MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project

E. Tagliabue, S. Gandini, R. Bellocco, P. Maisonneuve, J. Newton-Bishop, D. Polsky, D. Lazovich, P.A. Kanetsky, P. Ghiorzo, N.A. Gruis, M.T. Landi, C. Menin, M.C. Fargnoli, J.C. García-Borrón, J. Han, J. Little, F. Sera, S. Raimondi, M-SKIP Study Group

Research output: Contribution to journalArticle

Abstract

Purpose: Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics. Materials and methods: Data were collected within an international collaboration – the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case–control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype. Results: The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36–1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7% over a base clinical model (P=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%–30%). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%). Conclusion: The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype. © 2018 Tagliabue et al.
Original languageEnglish
Pages (from-to)1143-1154
Number of pages12
JournalCancer Management and Research
Volume10
DOIs
Publication statusPublished - 2018

Fingerprint

Melanoma
Hair
Genotype
Hair Color
Phenotype
Melanosis
Skin
ROC Curve
Early Diagnosis
Public Health
Mortality
Genes

Keywords

  • Cutaneous melanoma
  • Genetic epidemiology
  • Melanocortin 1 receptor
  • Pigmentation
  • Pooled analysis
  • melanocortin 1 receptor
  • Article
  • cancer growth
  • cancer risk
  • case control study
  • controlled study
  • cutaneous melanoma
  • diagnostic accuracy
  • diagnostic test accuracy study
  • disease association
  • disease classification
  • disease course
  • female
  • gene sequence
  • genetic variability
  • genotype
  • hair color
  • human
  • lentigo
  • major clinical study
  • male
  • MC1R gene
  • melanoma
  • phenotype
  • prediction
  • skin appendage

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MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project. / Tagliabue, E.; Gandini, S.; Bellocco, R.; Maisonneuve, P.; Newton-Bishop, J.; Polsky, D.; Lazovich, D.; Kanetsky, P.A.; Ghiorzo, P.; Gruis, N.A.; Landi, M.T.; Menin, C.; Fargnoli, M.C.; García-Borrón, J.C.; Han, J.; Little, J.; Sera, F.; Raimondi, S.; Group, M-SKIP Study.

In: Cancer Management and Research, Vol. 10, 2018, p. 1143-1154.

Research output: Contribution to journalArticle

Tagliabue, E, Gandini, S, Bellocco, R, Maisonneuve, P, Newton-Bishop, J, Polsky, D, Lazovich, D, Kanetsky, PA, Ghiorzo, P, Gruis, NA, Landi, MT, Menin, C, Fargnoli, MC, García-Borrón, JC, Han, J, Little, J, Sera, F, Raimondi, S & Group, M-SKIPS 2018, 'MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project', Cancer Management and Research, vol. 10, pp. 1143-1154. https://doi.org/10.2147/CMAR.S155283
Tagliabue, E. ; Gandini, S. ; Bellocco, R. ; Maisonneuve, P. ; Newton-Bishop, J. ; Polsky, D. ; Lazovich, D. ; Kanetsky, P.A. ; Ghiorzo, P. ; Gruis, N.A. ; Landi, M.T. ; Menin, C. ; Fargnoli, M.C. ; García-Borrón, J.C. ; Han, J. ; Little, J. ; Sera, F. ; Raimondi, S. ; Group, M-SKIP Study. / MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project. In: Cancer Management and Research. 2018 ; Vol. 10. pp. 1143-1154.
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title = "MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project",
abstract = "Purpose: Melanoma represents an important public health problem, due to its high case-fatality rate. Identification of individuals at high risk would be of major interest to improve early diagnosis and ultimately survival. The aim of this study was to evaluate whether MC1R variants predicted melanoma risk independently of at-risk phenotypic characteristics. Materials and methods: Data were collected within an international collaboration – the M-SKIP project. The present pooled analysis included data on 3,830 single, primary, sporadic, cutaneous melanoma cases and 2,619 controls from seven previously published case–control studies. All the studies had information on MC1R gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype. Results: The presence of any MC1R variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95{\%} CI 1.36–1.88). Inclusion of MC1R variants in a risk prediction model increased melanoma predictive accuracy (area under the receiver-operating characteristic curve) by 0.7{\%} over a base clinical model (P=0.002), and 24{\%} of participants were better assessed (net reclassification index 95{\%} CI 20{\%}–30{\%}). Subgroup analysis suggested a possibly stronger role of MC1R in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28{\%}) compared to paler skinned participants (15{\%}). Conclusion: The authors suggest that measuring the MC1R genotype might result in a benefit for melanoma prediction. The results could be a valid starting point to guide the development of scientific protocols assessing melanoma risk prediction tools incorporating the MC1R genotype. {\circledC} 2018 Tagliabue et al.",
keywords = "Cutaneous melanoma, Genetic epidemiology, Melanocortin 1 receptor, Pigmentation, Pooled analysis, melanocortin 1 receptor, Article, cancer growth, cancer risk, case control study, controlled study, cutaneous melanoma, diagnostic accuracy, diagnostic test accuracy study, disease association, disease classification, disease course, female, gene sequence, genetic variability, genotype, hair color, human, lentigo, major clinical study, male, MC1R gene, melanoma, phenotype, prediction, skin appendage",
author = "E. Tagliabue and S. Gandini and R. Bellocco and P. Maisonneuve and J. Newton-Bishop and D. Polsky and D. Lazovich and P.A. Kanetsky and P. Ghiorzo and N.A. Gruis and M.T. Landi and C. Menin and M.C. Fargnoli and J.C. Garc{\'i}a-Borr{\'o}n and J. Han and J. Little and F. Sera and S. Raimondi and Group, {M-SKIP Study}",
note = "Export Date: 5 February 2019 Correspondence Address: Raimondi, S.; Division of Epidemiology and Biostatistics, European Institute of Oncology, 16 Via Adamello, Italy; email: sara.raimondi@ieo.it Chemicals/CAS: melanocortin 1 receptor, 234764-00-2, 234764-02-4 Funding details: American Ornithologists' Union, AOU Funding details: National Cancer Institute, NCI Funding details: National Institutes of Health, NIH Funding details: Universit{\`a} degli Studi di Milano-Bicocca, 5Section Funding details: Karolinska Institutet, KI Funding details: University of Leeds Funding details: Universit{\`a} degli Studi di Genova Funding details: New York University, NYU Funding details: University of Ottawa, U of O Funding details: Leids Universitair Medisch Centrum, LUMC Funding details: 2Division Funding details: Universidad de Murcia Funding details: University of Minnesota, UM Funding details: School of Medicine, New York University, NYUSM Funding details: Indiana University, IU Funding details: Associazione Italiana per la Ricerca sul Cancro, AIRC, MFAG 11831 Funding details: National Cancer Institute, NCI, CA75434 Funding details: National Cancer Institute, NCI, CA80700 Funding details: National Cancer Institute, NCI, CA092428 Funding details: Associazione Italiana per la Ricerca sul Cancro, AIRC, IG 15460 Funding details: Menzies Research Institute Tasmania Funding details: University of California, Irvine, UCI Funding details: University of North Carolina, UNC Funding details: University of New South Wales, UNSW Funding details: University of Edinburgh Funding details: University of New Mexico, UNM Funding details: University of South Alabama, USA Funding details: State of New Jersey Department of Health and Senior Services, NJDOH Funding details: Memorial Sloan-Kettering Cancer Center, MSKCC Funding details: University of Pennsylvania, Penn Funding details: Cancer Care Ontario, CCO Funding details: Brookhaven National Laboratory, BNL Funding details: University of Tasmania, UTAS Funding details: University of Michigan, U-M Funding text 1: 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 Funding text 2: This work was supported by the Italian Association for Cancer Research (grant MFAG 11831). The Melanoma Susceptibility Study (PAK) was supported by the National Cancer Institute (CA75434, CA80700, CA092428). The Genoa study (PG) was supported by AIRC IG 15460. The M-SKIP study group consists of the following members: principal investigator (PI), Sara Raimondi (European Institute of Oncology, Milan, Italy); advisory committee members, Philippe Autier (International Prevention Research Institute, Lyon, France), Maria Concetta Fargnoli (University of L’Aquila, Italy), Jos{\'e} C Garc{\'i}a-Borr{\'o}n (University of Murcia, Spain), Jiali Han (Indiana University, Indianapolis, IN, USA), Peter A Kanetsky (Department of Cancer Epidemiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA), Maria Teresa Landi (National Funding text 3: Hospital, Oslo, Norway), Gabriella Guida, Stefania Guida (University of Bari, Bari, Italy), Terence H Wong (University of Edinburgh, Edinburgh, UK), and the GEM study group. Participants in the GEM study group are as follows: coordinating center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Marianne Berwick (PI, currently at the University of New Mexico), Colin Begg (co-PI), Irene Orlow (coinvestigator), Urvi Mujumdar (project coordinator), Amanda Hummer (biostatistician), Klaus Busam (dermato-pathologist), Pampa Roy (laboratory technician), Rebecca Canchola (laboratory technician), Brian Clas (laboratory technician), Javiar Cotignola (laboratory technician), and Yvette Monroe (interviewer); study centers; University of Sydney and Cancer Council New South Wales, Sydney (Australia), Bruce Armstrong (PI), Anne Kricker (co-PI), Melisa Litchfield (study coordinator); Menzies Institute for Medical Research, University of Tasmania, Hobart (Australia), Terence Dwyer (PI), Paul Tucker (dermatopathologist), Nicola Stephens (study coordinator); British Columbia Cancer Agency, Vancouver, BC (Canada), Richard Gallagher (PI), Teresa Switzer (coordinator), Cancer Care Ontario, Toronto, ON (Canada), Loraine Marrett (PI), Beth Theis (coinvestigator), Lynn From (dermatopathologist), Noori Chowdhury (coordinator), Louise Vanasse (coordinator), Mark Purdue (research officer), David Northrup (manager for CATI), Centro per la Prevenzione Oncologia Torino, Piedmont (Italy), Roberto Zanetti (PI), Stefano Rosso (data manager), Carlotta Sacerdote (coordinator); University of California, Irvine, CA (USA), Hoda Anton-Culver (PI), Nancy Leighton (coordinator), Maureen Gildea (data manager); University of Michigan, Ann Arbor, MI (USA), Stephen Gruber (PI), Joe Bonner (data manager), Joanne Jeter (Coordinator); New Jersey Department of Health and Senior Services, Trenton, NJ (USA), Judith Klotz (PI), Homer Wilcox (co-PI), Helen Weiss (coordinator); University of North Carolina, Chapel Hill, NC (USA), Robert Millikan (PI), Nancy Thomas (coinvestigator), Dianne Mattingly (coordinator), Jon Player (laboratory technician), Chiu-Kit Tse (data analyst); University of Pennsylvania, Philadelphia, PA (USA), Timothy Rebbeck (PI), Peter Kanetsky (coinvestigator), Amy Walker (laboratory technician), Saarene Panossian (laboratory technician); consultants, Harvey Mohrenweiser, University of California, Irvine, Irvine, CA (USA); Richard Setlow, Brookhaven National Laboratory, Upton, NY (USA). 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year = "2018",
doi = "10.2147/CMAR.S155283",
language = "English",
volume = "10",
pages = "1143--1154",
journal = "Cancer Management and Research",
issn = "1179-1322",
publisher = "Dove Medical Press Ltd.",

}

TY - JOUR

T1 - MC1R variants as melanoma risk factors independent of at-risk phenotypic characteristics: A pooled analysis from the M-SKIP project

AU - Tagliabue, E.

AU - Gandini, S.

AU - Bellocco, R.

AU - Maisonneuve, P.

AU - Newton-Bishop, J.

AU - Polsky, D.

AU - Lazovich, D.

AU - Kanetsky, P.A.

AU - Ghiorzo, P.

AU - Gruis, N.A.

AU - Landi, M.T.

AU - Menin, C.

AU - Fargnoli, M.C.

AU - García-Borrón, J.C.

AU - Han, J.

AU - Little, J.

AU - Sera, F.

AU - Raimondi, S.

AU - Group, M-SKIP Study

N1 - Export Date: 5 February 2019 Correspondence Address: Raimondi, S.; Division of Epidemiology and Biostatistics, European Institute of Oncology, 16 Via Adamello, Italy; email: sara.raimondi@ieo.it Chemicals/CAS: melanocortin 1 receptor, 234764-00-2, 234764-02-4 Funding details: American Ornithologists' Union, AOU Funding details: National Cancer Institute, NCI Funding details: National Institutes of Health, NIH Funding details: Università degli Studi di Milano-Bicocca, 5Section Funding details: Karolinska Institutet, KI Funding details: University of Leeds Funding details: Università degli Studi di Genova Funding details: New York University, NYU Funding details: University of Ottawa, U of O Funding details: Leids Universitair Medisch Centrum, LUMC Funding details: 2Division Funding details: Universidad de Murcia Funding details: University of Minnesota, UM Funding details: School of Medicine, New York University, NYUSM Funding details: Indiana University, IU Funding details: Associazione Italiana per la Ricerca sul Cancro, AIRC, MFAG 11831 Funding details: National Cancer Institute, NCI, CA75434 Funding details: National Cancer Institute, NCI, CA80700 Funding details: National Cancer Institute, NCI, CA092428 Funding details: Associazione Italiana per la Ricerca sul Cancro, AIRC, IG 15460 Funding details: Menzies Research Institute Tasmania Funding details: University of California, Irvine, UCI Funding details: University of North Carolina, UNC Funding details: University of New South Wales, UNSW Funding details: University of Edinburgh Funding details: University of New Mexico, UNM Funding details: University of South Alabama, USA Funding details: State of New Jersey Department of Health and Senior Services, NJDOH Funding details: Memorial Sloan-Kettering Cancer Center, MSKCC Funding details: University of Pennsylvania, Penn Funding details: Cancer Care Ontario, CCO Funding details: Brookhaven National Laboratory, BNL Funding details: University of Tasmania, UTAS Funding details: University of Michigan, U-M Funding text 1: 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 Funding text 2: This work was supported by the Italian Association for Cancer Research (grant MFAG 11831). The Melanoma Susceptibility Study (PAK) was supported by the National Cancer Institute (CA75434, CA80700, CA092428). The Genoa study (PG) was supported by AIRC IG 15460. The M-SKIP study group consists of the following members: principal investigator (PI), Sara Raimondi (European Institute of Oncology, Milan, Italy); advisory committee members, Philippe Autier (International Prevention Research Institute, Lyon, France), Maria Concetta Fargnoli (University of L’Aquila, Italy), José C García-Borrón (University of Murcia, Spain), Jiali Han (Indiana University, Indianapolis, IN, USA), Peter A Kanetsky (Department of Cancer Epidemiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA), Maria Teresa Landi (National Funding text 3: Hospital, Oslo, Norway), Gabriella Guida, Stefania Guida (University of Bari, Bari, Italy), Terence H Wong (University of Edinburgh, Edinburgh, UK), and the GEM study group. Participants in the GEM study group are as follows: coordinating center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, Marianne Berwick (PI, currently at the University of New Mexico), Colin Begg (co-PI), Irene Orlow (coinvestigator), Urvi Mujumdar (project coordinator), Amanda Hummer (biostatistician), Klaus Busam (dermato-pathologist), Pampa Roy (laboratory technician), Rebecca Canchola (laboratory technician), Brian Clas (laboratory technician), Javiar Cotignola (laboratory technician), and Yvette Monroe (interviewer); study centers; University of Sydney and Cancer Council New South Wales, Sydney (Australia), Bruce Armstrong (PI), Anne Kricker (co-PI), Melisa Litchfield (study coordinator); Menzies Institute for Medical Research, University of Tasmania, Hobart (Australia), Terence Dwyer (PI), Paul Tucker (dermatopathologist), Nicola Stephens (study coordinator); British Columbia Cancer Agency, Vancouver, BC (Canada), Richard Gallagher (PI), Teresa Switzer (coordinator), Cancer Care Ontario, Toronto, ON (Canada), Loraine Marrett (PI), Beth Theis (coinvestigator), Lynn From (dermatopathologist), Noori Chowdhury (coordinator), Louise Vanasse (coordinator), Mark Purdue (research officer), David Northrup (manager for CATI), Centro per la Prevenzione Oncologia Torino, Piedmont (Italy), Roberto Zanetti (PI), Stefano Rosso (data manager), Carlotta Sacerdote (coordinator); University of California, Irvine, CA (USA), Hoda Anton-Culver (PI), Nancy Leighton (coordinator), Maureen Gildea (data manager); University of Michigan, Ann Arbor, MI (USA), Stephen Gruber (PI), Joe Bonner (data manager), Joanne Jeter (Coordinator); New Jersey Department of Health and Senior Services, Trenton, NJ (USA), Judith Klotz (PI), Homer Wilcox (co-PI), Helen Weiss (coordinator); University of North Carolina, Chapel Hill, NC (USA), Robert Millikan (PI), Nancy Thomas (coinvestigator), Dianne Mattingly (coordinator), Jon Player (laboratory technician), Chiu-Kit Tse (data analyst); University of Pennsylvania, Philadelphia, PA (USA), Timothy Rebbeck (PI), Peter Kanetsky (coinvestigator), Amy Walker (laboratory technician), Saarene Panossian (laboratory technician); consultants, Harvey Mohrenweiser, University of California, Irvine, Irvine, CA (USA); Richard Setlow, Brookhaven National Laboratory, Upton, NY (USA). 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PY - 2018

Y1 - 2018

N2 - 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. © 2018 Tagliabue et al.

AB - 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. © 2018 Tagliabue et al.

KW - Cutaneous melanoma

KW - Genetic epidemiology

KW - Melanocortin 1 receptor

KW - Pigmentation

KW - Pooled analysis

KW - melanocortin 1 receptor

KW - Article

KW - cancer growth

KW - cancer risk

KW - case control study

KW - controlled study

KW - cutaneous melanoma

KW - diagnostic accuracy

KW - diagnostic test accuracy study

KW - disease association

KW - disease classification

KW - disease course

KW - female

KW - gene sequence

KW - genetic variability

KW - genotype

KW - hair color

KW - human

KW - lentigo

KW - major clinical study

KW - male

KW - MC1R gene

KW - melanoma

KW - phenotype

KW - prediction

KW - skin appendage

U2 - 10.2147/CMAR.S155283

DO - 10.2147/CMAR.S155283

M3 - Article

VL - 10

SP - 1143

EP - 1154

JO - Cancer Management and Research

JF - Cancer Management and Research

SN - 1179-1322

ER -