Tobiasz Joanna, Polanska Joanna
Department of Data Science and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland.
Department of Computer Graphics, Vision and Digital Systems, Silesian University of Technology, 44-100 Gliwice, Poland.
Cancers (Basel). 2023 Aug 24;15(17):4230. doi: 10.3390/cancers15174230.
As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival ( = 0.0160) and Progression-Free Interval ( = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications.
作为一种高度异质性疾病,乳腺癌(BRCA)呈现出多样的分子特征。已确立的分子分类(PAM50)依赖于基因表达谱分析。它不足以解释所观察到的BRCA的临床和组织病理学多样性。本研究旨在从人口统计学和临床角度对通过蛋白质组学特征揭示的六个BRCA亚群(基底型、HER2富集型和四个管腔型)进行特征描述。基于高斯混合模型(GMM)的高变量蛋白质选择结合主成分分析(PCA)/均匀流形近似和投影(UMAP)用于降维,而k均值算法用于患者聚类。统计分析(对数秩检验和Gehan-Wilcoxon检验,风险比HR作为效应量ES)显示,在疾病特异性生存(P = 0.0160)和无进展生存期(P = 0.0264)方面,所确定的亚群之间存在显著差异。管腔型亚群的预后有所不同(无病生存期,P = 0.0277)。A2亚群的预后最差,与HER2富集型亚群相当(HR = 1.748,以管腔B型为参照,效应量小),而A3亚群的预后最佳(HR = 0.250,效应量大)。与PAM50亚型相似,通过χ检验和Cramér's V测量效应量,以及分别结合η或Cohen's d型效应量的ANOVA和适当的事后检验,在管腔型亚群中未检测到对人口统计学和临床因素的实质性依赖。孕酮受体可作为管腔型患者中潜在的A2生物标志物。需要进一步研究分子差异,以检验其潜在的预后或临床应用。