Thomas Cecilia E, Dahl Leo, Byström Sanna, Chen Yan, Uhlén Mathias, Mälarstig Anders, Czene Kamila, Hall Per, Schwenk Jochen M, Gabrielson Marike
Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Transl Oncol. 2022 Mar;17:101339. doi: 10.1016/j.tranon.2022.101339. Epub 2022 Jan 13.
Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.
可获取的风险预测指标对于改善乳腺癌的早期检测和预后至关重要。血液样本广泛可得且含有能提供有关人类健康和疾病重要信息的蛋白质,然而,关于循环蛋白对乳腺癌风险预测的贡献仍知之甚少。我们对瑞典KARMA乳腺癌队列中在诊断前采集的EDTA血浆样本进行了分析,以评估循环蛋白作为分子预测指标的作用。一种数据驱动的分析策略被应用于基于700种循环蛋白构建的分子表型,以识别和注释女性群体。对183例未来乳腺癌病例和366例年龄匹配的对照进行的无监督分析揭示了五个具有不同蛋白质组血浆谱的稳定群体。在这些女性中,处于最稳定群体(N = 19;平均杰卡德指数:0.70 ± 0.29)的女性相比其他群体更有可能使用过绝经激素治疗(MHT)、被诊断患有乳腺癌,且年龄更大。与该群体相关的循环蛋白(FDR < 0.001)代表了与细胞连接(F11R、CLDN15、ITGAL)、DNA修复(RBBP8)、细胞复制(TJP3)相关的生理过程,并且包括在女性生殖组织中发现的蛋白质(PTCH1、ZP4)。对血浆蛋白质组学数据采用数据驱动的方法揭示了绝经激素治疗(MHT)对循环蛋白质组潜在的长期分子影响,即使在女性结束治疗后也是如此。这为蛋白质组学在识别乳腺癌风险预测分子标志物方面的努力提供了有价值的见解。