1
|
Tagliabue E, Gandini S, Bellocco R, Maisonneuve P, Newton-Bishop J, Polsky D, Lazovich D, Kanetsky PA, Ghiorzo P, Gruis NA, Landi MT, Menin C, Fargnoli MC, García-Borrón JC, Han J, Little J, Sera F, Raimondi S. MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project. Cancer Manag Res 2018; 10:1143-1154. [PMID: 29795986 PMCID: PMC5958947 DOI: 10.2147/cmar.s155283] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics. MATERIALS AND METHODS Data were collected within an international collaboration - the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case-control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype. RESULTS The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36-1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7% over a base clinical model (P=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%-30%). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%). CONCLUSION The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype.
Collapse
Affiliation(s)
- Elena Tagliabue
- Clinical Trial Center, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori
| | - Sara Gandini
- Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy
| | - Rino Bellocco
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Patrick Maisonneuve
- Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy
| | - Julia Newton-Bishop
- Section of Epidemiology and Biostatistics, Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - David Polsky
- Ronald O. Perelman Department of Dermatology, New York University School of Medicine, NYU Langone Medical Center, New York, NY
| | - DeAnn Lazovich
- Division of Epidemiology and Community Health, University of Minnesota, MN
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Paola Ghiorzo
- Department of Internal Medicine and Medical Specialties, University of Genoa
- IRCCS AOU San Martino-IST, Genoa, Italy
| | - Nelleke A Gruis
- Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Chiara Menin
- Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, IOV-IRCCS, Padua
| | | | - Jose Carlos García-Borrón
- Department of Biochemistry, Molecular Biology, and Immunology, University of Murcia
- IMIB-Arrixaca, Murcia, Spain
| | - Jiali Han
- Department of Epidemiology, Richard M Fairbanks School of Public Health, Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, IN, USA
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Francesco Sera
- Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK
| | - Sara Raimondi
- Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy
| |
Collapse
|
2
|
Chasman DI, Schürks M, Kurth T. Population-based approaches to genetics of migraine. Cephalalgia 2016; 36:692-703. [PMID: 27013237 DOI: 10.1177/0333102416638519] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 02/06/2016] [Indexed: 01/03/2023]
Abstract
BACKGROUND While the most accurate diagnosis of migraine typically requires a clinical interview guided by strict diagnostic criteria, an alternative approach that ascertains migraine by questionnaire in population-based settings has been instrumental in the discovery of common genetic variants influencing migraine risk. This result may be surprising. Population-based approaches are often criticized for limited ability to distinguish migraine from other forms of primary headache. It is thus useful to revisit prevailing ideas about population-based ascertainment of migraine to evaluate the extent to which this approach has potential for additional insights into migraine genetics and therefore pathophysiology. OVERVIEW We review recent findings suggesting that the success of the population-based approach is derived from the possibility of collecting much larger samples than in the clinic-based setting even at the risk of introducing phenotypic and genetic heterogeneity. The findings are also consistent with new appreciations for the genetic basis of many other common, complex clinical characteristics. However, clinic-based ascertainment and other settings will remain more effective than population-based approaches for investigating certain, often very specific aspects of migraine genetics. CONCLUSION We argue that the detailed genetic architecture of migraine, various aspects of methodology, and the ultimate sample size achieved by population-based ascertainment will be critical determinants of the future success of this approach to genetic analysis of migraine and its comorbidities.
Collapse
Affiliation(s)
- Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, USA Harvard Medical School, USA
| | - Markus Schürks
- Department of Neurology, University Hospital Essen, Germany
| | - Tobias Kurth
- Division of Preventive Medicine, Brigham and Women's Hospital, USA Institut of Public Health, Charité-Universitätsmedizin Berlin, Germany
| |
Collapse
|
3
|
A weighted polygenic risk score using 14 known susceptibility variants to estimate risk and age onset of psoriasis in Han Chinese. PLoS One 2015; 10:e0125369. [PMID: 25933357 PMCID: PMC4416725 DOI: 10.1371/journal.pone.0125369] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 03/23/2015] [Indexed: 02/07/2023] Open
Abstract
With numbers of common variants identified mainly through genome-wide association studies (GWASs), there is great interest in incorporating the findings into screening individuals at high risk of psoriasis. The purpose of this study is to establish genetic prediction models and evaluate its discriminatory ability in psoriasis in Han Chinese population. We built the genetic prediction models through weighted polygenic risk score (PRS) using 14 susceptibility variants in 8,819 samples. We found the risk of psoriasis among individuals in the top quartile of PRS was significantly larger than those in the lowest quartile of PRS (OR = 28.20, P < 2.0×10-16). We also observed statistically significant associations between the PRS, family history and early age onset of psoriasis. We also built a predictive model with all 14 known susceptibility variants and alcohol consumption, which achieved an area under the curve statistic of ~ 0.88. Our study suggests that 14 psoriasis known susceptibility loci have the discriminating potential, as is also associated with family history and age of onset. This is the genetic predictive model in psoriasis with the largest accuracy to date.
Collapse
|
4
|
Development of a melanoma risk prediction model incorporating MC1R genotype and indoor tanning exposure: impact of mole phenotype on model performance. PLoS One 2014; 9:e101507. [PMID: 25003831 PMCID: PMC4086828 DOI: 10.1371/journal.pone.0101507] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2014] [Accepted: 06/08/2014] [Indexed: 12/21/2022] Open
Abstract
Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals.
Collapse
|
5
|
Lee CPL, Irwanto A, Salim A, Yuan JM, Liu J, Koh WP, Hartman M. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population. Breast Cancer Res 2014; 16:R64. [PMID: 24941967 PMCID: PMC4095592 DOI: 10.1186/bcr3678] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 06/04/2014] [Indexed: 01/16/2023] Open
Abstract
Introduction Genetic variants for breast cancer risk identified in genome-wide association studies (GWAS) in Western populations require further testing in Asian populations. A risk assessment model incorporating both validated genetic variants and established risk factors may improve its performance in risk prediction of Asian women. Methods A nested case-control study of female breast cancer (411 cases and 1,212 controls) within the Singapore Chinese Health Study was conducted to investigate the effects of 51 genetic variants identified in previous GWAS on breast cancer risk. The independent effect of these genetic variants was assessed by creating a summed genetic risk score (GRS) after adjustment for body mass index and the Gail model risk factors for breast cancer. Results The GRS was an independent predictor of breast cancer risk in Chinese women. The multivariate-adjusted odds ratios (95% confidence intervals) of breast cancer for the second, third, and fourth quartiles of the GRS were 1.26 (0.90 to 1.76), 1.47 (1.06 to 2.04) and 1.75 (1.27 to 2.41) respectively (P for trend <0.001). In addition to established risk factors, the GRS improved the classification of 6.2% of women for their absolute risk of breast cancer in the next five years. Conclusions Genetic variants on top of conventional risk factors can improve the risk prediction of breast cancer in Chinese women.
Collapse
|
6
|
Pu X, Ye Y, Wu X. Development and validation of risk models and molecular diagnostics to permit personalized management of cancer. Cancer 2013; 120:11-9. [PMID: 24114238 DOI: 10.1002/cncr.28393] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Revised: 08/25/2013] [Accepted: 08/29/2013] [Indexed: 01/29/2023]
Abstract
Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation.
Collapse
Affiliation(s)
- Xia Pu
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | | |
Collapse
|
7
|
Park JH, Gail MH, Greene MH, Chatterjee N. Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers. J Clin Oncol 2012; 30:2157-62. [PMID: 22585702 DOI: 10.1200/jco.2011.40.1943] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
PURPOSE To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers. METHODS From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non-patient cases to patient cases (false-positive ratio) for various risk thresholds. RESULTS Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively. CONCLUSION Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.
Collapse
Affiliation(s)
- Ju-Hyun Park
- National Cancer Institute, Rockville, MD 20852-7244, USA
| | | | | | | |
Collapse
|
8
|
Howell A, Astley S, Warwick J, Stavrinos P, Sahin S, Ingham S, McBurney H, Eckersley B, Harvie M, Wilson M, Beetles U, Warren R, Hufton A, Sergeant J, Newman W, Buchan I, Cuzick J, Evans DG. Prevention of breast cancer in the context of a national breast screening programme. J Intern Med 2012; 271:321-30. [PMID: 22292490 DOI: 10.1111/j.1365-2796.2012.02525.x] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Breast cancer is not only increasing in the west but also particularly rapidly in eastern countries where traditionally the incidence has been low. The rise in incidence is mainly related to changes in reproductive patterns and lifestyle. These trends could potentially be reversed by defining women at greatest risk and offering appropriate preventive measures. A model for this approach was the establishment of Family History Clinics (FHCs), which have resulted in improved survival in younger women at high risk. New predictive models of risk that include reproductive and lifestyle factors, mammographic density and measurement of risk-associated single nucleotide polymorphisms (SNPs) may give more precise information concerning risk and enable better targeting for mammographic screening programmes and of preventive measures. Endocrine prevention using anti-oestrogens and aromatase inhibitors is effective, and observational studies suggest lifestyle modification may also be effective. However, referral to FHCs is opportunistic and predominantly includes younger women. A better approach for identifying older women at risk may be to use national breast screening programmes. Here were described pilot studies to assess whether the routine assessment of breast cancer risk is feasible within a population-based screening programme, whether the feedback and advice on risk-reducing interventions would be welcomed and taken up, and to consider whether the screening interval should be modified according to breast cancer risk.
Collapse
Affiliation(s)
- A Howell
- Genesis Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, UK.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Thomas DC. Genetic epidemiology with a capital E: where will we be in another 10 years? Genet Epidemiol 2012; 36:179-82. [PMID: 22311722 DOI: 10.1002/gepi.21612] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2011] [Accepted: 12/09/2011] [Indexed: 12/20/2022]
Abstract
In a commentary on the evolution of the field of genetic epidemiology over the past 10 years, Khoury et al. (2011) highlight several important developments, including the emergence of evaluation of genetic discoveries for their translational utility and of standards for reporting genetic findings. In this companion to their article, I reflect on some of these trends and speculate about the direction of the field in the future. In particular, I emphasize the opportunities posed by novel technologies like next-generation sequencing and the biological insights emerging from integrative genomics, but I also question the utility of large consortia. The basic principles of population-based research and the importance of taking account of the environment remain important to the field.
Collapse
|