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Kaiser I, Pfahlberg AB, Mathes S, Uter W, Diehl K, Steeb T, Heppt MV, Gefeller O. Inter-Rater Agreement in Assessing Risk of Bias in Melanoma Prediction Studies Using the Prediction Model Risk of Bias Assessment Tool (PROBAST): Results from a Controlled Experiment on the Effect of Specific Rater Training. J Clin Med 2023; 12:jcm12051976. [PMID: 36902763 PMCID: PMC10003882 DOI: 10.3390/jcm12051976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Assessing the risk of bias (ROB) of studies is an important part of the conduct of systematic reviews and meta-analyses in clinical medicine. Among the many existing ROB tools, the Prediction Model Risk of Bias Assessment Tool (PROBAST) is a rather new instrument specifically designed to assess the ROB of prediction studies. In our study we analyzed the inter-rater reliability (IRR) of PROBAST and the effect of specialized training on the IRR. Six raters independently assessed the risk of bias (ROB) of all melanoma risk prediction studies published until 2021 (n = 42) using the PROBAST instrument. The raters evaluated the ROB of the first 20 studies without any guidance other than the published PROBAST literature. The remaining 22 studies were assessed after receiving customized training and guidance. Gwet's AC1 was used as the primary measure to quantify the pairwise and multi-rater IRR. Depending on the PROBAST domain, results before training showed a slight to moderate IRR (multi-rater AC1 ranging from 0.071 to 0.535). After training, the multi-rater AC1 ranged from 0.294 to 0.780 with a significant improvement for the overall ROB rating and two of the four domains. The largest net gain was achieved in the overall ROB rating (difference in multi-rater AC1: 0.405, 95%-CI 0.149-0.630). In conclusion, without targeted guidance, the IRR of PROBAST is low, questioning its use as an appropriate ROB instrument for prediction studies. Intensive training and guidance manuals with context-specific decision rules are needed to correctly apply and interpret the PROBAST instrument and to ensure consistency of ROB ratings.
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Affiliation(s)
- Isabelle Kaiser
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany
- Correspondence:
| | - Annette B. Pfahlberg
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany
| | - Sonja Mathes
- Department of Dermatology and Allergy Biederstein, Faculty of Medicine, Technical University of Munich, 80802 Munich, Germany
| | - Wolfgang Uter
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany
| | - Katharina Diehl
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany
| | - Theresa Steeb
- Department of Dermatology, University Hospital Erlangen, 91054 Erlangen, Germany
| | - Markus V. Heppt
- Department of Dermatology, University Hospital Erlangen, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-European Metropolitan Area of Nuremberg (CCC ER-EMN), 91054 Erlangen, Germany
| | - Olaf Gefeller
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany
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Kontogianni G, Voutetakis K, Piroti G, Kypreou K, Stefanaki I, Vlachavas EI, Pilalis E, Stratigos A, Chatziioannou A, Papadodima O. A Comprehensive Analysis of Cutaneous Melanoma Patients in Greece Based on Multi-Omic Data. Cancers (Basel) 2023; 15:cancers15030815. [PMID: 36765773 PMCID: PMC9913631 DOI: 10.3390/cancers15030815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/01/2023] Open
Abstract
Cutaneous melanoma (CM) is the most aggressive type of skin cancer, and it is characterised by high mutational load and heterogeneity. In this study, we aimed to analyse the genomic and transcriptomic profile of primary melanomas from forty-six Formalin-Fixed, Paraffin-Embedded (FFPE) tissues from Greek patients. Molecular analysis for both germline and somatic variations was performed in genomic DNA from peripheral blood and melanoma samples, respectively, exploiting whole exome and targeted sequencing, and transcriptomic analysis. Detailed clinicopathological data were also included in our analyses and previously reported associations with specific mutations were recognised. Most analysed samples (43/46) were found to harbour at least one clinically actionable somatic variant. A subset of samples was profiled at the transcriptomic level, and it was shown that specific melanoma phenotypic states could be inferred from bulk RNA isolated from FFPE primary melanoma tissue. Integrative bioinformatics analyses, including variant prioritisation, differential gene expression analysis, and functional and gene set enrichment analysis by group and per sample, were conducted and molecular circuits that are implicated in melanoma cell programmes were highlighted. Integration of mutational and transcriptomic data in CM characterisation could shed light on genes and pathways that support the maintenance of phenotypic states encrypted into heterogeneous primary tumours.
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Affiliation(s)
- Georgia Kontogianni
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
- Centre of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | | | - Georgia Piroti
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
| | - Katerina Kypreou
- 1st Department of Dermatology, Andreas Syggros Hospital, Medical School, National and Kapodistrian University of Athens, 16121 Athens, Greece
| | - Irene Stefanaki
- 1st Department of Dermatology, Andreas Syggros Hospital, Medical School, National and Kapodistrian University of Athens, 16121 Athens, Greece
| | | | | | - Alexander Stratigos
- 1st Department of Dermatology, Andreas Syggros Hospital, Medical School, National and Kapodistrian University of Athens, 16121 Athens, Greece
| | - Aristotelis Chatziioannou
- Centre of Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
- e-NIOS Applications Private Company, 17671 Kallithea, Greece
- Correspondence: (A.C.); (O.P.); Tel.: +30-210-727-3721 (A.C. & O.P.)
| | - Olga Papadodima
- Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
- Correspondence: (A.C.); (O.P.); Tel.: +30-210-727-3721 (A.C. & O.P.)
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3
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Talli I, Dovrolis N, Oulas A, Stavrakaki S, Makedou K, Spyrou GM, Maroulakou I. Novel clinical, molecular and bioinformatics insights into the genetic background of autism. Hum Genomics 2022; 16:39. [PMID: 36117207 PMCID: PMC9482726 DOI: 10.1186/s40246-022-00415-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Background Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. Method Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. Results Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. Conclusions These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.
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Affiliation(s)
- Ioanna Talli
- Department of Italian Language and Literature, School of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolas Dovrolis
- Laboratory of Biology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Anastasis Oulas
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus.,The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus
| | - Stavroula Stavrakaki
- Department of Italian Language and Literature, School of Philosophy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kali Makedou
- Laboratory of Biochemistry, School of Medicine, AHEPA General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George M Spyrou
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus. .,The Cyprus School of Molecular Medicine, 6 International Airport Avenue, 2370 Nicosia, Cyprus, P.O. Box 23462, 1683, Nicosia, Cyprus.
| | - Ioanna Maroulakou
- Laboratory of Genetics, Department of Molecular Biology and Genetics, Democritus University of Thrace, 68100, Alexandroupolis, Greece.
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Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies. Cancers (Basel) 2022; 14:cancers14123033. [PMID: 35740698 PMCID: PMC9221327 DOI: 10.3390/cancers14123033] [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: 05/02/2022] [Revised: 06/01/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary The rising incidence of cutaneous melanoma over recent decades, combined with a general interest in cancer risk prediction, has led to a high number of published melanoma risk prediction models. The aim of our work was to assess the validity of these models in order to discuss the current state of knowledge about how to predict incident cutaneous melanoma. To assess the risk of bias, we used a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). Only one of the 42 studies identified was rated as having a low risk of bias. However, it was encouraging to observe a recent reduction of problematic statistical methods used in the analyses. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest. Abstract Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall ROB rating. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether, 42 studies were assessed, of which the vast majority (n = 34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our temporal analysis results showed a significant reduction in the number of studies with high ROB for the domain “analysis”. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.
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Reporting Quality of Studies Developing and Validating Melanoma Prediction Models: An Assessment Based on the TRIPOD Statement. Healthcare (Basel) 2022; 10:healthcare10020238. [PMID: 35206853 PMCID: PMC8871554 DOI: 10.3390/healthcare10020238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Transparent and accurate reporting is essential to evaluate the validity and applicability of risk prediction models. Our aim was to evaluate the reporting quality of studies developing and validating risk prediction models for melanoma according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) checklist. We included studies that were identified by a recent systematic review and updated the literature search to ensure that our TRIPOD rating included all relevant studies. Six reviewers assessed compliance with all 37 TRIPOD components for each study using the published “TRIPOD Adherence Assessment Form”. We further examined a potential temporal effect of the reporting quality. Altogether 42 studies were assessed including 35 studies reporting the development of a prediction model and seven studies reporting both development and validation. The median adherence to TRIPOD was 57% (range 29% to 78%). Study components that were least likely to be fully reported were related to model specification, title and abstract. Although the reporting quality has slightly increased over the past 35 years, there is still much room for improvement. Adherence to reporting guidelines such as TRIPOD in the publication of study results must be adopted as a matter of course to achieve a sufficient level of reporting quality necessary to foster the use of the prediction models in applications.
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Bakshi A, Yan M, Riaz M, Polekhina G, Orchard SG, Tiller J, Wolfe R, Joshi A, Cao Y, McInerney-Leo AM, Yanes T, Janda M, Soyer HP, Cust AE, Law MH, Gibbs P, McLean C, Chan AT, McNeil JJ, Mar VJ, Lacaze P. Genomic Risk Score for Melanoma in a Prospective Study of Older Individuals. J Natl Cancer Inst 2021; 113:1379-1385. [PMID: 33837773 PMCID: PMC8921762 DOI: 10.1093/jnci/djab076] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/16/2021] [Accepted: 03/30/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Recent genome-wide association meta-analysis for melanoma doubled the number of previously identified variants. We assessed the performance of an updated polygenic risk score (PRS) in a population of older individuals, where melanoma incidence and cumulative ultraviolet radiation exposure is greatest. METHODS We assessed a PRS for cutaneous melanoma comprising 55 variants in a prospective study of 12 712 individuals in the ASPirin in Reducing Events in the Elderly Trial. We evaluated incident melanomas diagnosed during the trial and prevalent melanomas diagnosed preenrolment (self-reported). Multivariable models examined associations between PRS as a continuous variable (per SD) and categorical (low-risk [0%-20%], medium-risk [21%-80%], high-risk [81%-100%] groups) with incident melanoma. Logistic regression examined the association between PRS and prevalent melanoma. RESULTS At baseline, mean participant age was 75 years; 55.0% were female, and 528 (4.2%) had prevalent melanomas. During follow-up (median = 4.7 years), 120 (1.0%) incident cutaneous melanomas occurred, 98 of which were in participants with no history. PRS was associated with incident melanoma (hazard ratio = 1.46 per SD, 95% confidence interval [CI] = 1.20 to 1.77) and prevalent melanoma (odds ratio [OR] = 1.55 per SD, 95% CI = 1.42 to 1.69). Participants in the highest-risk PRS group had increased risk compared with the low-risk group for incident melanoma (OR = 2.51, 95% CI = 1.28 to 4.92) and prevalent melanoma (OR = 3.66, 95% CI = 2.69 to 5.05). When stratifying by sex, only males had an association between the PRS and incident melanoma, whereas both sexes had an association between the PRS and prevalent melanoma. CONCLUSIONS A genomic risk score is associated with melanoma risk in older individuals and may contribute to targeted surveillance.
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Affiliation(s)
- Andrew Bakshi
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Mabel Yan
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Galina Polekhina
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Suzanne G Orchard
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Jane Tiller
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; MGH Cancer Center, Boston, MA, USA
| | - Yin Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA; Alvin J. Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
| | - Aideen M McInerney-Leo
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Tatiane Yanes
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Monika Janda
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
- Centre of Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - H Peter Soyer
- The University of Queensland Diamantina Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, USA
| | - Anne E Cust
- Sydney School of Public Health and Melanoma Institute Australia, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia, Personalised Oncology Division, Walter and Eliza Hall Institute Medical Research and Faculty of Medicine University of Melbourne, Australia
| | - Peter Gibbs
- Department of Anatomical Pathology, Alfred Hospital, Melbourne, Victoria, Australia
| | - Catriona McLean
- Department of Anatomical Pathology, Alfred Hospital, Melbourne, Victoria, Australia
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; MGH Cancer Center, Boston, MA, USA
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Victoria J Mar
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
- Victorian Melanoma Service, Alfred Health, Melbourne, Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Shetty A, Janda M, Fry K, Brown S, Yau B, Schuckmann LV, Thomas S, Rayner JE, Spelman L, Wagner G, Jenkins H, Lun K, Parbery J, Soyer HP, Neale RE, Green AC, Whiteman DC, Olsen CM, Khosrotehrani K. Clinical utility of skin cancer and melanoma risk scores for population screening: TRoPICS study. J Eur Acad Dermatol Venereol 2020; 35:1094-1098. [PMID: 33274462 DOI: 10.1111/jdv.17062] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/27/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND Screening for skin cancer can be cost-effective if focused on high-risk groups. Risk prediction tools have been developed for keratinocyte cancers and melanoma to optimize advice and management. However, few have been validated in a clinical setting over the past few years. OBJECTIVES To assess the clinical utility of risk assessment tools to identify individuals with prevalent skin cancers in a volunteer-based screening clinic. METHODS Participants were adults presenting for a skin check at a volunteer-based skin cancer screening facility. We used previously published tools, based on questionnaire responses, to predict melanoma and keratinocyte cancers [KCs; basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)] and classified each participant into one of five risk categories. Participants subsequently underwent a full skin examination by a dermatologist. All suspicious lesions were biopsied, and all cancers were histopathologically confirmed. RESULTS Of 789 people who presented to the clinic, 507 (64%) consented to the study. Twenty-two BCCs, 19 SCCs and eight melanomas were diagnosed. The proportion of keratinocyte cancers diagnosed increased according to risk category from <1% in the lowest to 24% in the highest risk category (P < 0.001). Subtype analysis revealed similar proportionate increases in BCC or SCC prevalence according to risk category. However, a similar proportion of melanoma cases were detected in the low-risk and high-risk groups. CONCLUSION The risk prediction model for keratinocyte cancers can reliably identify individuals with a significant skin cancer burden prior to a skin examination in the community setting. The prediction tool for melanoma needs to be tested in a larger sample exposed to a wider range of environmental risk factors.
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Affiliation(s)
- A Shetty
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - M Janda
- Centre of Health Services Research, The University of Queensland, Brisbane, QLD, Australia
| | - K Fry
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - S Brown
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - B Yau
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - L Von Schuckmann
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.,Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - S Thomas
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - J E Rayner
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.,Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - L Spelman
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - G Wagner
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - H Jenkins
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - K Lun
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - J Parbery
- Queensland Institute of Dermatology, Queensland Skin and Cancer Foundation, Brisbane, QLD, Australia
| | - H P Soyer
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - R E Neale
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - A C Green
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - D C Whiteman
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - C M Olsen
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia.,QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - K Khosrotehrani
- UQ Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
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8
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Kaiser I, Pfahlberg AB, Uter W, Heppt MV, Veierød MB, Gefeller O. Risk Prediction Models for Melanoma: A Systematic Review on the Heterogeneity in Model Development and Validation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17217919. [PMID: 33126677 PMCID: PMC7662952 DOI: 10.3390/ijerph17217919] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/15/2020] [Accepted: 10/26/2020] [Indexed: 12/13/2022]
Abstract
The rising incidence of cutaneous melanoma over the past few decades has prompted substantial efforts to develop risk prediction models identifying people at high risk of developing melanoma to facilitate targeted screening programs. We review these models, regarding study characteristics, differences in risk factor selection and assessment, evaluation, and validation methods. Our systematic literature search revealed 40 studies comprising 46 different risk prediction models eligible for the review. Altogether, 35 different risk factors were part of the models with nevi being the most common one (n = 35, 78%); little consistency in other risk factors was observed. Results of an internal validation were reported for less than half of the studies (n = 18, 45%), and only 6 performed external validation. In terms of model performance, 29 studies assessed the discriminative ability of their models; other performance measures, e.g., regarding calibration or clinical usefulness, were rarely reported. Due to the substantial heterogeneity in risk factor selection and assessment as well as methodologic aspects of model development, direct comparisons between models are hardly possible. Uniform methodologic standards for the development and validation of risk prediction models for melanoma and reporting standards for the accompanying publications are necessary and need to be obligatory for that reason.
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Affiliation(s)
- Isabelle Kaiser
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany; (I.K.); (A.B.P.); (W.U.)
| | - Annette B. Pfahlberg
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany; (I.K.); (A.B.P.); (W.U.)
| | - Wolfgang Uter
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany; (I.K.); (A.B.P.); (W.U.)
| | - Markus V. Heppt
- Department of Dermatology, University Hospital Erlangen, 91054 Erlangen, Germany;
| | - Marit B. Veierød
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo, Norway;
| | - Olaf Gefeller
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich Alexander University of Erlangen-Nuremberg, 91054 Erlangen, Germany; (I.K.); (A.B.P.); (W.U.)
- Correspondence:
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9
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Yanes T, McInerney-Leo AM, Law MH, Cummings S. The emerging field of polygenic risk scores and perspective for use in clinical care. Hum Mol Genet 2020; 29:R165-R176. [DOI: 10.1093/hmg/ddaa136] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023] Open
Abstract
Abstract
Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.
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Affiliation(s)
- Tatiane Yanes
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Aideen M McInerney-Leo
- Dermatology Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Herston QLD 4006, Australia
- Faculty of Health, School of Biomedical Sciences, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove QLD 4059, Australia
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10
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Puentes C, Estrada A, Bohórquez M, Vélez A, Giraldo C, Echeverry M. Melanoma: clinical-pathological and molecular analysis in patients of Ibague city, Colombia. DUAZARY 2020. [DOI: 10.21676/2389783x.3217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
This study aimed to establish the clinicopathological characteristics of patients with melanoma and its association with BRAF gene mutations. The pathology reports and paraffin-embedded tumor samples from 47 women and 30 men with melanoma, with an average age of diagnosis of 60 years, were reviewed at the Hospital Federico Lleras Acosta of Ibague, between 2010 and 2016. The presence of V600E mutation at the exon 15 of BRAF gene, was analyzed in these tumoral samples by Sanger sequencing and visual inspection of the electropherograms. We also studied the clinicopathological variables with X2, t-Student and the Kaplan Meier index. Most of the lesions were located in the lower limbs (46.6%). The most frequent subtype was Acral Lentiginous Melanoma (41.8%). Most lesions were of poor prognosis: Breslow depth greater than 4.1 mm (52.7%), ulceration (61.4%) and medium or high mitotic rate (> 30 %). The V600E mutation was identified in five patients with large, deep and ulcerated tumors, four of them had less than four years of survival. In conclusion, there was a higher frequency of melanoma in women, V600E BRAF mutation was present in patients with advanced disease (high Breslow index) and, the probability of five-year survival was less than 40%.
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11
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Lello L, Raben TG, Yong SY, Tellier LCAM, Hsu SDH. Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer. Sci Rep 2019; 9:15286. [PMID: 31653892 PMCID: PMC6814833 DOI: 10.1038/s41598-019-51258-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 09/26/2019] [Indexed: 01/09/2023] Open
Abstract
We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistant) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range ~0.58-0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of polygenic score, or PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.
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Affiliation(s)
- Louis Lello
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA.
| | - Timothy G Raben
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA.
| | - Soke Yuen Yong
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA.
| | - Laurent C A M Tellier
- Genomic Prediction, North Brunswick, NJ, USA.
- Cognitive Genomics Laboratory, Shenzhen Key Laboratory of Neurogenomics, China National GeneBank, BGI-Shenzhen, Shenzhen, China.
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, USA.
- Genomic Prediction, North Brunswick, NJ, USA.
- Cognitive Genomics Laboratory, Shenzhen Key Laboratory of Neurogenomics, China National GeneBank, BGI-Shenzhen, Shenzhen, China.
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12
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Roberts MR, Asgari MM, Toland AE. Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet? Br J Dermatol 2019; 181:1146-1155. [PMID: 30908599 DOI: 10.1111/bjd.17917] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2019] [Indexed: 12/27/2022]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified thousands of susceptibility variants, although most have been associated with small individual risk estimates that offer little predictive value. However, combining multiple variants into polygenic risk scores (PRS) may be more informative. Multiple studies have developed PRS composed of GWAS-identified variants for cutaneous cancers. This review highlights data from these studies. OBJECTIVES To review published GWAS and PRS studies for melanoma, cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), and discuss their potential clinical utility. METHODS We searched PubMed and the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue to identify relevant studies. RESULTS Results from 21 GWAS (11 melanoma, 3 cSCC, 7 BCC) and 11 PRS studies are summarized. Six loci in pigmentation genes overlap between these three cancers (ASIP/RALY, IRF4, MC1R, OCA2, SLC45A2 and TYR). Additional loci overlap for cSCC/BCC and BCC/melanoma, but no other loci are shared between cSCC and melanoma. PRS for melanoma show roughly two-to-threefold increases in risk and modest improvements in risk prediction (2-7% increases). PRS are associated with twofold and threefold increases in risk of cSCC and BCC, respectively, with small improvements (2% increase) in predictive ability. CONCLUSIONS Existing data indicate that PRS may offer small, but potentially meaningful, improvements to risk prediction. Additional research is needed to clarify the potential utility of PRS in cutaneous carcinomas. Clinical translation will require well-powered validation studies incorporating known risk factors to evaluate PRS as tools for screening. What's already known about this topic? Over 50 susceptibility loci for melanoma, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) have been identified in genome-wide association studies (GWAS). Polygenic risk scores (PRS) using variants identified from GWAS have also been developed for melanoma, BCC and cSCC, and investigated with respect to clinical risk prediction. What does this study add? This review provides an overview of GWAS findings and the potential clinical utility of PRS for melanoma, BCC and cSCC.
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Affiliation(s)
- M R Roberts
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, U.S.A.,Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, U.S.A
| | - M M Asgari
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, U.S.A.,Department of Population Medicine, Harvard Pilgrim Healthcare Institute, Boston, MA, U.S.A
| | - A E Toland
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, Ohio State University, 998 Biomedical Research Tower, 460 W 12th Ave, Columbus, OH, 43210, U.S.A
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13
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Fibroblast Growth Factor Receptor Signaling in Skin Cancers. Cells 2019; 8:cells8060540. [PMID: 31167513 PMCID: PMC6628025 DOI: 10.3390/cells8060540] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 05/31/2019] [Accepted: 06/03/2019] [Indexed: 12/19/2022] Open
Abstract
Fibroblast growth factor (FGF)/Fibroblast growth factor receptor (FGFR) signaling regulates various cellular processes during the embryonic development and in the adult organism. In the skin, fibroblasts and keratinocytes control proliferation and survival of melanocytes in a paracrine manner via several signaling molecules, including FGFs. FGF/FGFR signaling contributes to the skin surface expansion in childhood or during wound healing, and skin protection from UV light damage. Aberrant FGF/FGFR signaling has been implicated in many disorders, including cancer. In melanoma cells, the FGFR expression is low, probably because of the strong endogenous mutation-driven constitutive activation of the downstream mitogen-activated protein kinase-extracellular signal-regulated kinase (MAPK-ERK) signaling pathway. FGFR1 is exceptional as it is expressed in the majority of melanomas at a high level. Melanoma cells that acquired the capacity to synthesize FGFs can influence the neighboring cells in the tumor niche, such as endothelial cells, fibroblasts, or other melanoma cells. In this way, FGF/FGFR signaling contributes to intratumoral angiogenesis, melanoma cell survival, and development of resistance to therapeutics. Therefore, inhibitors of aberrant FGF/FGFR signaling are considered as drugs in combination treatment. The ongoing LOGIC-2 phase II clinical trial aims to find out whether targeting the FGF/FGFR signaling pathway with BGJ398 may be a good therapeutic strategy in melanoma patients who develop resistance to v-Raf murine sarcoma viral oncogene homolog B (BRAF)/MEK inhibitors.
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14
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Ntritsos G, Dimou N, Kypreou K, Stefanaki I, Loizidou MA, Hadjisavvas A, Kyriacou K, MacGregor S, Law MH, Iles MM, Stratigos AJ, Evangelou E. Assessment of melanoma candidate genes in a meta-analysis of 16 534 melanoma cases. J Eur Acad Dermatol Venereol 2019; 33:e369-e370. [PMID: 31071243 DOI: 10.1111/jdv.15662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- G Ntritsos
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - N Dimou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - K Kypreou
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens School of Medicine, Athens, Greece
| | - I Stefanaki
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens School of Medicine, Athens, Greece
| | - M A Loizidou
- Department of EM/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - A Hadjisavvas
- Department of EM/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - K Kyriacou
- Department of EM/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus.,Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | | | - S MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - M H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - M M Iles
- Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds, UK
| | - A J Stratigos
- 1st Department of Dermatology-Venereology, Andreas Sygros Hospital, National and Kapodistrian University of Athens School of Medicine, Athens, Greece
| | - E Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.,Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, UK
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15
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Shi Z, Yu H, Wu Y, Lin X, Bao Q, Jia H, Perschon C, Duggan D, Helfand BT, Zheng SL, Xu J. Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts. Cancer Med 2019; 8:3196-3205. [PMID: 30968590 PMCID: PMC6558466 DOI: 10.1002/cam4.2143] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/01/2019] [Accepted: 03/18/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers. METHODS Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts. RESULTS Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P-trend < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers. CONCLUSION Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.
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Affiliation(s)
- Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.,State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Hongjie Yu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Yishuo Wu
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoling Lin
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Quanwa Bao
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Haifei Jia
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chelsea Perschon
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - David Duggan
- Translational Genomics Research Institute, Phoenix, Arizona
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Siqun L Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.,State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
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16
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Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts. Sci Rep 2019; 9:3266. [PMID: 30824863 PMCID: PMC6397233 DOI: 10.1038/s41598-019-39796-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 01/31/2019] [Indexed: 12/12/2022] Open
Abstract
Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are being detected in research laboratories as well as in the health sector. Motivated by this overabundance of VUS, we propose a novel computational methodology, termed VariantClassifier (VarClass), which utilizes gene-association networks and polygenic risk prediction models to shed light into this grey area of genetic variation in association with disease. VarClass has been evaluated using numerous validation steps and proves to be very successful in assigning significance to VUS in association with specific diseases of interest. Notably, using VUS that are deemed significant by VarClass, we improved risk prediction accuracy in four large case-studies involving disease-control cohorts from GWAS as well as WES, when compared to traditional odds ratio analysis. Biological interpretation of selected high scoring VUS revealed interesting biological themes relevant to the diseases under investigation. VarClass is available as a standalone tool for large-scale data analyses, as well as a web-server with additional functionalities through a user-friendly graphical interface.
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17
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Gu F, Chen TH, Pfeiffer RM, Fargnoli MC, Calista D, Ghiorzo P, Peris K, Puig S, Menin C, De Nicolo A, Rodolfo M, Pellegrini C, Pastorino L, Evangelou E, Zhang T, Hua X, DellaValle CT, Timothy Bishop D, MacGregor S, Iles MI, Law MH, Cust A, Brown KM, Stratigos AJ, Nagore E, Chanock S, Shi J, Consortium MMA, Consortium M, Landi MT. Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma. Hum Mol Genet 2018; 27:4145-4156. [PMID: 30060076 PMCID: PMC6240742 DOI: 10.1093/hmg/ddy282] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 06/14/2018] [Accepted: 07/24/2018] [Indexed: 02/04/2023] Open
Abstract
Melanoma heritability is among the highest for cancer and single nucleotide polymorphisms (SNPs) contribute to it. To date, only SNPs that reached statistical significance in genome-wide association studies or few candidate SNPs have been included in melanoma risk prediction models. We compared four approaches for building polygenic risk scores (PRS) using 12 874 melanoma cases and 23 203 controls from Melanoma Meta-Analysis Consortium as a training set, and newly genotyped 3102 cases and 2301 controls from the MelaNostrum consortium for validation. We estimated adjusted odds ratios (ORs) for melanoma risk using traditional melanoma risk factors and the PRS with the largest area under the receiver operator characteristics curve (AUC). We estimated absolute risks combining the PRS and other risk factors, with age- and sex-specific melanoma incidence and competing mortality rates from Italy as an example. The best PRS, including 204 SNPs (AUC = 64.4%; 95% confidence interval (CI) = 63-65.8%), developed using winner's curse estimate corrections, had a per-quintile OR = 1.35 (95% CI = 1.30-1.41), corresponding to a 3.33-fold increase comparing the 5th to the 1st PRS quintile. The AUC improvement by adding the PRS was up to 7%, depending on adjusted factors and country. The 20-year absolute risk estimates based on the PRS, nevus count and pigmentation characteristics for a 60-year-old Italian man ranged from 0.5 to 11.8% (relative risk = 26.34), indicating good separation.
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Affiliation(s)
- Fangyi Gu
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ting-Huei Chen
- Department of Mathematics and Statistics, Laval University, Quebec, Canada
| | - Ruth M Pfeiffer
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Donato Calista
- Department of Dermatology, Maurizio Bufalini Hospital, Cesena, Italy
| | - Paola Ghiorzo
- Department of Internal Medicine and Medical Specialties, University of Genoa and Genetics of Rare Cancers, Ospedale Policlinico San Martino, Genoa, Italy
| | - Ketty Peris
- Institute of Dermatology, Catholic University, Rome, Italy
| | - Susana Puig
- Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Valencia, Spain
| | - Chiara Menin
- Department of Immunology and Molecular Oncology, Veneto Institute of Oncology IOV–IRCCS, Padua, Italy
| | - Arcangela De Nicolo
- Cancer Genomics Program, Veneto Institute of Oncology IOV–IRCCS, Padua, Italy
| | - Monica Rodolfo
- Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Lorenza Pastorino
- Department of Internal Medicine and Medical Specialties, University of Genoa and Genetics of Rare Cancers, Ospedale Policlinico San Martino, Genoa, Italy
| | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Curt T DellaValle
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - D Timothy Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, UK
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mark I Iles
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, UK
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Anne Cust
- Sydney School of Public Health, and Melanoma Institute Australia, The University of Sydney, Sydney, Australia
| | - Kevin M Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alexander J Stratigos
- 1 Department of Dermatology–Venereology, National and Kapodistrian University of Athens School of Medicine, Andreas Sygros Hospital, Athens, Greece
| | - Eduardo Nagore
- Department of Dermatology, Instituto Valenciano de Oncología, València, Spain
| | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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18
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Cust AE, Drummond M, Kanetsky PA, Goldstein AM, Barrett JH, MacGregor S, Law MH, Iles MM, Bui M, Hopper JL, Brossard M, Demenais F, Taylor JC, Hoggart C, Brown KM, Landi MT, Newton-Bishop JA, Mann GJ, Bishop DT. Assessing the Incremental Contribution of Common Genomic Variants to Melanoma Risk Prediction in Two Population-Based Studies. J Invest Dermatol 2018; 138:2617-2624. [PMID: 29890168 PMCID: PMC6249137 DOI: 10.1016/j.jid.2018.05.023] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 05/29/2018] [Accepted: 05/30/2018] [Indexed: 01/02/2023]
Abstract
It is unclear to what degree genomic and traditional (phenotypic and environmental) risk factors overlap in their prediction of melanoma risk. We evaluated the incremental contribution of common genomic variants (in pigmentation, nevus, and other pathways) and their overlap with traditional risk factors, using data from two population-based case-control studies from Australia (n = 1,035) and the United Kingdom (n = 1,460) that used the same questionnaires. Polygenic risk scores were derived from 21 gene regions associated with melanoma and odds ratios from published meta-analyses. Logistic regression models were adjusted for age, sex, center, and ancestry. Adding the polygenic risk score to a model with traditional risk factors increased the area under the receiver operating characteristic curve (AUC) by 2.3% (P = 0.003) for Australia and by 2.8% (P = 0.002) for Leeds. Gene variants in the pigmentation pathway, particularly MC1R, were responsible for most of the incremental improvement. In a cross-tabulation of polygenic by traditional tertile risk scores, 59% (Australia) and 49% (Leeds) of participants were categorized in the same (concordant) tertile. Of participants with low traditional risk, 9% (Australia) and 21% (Leeds) had high polygenic risk. Testing of genomic variants can identify people who are susceptible to melanoma despite not having a traditional phenotypic risk profile.
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Affiliation(s)
- Anne E Cust
- Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, Sydney, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
| | - Martin Drummond
- Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, Sydney, Australia; Melanoma Institute Australia, The University of Sydney, Sydney, Australia
| | - Peter A Kanetsky
- Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Alisa M Goldstein
- Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Jennifer H Barrett
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Stuart MacGregor
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Matthew H Law
- Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Mark M Iles
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, University of Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population Health, University of Melbourne, Australia
| | - Myriam Brossard
- INSERM, UMR 946, Genetic Variation and Human Diseases Unit, Paris, France; Institut Universitaire d'Hématologie, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Florence Demenais
- INSERM, UMR 946, Genetic Variation and Human Diseases Unit, Paris, France; Institut Universitaire d'Hématologie, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - John C Taylor
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Clive Hoggart
- Section of Paediatrics, Division of Infectious Diseases, Department of Medicine, Imperial College London, London, UK
| | - Kevin M Brown
- Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Maria Teresa Landi
- Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Julia A Newton-Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Graham J Mann
- Melanoma Institute Australia, The University of Sydney, Sydney, Australia; Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia
| | - D Timothy Bishop
- Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
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19
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Karagianni F, Njauw CN, Kypreou KP, Stergiopoulou A, Plaka M, Polydorou D, Chasapi V, Pappas L, Stratigos IA, Champsas G, Panagiotou P, Gogas H, Evangelou E, Tsao H, Stratigos AJ, Stefanaki I. CDKN2A/CDK4 Status in Greek Patients with Familial Melanoma and Association with Clinico-epidemiological Parameters. Acta Derm Venereol 2018; 98:862-866. [PMID: 29774366 PMCID: PMC6572781 DOI: 10.2340/00015555-2969] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Approximately 5–10% of melanoma cases occur in a familial context. CDKN2A/CDK4 were the first high- penetrance melanoma genes identified. The aims of this study were to evaluate CDKN2A/CDK4 variants in Greek familial melanoma patients and to correlate the mutational status with specific clinico-epidemiological characteristics. A cross-sectional study was conducted by genotyping CDKN2A/CDK4 variants and selected MC1R polymorphisms in 52 melanoma-prone families. Descriptive statistics were calculated and comparisons were made using the X2 test, Fisher’s exact test and Student’s t-test for statistical analysis, as appropriate. CDKN2A variants were detected in 46.2% of melanoma-prone families, while a CDK4 variant was found in only one family. This study confirmed that, in the Greek population, the age at melanoma diagnosis was lower in patients carrying a variant in CDKN2A compared with wild-type patients. No statistically significant associations were found between CDKN2A mutational status and MC1R polymorphisms.
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Affiliation(s)
- Fani Karagianni
- 1st Department of Dermatology, Andreas Sygros Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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Wang N, Wang Z, Wang C, Fu X, Yu G, Yue Z, Liu T, Zhang H, Li L, Chen M, Wang H, Niu G, Liu D, Zhang M, Xu Y, Zhang Y, Li J, Li Z, You J, Chu T, Li F, Liu D, Liu H, Zhang F. Prediction of leprosy in the Chinese population based on a weighted genetic risk score. PLoS Negl Trop Dis 2018; 12:e0006789. [PMID: 30231057 PMCID: PMC6166985 DOI: 10.1371/journal.pntd.0006789] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 10/01/2018] [Accepted: 08/26/2018] [Indexed: 01/03/2023] Open
Abstract
Genome wide association studies (GWASs) have revealed multiple genetic variants associated with leprosy in the Chinese population. The aim of our study was to utilize the genetic variants to construct a risk prediction model through a weighted genetic risk score (GRS) in a Chinese set and to further assess the performance of the model in identifying higher-risk contact individuals in an independent set. The highest prediction accuracy, with an area under the curve (AUC) of 0.743 (95% confidence interval (CI): 0.729-0.757), was achieved with a GRS encompassing 25 GWAS variants in a discovery set that included 2,144 people affected by leprosy and 2,671 controls. Individuals in the high-risk group, based on genetic factors (GRS > 28.06), have a 24.65 higher odds ratio (OR) for developing leprosy relative to those in the low-risk group (GRS≤18.17). The model was then applied to a validation set consisting of 1,385 people affected by leprosy and 7,541 individuals in contact with leprosy, which yielded a discriminatory ability with an AUC of 0.707 (95% CI: 0.691-0.723). When a GRS cut-off value of 22.38 was selected with the optimal sensitivity and specificity, it was found that 39.31% of high risk contact individuals should be screened in order to detect leprosy in 64.9% of those people affected by leprosy. In summary, we developed and validated a risk model for the prediction of leprosy that showed good discrimination capabilities, which may help physicians in the identification of patients coming into contact with leprosy and are at a higher-risk of developing this condition.
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Affiliation(s)
- Na Wang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Zhenzhen Wang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Chuan Wang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Xi'an Fu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Gongqi Yu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Zhenhua Yue
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Tingting Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Huimin Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Lulu Li
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Mingfei Chen
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Honglei Wang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Guiye Niu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Dan Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Mingkai Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Yuanyuan Xu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Yan Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
| | - Jinghui Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Zhen Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Jiabao You
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Tongsheng Chu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Furong Li
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Dianchang Liu
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
| | - Hong Liu
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
- * E-mail: (HL); (FZ)
| | - Furen Zhang
- Shandong Provincial Hospital for Skin Diseases, Shandong University, Jinan, Shandong, China
- Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Sciences, Jinan, Shandong, China
- Shandong Provincial Key Lab for Dermatovenereology, Jinan, Shandong, China
- Shandong Provincial Medical Center for Dermatovenereology, Jinan, Shandong, China
- * E-mail: (HL); (FZ)
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Smit AK, Newson AJ, Morton RL, Kimlin M, Keogh L, Law MH, Kirk J, Dobbinson S, Kanetsky PA, Fenton G, Allen M, Butow P, Dunlop K, Trevena L, Lo S, Savard J, Dawkins H, Wordsworth S, Jenkins M, Mann GJ, Cust AE. The melanoma genomics managing your risk study: A protocol for a randomized controlled trial evaluating the impact of personal genomic risk information on skin cancer prevention behaviors. Contemp Clin Trials 2018; 70:106-116. [PMID: 29802966 DOI: 10.1016/j.cct.2018.05.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/17/2018] [Accepted: 05/22/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Reducing ultraviolet radiation (UV) exposure and improving early detection may reduce melanoma incidence, mortality and health system costs. This study aims to evaluate the efficacy and cost-effectiveness of providing information on personal genomic risk of melanoma in reducing UV exposure at 12 months, according to low and high traditional risk. METHODS In this randomized controlled trial, participants (target sample = 892) will be recruited from the general population, and randomized (1:1 ratio, intervention versus control). Intervention arm participants provide a saliva sample, receive personalized melanoma genomic risk information, a genetic counselor phone call, and an educational booklet on melanoma prevention. Control arm participants receive only the educational booklet. Eligible participants are aged 18-69 years, have European ancestry and no personal history of melanoma. All participants will complete a questionnaire and wear a UV dosimeter to objectively measure their sun exposure at baseline, 1- and 12-month time-points, except 1-month UV dosimetry will be limited to ~250 participants. The primary outcome is total daily Standard Erythemal Doses at 12 months. Secondary outcomes include objectively measured UV exposure for specific time periods (e.g. midday hours), self-reported sun protection and skin-examination behaviors, psycho-social outcomes, and ethical considerations surrounding offering genomic testing at a population level. A within-trial and modelled economic evaluation will be undertaken from an Australian health system perspective to assess the intervention costs and outcomes. DISCUSSION This trial will inform the clinical and personal utility of introducing genomic testing into the health system for melanoma prevention and early detection at a population-level. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ACTRN12617000691347.
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Affiliation(s)
- Amelia K Smit
- Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia; Sydney Health Ethics, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia; Melanoma Institute Australia, The University of Sydney, NSW 2006, Australia.
| | - Ainsley J Newson
- Sydney Health Ethics, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia
| | - Rachael L Morton
- NHMRC Clinical Trials Centre, The University of Sydney, NSW 2006, Australia
| | - Michael Kimlin
- University of the Sunshine Coast and Cancer Council Queensland, PO Box 201, Spring Hill, QLD 4004, Australia
| | - Louise Keogh
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Locked Bag 2000, Brisbane, QLD 4029, Australia
| | - Judy Kirk
- Westmead Clinical School and Westmead Institute for Medical Research, Sydney Medical School, The University of Sydney, NSW 2006, Australia
| | - Suzanne Dobbinson
- Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
| | - Peter A Kanetsky
- H. Lee Moffitt Cancer Center and Research Institute and University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA
| | - Georgina Fenton
- Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia
| | - Martin Allen
- Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Phyllis Butow
- Centre for Medical Psychology and Evidence-based Decision-making, School of Psychology, The University of Sydney, NSW 2006, Australia
| | - Kate Dunlop
- The Centre for Genetics Education, NSW Health, Level 5 2c Herbert Street St Leonards, NSW 2065, Australia
| | - Lyndal Trevena
- Sydney School of Public Health, The University of Sydney, NSW 2006, Australia
| | - Serigne Lo
- Melanoma Institute Australia, The University of Sydney, NSW 2006, Australia
| | - Jacqueline Savard
- Sydney Health Ethics, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia
| | - Hugh Dawkins
- Office of Population Health Genomics, Public Health Division, Government of Western Australia, Level 3 C Block 189 Royal Street, East Perth, WA 6004, Australia
| | - Sarah Wordsworth
- Health Economics Research Centre, The University of Oxford, Oxford OX1 2JD, UK
| | - Mark Jenkins
- Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Graham J Mann
- Melanoma Institute Australia, The University of Sydney, NSW 2006, Australia; Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, NSW 2006, Australia
| | - Anne E Cust
- Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, NSW 2006, Australia; Melanoma Institute Australia, The University of Sydney, NSW 2006, Australia
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Dissecting the Mutational Landscape of Cutaneous Melanoma: An Omic Analysis Based on Patients from Greece. Cancers (Basel) 2018; 10:cancers10040096. [PMID: 29596374 PMCID: PMC5923351 DOI: 10.3390/cancers10040096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 03/20/2018] [Accepted: 03/27/2018] [Indexed: 12/21/2022] Open
Abstract
Melanoma is a lethal type of skin cancer, unless it is diagnosed early. Formalin-fixed, paraffin-embedded (FFPE) tissue is a valuable source for molecular assays after diagnostic examination, but isolated nucleic acids often suffer from degradation. Here, for the first time, we examine primary melanomas from Greek patients, using whole exome sequencing, so as to derive their mutational profile. Application of a bioinformatic framework revealed a total of 10,030 somatic mutations. Regarding the genes containing putative protein-altering mutations, 73 were common in at least three patients. Sixty-five of these 73 top common genes have been previously identified in melanoma cases. Biological processes related to melanoma were affected by varied genes in each patient, suggesting differences in the components of a pathway possibly contributing to pathogenesis. We performed a multi-level analysis highlighting a short list of candidate genes with a probable causative role in melanoma.
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23
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Cho HG, Ransohoff KJ, Yang L, Hedlin H, Assimes T, Han J, Stefanick M, Tang JY, Sarin KY. Melanoma risk prediction using a multilocus genetic risk score in the Women's Health Initiative cohort. J Am Acad Dermatol 2018; 79:36-41.e10. [PMID: 29499294 DOI: 10.1016/j.jaad.2018.02.052] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 01/29/2018] [Accepted: 02/11/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Single-nucleotide polymorphisms (SNPs) associated with melanoma have been identified though genome-wide association studies. However, the combined impact of these SNPs on melanoma development remains unclear, particularly in postmenopausal women who carry a lower melanoma risk. OBJECTIVE We examine the contribution of a combined polygenic risk score on melanoma development in postmenopausal women. METHODS Genetic risk scores were calculated using 21 genome-wide association study-significant SNPs. Their combined effect on melanoma development was evaluated in 19,102 postmenopausal white women in the clinical trial and observational study arms of the Women's Health Initiative dataset. RESULTS Compared to the tertile of weighted genetic risk score with the lowest genetic risk, the women in the tertile with the highest genetic risk were 1.9 times more likely to develop melanoma (95% confidence interval 1.50-2.42). The incremental change in c-index from adding genetic risk scores to age were 0.075 (95% confidence interval 0.041-0.109) for incident melanoma. LIMITATIONS Limitations include a lack of information on nevi count, Fitzpatrick skin type, family history of melanoma, and potential reporting and selection bias in the Women's Health Initiative cohort. CONCLUSION Higher genetic risk is associated with increased melanoma prevalence and incidence in postmenopausal women, but current genetic information may have a limited role in risk prediction when phenotypic information is available.
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Affiliation(s)
- Hyunje G Cho
- Department of Dermatology, Stanford University School of Medicine, Stanford, California
| | - Katherine J Ransohoff
- Department of Dermatology, Stanford University School of Medicine, Stanford, California
| | - Lingyao Yang
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
| | - Haley Hedlin
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, California
| | - Themistocles Assimes
- Department of Medicine (Cardiovascular), Stanford University School of Medicine, Stanford, California
| | - Jiali Han
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana
| | - Marcia Stefanick
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Jean Y Tang
- Department of Dermatology, Stanford University School of Medicine, Stanford, California
| | - Kavita Y Sarin
- Department of Dermatology, Stanford University School of Medicine, Stanford, California.
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Evangelou E, Stratigos AJ. Lessons from genome-wide studies of melanoma: towards precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016. [DOI: 10.1080/23808993.2016.1240586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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