Rennhack Jonathan, To Briana, Wermuth Harrison, Andrechek Eran R
Department of Physiology, Michigan State University, 2194 BPS Building, 567 Wilson Road, East Lansing, MI, 48824, USA.
J Mammary Gland Biol Neoplasia. 2017 Mar;22(1):71-84. doi: 10.1007/s10911-017-9374-y. Epub 2017 Jan 26.
Breast tumor heterogeneity has been well documented through the use of multiplatform -omic studies in human tumors. However, there is no integrative database to capture the heterogeneity within mouse models of breast cancer. This project identifies genomic copy number alterations (CNAs) in 600 tumors across 27 major mouse models of breast cancer through the application of a predictive algorithm to publicly available gene expression data. It was found that despite the presence of strong oncogenic drivers in most mouse models, CNAs are extremely common but heterogeneous both between models and within models. Many mouse CNA events are largely conserved in human tumors and in the mouse we show that they are associated with secondary tumor characteristics such as tumor histology, metastasis, as well as enhanced oncogenic signaling. These data serve as an important resource in guiding investigators when choosing a mouse model to understand the gene copy number changes relevant to human breast cancer.
通过在人类肿瘤中使用多平台组学研究，乳腺癌的肿瘤异质性已得到充分记录。然而，目前尚无综合数据库来捕捉乳腺癌小鼠模型中的异质性。该项目通过将预测算法应用于公开可用的基因表达数据，在27种主要乳腺癌小鼠模型的600个肿瘤中识别基因组拷贝数改变（CNA）。研究发现，尽管大多数小鼠模型中存在强大的致癌驱动因素，但CNA极为常见，且在不同模型之间以及同一模型内都具有异质性。许多小鼠CNA事件在人类肿瘤中基本保守，并且在小鼠中，我们表明它们与继发性肿瘤特征相关，如肿瘤组织学、转移以及致癌信号增强。这些数据为研究人员在选择小鼠模型以了解与人类乳腺癌相关的基因拷贝数变化时提供了重要资源。
