Département d'oncologie chirurgicale, Georges-François Leclerc Cancer Centre, UNICANCER, 1 rue du Professeur Marion, 21000, Dijon, France.
Breast and Gynaecologic Cancer Registry of Côte d'Or, Georges-François Leclerc Cancer Centre, UNICANCER, 1 rue du Professeur Marion, 21000, Dijon, France.
BMC Cancer. 2018 Feb 7;18(1):161. doi: 10.1186/s12885-018-4080-8.
The incidence and incidence trends of breast cancer according to molecular subtype are unknown at a population level in France. The registry data enables this study and may give this information, that is crucial to describe and understand breast cancer epidemiology.
We estimated the incidence rates of breast cancer for each molecular subtype using data from three cancer registries in France for the period from 2007 to 2012. Molecular subtypes were defined with immunohistochemical data. Poisson models were estimated to modelize the course of breast cancer incidence and to test the trends.
The study included 12,040 patients diagnosed between 2007 and 2012 in the three administrative areas covered by the registries. There was no significant trends in the proportion of each molecular subtype year by year. The age distribution of incident cases was different depending on the molecular subtypes (p < 0.001). The course of incidence between 2007 and 2012 was also different depending on molecular subtype according to the multivariate Poisson model (p < 0.001).
The description of incident cases of breast cancer according to molecular subtype at a population level showed differences in trends. The trends in incidence differed according to molecular subtype, and this should improve our understanding of overall changes in incidence. This analysis is important to plan screening and treatment resources at a population level.
在法国,基于分子亚型的乳腺癌发病率和发病趋势在人群层面上尚不清楚。该登记处的数据能够提供这些信息,这些信息对于描述和理解乳腺癌流行病学至关重要。
我们使用法国三个癌症登记处的数据,对 2007 年至 2012 年期间的乳腺癌分子亚型的发病率进行了估计。使用免疫组织化学数据对分子亚型进行定义。利用泊松模型来模拟乳腺癌发病率的变化过程,并检验趋势。
本研究纳入了 2007 年至 2012 年期间在三个登记处覆盖的行政区域中诊断的 12,040 名患者。各分子亚型的比例每年没有显著变化。根据分子亚型,发病病例的年龄分布不同(p < 0.001)。根据多变量泊松模型,2007 年至 2012 年期间的发病情况也因分子亚型而异(p < 0.001)。
在人群层面上,对乳腺癌分子亚型发病病例的描述显示出不同的趋势。发病率的趋势因分子亚型而异,这应有助于我们更好地了解整体发病率的变化。这种分析对于在人群层面上规划筛查和治疗资源非常重要。