Tagliabue Elena, Gandini Sara, Bellocco Rino, Maisonneuve Patrick, Newton-Bishop Julia, Polsky David, Lazovich DeAnn, Kanetsky Peter A, Ghiorzo Paola, Gruis Nelleke A, Landi Maria Teresa, Menin Chiara, Fargnoli Maria Concetta, García-Borrón Jose Carlos, Han Jiali, Little Julian, Sera Francesco, Raimondi Sara
Clinical Trial Center, Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori.
Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy.
Cancer Manag Res. 2018 May 14;10:1143-1154. doi: 10.2147/CMAR.S155283. eCollection 2018.
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 variants predicted melanoma risk independently of at-risk phenotypic characteristics.
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 gene variants by sequencing analysis and on hair color, skin phototype, and freckles, ie, the phenotypic characteristics used to define the red hair phenotype.
The presence of any variant was associated with melanoma risk independently of phenotypic characteristics (OR 1.60; 95% CI 1.36-1.88). Inclusion of 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 (=0.002), and 24% of participants were better assessed (net reclassification index 95% CI 20%-30%). Subgroup analysis suggested a possibly stronger role of in melanoma prediction for participants without the red hair phenotype (net reclassification index: 28%) compared to paler skinned participants (15%).
The authors suggest that measuring the 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 genotype.
黑色素瘤因其高病死率而成为一个重要的公共卫生问题。识别高危个体对于改善早期诊断并最终提高生存率至关重要。本研究的目的是评估某些变异是否能独立于高危表型特征来预测黑色素瘤风险。
数据收集于一项国际合作项目——M-SKIP项目。本次汇总分析纳入了来自七项先前发表的病例对照研究的3830例单发、原发性、散发性皮肤黑色素瘤病例及2619例对照的数据。所有研究均通过测序分析获得了基因变异信息,以及关于头发颜色、皮肤光类型和雀斑的信息,即用于定义红发表型的表型特征。
任何一种变异的存在都与黑色素瘤风险相关,且独立于表型特征(比值比1.60;95%置信区间1.36 - 1.88)。在风险预测模型中纳入这些变异,相对于基础临床模型,黑色素瘤预测准确性(受试者工作特征曲线下面积)提高了0.7%(P = 0.002),并且24%的参与者得到了更好的评估(净重新分类指数95%置信区间20% - 30%)。亚组分析表明,与皮肤较白的参与者(15%)相比,对于没有红发表型的参与者,该变异在黑色素瘤预测中可能发挥更强的作用(净重新分类指数:28%)。
作者认为检测该基因型可能对黑色素瘤预测有益。这些结果可能是指导制定评估纳入该基因型的黑色素瘤风险预测工具的科学方案的一个有效起点。