文章：

小鼠特异性模型用于检测肿瘤中受选择基因

A Mouse-Specific Model to Detect Genes under Selection in Tumors

原文发布日期：26 October 2023

DOI: 10.3390/cancers15215156

类型: Article

开放获取: 是

英文摘要：

The mouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (Genes Under Selection in Tumors) model, originally trained on human exomes, to mouse exomes via transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high-throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models and 17 exomes of mouse leukemia models. Comparing the predictions between cancer types and between human and mouse tumors revealed common and unique driver genes. The GUST-mouse method is available as an open-source R package on github.

摘要翻译： 

小鼠是癌症研究中广泛使用的模式生物。然而，由于缺乏标记训练数据，目前尚无计算方法可用于识别小鼠癌症驱动基因。为填补这一知识空白，我们通过迁移学习将最初基于人类外显子组训练的GUST（肿瘤中选择基因）模型适配至小鼠外显子组。由此开发的工具GUST-mouse能够评估小鼠肿瘤中长期与短期进化选择，并利用高通量测序数据区分癌基因、肿瘤抑制基因和乘客基因。我们应用GUST-mouse分析了65个小鼠原发性乳腺癌模型外显子组和17个小鼠白血病模型外显子组。通过比较不同癌症类型之间以及人与小鼠肿瘤之间的预测结果，揭示了共有和特有的驱动基因。GUST-mouse方法已在GitHub上以开源R软件包形式发布。

原文链接：

A Mouse-Specific Model to Detect Genes under Selection in Tumors