Ricardo Sara, Marcos-Silva Lara, Pereira Daniela, Pinto Rita, Almeida Raquel, Söderberg Ola, Mandel Ulla, Clausen Henrik, Felix Ana, Lunet Nuno, David Leonor
IPATIMUP, Institute of Molecular Pathology and Immunology of The University of Porto, Portugal.
IPATIMUP, Institute of Molecular Pathology and Immunology of The University of Porto, Portugal; Faculty of Medicine of The University of Porto, Porto, Portugal; Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine and School of Dentistry, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
Mol Oncol. 2015 Feb;9(2):503-12. doi: 10.1016/j.molonc.2014.10.005. Epub 2014 Oct 22.
The CA125 assay detects circulating MUC16 and is one of the most widely used cancer biomarkers for the follow-up of ovarian cancer. We previously demonstrated that detection of aberrant cancer-associated glycoforms of MUC16 as well as MUC1 in circulation could improve the yield of these serum assays. Our aim was to refine ovarian cancer biomarkers by detection of aberrant glycoforms (Tn, STn, and T) of MUC16 and MUC1 in ovarian cancer tissue using Proximity Ligation Assays (PLA). We studied two series of serous ovarian tumours, a pilot series of 66 ovarian tumours (27 cystadenomas, 16 borderline tumours and 23 adenocarcinomas) from Centro Hospitalar S. João, Porto and a validation series of 89 ovarian tumours (17 cystadenomas, 25 borderline tumours and 47 adenocarcinomas) from the Portuguese Institute of Oncology Francisco Gentil, Lisbon. PLA reactions for MUC16/Tn, MUC16/STn, MUC1/Tn and MUC1/STn were negative in benign lesions but often positive in borderline and malignant lesions, in both series. An even better yield was obtained based on positivity for any of the four glyco-mucin profiles, further increasing sensitivity to 72% and 83% in the two series, respectively, with 100% specificity. The strategy is designated glyco-mucin profiling and provides strong support for development of PLA-based serum assays for early diagnosis.
CA125检测可检测循环中的MUC16，是卵巢癌随访中使用最广泛的癌症生物标志物之一。我们之前证明，检测循环中MUC16以及MUC1异常的癌症相关糖型可以提高这些血清检测的阳性率。我们的目的是通过使用邻近连接分析（PLA）检测卵巢癌组织中MUC16和MUC1的异常糖型（Tn、STn和T）来优化卵巢癌生物标志物。我们研究了两个系列的浆液性卵巢肿瘤，一个试点系列包括来自波尔图圣若昂医院中心的66例卵巢肿瘤（27例囊腺瘤、16例交界性肿瘤和23例腺癌），以及一个验证系列包括来自里斯本弗朗西斯科·根蒂尔葡萄牙肿瘤研究所的89例卵巢肿瘤（17例囊腺瘤、25例交界性肿瘤和47例腺癌）。在这两个系列中，MUC16/Tn、MUC16/STn、MUC1/Tn和MUC1/STn的PLA反应在良性病变中为阴性，但在交界性和恶性病变中通常为阳性。基于四种糖蛋白聚糖谱中任何一种的阳性结果，获得了更高的阳性率，两个系列的敏感性分别进一步提高到72%和83%，特异性均为100%。该策略被称为糖蛋白聚糖谱分析，为开发基于PLA的早期诊断血清检测提供了有力支持。
