Azevedo Alexandre Luiz Korte, Gomig Talita Helen Bombardelli, Batista Michel, Marchini Fabricio Klerynton, Spautz Cleverton César, Rabinovich Iris, Sebastião Ana Paula Martins, Oliveira Jaqueline Carvalho, Gradia Daniela Fiori, Cavalli Iglenir João, Ribeiro Enilze Maria de Souza Fonseca
Genetics Post-Graduation Program, Genetics Department, Federal University of Parana, Curitiba, Parana, Brazil.
Research Institute Pele Pequeno Principe, Curitiba, Parana, Brazil.
J Proteomics. 2023 Aug 15;285:104955. doi: 10.1016/j.jprot.2023.104955. Epub 2023 Jun 28.
The actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype's biological and clinical characterization, and with protein panels for subtype discrimination.
In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes.
We identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00).
In general, our results expand the accuracy of breast cancer subtypes' proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers.
Breast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype's proteomes. Here, we indicate how the variations in the subtype's proteome can influence the tumor's biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor suppressor proteins between subtypes. Also, through our machine-learning approach, we propose multi-protein panels with the potential to discriminate the breast cancer subtypes. Our panels achieved high classification performance in our cohort and in the independent validation cohort, demonstrating their potential to improve the current tumor discrimination system as complements to the classical immunohistochemical classification.
乳腺肿瘤亚型的实际分类旨在将患者分层为临床特征一致的群体，然而，临床医生仍缺乏用于区分乳腺癌亚型的可重复且可靠的蛋白质生物标志物。在本研究中，我们旨在探寻这些肿瘤之间差异表达的蛋白质及其生物学意义，为亚型的生物学和临床特征描述提供依据，并构建用于亚型区分的蛋白质组。
在我们的研究中，我们应用高通量质谱、生物信息学和机器学习方法来研究不同乳腺癌亚型的蛋白质组。
我们发现每种亚型依赖不同的蛋白质表达模式来维持其恶性程度，并且与每种亚型及其生物学和临床行为相关的信号通路和生物学过程也存在改变。关于亚型生物标志物，我们的蛋白质组实现了至少75%的灵敏度和92%的特异性。在验证队列中，这些蛋白质组取得了可接受至优异的表现（曲线下面积=0.740至1.00）。
总体而言，我们的结果扩展了乳腺癌亚型蛋白质组图谱的准确性，并增进了对其生物学异质性的理解。此外，我们鉴定出了用于乳腺癌患者分层的潜在蛋白质生物标志物，增加了可靠蛋白质生物标志物的种类。
乳腺癌是全球诊断率最高的癌症类型，也是女性中最致命的癌症。作为一种异质性疾病，乳腺癌肿瘤可分为四种主要亚型，每种亚型都有特定的分子改变、临床行为和治疗反应。因此，在患者管理和临床决策中的关键一步是准确分类乳腺肿瘤亚型。目前，这种分类是通过免疫组织化学检测四种经典标志物（雌激素受体、孕激素受体、HER2受体和Ki-67指数）来进行的；然而，众所周知，仅这些标志物并不能完全区分乳腺肿瘤亚型。此外，对每种亚型分子改变的了解不足导致在治疗选择和预后判定方面的决策过程具有挑战性。本研究通过高通量无标记质谱数据采集和下游生物信息学分析，在乳腺肿瘤的蛋白质组区分方面取得进展，并实现了对亚型蛋白质组的深入表征。在此，我们指出亚型蛋白质组的变化如何影响肿瘤的生物学和临床差异，强调亚型之间癌蛋白和抑癌蛋白表达模式的差异。此外，通过我们的机器学习方法，我们提出了具有区分乳腺癌亚型潜力的多蛋白组。我们的蛋白质组在我们的队列和独立验证队列中取得了高分类性能，证明了它们作为经典免疫组织化学分类的补充来改进当前肿瘤区分系统的潜力。
