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1. MC1R variants in childhood and adolescent melanoma: a retrospective pooled analysis of a multicentre cohort

Author

Pellegrini, C. Botta, F. Massi, D. Martorelli, C. Facchetti, F. Gandini, S. Maisonneuve, P. Avril, M.-F. Demenais, F. Bressac-de Paillerets, B. Hoiom, V. Cust, A.E. Anton-Culver, H. Gruber, S.B. Gallagher, R.P. Marrett, L. Zanetti, R. Dwyer, T. Thomas, N.E. Begg, C.B. Berwick, M. Puig, S. Potrony, M. Nagore, E. Ghiorzo, P. Menin, C. Manganoni, A.M. Rodolfo, M. Brugnara, S. Passoni, E. Sekulovic, L.K. Baldini, F. Guida, G. Stratigos, A. Ozdemir, F. Ayala, F. Fernandez-de-Misa, R. Quaglino, P. Ribas, G. Romanini, A. Migliano, E. Stanganelli, I. Kanetsky, P.A. Pizzichetta, M.A. García-Borrón, J.C. Nan, H. Landi, M.T. Little, J. Newton-Bishop, J. Sera, F. Fargnoli, M.C. Raimondi, S. Alaibac, M. Ferrari, A. Valeri, B. Sicher, M. Mangiola, D. Nazzaro, G. Tosti, G. Mazzarol, G. Giudice, G. Ribero, S. Astrua, C. Mazzoni, L. Orlow, I. Mujumdar, U. Hummer, A. Busam, K. Roy, P. Canchola, R. Clas, B. Cotignola, J. Monroe, Y. Armstrong, B. Kricker, A. Litchfield, M. Tucker, P. Stephens, N. Switzer, T. Theis, B. From, L. Chowdhury, N. Vanasse, L. Purdue, M. Northrup, D. Rosso, S. Sacerdote, C. Leighton, N. Gildea, M. Bonner, J. Jeter, J. Klotz, J. Wilcox, H. Weiss, H. Millikan, R. Mattingly, D. Player, J. Tse, C.-K. Rebbeck, T. Walker, A. Panossian, S. Setlow, R. Mohrenweiser, H. Autier, P. Han, J. Caini, S. Hofman, A. Kayser, M. Liu, F. Nijsten, T. Uitterlinden, A.G. Kumar, R. Bishop, T. Elliott, F. Lazovich, D. Polsky, D. Hansson, J. Pastorino, L. Gruis, N.A. Bouwes Bavinck, J.N. Aguilera, P. Badenas, C. Carrera, C. Gimenez-Xavier, P. Malvehy, J. Puig-Butille, J.A. Tell-Marti, G. Blizzard, L. Cochrane, J. Branicki, W. Debniak, T. Morling, N. Johansen, P. Mayne, S. Bale, A. Cartmel, B. Ferrucci, L. Pfeiffer, R. Palmieri, G. Kypreou, K. Bowcock, A. Cornelius, L. Council, M.L. Motokawa, T. Anno, S. Helsing, P. Andresen, P.A. Guida, S. Wong, T.H. IMI Study Group GEM Study Group M-SKIP Study Group

Abstract

Background: Germline variants in the melanocortin 1 receptor gene (MC1R) might increase the risk of childhood and adolescent melanoma, but a clear conclusion is challenging because of the low number of studies and cases. We assessed the association of MC1R variants with childhood and adolescent melanoma in a large study comparing the prevalence of MC1R variants in child or adolescent patients with melanoma to that in adult patients with melanoma and in healthy adult controls. Methods: In this retrospective pooled analysis, we used the M-SKIP Project, the Italian Melanoma Intergroup, and other European groups (with participants from Australia, Canada, France, Greece, Italy, the Netherlands, Serbia, Spain, Sweden, Turkey, and the USA) to assemble an international multicentre cohort. We gathered phenotypic and genetic data from children or adolescents diagnosed with sporadic single-primary cutaneous melanoma at age 20 years or younger, adult patients with sporadic single-primary cutaneous melanoma diagnosed at age 35 years or older, and healthy adult individuals as controls. We calculated odds ratios (ORs) for childhood and adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged 18 or younger and 14 years or younger. Findings: We analysed data from 233 young patients, 932 adult patients, and 932 healthy adult controls. Children and adolescents had higher odds of carrying MC1R r variants than did adult patients (OR 1·54, 95% CI 1·02–2·33), including when analysis was restricted to patients aged 18 years or younger (1·80, 1·06–3·07). All investigated variants, except Arg160Trp, tended, to varying degrees, to have higher frequencies in young patients than in adult patients, with significantly higher frequencies found for Val60Leu (OR 1·60, 95% CI 1·05–2·44; p=0·04) and Asp294His (2·15, 1·05–4·40; p=0·04). Compared with those of healthy controls, young patients with melanoma had significantly higher frequencies of any MC1R variants. Interpretation: Our pooled analysis of MC1R genetic data of young patients with melanoma showed that MC1R r variants were more prevalent in childhood and adolescent melanoma than in adult melanoma, especially in patients aged 18 years or younger. Our findings support the role of MC1R in childhood and adolescent melanoma susceptibility, with a potential clinical relevance for developing early melanoma detection and preventive strategies. Funding: SPD-Pilot/Project-Award-2015; AIRC-MFAG-11831. © 2019 Elsevier Ltd

Published
2019

2. Association of Melanocortin-1 Receptor Variants with Pigmentary Traits in Humans: A Pooled Analysis from the M-Skip Project

Author

Tagliabue, E. Gandini, S. García-Borrón, J.C. Maisonneuve, P. Newton-Bishop, J. Polsky, D. Lazovich, D. Kumar, R. Ghiorzo, P. Ferrucci, L. Gruis, N.A. Puig, S. Kanetsky, P.A. Motokawa, T. Ribas, G. Landi, M.T. Fargnoli, M.C. Wong, T.H. Stratigos, A. Helsing, P. Guida, G. Autier, P. Han, J. Little, J. Sera, F. Raimondi, S. Caini, S. Hofman, A. Kayser, M. Liu, F. Nijsten, T. Uitterlinden, A.G. Scherer, D. Bishop, T. Elliott, F. Nagore, E. Hansson, J. Hoiom, V. Pastorino, L. Bouwes Bavinck, J.N. Aguilera, P. Badenas, C. Carrera, C. Gimenez-Xavier, P. Malvehy, J. Potrony, M. Puig-Butille, J.A. Tell-Marti, G. Dwyer, T. Blizzard, L. Cochrane, J. Fernandez-de-Misa, R. Branicki, W. Debniak, T. Morling, N. Johansen, P. Mayne, S. Bale, A. Cartmel, B. Pfeiffer, R. Palmieri, G. Menin, C. Kypreou, K. Bowcock, A. Cornelius, L. Council, M.L. Anno, S. Andresen, P.A. Guida, S.

Published
2016

3. MC1R variants increased the risk of sporadic cutaneous melanoma in darker-pigmented Caucasians: A pooled-analysis from the M-SKIP project

Author

Pasquali, E. García-Borrón, J.C. Fargnoli, M.C. Gandini, S. Maisonneuve, P. Bagnardi, V. Specchia, C. Liu, F. Kayser, M. Nijsten, T. Nagore, E. Kumar, R. Hansson, J. Kanetsky, P.A. Ghiorzo, P. Debniak, T. Branicki, W. Gruis, N.A. Han, J. Dwyer, T. Blizzard, L. Landi, M.T. Palmieri, G. Ribas, G. Stratigos, A. Council, M.L. Autier, P. Little, J. Newton-Bishop, J. Sera, F. Raimondi, S. Caini, S. Hofman, A. Uitterlinden, A.G. Scherer, D. Hoiom, V. Pastorino, L. Cochrane, J. Fernandez-De-Misa, R. Morling, N. Johansen, P. Pfeiffer, R. Kypreou, K. Bowcock, A. Cornelius, L. Motokawa, T. Anno, S. Helsing, P. Andresen, P.A. Wong, T.H. M-SKIP Study Group

Abstract

The MC1R gene is a key regulator of skin pigmentation. We aimed to evaluate the association between MC1R variants and the risk of sporadic cutaneous melanoma (CM) within the M-SKIP project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. Data included 5,160 cases and 12,119 controls from 17 studies. We calculated a summary odds ratio (SOR) for the association of each of the nine most studied MC1R variants and of variants combined with CM by using random-effects models. Stratified analysis by phenotypic characteristics were also performed. Melanoma risk increased with presence of any of the main MC1R variants: the SOR for each variant ranged from 1.47 (95%CI: 1.17-1.84) for V60L to 2.74 (1.53-4.89) for D84E. Carriers of any MC1R variant had a 66% higher risk of developing melanoma compared with wildtype subjects (SOR; 95%CI: 1.66; 1.41-1.96) and the risk attributable to MC1R variants was 28%. When taking into account phenotypic characteristics, we found that MC1R-associated melanoma risk increased only for darker-pigmented Caucasians: SOR (95%CI) was 3.14 (2.06-4.80) for subjects with no freckles, no red hair and skin Type III/IV. Our study documents the important role of all the main MC1R variants in sporadic CM and suggests that they have a direct effect on melanoma risk, independently on the phenotypic characteristics of carriers. This is of particular importance for assessing preventive strategies, which may be directed to darker-pigmented Caucasians with MC1R variants as well as to lightly pigmented, fairskinned subjects. © 2014 UICC.

Published
2015

4. MC1R variants increased the risk of sporadic cutaneous melanoma in darker-pigmented Caucasians: A pooled-analysis from the M-SKIP project

Author

Pasquali, E, García Borrón, J, Fargnoli, M, Gandini, S, Maisonneuve, P, Bagnardi, V, Specchia, C, Liu, F, Kayser, M, Nijsten, T, Nagore, E, Kumar, R, Hansson, J, Kanetsky, P, Ghiorzo, P, Debniak, T, Branicki, W, Gruis, N, Han, J, Dwyer, T, Blizzard, L, Landi, M, Palmieri, G, Ribas, G, Stratigos, A, Council, M, Autier, P, Little, J, Newton Bishop, J, Sera, F, Raimondi, S, Caini, S, Hofman, A, Uitterlinden, A, Scherer, D, Hoiom, V, Pastorino, L, Cochrane, J, Fernandez De Misa, R, Morling, N, Johansen, P, Pfeiffer, R, Kypreou, K, Bowcock, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Andresen, P, Wong, T, Wong, T., BAGNARDI, VINCENZO, Pasquali, E, García Borrón, J, Fargnoli, M, Gandini, S, Maisonneuve, P, Bagnardi, V, Specchia, C, Liu, F, Kayser, M, Nijsten, T, Nagore, E, Kumar, R, Hansson, J, Kanetsky, P, Ghiorzo, P, Debniak, T, Branicki, W, Gruis, N, Han, J, Dwyer, T, Blizzard, L, Landi, M, Palmieri, G, Ribas, G, Stratigos, A, Council, M, Autier, P, Little, J, Newton Bishop, J, Sera, F, Raimondi, S, Caini, S, Hofman, A, Uitterlinden, A, Scherer, D, Hoiom, V, Pastorino, L, Cochrane, J, Fernandez De Misa, R, Morling, N, Johansen, P, Pfeiffer, R, Kypreou, K, Bowcock, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Andresen, P, Wong, T, Wong, T., and BAGNARDI, VINCENZO

Abstract

The MC1R gene is a key regulator of skin pigmentation. We aimed to evaluate the association between MC1R variants and the risk of sporadic cutaneous melanoma (CM) within the M-SKIP project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. Data included 5,160 cases and 12,119 controls from 17 studies. We calculated a summary odds ratio (SOR) for the association of each of the nine most studied MC1R variants and of variants combined with CM by using random-effects models. Stratified analysis by phenotypic characteristics were also performed. Melanoma risk increased with presence of any of the main MC1R variants: the SOR for each variant ranged from 1.47 (95%CI: 1.17-1.84) for V60L to 2.74 (1.53-4.89) for D84E. Carriers of any MC1R variant had a 66% higher risk of developing melanoma compared with wildtype subjects (SOR; 95%CI: 1.66; 1.41-1.96) and the risk attributable to MC1R variants was 28%. When taking into account phenotypic characteristics, we found that MC1R-associated melanoma risk increased only for darker-pigmented Caucasians: SOR (95%CI) was 3.14 (2.06-4.80) for subjects with no freckles, no red hair and skin Type III/IV. Our study documents the important role of all the main MC1R variants in sporadic CM and suggests that they have a direct effect on melanoma risk, independently on the phenotypic characteristics of carriers. This is of particular importance for assessing preventive strategies, which may be directed to darker-pigmented Caucasians with MC1R variants as well as to lightly pigmented, fairskinned subjects.

Published
2015

5. Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: Study design and methods for pooling results of genetic epidemiological studies

Author

Raimondi, S. Gandini, S. Fargnoli, M.C. Bagnardi, V. Maisonneuve, P. Specchia, C. Kumar, R. Nagore, E. Han, J. Hansson, J. Kanetsky, P.A. Ghiorzo, P. Gruis, N.A. Dwyer, T. Blizzard, L. Fernandez-De-Misa, R. Branicki, W. Debniak, T. Morling, N. Landi, M.T. Palmieri, G. Ribas, G. Stratigos, A. Cornelius, L. Motokawa, T. Anno, S. Helsing, P. Wong, T.H. Autier, P. García-Borrón, J.C. Little, J. Newton-Bishop, J. Sera, F. Liu, F. Kayser, M. Nijsten, T.

Abstract

Background: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods. Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion. Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields. © 2012 Raimondi et al.

Published
2012

7. Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

Author

Raimondi, S, Gandini, S, Fargnoli, MC, Bagnardi, V, Maisonneuve, P, Specchia, C, Kumar, R, Nagore, E, Han, J, Hansson, J, Kanetsky, PA, Ghiorzo, P, Gruis, NA, Dwyer, T, Blizzard, L, Fernandez-de-Misa, R, Branicki, W, Debniak, T, Morling, N, Landi, MT, Palmieri, G, Ribas, G, Stratigos, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Wong, TH, Autier, P, Garcia-Borron, JC, Little, J, Newton-Bishop, J, Sera, F, Liu, F, Kayser, M, Nijsten, T, Raimondi, S, Gandini, S, Fargnoli, MC, Bagnardi, V, Maisonneuve, P, Specchia, C, Kumar, R, Nagore, E, Han, J, Hansson, J, Kanetsky, PA, Ghiorzo, P, Gruis, NA, Dwyer, T, Blizzard, L, Fernandez-de-Misa, R, Branicki, W, Debniak, T, Morling, N, Landi, MT, Palmieri, G, Ribas, G, Stratigos, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Wong, TH, Autier, P, Garcia-Borron, JC, Little, J, Newton-Bishop, J, Sera, F, Liu, F, Kayser, M, and Nijsten, T

Abstract

BACKGROUND: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. DESIGN AND METHODS: Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. DISCUSSION: Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.

Published
2012

8. Melanocortin-1receptor, skincancer and phenotypic characteristics (MSKIP) project: study design and methods for pooling results of genetic epidemiological studies.

Author

Raimondi, S., Gandini, S., Fargnoli, M.C., Bagnardi, V., Maissonneuve, P., Specchia, C., Kumar, R., Nagore, E., Han, J., Hansson, J., Kanetsky, P.A., Ghiorzo, P., Gruis, N.A., Dwyer, T., Blizzard, L., Fernandez-de-Misa, R., Brainicki, W., Debniak, T., Landi, M.T., Morling, Niels, Palmieri, G., Ribas, G., Stratigos, A., Cornelius, L., Motokawa, T., Anno, S., Helsing, P., Wong, T.H., Autier, P., Garcia-Borron, J.C., Little, J., Newton-Bishop, J., Liu, F., Kayser, M., Nijsten, T., Raimondi, S., Gandini, S., Fargnoli, M.C., Bagnardi, V., Maissonneuve, P., Specchia, C., Kumar, R., Nagore, E., Han, J., Hansson, J., Kanetsky, P.A., Ghiorzo, P., Gruis, N.A., Dwyer, T., Blizzard, L., Fernandez-de-Misa, R., Brainicki, W., Debniak, T., Landi, M.T., Morling, Niels, Palmieri, G., Ribas, G., Stratigos, A., Cornelius, L., Motokawa, T., Anno, S., Helsing, P., Wong, T.H., Autier, P., Garcia-Borron, J.C., Little, J., Newton-Bishop, J., Liu, F., Kayser, M., and Nijsten, T.

Published
2012

10. Nonliquid Reagent for Detecting Nitrate Reduction

Author

Anno S. Lampe

Subjects
Microbiology (medical), Bacteriological Techniques, Nitrates, Bacteria, Ethylenediamines, Oxidation reduction, Naphthalenes, Reduction (complexity), chemistry.chemical_compound, Nitrate, chemistry, Reagent, ETHYLENEDIAMINE DIHYDROCHLORIDE, General Clinical Microbiology, Indicators and Reagents, Oxidation-Reduction, Nuclear chemistry
Abstract

A nonliquid reagent for detecting nitrate reduction is described. The reagent contains N -(1-naphthyl)ethylenediamine dihydrochloride as a substitute for the carcinogenic α-naphthylamine. The reagent was tested on 135 strains and gave reliable results.

Published
1981

12. MC1R variants in childhood and adolescent melanoma: a retrospective pooled analysis of a multicentre cohort

Author

Pellegrini, Cristina, Botta, Francesca, Massi, Daniela, Martorelli, Claudia, Facchetti, Fabio, Gandini, Sara, Maisonneuve, Patrick, Avril, Marie-Françoise, Demenais, Florence, Bressac-de Paillerets, Brigitte, Hoiom, Veronica, Cust, Anne E, Anton-Culver, Hoda, Gruber, Stephen B, Gallagher, Richard P, Marrett, Loraine, Zanetti, Roberto, Dwyer, Terence, Thomas, Nancy E, Begg, Colin B, Berwick, Marianne, Puig, Susana, Potrony, Miriam, Nagore, Eduardo, Ghiorzo, Paola, Menin, Chiara, Manganoni, Ausilia Maria, Rodolfo, Monica, Brugnara, Sonia, Passoni, Emanuela, Sekulovic, Lidija Kandolf, Baldini, Federica, Guida, Gabriella, Stratigos, Alexandros, Ozdemir, Fezal, Ayala, Fabrizio, Fernandez-de-Misa, Ricardo, Quaglino, Pietro, Ribas, Gloria, Romanini, Antonella, Migliano, Emilia, Stanganelli, Ignazio, Kanetsky, Peter A, Pizzichetta, Maria Antonietta, García-Borrón, Jose Carlos, Nan, Hongmei, Landi, Maria Teresa, Little, Julian, Newton-Bishop, Julia, Sera, Francesco, Fargnoli, Maria Concetta, Raimondi, Sara, Alaibac, Mauro, Ferrari, Andrea, Valeri, Barbara, Sicher, Mariacristina, Mangiola, Daniela, Nazzaro, Gianluca, Tosti, Giulio, Mazzarol, Giovanni, Giudice, Giuseppe, Ribero, Simone, Astrua, Chiara, Mazzoni, Laura, Orlow, Irene, Mujumdar, Urvi, Hummer, Amanda, Busam, Klaus, Roy, Pampa, Canchola, Rebecca, Clas, Brian, Cotignola, Javiar, Monroe, Yvette, Armstrong, Bruce, Kricker, Anne, Litchfield, Melisa, Tucker, Paul, Stephens, Nicola, Switzer, Teresa, Theis, Beth, From, Lynn, Chowdhury, Noori, Vanasse, Louise, Purdue, Mark, Northrup, David, Rosso, Stefano, Sacerdote, Carlotta, Leighton, Nancy, Gildea, Maureen, Bonner, Joe, Jeter, Joanne, Klotz, Judith, Wilcox, Homer, Weiss, Helen, Millikan, Robert, Mattingly, Dianne, Player, Jon, Tse, Chiu-Kit, Rebbeck, Timothy, Walker, Amy, Panossian, Saarene, Setlow, Richard, Mohrenweiser, Harvey, Autier, Philippe, Han, Jiali, Caini, Saverio, Hofman, Albert, Kayser, Manfred, Liu, Fan, Nijsten, Tamar, Uitterlinden, Andre G., Kumar, Rajiv, Bishop, Tim, Elliott, Faye, Lazovich, Deann, Polsky, David, Hansson, Johan, Pastorino, Lorenza, Gruis, Nelleke A., Bouwes Bavinck, Jan Nico, Aguilera, Paula, Badenas, Celia, Carrera, Cristina, Gimenez-Xavier, Pol, Malvehy, Josep, Puig-Butille, Joan Anton, Tell-Marti, Gemma, Blizzard, Leigh, Cochrane, Jennifer, Branicki, Wojciech, Debniak, Tadeusz, Morling, Niels, Johansen, Peter, Mayne, Susan, Bale, Allen, Cartmel, Brenda, Ferrucci, Leah, Pfeiffer, Ruth, Palmieri, Giuseppe, Kypreou, Katerina, Bowcock, Anne, Cornelius, Lynn, Council, M. Laurin, Motokawa, Tomonori, Anno, Sumiko, Helsing, Per, Andresen, Per Arne, Guida, Stefania, Wong, Collaborators (98): Alaibac M, Terence H., Ferrari, A, Valeri, B, Et, Al., Pellegrini, C., Botta, F., Massi, D., Martorelli, C., Facchetti, F., Gandini, S., Maisonneuve, P., Avril, M. -F., Demenais, F., Bressac-de Paillerets, B., Hoiom, V., Cust, A. E., Anton-Culver, H., Gruber, S. B., Gallagher, R. P., Marrett, L., Zanetti, R., Dwyer, T., Thomas, N. E., Begg, C. B., Berwick, M., Puig, S., Potrony, M., Nagore, E., Ghiorzo, P., Menin, C., Manganoni, A. M., Rodolfo, M., Brugnara, S., Passoni, E., Sekulovic, L. K., Baldini, F., Guida, G., Stratigos, A., Ozdemir, F., Ayala, F., Fernandez-de-Misa, R., Quaglino, P., Ribas, G., Romanini, A., Migliano, E., Stanganelli, I., Kanetsky, P. A., Pizzichetta, M. A., Garcia-Borron, J. C., Nan, H., Landi, M. T., Little, J., Newton-Bishop, J., Sera, F., Fargnoli, M. C., Raimondi, S., Alaibac, M., Ferrari, A., Valeri, B., Sicher, M., Mangiola, D., Nazzaro, G., Tosti, G., Mazzarol, G., Giudice, G., Ribero, S., Astrua, C., Mazzoni, L., Orlow, I., Mujumdar, U., Hummer, A., Busam, K., Roy, P., Canchola, R., Clas, B., Cotignola, J., Monroe, Y., Armstrong, B., Kricker, A., Litchfield, M., Tucker, P., Stephens, N., Switzer, T., Theis, B., From, L., Chowdhury, N., Vanasse, L., Purdue, M., Northrup, D., Rosso, S., Sacerdote, C., Leighton, N., Gildea, M., Bonner, J., Jeter, J., Klotz, J., Wilcox, H., Weiss, H., Millikan, R., Mattingly, D., Player, J., Tse, C. -K., Rebbeck, T., Walker, A., Panossian, S., Setlow, R., Mohrenweiser, H., Autier, P., Han, J., Caini, S., Hofman, A., Kayser, M., Liu, F., Nijsten, T., Uitterlinden, A. G., Kumar, R., Bishop, T., Elliott, F., Lazovich, D., Polsky, D., Hansson, J., Pastorino, L., Gruis, N. A., Bouwes Bavinck, J. N., Aguilera, P., Badenas, C., Carrera, C., Gimenez-Xavier, P., Malvehy, J., Puig-Butille, J. A., Tell-Marti, G., Blizzard, L., Cochrane, J., Branicki, W., Debniak, T., Morling, N., Johansen, P., Mayne, S., Bale, A., Cartmel, B., Ferrucci, L., Pfeiffer, R., Palmieri, G., Kypreou, K., Bowcock, A., Cornelius, L., Council, M. L., Motokawa, T., Anno, S., Helsing, P., Andresen, P. A., Guida, S., Wong, T. H., Ege Üniversitesi, Epidemiology, Genetic Identification, and Dermatology

Subjects
Male, Skin Neoplasms, Pediatrics, Cohort Studies, 0302 clinical medicine, Odds Ratio, Developmental and Educational Psychology, Pediatrics, Perinatology and Child Health, 030212 general & internal medicine, Child, Cancer, Pediatric, Tumor, childhood disease, Middle Aged, Perinatology and Child Health, cohort analysis, Meta-analysis, Melanocortin, Cohort, Female, MC1R gene, Receptor, Melanocortin, Type 1, Receptor, Type 1, Cohort study, Adult, medicine.medical_specialty, adolescent, melanoma, cohort analysi, Subgroup analysis, Article, 03 medical and health sciences, Genetic, Clinical Research, 030225 pediatrics, Internal medicine, Genetics, Biomarkers, Tumor, medicine, Humans, Genetic Predisposition to Disease, Polymorphism, Germ-Line Mutation, Aged, Retrospective Studies, Polymorphism, Genetic, business.industry, Prevention, Case-control study, Retrospective cohort study, GEM Study Group, Odds ratio, Logistic Models, M-SKIP Study Group, Case-Control Studies, Cutaneous melanoma, IMI Study Group, business, Biomarkers
Abstract

Ferrari, Andrea/0000-0002-4724-0517; Pellegrini, Cristina/0000-0003-2168-8097; Migliano, Emilia/0000-0002-5316-8937; Maisonneuve, Patrick/0000-0002-5309-4704; Guida, Stefania/0000-0002-8221-6694; Pastorino, Lorenza/0000-0002-2575-8331; CARRERA, CRISTINA/0000-0003-1608-8820; Paillerets, Brigitte Bressac-de/0000-0003-0245-8608; Sekulovic, Lidija Kandolf/0000-0002-5221-5068; Caini, Saverio/0000-0002-2262-1102; Potrony, Miriam/0000-0003-2766-0765; Pizzichetta, Maria Antonietta/0000-0002-4201-8490; Little, Julian/0000-0001-5026-5531; Nagore, Eduardo/0000-0003-3433-8707; Polsky, David/0000-0001-9554-5289; Demenais, Florence/0000-0001-8361-0936; Nazzaro, Gianluca/0000-0001-8534-6497; gandini, sara/0000-0002-1348-4548; Cornelius, Lynn A/0000-0002-6329-2819; Palmieri, Giuseppe/0000-0002-4350-2276; Cotignola, Javier/0000-0003-4473-9854; Ghiorzo, Paola/0000-0002-3651-8173; Autier, Philippe/0000-0003-1538-5321; Bishop, Tim/0000-0002-8752-8785; Sera, Francesco/0000-0002-8890-6848; Newton-Bishop, Julia/0000-0001-9147-6802; Litchfield, Melisa/0000-0003-0002-7724, WOS: 000464254100018, PubMed: 30872112, Background Germline variants in the melanocortin 1 receptor gene (MC1R) might increase the risk of childhood and adolescent melanoma, but a clear conclusion is challenging because of the low number of studies and cases. We assessed the association of MC1R variants with childhood and adolescent melanoma in a large study comparing the prevalence of MC1R variants in child or adolescent patients with melanoma to that in adult patients with melanoma and in healthy adult controls. Methods in this retrospective pooled analysis, we used the M-SKIP Project, the Italian Melanoma Intergroup, and other European groups (with participants from Australia, Canada, France, Greece, Italy, the Netherlands, Serbia, Spain, Sweden, Turkey, and the USA) to assemble an international multicentre cohort. We gathered phenotypic and genetic data from children or adolescents diagnosed with sporadic single-primary cutaneous melanoma at age 20 years or younger, adult patients with sporadic single-primary cutaneous melanoma diagnosed at age 35 years or older, and healthy adult individuals as controls. We calculated odds ratios (ORs) for childhood and adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged 18 or younger and 14 years or younger. Findings We analysed data from 233 young patients, 932 adult patients, and 932 healthy adult controls. Children and adolescents had higher odds of carrying MC1R r variants than did adult patients (OR 1.54, 95% CI 1.02-2.33), including when analysis was restricted to patients aged 18 years or younger (1.80, 1.06-3.07). All investigated variants, except Arg160Trp, tended, to varying degrees, to have higher frequencies in young patients than in adult patients, with significantly higher frequencies found for Val60Leu (OR 1.60, 95% CI 1.05-2.44; p=0.04) and Asp294His (2.15, 1.05-4.40; p=0.04). Compared with those of healthy controls, young patients with melanoma had significantly higher frequencies of any MC1R variants. Interpretation Our pooled analysis of MC1R genetic data of young patients with melanoma showed that MC1R r variants were more prevalent in childhood and adolescent melanoma than in adult melanoma, especially in patients aged 18 years or younger. Our findings support the role of MC1R in childhood and adolescent melanoma susceptibility, with a potential clinical relevance for developing early melanoma detection and preventive strategies. Copyright (c) 2019 Elsevier Ltd. All rights reserved., [AIRC-MFAG-11831]; NATIONAL CANCER INSTITUTEUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P30CA016086, P01CA206980, P30CA016086, P30CA016086] Funding Source: NIH RePORTER, SPD-Pilot/Project-Award-2015; AIRC-MFAG-11831.

Published
2019

13. MC1R variants increased the risk of sporadic cutaneous melanoma in darker-pigmented Caucasians: a pooled-analysis from the M-SKIP project

Author

Pasquali, Elena, García Borrón, José C., Fargnoli, Maria Concetta, Gandini, Sara, Maisonneuve, Patrick, Bagnardi, Vincenzo, Specchia, Claudia, Liu, Fan, Kayser, Manfred, Nijsten, Tamar, Nagore, Eduardo, Kumar, Rajiv, Hansson, Johan, Kanetsky, Peter A., Ghiorzo, Paola, Debniak, Tadeusz, Branicki, Wojciech, Gruis, Nelleke A., Han, Jiali, Dwyer, Terry, Blizzard, Leigh, Landi, Maria Teresa, Palmieri, Giuseppe, Ribas, Gloria, Stratigos, Alexander, Council, M. Laurin, Autier, Philippe, Little, Julian, Newton Bishop, Julia, Sera, Francesco, Raimondi, Sara, Caini, Saverio, Hofman, Albert, Uitterlinden, Andre G., Scherer, Dominique, Hoiom, Veronica, Pastorino, Lorenza, Cochrane, Jennifer, Fernandez De Misa, Ricardo, Morling, Niels, Johansen, Peter, Pfeiffer, Ruth, Kypreou, Katerina, Bowcock, Anne, Cornelius, Lynn, Motokawa, Tomonori, Anno, Sumiko, Helsing, Per, Andresen, Per Arne, Wong, Terence H., International Prevention Research Institute (IPRI), Strathclyde Institute of Global Public Health at iPRI (SIGPH@iPRI), Pasquali, E, García Borrón, J, Fargnoli, M, Gandini, S, Maisonneuve, P, Bagnardi, V, Specchia, C, Liu, F, Kayser, M, Nijsten, T, Nagore, E, Kumar, R, Hansson, J, Kanetsky, P, Ghiorzo, P, Debniak, T, Branicki, W, Gruis, N, Han, J, Dwyer, T, Blizzard, L, Landi, M, Palmieri, G, Ribas, G, Stratigos, A, Council, M, Autier, P, Little, J, Newton Bishop, J, Sera, F, Raimondi, S, Caini, S, Hofman, A, Uitterlinden, A, Scherer, D, Hoiom, V, Pastorino, L, Cochrane, J, Fernandez De Misa, R, Morling, N, Johansen, P, Pfeiffer, R, Kypreou, K, Bowcock, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Andresen, P, Wong, T, Genetic Identification, and Dermatology

Subjects
Risk, Cancer Research, genetic epidemiology, Skin Neoplasms, European Continental Ancestry Group, Skin Pigmentation, Type 1/*genetics Risk Skin Neoplasms/etiology/*genetics Skin Pigmentation genetic epidemiology melanocortin-1 receptor melanoma meta-analysis, Article, White People, SDG 3 - Good Health and Well-being, Melanocortin-1 receptor, European Continental Ancestry Group *Genetic Predisposition to Disease Humans Melanoma/etiology/*genetics Middle Aged Phenotype Receptor, melanoma, Humans, Genetic epidemiology, Meta-analysi, Genetic Predisposition to Disease, Melanoma, melanocortin-1 receptor, Middle Aged, meta-analysis, Phenotype, Oncology, Melanocortin, Meta-analysis, Receptor, Melanocortin, Type 1, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Receptor, Type 1
Abstract

Pasquali, Elena Garcia-Borron, Jose C Fargnoli, Maria Concetta Gandini, Sara Maisonneuve, Patrick Bagnardi, Vincenzo Specchia, Claudia Liu, Fan Kayser, Manfred Nijsten, Tamar Nagore, Eduardo Kumar, Rajiv Hansson, Johan Kanetsky, Peter A Ghiorzo, Paola Debniak, Tadeusz Branicki, Wojciech Gruis, Nelleke A Han, Jiali Dwyer, Terry Blizzard, Leigh Landi, Maria Teresa Palmieri, Giuseppe Ribas, Gloria Stratigos, Alexander Council, M Laurin Autier, Philippe Little, Julian Newton-Bishop, Julia Sera, Francesco Raimondi, Sara eng 10589/Cancer Research UK/United Kingdom K05 CA131675/CA/NCI NIH HHS/ U01 CA083180/CA/NCI NIH HHS/ Research Support, Non-U.S. Gov't 2014/06/12 06:00 Int J Cancer. 2015 Feb 1;136(3):618-31. doi: 10.1002/ijc.29018. Epub 2014 Jun 18.; International audience; The MC1R gene is a key regulator of skin pigmentation. We aimed to evaluate the association between MC1R variants and the risk of sporadic cutaneous melanoma (CM) within the M-SKIP project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. Data included 5,160 cases and 12,119 controls from 17 studies. We calculated a summary odds ratio (SOR) for the association of each of the nine most studied MC1R variants and of variants combined with CM by using random-effects models. Stratified analysis by phenotypic characteristics were also performed. Melanoma risk increased with presence of any of the main MC1R variants: the SOR for each variant ranged from 1.47 (95%CI: 1.17-1.84) for V60L to 2.74 (1.53-4.89) for D84E. Carriers of any MC1R variant had a 66% higher risk of developing melanoma compared with wild-type subjects (SOR; 95%CI: 1.66; 1.41-1.96) and the risk attributable to MC1R variants was 28%. When taking into account phenotypic characteristics, we found that MC1R-associated melanoma risk increased only for darker-pigmented Caucasians: SOR (95%CI) was 3.14 (2.06-4.80) for subjects with no freckles, no red hair and skin Type III/IV. Our study documents the important role of all the main MC1R variants in sporadic CM and suggests that they have a direct effect on melanoma risk, independently on the phenotypic characteristics of carriers. This is of particular importance for assessing preventive strategies, which may be directed to darker-pigmented Caucasians with MC1R variants as well as to lightly pigmented, fair-skinned subjects.

Published
2015
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14. Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

Author

Raimondi, Sara, Gandini, Sara, Fargnoli, Maria Concetta, Bagnardi, Vincenzo, Maisonneuve, Patrick, Specchia, Claudia, Kumar, Rajiv, Nagore, Eduardo, Han, Jiali, Hansson, Johan, Kanetsky, Peter A., Ghiorzo, Paola, Gruis, Nelleke A., Dwyer, Terry, Blizzard, Leigh, Fernandez-De-Misa, Ricardo, Branicki, Wojciech, Debniak, Tadeusz, Morling, Niels, Landi, Maria Teresa, Palmieri, Giuseppe, Ribas, Gloria, Stratigos, Alexander, Cornelius, Lynn, Motokawa, Tomonori, Anno, Sumiko, Helsing, Per, Wong, Terence H., Autier, Philippe, García-Borrón, José C., Little, Julian, Newton-Bishop, Julia, Sera, Francesco, Liu, Fan, Kayser, Manfred, Nijsten, Tamar, Genetic Identification, Dermatology, International Prevention Research Institute (IPRI), Raimondi, S, Gandini, S, Fargnoli, M, Bagnardi, V, Maisonneuve, P, Specchia, C, Kumar, R, Nagore, E, Han, J, Hansson, J, Kanetsky, P, Ghiorzo, P, Gruis, N, Dwyer, T, Blizzard, L, Fernandez de Misa, R, Branicki, W, Debniak, T, Morling, N, Landi, M, Palmieri, G, Ribas, G, Stratigos, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Wong, T, Autier, P, García Borrón, J, Little, J, Newton Bishop, J, Sera, F, Liu, F, Kayser, M, Nijsten, T, and Study Group, G

Subjects
Male, Skin Neoplasms, Standardization, Epidemiology, Pooling, Disease, Bioinformatics, Logistic regression, Study Protocol, 0302 clinical medicine, Statistical Analysis Plan, Adult Case-Control Studies Data Collection/standards Data Interpretation, Skin cancer, Genetic epidemiology, Statistical *Epidemiologic Research Design Female *Genetic Predisposition to Disease Hospitalization Humans Logistic Models Male Meta-Analysis as Topic Phenotype *Receptor, Melanoma, lcsh:R5-920, 0303 health sciences, Data Collection, Smoking, Pooled-analysis, 3. Good health, Hospitalization, Phenotype, 030220 oncology & carcinogenesis, Meta-analysis, Data Interpretation, Statistical, Genetic epidemiology, Melanoma, Meta-analysis, Pooled-analysis, Skin cancer, Study design, Melanocortin, Sunlight, Female, lcsh:Medicine (General), Receptor, Melanocortin, Type 1, Adult, Health Informatics, Computational biology, 03 medical and health sciences, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, medicine, Humans, Genetic Predisposition to Disease, MED/01 - STATISTICA MEDICA, 030304 developmental biology, business.industry, Study design, medicine.disease, Logistic Models, Case-Control Studies, Epidemiologic Research Design, Type 1 Skin Neoplasms/*genetics/physiopathology/secondary Smoking Sunlight/*adverse effects, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

Raimondi, Sara Gandini, Sara Fargnoli, Maria Concetta Bagnardi, Vincenzo Maisonneuve, Patrick Specchia, Claudia Kumar, Rajiv Nagore, Eduardo Han, Jiali Hansson, Johan Kanetsky, Peter A Ghiorzo, Paola Gruis, Nelleke A Dwyer, Terry Blizzard, Leigh Fernandez-de-Misa, Ricardo Branicki, Wojciech Debniak, Tadeusz Morling, Niels Landi, Maria Teresa Palmieri, Giuseppe Ribas, Gloria Stratigos, Alexander Cornelius, Lynn Motokawa, Tomonori Anno, Sumiko Helsing, Per Wong, Terence H Autier, Philippe Garcia-Borron, Jose C Little, Julian Newton-Bishop, Julia Sera, Francesco Liu, Fan Kayser, Manfred Nijsten, Tamar eng CA112243-05 S1/CA/NCI NIH HHS/ R01 CA112243/CA/NCI NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't England 2012/08/07 06:00 BMC Med Res Methodol. 2012 Aug 3;12:116. doi: 10.1186/1471-2288-12-116.; International audience; BACKGROUND: For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. DESIGN AND METHODS: Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. DISCUSSION: Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.

Published
2012
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15. Comparison of creatine kinase elevation caused by Janus kinase inhibitors and interleukin-6 inhibitors in patients with rheumatoid arthritis: A propensity score-matched study.

Author

Tada M, Okano T, Mamaoto K, Yamada Y, Orita K, Mandai K, Anno S, Iida T, Inui K, and Koike T

Abstract

Objectives: This study aimed to examine whether creatine kinase (CK) elevation occurs with interleukin (IL)-6 inhibitors, as in Janus kinase (JAK) inhibitors, which are reported to increase CK levels in rheumatoid arthritis., Patients and Methods: A multicenter database of JAK inhibitor and IL-6 inhibitor treatment was retrospectively searched between January 2016 to December 2022; 142 cases (117 females, 25 males, mean age: 63.8±13.0 years; range, 20 to 85 years), with 71 cases in each group, were extracted by propensity score matching using age, sex, body mass index, and CK at 0 weeks. The outlier rate was compared. Patients' background characteristics related to elevated CK levels at 24 weeks were investigated by univariate and multivariate analyses., Results: Creatine kinase levels at 4 and 12 weeks were significantly higher with JAK inhibitors than with IL-6 inhibitors (four weeks, 72 vs. 87.5 IU/mL, p=0.016; 12 weeks, 71 vs. 95.5 IU/mL, p=0.028). The outlier rate (Grade 1) with JAK inhibitors increased significantly over time (0 weeks, 4.2%; four weeks, 18.1%; 12 weeks, 21.7%; 24 weeks, 18.3%; p=0.015), whereas that with IL-6 inhibitors increased slightly (0 weeks, 5.6%; four weeks, 9.2%; 12 weeks, 8.6%; 24 weeks, 8.5%; p=0.745), with a significant difference between the groups (p=0.035). No patients discontinued treatment due to myalgia or renal dysfunction. The factors significantly positively related to elevated CK levels at 24 weeks were male sex and creatinine. Those significantly negatively related were Steinbrocker stage and class, modified health assessment questionnaire scores, estimated glomerular filtration rate, and glucocorticoid dose., Conclusion: Mild CK elevations with JAK inhibitors are not a particular clinical problem. CK elevation might be specific to JAK inhibitors., Competing Interests: Conflict of Interest: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article., (Copyright © 2024, Turkish League Against Rheumatism.)

Published
2024
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16. Suspension culture of human induced pluripotent stem cell-derived intestinal organoids using natural polysaccharides.

Author

Ogawa I, Onozato D, Anno S, Hayashi H, Kanaki T, Iwao T, and Matsunaga T

Subjects
Animals, Cell Differentiation genetics, Humans, Intestinal Mucosa metabolism, Intestines, Polysaccharides metabolism, Sulfur Compounds, Induced Pluripotent Stem Cells, Organoids
Abstract

Currently, there are many challenges in the culture of human induced pluripotent stem (iPS) cell-derived intestinal organoids (HIOs) for use in drug discovery, disease research, and regenerative medicine. For example, the main culture method, embedding culture, makes industrial large-scale culture difficult, and Matrigel, which is used for almost all HIO cultures, is not respected for its application in regenerative medicine. To overcome these challenges, we herein propose a new culture method using low concentrations of natural polysaccharides in a suspension culture. In the present study, five natural polysaccharides free from heterologous animal-derived components were used, and HIOs were successfully cultured in suspension with FP001 and FP003, which are microbial exopolysaccharide analogs of gellan gum. The fabricated HIOs were similar to living intestinal tracts with respect to their gene expression, microstructure, and protein expression. The observed activities of the drug metabolizing enzymes and drug transporters in the generated HIOs suggested that they have pharmacokinetic functions. We believe that suspension culture of HIOs using FP001 or FP003 can be widely applied to not only drug discovery research but also disease research and regenerative medicine., Competing Interests: Declaration of competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Ltd.)

Published
2022
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17. A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries.

Author

Anno S, Hirakawa T, Sugita S, and Yasumoto S

Subjects
Forecasting, Humans, Neural Networks, Computer, Public Health, COVID-19 epidemiology, Pandemics
Abstract

Introduction: Coronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue., Methods: This study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error., Results: Our results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability., Discussion: The proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Anno, Hirakawa, Sugita and Yasumoto.)

Published
2022
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18. Gellan Gum Promotes the Differentiation of Enterocytes from Human Induced Pluripotent Stem Cells.

Author

Qiu S, Kabeya T, Ogawa I, Anno S, Hayashi H, Kanaki T, Hashita T, Iwao T, and Matsunaga T

Abstract

The evaluation of drug pharmacokinetics in the small intestine is critical for developing orally administered drugs. Caucasian colon adenocarcinoma (Caco-2) cells are employed to evaluate drug absorption in preclinical trials of drug development. However, the pharmacokinetic characteristics of Caco-2 cells are different from those of the normal human small intestine. Besides this, it is almost impossible to obtain primary human intestinal epithelial cells of the same batch. Therefore, human iPS cell-derived enterocytes (hiPSEs) with pharmacokinetic functions similar to human intestinal epithelial cells are expected to be useful for the evaluation of drug absorption. Previous studies have been limited to the use of cytokines and small molecules to generate hiPSEs. Dietary fibers play a critical role in maintaining intestinal physiology. We used gellan gum (GG), a soluble dietary fiber, to optimize hiPSE differentiation. hiPSEs cocultured with GG had significantly higher expression of small intestine- and pharmacokinetics-related genes and proteins. The activities of drug-metabolizing enzymes, such as cytochrome P450 2C19, and peptide transporter 1 were significantly increased in the GG treatment group compared to the control group. At the end point of differentiation, the percentage of senescent cells increased. Therefore, GG could improve the differentiation efficiency of human iPS cells to enterocytes and increase intestinal maturation by extending the life span of hiPSEs.

Published
2020
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19. Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.

Author

Anno S, Hara T, Kai H, Lee MA, Chang Y, Oyoshi K, Mizukami Y, and Tadono T

Subjects
Disease Outbreaks, Geographic Information Systems, Humans, Reproducibility of Results, Risk Factors, Taiwan epidemiology, Temperature, Climate, Dengue epidemiology, Machine Learning, Spatio-Temporal Analysis
Abstract

Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of development, introduction and safety concerns. Understanding how dengue spreads and identifying the risk factors will facilitate the development of a dengue EWS, for which a climate-based model is still needed. An analysis was conducted to examine emerging spatiotemporal hotspots of dengue fever at the township level in Taiwan, associated with climatic factors obtained from remotely sensed data in order to identify the risk factors. Machinelearning was applied to support the search for factors with a spatiotemporal correlation with dengue fever outbreaks. Three dengue fever hotspot categories were found in southwest Taiwan and shown to be spatiotemporally associated with five kinds of sea surface temperatures. Machine-learning, based on the deep AlexNet model trained by transfer learning, yielded an accuracy of 100% on an 8-fold cross-validation test dataset of longitudetime sea surface temperature images.

Published
2019
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20. Space-time clustering characteristics of dengue based on ecological, socio-economic and demographic factors in northern Sri Lanka.

Author

Anno S, Imaoka K, Tadono T, Igarashi T, Sivaganesh S, Kannathasan S, Kumaran V, and Surendran SN

Subjects
Climate, Demography, Disease Outbreaks, Humans, Retrospective Studies, Socioeconomic Factors, Space-Time Clustering, Sri Lanka epidemiology, Topography, Medical, Dengue epidemiology, Remote Sensing Technology, Satellite Imagery
Abstract

The aim of the present study was to identify geographical areas and time periods of potential clusters of dengue cases based on ecological, socio-economic and demographic factors in northern Sri Lanka from January 2010 to December 2013. Remote sensing (RS) was used to develop an index comprising rainfall, humidity and temperature data. Remote sensing data gathered by the AVNIR-2 instrument onboard the ALOS satellite were used to detect urbanisation, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analysed RS data and databases were integrated into a geographical information system (GIS) enabling space-time clustering analysis. Our results indicate that increases in the number of combinations of ecological, socio-economic and demographic factors that are present or above the average contribute to significantly high rates of space-time dengue clusters. The spatio-temporal association that consolidates the two kinds of associations into one can ensure a more stable model for forecasting. An integrated spatiotemporal prediction model at a smaller level using ecological, socioeconomic and demographic factors could lead to substantial improvements in dengue control and prevention by allocating the right resources to the appropriate places at the right time.

Published
2015
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21. Approaches to understanding adaptations of skin color variation by detecting gene-environment interactions.

Author

Anno S, Ohshima K, and Abe T

Subjects
Environment, Gene Expression, Humans, Phenotype, Polymorphism, Single Nucleotide, Selection, Genetic, Ultraviolet Rays, Adaptation, Biological genetics, Genetic Variation, Skin Pigmentation genetics
Abstract

Genetic and environmental factors are both part of an elaborate feedback mechanism whereby the human adaptive form reacts to environmental stimuli via internal adjustments. Human survival may ultimately depend on understanding two important components of future environmental adaptation. First, we must elucidate the dynamics of the human genome underpinning the complex human phenotype. Second, we must understand how the environment pressures and affects the genome, helping to determine human traits. This article reviews current approaches to detecting the natural selection of skin color variation in human populations. We include statistical methods for clarifying gene-environment interactions applicable to the interactions with UV radiation levels. We recommend spatial data mining as an efficient approach that applies environmental association rules, extending our knowledge of adaptation to the environment.

Published
2010
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22. Interactions between SNP alleles at multiple loci contribute to skin color differences between caucasoid and mongoloid subjects.

Author

Anno S, Abe T, and Yamamoto T

Subjects
Agouti Signaling Protein genetics, Alleles, Back, Cheek, Humans, Japan, Linkage Disequilibrium genetics, Membrane Glycoproteins genetics, Membrane Transport Proteins genetics, Microphthalmia-Associated Transcription Factor genetics, Myosin Heavy Chains genetics, Myosin Type V genetics, Ohio, Oxidoreductases genetics, Polymorphism, Single Nucleotide, Receptor, Melanocortin, Type 1 genetics, Asian People genetics, Monophenol Monooxygenase genetics, Skin Pigmentation genetics, White People genetics
Abstract

This study aimed to identify single nucleotide polymorphism (SNP) alleles at multiple loci associated with racial differences in skin color using SNP genotyping. A total of 122 Caucasians in Toledo, Ohio and 100 Mongoloids in Japan were genotyped for 20 SNPs in 7 candidate genes, encoding the Agouti signaling protein (ASIP), tyrosinase-related protein 1 (TYRP1), tyrosinase (TYR), melanocortin 1 receptor (MC1R), oculocutaneous albinism II (OCA2), microphthalmia-associated transcription factor (MITF), and myosin VA (MYO5A). Data were used to analyze associations between the 20 SNP alleles using linkage disequilibrium (LD). Combinations of SNP alleles were jointly tested under LD for associations with racial groups by performing a chi(2) test for independence. Results showed that SNP alleles at multiple loci can be considered the haplotype that contributes to significant differences between the two population groups and suggest a high probability of LD. Confirmation of these findings requires further study with other ethnic groups to analyze the associations between SNP alleles at multiple loci and skin color variation among races.

Published
2008
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23. Interactions between SNP alleles at multiple Loci and variation in skin pigmentation in 122 Caucasians.

Author

Anno S, Abe T, Sairyo K, Kudo S, Yamamoto T, Ogata K, and Goel VK

Abstract

This study was undertaken to clarify the molecular basis for human skin color variation and the environmental adaptability to ultraviolet irradiation, with the ultimate goal of predicting the impact of changes in future environments on human health risk. One hundred twenty-two Caucasians living in Toledo, Ohio participated. Back and cheek skin were assayed for melanin as a quantitative trait marker. Buccal cell samples were collected and used for DNA extraction. DNA was used for SNP genotyping using the Masscode system, which entails two-step PCR amplification and a platform chemistry which allows cleavable mass spectrometry tags. The results show gene-gene interaction between SNP alleles at multiple loci (not necessarily on the same chromosome) contributes to inter-individual skin color variation while suggesting a high probability of linkage disequilibrium. Confirmation of these findings requires further study with other ethic groups to analyze the associations between SNP alleles at multiple loci and human skin color variation. Our overarching goal is to use remote sensing data to clarify the interaction between atmospheric environments and SNP allelic frequency and investigate human adaptability to ultraviolet irradiation. Such information should greatly assist in the prediction of the health effects of future environmental changes such as ozone depletion and increased ultraviolet exposure. If such health effects are to some extent predictable, it might be possible to prepare for such changes in advance and thus reduce the extent of their impact.

Published
2007

24. Techniques of SNP genotyping and remote sensing applied for elucidation of the contribution of polygene and environmental factors to inter-individual variation in skin pigmentation phenotype.

Author

Anno S, Kudo S, Hamasaki K, Matsumura K, Yamamoto T, and Ogata K

Subjects
Atmosphere analysis, Databases, Genetic, Genotype, Humans, Spectrophotometry methods, Telemetry methods, Computational Biology methods, Environment, Genetic Variation, Genome, Human, Polymorphism, Single Nucleotide genetics, Skin Pigmentation genetics
Abstract

We present a conceptual framework for applying techniques of SNP genotyping as a molecular biological approach and remote sensing as an ecological approach to elucidation of the contribution of polygene and environmental factors to inter-individual variation in skin pigmentation phenotype. Additionally, we discuss the obstacles that frustrate our efforts to identify how the human genome encodes the complex phenotype and suggest the use of computational methods designed for knowledge discovery within hereditary database.

Published
2005
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