Skoog Petter, Ohlsson Mattias, Fernö Mårten, Rydén Lisa, Borrebaeck Carl A K, Wingren Christer
Dept. of Immunotechnology, Lund University, Medicon Village, Lund, Sweden.
Computational Biology & Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
PLoS One. 2017 Jun 26;12(6):e0179775. doi: 10.1371/journal.pone.0179775. eCollection 2017.
Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.
组织学分级是诊断为乳腺癌患者最常用的预后因素之一。然而，传统分级已证明在技术上具有挑战性，高达60%的肿瘤被归类为组织学2级，这代表了一个异质性队列，对临床决策的信息较少。为了研究和扩展组织学分级乳腺癌的分子谜题，在这个试点项目中，我们在蛋白质组的一个新领域寻找额外的蛋白质生物标志物。为此，我们首次使用主要针对免疫调节蛋白的重组抗体微阵列对乳腺癌肿瘤组织进行了蛋白质表达谱分析。因此，我们将免疫系统作为一种疾病特异性传感器进行了探索（临床免疫蛋白质组学）。独特的是，结果表明可以描绘出几种反映组织学分级的生物学相关蛋白质。更详细地说，暂定的生物标志物面板可用于：i）建立一个区分1级与3级肿瘤的候选模型；ii）证明2级肿瘤之间的分子异质性；iii）潜在地将一些2级肿瘤重新分类为更类似于1级或3级肿瘤。从长远来看，这可能会改善预后，患者可以从改进的个性化护理中受益。
