Molecular characterization of patient-derived human pancreatic tumor xenograft models for preclinical and translational development of cancer therapeutics

患者来源的人胰腺肿瘤异种移植模型的分子特征分析及其在癌症治疗临床前和转化开发中的应用

阅读:1

Abstract

Preclinical evaluation of novel cancer agents requires models that accurately reflect the biology and molecular characteristics of human tumors. Molecular profiles of eight pancreatic ductal adenocarcinoma patient tumors were compared to corresponding passages of xenografts obtained by grafting tumor fragments into immunocompromised mice. Molecular characterization was performed by copy number analysis, gene expression and microRNA microarrays, mutation analysis, short tandem repeat (STR) profiling, and immunohistochemistry. Xenografts were found to be highly representative of their respective tumors, with a high degree of genetic stability observed by STR profiling and mutation analysis. Copy number variation (CNV) profiles of early and late xenograft passages were similar, with recurrent losses on chromosomes 1p, 3p, 4q, 6, 8p, 9, 10, 11q, 12p, 15q, 17, 18, 20p, and 21 and gains on 1q, 5p, 8q, 11q, 12q, 13q, 19q, and 20q. Pearson correlations of gene expression profiles of tumors and xenograft passages were above 0.88 for all models. Gene expression patterns between early and late passage xenografts were highly stable for each individual model. Changes observed in xenograft passages largely corresponded to human stromal compartment genes and inflammatory processes. While some differences exist between the primary tumors and corresponding xenografts, the molecular profiles remain stable after extensive passaging. Evidence for stability in molecular characteristics after several rounds of passaging lends confidence to clinical relevance and allows for expansion of models to generate the requisite number of animals required for cohorts used in drug screening and development studies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。