Gajbhiye Akshada, Dabhi Raju, Taunk Khushman, Jagadeeshaprasad Mashanipalya G, RoyChoudhury Sourav, Mane Anupama, Bayatigeri Santhakumari, Chaudhury Koel, Santra Manas K, Rapole Srikanth
Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune 411007, MH, India; Savitribai Phule Pune University, Ganeshkhind, Pune 411007, MH, India.
Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune 411007, MH, India.
J Proteomics. 2017 Jun 23;163:1-13. doi: 10.1016/j.jprot.2017.05.007. Epub 2017 May 8.
Being molecularly heterogeneous, breast cancer tends to be a complicated oncological disease with high incidence rates throughout the world. The primary aim of this study was to identify the set of serum proteins with discriminatory capabilities towards the four major subtypes of breast cancer. We employed multipronged quantitative proteomic approaches like 2D-DIGE, iTRAQ and SWATH-MS and identified 307 differentially regulated proteins. Luminal A subtype consisted of 24, Luminal B subtype 38, HER2 Enriched subtype 17 and Triple negative breast cancer subtype 10 differentially regulated subtype specific proteins. These specific proteins were further subjected to bioinformatic tools which revealed the involvement in platelet degranulation, fibrinolysis, lipid metabolism, immune response, complement activation, blood coagulation, glycolysis and cancer signaling pathways in the subtypes of the breast cancer. The significant discrimination efficiency of the models generated through multivariate statistical analysis was decent to distinguish each of the four subtypes from controls. Further, some of the statistically significant differentially regulated proteins were verified and validated by immunoblotting and mass spectrometry based selected reaction monitoring (SRM) approach. Our Multipronged proteomics approaches revealed panel of serum proteins specifically altered for individual subtypes of breast cancer. The mass spectrometry data are available via ProteomeXchange with identifier PXD006441.
Worldwide, breast cancer continues to be one of the leading causes of cancer related deaths in women and it encompasses four major molecular subtypes. As breast cancer treatment majorly depends on identification of specific subtype, it is important to diagnosis the disease at subtype level. Our results using multipronged quantitative proteomics identified 307 differentially regulated proteins in which 24 were specific for Luminal A, 38 for Luminal B, 17 for HER2 enriched and 10 proteins were specific for TN subtype. Bioinformatic analysis of these proteins revealed certain biological processes and pathways altered at subtype level and validation experiments of some of these proteins using immunoblotting and SRM assays are consistent with discovery data. This is the first comprehensive proteomic study on serum proteome alterations at subtype level which will not only help to distinguish subtype of breast cancer but also contribute to a better understanding of the molecular characteristic of breast cancer at individual subtype level.
乳腺癌在分子水平上具有异质性，是一种复杂的肿瘤疾病，在全球发病率很高。本研究的主要目的是确定对乳腺癌四种主要亚型具有鉴别能力的血清蛋白组。我们采用了多种定量蛋白质组学方法，如二维差异凝胶电泳（2D-DIGE）、同位素标记相对和绝对定量（iTRAQ）以及数据非依赖采集质谱（SWATH-MS），并鉴定出307种差异表达的蛋白质。腔面A型亚型包含24种差异表达的亚型特异性蛋白质，腔面B型亚型有38种，人表皮生长因子受体2（HER2）富集型亚型有17种，三阴性乳腺癌亚型有10种。这些特异性蛋白质进一步通过生物信息学工具进行分析，结果显示它们参与了乳腺癌各亚型中的血小板脱颗粒、纤维蛋白溶解、脂质代谢、免疫反应、补体激活、血液凝固、糖酵解和癌症信号通路。通过多变量统计分析生成的模型具有显著的鉴别效率，能够很好地将四种亚型与对照区分开来。此外，一些具有统计学意义的差异表达蛋白质通过免疫印迹和基于质谱的选择反应监测（SRM）方法进行了验证。我们的多种蛋白质组学方法揭示了乳腺癌各亚型血清蛋白的特异性变化。质谱数据可通过蛋白质组交换库获取，标识符为PXD006441。
在全球范围内，乳腺癌仍然是女性癌症相关死亡的主要原因之一，它包含四种主要分子亚型。由于乳腺癌的治疗主要依赖于特定亚型的识别，因此在亚型水平上诊断疾病很重要。我们使用多种定量蛋白质组学方法的研究结果鉴定出307种差异表达蛋白质，其中24种对腔面A型特异，38种对腔面B型特异，17种对HER2富集型特异，10种蛋白质对三阴性亚型特异。对这些蛋白质的生物信息学分析揭示了在亚型水平上某些生物学过程和通路的改变，并且使用免疫印迹和SRM分析对其中一些蛋白质进行的验证实验与发现数据一致。这是首次关于亚型水平血清蛋白质组改变的全面蛋白质组学研究，不仅有助于区分乳腺癌的亚型，还有助于更好地理解个体亚型水平上乳腺癌的分子特征。
