Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes

被引:1
作者
Tobiasz, Joanna [1 ,2 ]
Polanska, Joanna [1 ]
机构
[1] Silesian Tech Univ, Dept Data Sci & Engn, PL-44100 Gliwice, Poland
[2] Silesian Tech Univ, Dept Comp Graph Vis & Digital Syst, PL-44100 Gliwice, Poland
关键词
breast cancer; machine learning; survival analysis; subtyping; effect size; MOLECULAR PORTRAITS; CAVEOLIN-1; CARCINOMAS; PATTERNS; SUBTYPES; MODELS;
D O I
10.3390/cancers15174230
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Simple Summary Breast cancer is a heterogeneous disease, and several attempts have been made to subtype it. Based on the abundance of 86 proteins, we identified six subpopulations of breast cancer patients: one basal subtype, one HER2-enriched subtype, and four luminal subtypes. We then evaluated them demographically and clinically, and we found significant differences in survival. The new luminal subpopulations vary in prognoses. One, marked as A2, showed similar or even worse survival than HER2-enriched and basal cases. The observed poorer survival cannot be explained by clinical and demographic factors, as there was no substantial association with race, ethnicity, metastasis, tumor size, cancer stage, and age at diagnosis. It suggests that the molecular profiles underlie the cohort heterogeneity rather than the patient background. The subpopulations identified may potentially complement established breast cancer classifications and, with further molecular investigation, may find application in clinical routine.Abstract 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 (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 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 & chi;2 test and Cramer's V for ES, and ANOVA with appropriate post hocs combined with & eta;2 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.
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页数:17
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