Castellani Patrizia, Borsi Laura, Carnemolla Barbara, Birò Attila, Dorcaratto Alessandra, Viale Giuseppe L, Neri Dario, Zardi Luciano
Laboratory of Cell Biology, Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy.
Am J Pathol. 2002 Nov;161(5):1695-700. doi: 10.1016/S0002-9440(10)64446-X.
Different fibronectin (FN) isoforms are generated by the alternative splicing of the primary FN transcript. We previously demonstrated that the isoform containing the extra domain B sequence of fibronectin (B-FN), a complete type-III-homology repeat, is a marker of angiogenesis that accumulates around neovasculature only during angiogenic processes. We produced a single-chain human recombinant antibody (scFv), L19, which reacts specifically with B-FN and selectively targets tumor vasculature in vivo. We used this scFv and an antibody against a pan-endothelial marker (Factor VIII) in a double-staining procedure on specimens of low- and high-grade astrocytomas to determine the percentage of B-FN-positive vessels, (denominating the resulting value angiogenic index [AI]). Compared to vascular density and proliferative activity (evaluated using antibodies to Factor VIII and Ki67, respectively), AI correlated better with tumor grade (1.6 +/- 2.6% and 92.0 +/- 8.7% of B-FN-positive vessels in low- and high-grade astrocytomas, respectively) and was a more precise diagnostic tool than either of the two conventional methods. In fact, discriminating analysis using these three parameters showed that only AI accurately classified 100% of the cases studied, compared to 64% and 89% correctly diagnosed by vascular density and of proliferating cells, respectively.
不同的纤连蛋白(FN)同工型是由初级FN转录本的可变剪接产生的。我们之前证明,包含纤连蛋白额外结构域B序列(B-FN)的同工型,即一个完整的III型同源重复序列,是血管生成的标志物,仅在血管生成过程中在新生血管周围积累。我们制备了一种单链人重组抗体(scFv)L19,它能与B-FN特异性反应,并在体内选择性地靶向肿瘤血管。我们在低级别和高级别星形细胞瘤标本的双重染色程序中使用了这种scFv和一种针对泛内皮标志物(因子VIII)的抗体,以确定B-FN阳性血管的百分比(将所得值称为血管生成指数[AI])。与血管密度和增殖活性(分别使用针对因子VIII和Ki67的抗体进行评估)相比,AI与肿瘤分级的相关性更好(低级别和高级别星形细胞瘤中B-FN阳性血管分别为1.6 +/- 2.6%和92.0 +/- 8.7%),并且是比两种传统方法更精确的诊断工具。事实上,使用这三个参数的判别分析表明,只有AI能准确分类100%的研究病例,相比之下,血管密度和增殖细胞分别正确诊断了64%和89%的病例。