancer prediction models developed with AI may represent the next frontier in research.
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Affiliation(s)
- Shuai Huang
- Department of Breast Oncology, Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China
| | - Jun Tao Xu
- Joint Turing‐Darwin Laboratory of Phil Rivers Technology Ltd. and Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China Department of Computational Biology, Phil Rivers Technology Ltd, Beijing, China West Institute of Computing Technology, Chinese Academy of Sciences, Chongqing, China
| | - Mei Yang
- Department of Breast Oncology, Guangdong Provincial People's Hospital(Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong, China
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92
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Lee YH, Thaweethai T, Sheu YH, Feng YCA, Karlson EW, Ge T, Kraft P, Smoller JW. Impact of selection bias on polygenic risk score estimates in healthcare settings. Psychol Med 2023; 53:7435-7445. [PMID: 37226828 DOI: 10.1017/s0033291723001186] [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] [Indexed: 05/26/2023]
Abstract
BACKGROUND Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions. METHODS PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals. RESULTS Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8-11.2%) in the unweighted analysis but only 6.2% (5.0-7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7-35.4%) to 28.9% (25.8-31.9%) after IP weighting. CONCLUSIONS Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.
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Affiliation(s)
- Younga Heather Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Tanayott Thaweethai
- Harvard Medical School, Boston, Massachusetts, USA
- Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yi-Han Sheu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Yen-Chen Anne Feng
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Elizabeth W Karlson
- Harvard Medical School, Boston, Massachusetts, USA
- Division of Rheumatology, Immunity, and Inflammation, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
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93
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Fatumo S, Sathan D, Samtal C, Isewon I, Tamuhla T, Soremekun C, Jafali J, Panji S, Tiffin N, Fakim YJ. Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions. Genome Med 2023; 15:87. [PMID: 37904243 PMCID: PMC10614359 DOI: 10.1186/s13073-023-01245-9] [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: 03/04/2023] [Accepted: 10/12/2023] [Indexed: 11/01/2023] Open
Abstract
Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.
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Affiliation(s)
- Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- Department of Non-Communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
| | - Dassen Sathan
- H3Africa Bioinformatics Network (H3ABioNet) Node, University of Mauritius, Reduit, Mauritius
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-Food and Health, Faculty of Sciences Dhar El Mahraz-Sidi Mohammed Ben Abdellah University, 30000, Fez, Morocco
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, P. M. B. 1023, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Km 10 Idiroko Road, P.M.B. 1023, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Covenant University, P.M.B. 1023, Ota, Ogun State, Nigeria
| | - Tsaone Tamuhla
- Division of Computational Biology, Integrative Biomedical Sciences Department, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
| | - James Jafali
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi
- Clinical Infection, Microbiology & Immunology, The University of Liverpool, Liverpool, UK
| | - Sumir Panji
- Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Nicki Tiffin
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
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94
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Suzuki Y, Hanaoka S, Tanabe M, Yoshikawa T, Seto Y. Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images. J Pers Med 2023; 13:1528. [PMID: 38003843 PMCID: PMC10672551 DOI: 10.3390/jpm13111528] [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: 09/14/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the 'starting point set' of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as 'wavelet-H_glcm_Correlation' and 'wavelet-H_firstorder_Maximum', showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was ≥0.95. Radiomics features from mammography images can help predict breast cancer risk.
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Affiliation(s)
- Yusuke Suzuki
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shouhei Hanaoka
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan;
| | - Masahiko Tanabe
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yasuyuki Seto
- Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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95
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Liu R, Li D, Haritunians T, Ruan Y, Daly MJ, Huang H, McGovern DP. Profiling the inflammatory bowel diseases using genetics, serum biomarkers, and smoking information. iScience 2023; 26:108053. [PMID: 37841595 PMCID: PMC10568094 DOI: 10.1016/j.isci.2023.108053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/28/2023] [Accepted: 09/22/2023] [Indexed: 10/17/2023] Open
Abstract
Crohn's disease (CD) and ulcerative colitis (UC) are two etiologically related yet distinctive subtypes of the inflammatory bowel diseases (IBD). Differentiating CD from UC can be challenging using conventional clinical approaches in a subset of patients. We designed and evaluated a novel molecular-based prediction model aggregating genetics, serum biomarkers, and tobacco smoking information to assist the diagnosis of CD and UC in over 30,000 samples. A joint model combining genetics, serum biomarkers and smoking explains 46% (42-50%, 95% CI) of phenotypic variation. Despite modest overlaps with serum biomarkers, genetics makes unique contributions to distinguishing IBD subtypes. Smoking status only explains 1% (0-6%, 95% CI) of the phenotypic variance suggesting it may not be an effective biomarker. This study reveals that molecular-based models combining genetics, serum biomarkers, and smoking information could complement current diagnostic strategies and help classify patients based on biologic state rather than imperfect clinical parameters.
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Affiliation(s)
- Ruize Liu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dalin Li
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Talin Haritunians
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Yunfeng Ruan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Dermot P.B. McGovern
- F. Widjaja Family Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
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96
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Su P, Wu H, Huang Y, Lu X, Yin J, Zhang Q, Lan X. The Hoof Color of Australian White Sheep Is Associated with Genetic Variation of the MITF Gene. Animals (Basel) 2023; 13:3218. [PMID: 37893942 PMCID: PMC10603658 DOI: 10.3390/ani13203218] [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: 08/08/2023] [Revised: 09/28/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
Studying the characteristics of mammalian hoof colors is important for genetic improvements in animals. A deeper black hoof color is the standard for breeding purebred Australian White (AUW) sheep and this phenotype could be used as a phenotypic marker of purebred animals. We conducted a genome-wide association study (GWAS) analysis using restriction site associated DNA sequencing (RAD-seq) data from 577 Australian White sheep (black hoof color = 283, grey hoof color = 106, amber hoof color = 186) and performed association analysis utilizing the mixed linear model in EMMAX. The results of GWAS demonstrated that a specific single-nucleotide polymorphism (SNP; g. 33097911G>A) in intron 14 of the microphthalmia-associated transcription factor (MITF) gene was significantly associated with the hoof color in AUW sheep (p = 9.40 × 10-36). The MITF gene plays a key role in the development, differentiation, and functional regulation of melanocytes. Furthermore, the association between this locus and hoof color was validated in a cohort of 212 individuals (black hoof color = 122, grey hoof color = 38, amber hoof color = 52). The results indicated that the hoof color of AUW sheep with GG, AG, and AA genotypes tended to be black, grey, and amber, respectively. This study provided novel insights into hoof color genetics in AUW sheep, enhancing our comprehension of the genetic mechanisms underlying the diverse range of hoof colors. Our results agree with previous studies and provide molecular markers for marker-assisted selection for hoof color in sheep.
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Affiliation(s)
- Peng Su
- Tianjin Aoqun Animal Husbandry Co., Ltd., Tianjin 301607, China; (P.S.)
- Key Laboratory of Animal Genetics Breeding and Reproduction of Shanxi Province, College Animal Science and Technology, Northwest A&F University, Yangling 712100, China
- National Germplasm Center of Domestic Animal Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
| | - Hui Wu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yangming Huang
- Key Laboratory of Animal Genetics Breeding and Reproduction of Shanxi Province, College Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Xiaofang Lu
- Tianjin Aoqun Animal Husbandry Co., Ltd., Tianjin 301607, China; (P.S.)
- Tianjin Aoqun Sheep Industry Academy Company, Tianjin 301607, China
| | - Jing Yin
- Tianjin Aoqun Animal Husbandry Co., Ltd., Tianjin 301607, China; (P.S.)
- Tianjin Aoqun Sheep Industry Academy Company, Tianjin 301607, China
| | - Qingfeng Zhang
- Tianjin Aoqun Animal Husbandry Co., Ltd., Tianjin 301607, China; (P.S.)
- Tianjin Aoqun Sheep Industry Academy Company, Tianjin 301607, China
| | - Xianyong Lan
- Key Laboratory of Animal Genetics Breeding and Reproduction of Shanxi Province, College Animal Science and Technology, Northwest A&F University, Yangling 712100, China
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97
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Padrik P, Puustusmaa M, Tõnisson N, Kolk B, Saar R, Padrik A, Tasa T. Implementation of Risk-Stratified Breast Cancer Prevention With a Polygenic Risk Score Test in Clinical Practice. Breast Cancer (Auckl) 2023; 17:11782234231205700. [PMID: 37842230 PMCID: PMC10571698 DOI: 10.1177/11782234231205700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 09/18/2023] [Indexed: 10/17/2023] Open
Abstract
Background Breast cancer (BC) screening with mammography reduces mortality but considers currently only age as a risk factor. Personalized risk-based screening has been proposed as a more efficient alternative. For that, risk prediction tools are necessary. Genome-wide association studies have identified numerous genetic variants (single-nucleotide polymorphisms [SNPs]) associated with BC. The effects of SNPs are combined into a polygenic risk score (PRS) as a risk prediction tool. Objectives We aimed to develop a clinical-grade PRS test suitable for BC risk-stratified screening with clinical recommendations and implementation in clinical practice. Design and methods In the first phase of our study, we gathered previously published PRS models for predicting BC risk from the literature and validated them using the Estonian Biobank and UK Biobank data sets. We selected the best performing model based on prevalent data and independently validated it in both incident data sets. We then conducted absolute risk simulations, developed risk-based recommendations, and implemented the PRS test in clinical practice. In the second phase, we carried out a retrospective analysis of the PRS test's performance results in clinical practice. Results The best performing PRS included 2803 SNPs. The C-index of the Cox regression model associating BC status with PRS was 0.656 (SE = 0.05) with a hazard ratio of 1.66. The PRS can stratify individuals with more than a 3-fold risk increase. A total of 2637 BC PRS tests have been performed for women between the ages 30 and 83. Results in clinical use overlap well with expected PRS performance with 5.7% of women with more than 2-fold and 1.4% with more than 3-fold higher risk than the population average. Conclusion The PRS test separates different BC risk levels and is feasible to implement in clinical practice.
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Affiliation(s)
- Peeter Padrik
- OÜ Antegenes, Tartu, Estonia
- Clinic of Hematology and Oncology, Tartu University Hospital, Tartu, Estonia
| | | | - Neeme Tõnisson
- OÜ Antegenes, Tartu, Estonia
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia
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98
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Hopper JL, Dowty JG, Nguyen TL, Li S, Dite GS, MacInnis RJ, Makalic E, Schmidt DF, Bui M, Stone J, Sung J, Jenkins MA, Giles GG, Southey MC, Mathews JD. Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks. Int J Epidemiol 2023; 52:1557-1568. [PMID: 37349888 PMCID: PMC10655167 DOI: 10.1093/ije/dyad086] [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: 07/11/2022] [Accepted: 06/16/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. CONCLUSION VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Genetic Technologies Ltd., Fitzroy, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Jennifer Stone
- School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - John D Mathews
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
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99
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Xu C, Ganesh SK, Zhou X. mtPGS: Leverage multiple correlated traits for accurate polygenic score construction. Am J Hum Genet 2023; 110:1673-1689. [PMID: 37716346 PMCID: PMC10577082 DOI: 10.1016/j.ajhg.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/18/2023] [Accepted: 08/27/2023] [Indexed: 09/18/2023] Open
Abstract
Accurate polygenic scores (PGSs) facilitate the genetic prediction of complex traits and aid in the development of personalized medicine. Here, we develop a statistical method called multi-trait assisted PGS (mtPGS), which can construct accurate PGSs for a target trait of interest by leveraging multiple traits relevant to the target trait. Specifically, mtPGS borrows SNP effect size similarity information between the target trait and its relevant traits to improve the effect size estimation on the target trait, thus achieving accurate PGSs. In the process, mtPGS flexibly models the shared genetic architecture between the target and the relevant traits to achieve robust performance, while explicitly accounting for the environmental covariance among them to accommodate different study designs with various sample overlap patterns. In addition, mtPGS uses only summary statistics as input and relies on a deterministic algorithm with several algebraic techniques for scalable computation. We evaluate the performance of mtPGS through comprehensive simulations and applications to 25 traits in the UK Biobank, where in the real data mtPGS achieves an average of 0.90%-52.91% accuracy gain compared to the state-of-the-art PGS methods. Overall, mtPGS represents an accurate, fast, and robust solution for PGS construction in biobank-scale datasets.
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Affiliation(s)
- Chang Xu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Santhi K Ganesh
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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100
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Jia Z, Zhang H, Yu L, Qiu F, Lv Y, Guan J, Gang H, Zuo J, Zheng T, Liu H, Xia W, Xu S, Li Y. Prenatal Lead Exposure, Genetic Factors, and Cognitive Developmental Delay. JAMA Netw Open 2023; 6:e2339108. [PMID: 37870833 PMCID: PMC10594149 DOI: 10.1001/jamanetworkopen.2023.39108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/04/2023] [Indexed: 10/24/2023] Open
Abstract
Importance Although the effects of lead (Pb) exposure on neurocognition in children have been confirmed, the individual associations of prenatal Pb exposure and its interaction with genetic factors on cognitive developmental delay (CDD) in children remain unclear. Objective To investigate the association of prenatal Pb exposure and its interaction with genetic factors with CDD risk. Design, Setting, and Participants Women in Wuhan, China, who had an expected delivery date between March 2014 and December 2017, were recruited for this prospective cohort study. Children were assessed for cognitive development at approximately 2 years of age (March 2016 to December 2019). Maternal venous blood, cord blood, and venous blood from children were collected in a longitudinal follow-up. Data analysis was performed from March 2022 to February 2023. Exposure Prenatal Pb exposure, and genetic risk for cognitive ability evaluated by polygenic risk score constructed with 58 genetic variations. Main Outcomes and Measures Cognitive developmental delay of children aged approximately 2 years was assessed using the Chinese revision of the Bayley Scale of Infant Development. A series of multivariable logistic regressions was estimated to determine associations between prenatal Pb exposure and CDD among children with various genetic backgrounds, adjusting for confounding variables. Results This analysis included 2361 eligible mother-child pairs (1240 boys [52.5%] and 1121 girls [47.5%]; mean [SD] ages of mothers and children, 28.9 [3.6] years and 24.8 [1.0] months, respectively), with 292 children (12.4%) having CDD. Higher maternal Pb levels were significantly associated with increased risk of CDD (highest vs lowest tertile: odds ratio, 1.55; 95% CI, 1.13-2.13), adjusting for demographic confounders. The association of CDD with maternal Pb levels was more evident among children with higher genetic risk (highest vs lowest tertile: odds ratio, 2.59; 95% CI, 1.48-4.55), adjusting for demographic confounders. Conclusions and Relevance In this cohort study, prenatal Pb exposure was associated with an increased risk of CDD in children, especially in those with a high genetic risk. These findings suggest that prenatal Pb exposure and genetic background may jointly contribute to an increased risk of CDD for children and indicate the possibility for an integrated strategy to assess CDD risk and improve children's cognitive ability.
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Affiliation(s)
- Zhenxian Jia
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | | | - Ling Yu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feng Qiu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiqing Lv
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jing Guan
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huiqing Gang
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jingwen Zuo
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Tongzhang Zheng
- Department of Epidemiology, School of Public Health, Brown University, Providence, Rhode Island
| | - Hongxiu Liu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Xia
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shunqing Xu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanyuan Li
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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