Fröhlich Holger, Patjoshi Sabyasachi, Yeghiazaryan Kristina, Kehrer Christina, Kuhn Walther, Golubnitschaja Olga
1Bonn-Aachen International Centre for IT, Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
2Radiological Clinic, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
EPMA J. 2018 Apr 11;9(2):175-186. doi: 10.1007/s13167-018-0131-0. eCollection 2018 Jun.
The breast cancer (BC) epidemic is a multifactorial disease attributed to the early twenty-first century: about two million of new cases and half a million deaths are registered annually worldwide. New trends are emerging now: on the one hand, with respect to the geographical BC prevalence and, on the other hand, with respect to the age distribution. Recent statistics demonstrate that young populations are getting more and more affected by BC in both Eastern and Western countries. Therefore, the old rule "the older the age, the higher the BC risk" is getting relativised now. Accumulated evidence shows that young premenopausal women deal with particularly unpredictable subtypes of BC such as triple-negative BC, have lower survival rates and respond less to conventional chemotherapy compared to the majority of postmenopausal BC.
Here we hypothesised that a multi-level diagnostic approach may lead to the identification of a molecular signature highly specific for the premenopausal BC. A multi-omic approach using machine learning was considered as a potent tool for stratifying patients with benign breast alterations into well-defined risk groups, namely individuals at high versus low risk for breast cancer development.
The study resulted in identifying multi-omic signature specific for the premenopausal BC that can be used for stratifying patients with benign breast alterations. Our predictive model is capable of discriminating individually between high and low BC-risk with high confidence (>90%) and considered of potential clinical utility. Novel risk assessment approaches and advanced screening programmes-as the long-term target of this project-are of particular importance for predictive, preventive and personalised medicine as the medicine of the future, due to the expected health benefits for young subpopulations and the healthcare system as a whole.
乳腺癌流行是一种多因素疾病，可追溯到21世纪初：全球每年登记约200万新病例和50万例死亡。现在正在出现新的趋势：一方面，关于乳腺癌的地理患病率；另一方面，关于年龄分布。最近的统计数据表明，在东方和西方国家，年轻人群受乳腺癌影响的程度越来越高。因此，“年龄越大，患乳腺癌风险越高”这一旧规则现在正变得相对化。越来越多的证据表明，绝经前年轻女性患的是特别难以预测的乳腺癌亚型，如三阴性乳腺癌，与大多数绝经后乳腺癌相比，其生存率较低，对传统化疗的反应也较小。
在此，我们假设一种多层次诊断方法可能会导致识别出一种对绝经前乳腺癌具有高度特异性的分子特征。使用机器学习的多组学方法被认为是一种有效的工具，可将患有良性乳腺病变的患者分层为明确的风险组，即乳腺癌发生风险高与低的个体。
该研究结果是识别出了一种对绝经前乳腺癌具有特异性的多组学特征，可用于对患有良性乳腺病变的患者进行分层。我们的预测模型能够以高置信度（>90%）分别区分乳腺癌高风险和低风险个体，并被认为具有潜在的临床应用价值。作为本项目的长期目标，新的风险评估方法和先进的筛查计划对作为未来医学的预测性、预防性和个性化医学尤为重要；这是因为预期会给年轻亚人群体和整个医疗保健系统带来健康益处。
