Venables Julian P, Klinck Roscoe, Bramard Anne, Inkel Lyna, Dufresne-Martin Geneviève, Koh ChuShin, Gervais-Bird Julien, Lapointe Elvy, Froehlich Ulrike, Durand Mathieu, Gendron Daniel, Brosseau Jean-Philippe, Thibault Philippe, Lucier Jean-Francois, Tremblay Karine, Prinos Panagiotis, Wellinger Raymund J, Chabot Benoit, Rancourt Claudine, Elela Sherif Abou
Laboratoire de génomique fonctionnelle de l'Université de Sherbrooke, Québec, Canada.
Cancer Res. 2008 Nov 15;68(22):9525-31. doi: 10.1158/0008-5472.CAN-08-1769.
Breast cancer is the most common cause of cancer death among women under age 50 years, so it is imperative to identify molecular markers to improve diagnosis and prognosis of this disease. Here, we present a new approach for the identification of breast cancer markers that does not measure gene expression but instead uses the ratio of alternatively spliced mRNAs as its indicator. Using a high-throughput reverse transcription-PCR-based system for splicing annotation, we monitored the alternative splicing profiles of 600 cancer-associated genes in a panel of 21 normal and 26 cancerous breast tissues. We validated 41 alternative splicing events that significantly differed in breast tumors relative to normal breast tissues. Most cancer-specific changes in splicing that disrupt known protein domains support an increase in cell proliferation or survival consistent with a functional role for alternative splicing in cancer. In a blind screen, a classifier based on the 12 best cancer-associated splicing events correctly identified cancer tissues with 96% accuracy. Moreover, a subset of these alternative splicing events could order tissues according to histopathologic grade, and 5 markers were validated in a further blind set of 19 grade 1 and 19 grade 3 tumor samples. These results provide a simple alternative for the classification of normal and cancerous breast tumor tissues and underscore the putative role of alternative splicing in the biology of cancer.
乳腺癌是50岁以下女性癌症死亡的最常见原因,因此确定分子标志物以改善该疾病的诊断和预后势在必行。在此,我们提出一种鉴定乳腺癌标志物的新方法,该方法不测量基因表达,而是使用可变剪接mRNA的比率作为指标。利用基于高通量逆转录PCR的剪接注释系统,我们监测了21个正常乳腺组织和26个癌性乳腺组织样本中600个癌症相关基因的可变剪接图谱。我们验证了41个在乳腺肿瘤中相对于正常乳腺组织有显著差异的可变剪接事件。大多数破坏已知蛋白质结构域的癌症特异性剪接变化支持细胞增殖或存活增加,这与可变剪接在癌症中的功能作用一致。在一项盲筛中,基于12个最佳癌症相关剪接事件的分类器以96%的准确率正确识别了癌组织。此外,这些可变剪接事件中的一部分可以根据组织病理学分级对组织进行排序,并且在另外一组19个1级和19个3级肿瘤样本的盲测中验证了5个标志物。这些结果为正常和癌性乳腺肿瘤组织的分类提供了一种简单的替代方法,并强调了可变剪接在癌症生物学中的假定作用。