Department of Immunotechnology and CREATE HEALTH, Lund University, Medicon Village, SE-223 81 Lund, Sweden;
Mol Cell Proteomics. 2013 Dec;12(12):3612-23. doi: 10.1074/mcp.M113.030379. Epub 2013 Aug 27.
Tumor progression and prognosis in breast cancer patients are difficult to assess using current clinical and laboratory parameters, where a pathological grading is indicative of tumor aggressiveness. This grading is based on assessments of nuclear grade, tubule formation, and mitotic rate. We report here the first protein signatures associated with histological grades of breast cancer, determined using a novel affinity proteomics approach. We profiled 52 breast cancer tissue samples by combining nine antibodies and label-free LC-MS/MS, which generated detailed quantified proteomic maps representing 1,388 proteins. The results showed that we could define in-depth molecular portraits of histologically graded breast cancer tumors. Consequently, a 49-plex candidate tissue protein signature was defined that discriminated between histological grades 1, 2, and 3 of breast cancer tumors with high accuracy. Highly biologically relevant proteins were identified, and the differentially expressed proteins indicated further support for the current hypothesis regarding remodeling of the tumor microenvironment during tumor progression. The protein signature was corroborated using meta-analysis of transcriptional profiling data from an independent patient cohort. In addition, the potential for using the markers to estimate the likelihood of long-term metastasis-free survival was also indicated. Taken together, these molecular portraits could pave the way for improved classification and prognostication of breast cancer.
使用当前的临床和实验室参数来评估乳腺癌患者的肿瘤进展和预后是困难的，其中病理分级表示肿瘤的侵袭性。这种分级是基于核分级、管腔形成和有丝分裂率的评估。我们在这里报告了与乳腺癌组织学分级相关的第一个蛋白质特征，该特征是使用新型亲和蛋白质组学方法确定的。我们通过结合 9 种抗体和无标记 LC-MS/MS 对 52 个乳腺癌组织样本进行了分析，这生成了代表 1388 种蛋白质的详细定量蛋白质组图谱。结果表明，我们可以深入定义组织学分级乳腺癌肿瘤的分子特征。因此，定义了一个 49 个蛋白质的候选组织蛋白特征，可以高精度地区分乳腺癌肿瘤的组织学分级 1、2 和 3。鉴定出了高度生物学相关的蛋白质，差异表达的蛋白质进一步支持了当前关于肿瘤进展过程中肿瘤微环境重塑的假说。该蛋白质特征使用来自独立患者队列的转录谱数据分析的荟萃分析进行了验证。此外，还表明了使用这些标志物来估计长期无转移生存的可能性。总之，这些分子图谱可以为改善乳腺癌的分类和预后提供依据。
