MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: a pooled analysis from the M-SKIP project
Bibliographic record
Abstract
Elena Tagliabue,1 Sara Gandini,2 Rino Bellocco,3,4 Patrick Maisonneuve,2 Julia Newton-Bishop,5 David Polsky,6 DeAnn Lazovich,7 Peter A Kanetsky,8 Paola Ghiorzo,9,10 Nelleke A Gruis,11 Maria Teresa Landi,12 Chiara Menin,13 Maria Concetta Fargnoli,14 Jose Carlos García-Borrón,15,16 Jiali Han,17 Julian Little,18 Francesco Sera,19 Sara Raimondi2 On behalf of the M-SKIP Study Group 1Clinical Trial Center, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori, 2Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy; 3Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; 4Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy; 5Section of Epidemiology and Biostatistics, Institute of Cancer and Pathology, University of Leeds, Leeds, UK; 6Ronald O. Perelman Department of Dermatology, New York University School of Medicine, NYU Langone Medical Center, New York, NY, 7Division of Epidemiology and Community Health, University of Minnesota, MN, 8Department of Cancer Epidemiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA; 9Department of Internal Medicine and Medical Specialties, University of Genoa, 10IRCCS AOU San Martino-IST, Genoa, Italy; 11Department of Dermatology, Leiden University Medical Center, Leiden, the Netherlands; 12Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA; 13Immunology and Molecular Oncology Unit, Veneto Institute of Oncology, IOV-IRCCS, Padua, 14Department of Dermatology, University of L’Aquila, L’Aquila, Italy; 15Department of Biochemistry, Molecular Biology, and Immunology, University of Murcia, 16IMIB-Arrixaca, Murcia, Spain; 17Department of Epidemiology, Richard M Fairbanks School of Public Health, Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, IN, USA; 18School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada; 19Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK 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. Keywords: pooled analysis, genetic epidemiology, cutaneous melanoma, melanocortin 1 receptor, pigmentation
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".