转移
乳腺癌
生物
脑转移
癌症
癌症研究
体内
癌症转移
疾病
癌细胞
计算生物学
生物信息学
医学
病理
遗传学
作者
Xin Jin,Zelalem Demere,Karthik Nair,Ahmed Ali,Gino B. Ferraro,Ted Natoli,Amy Deik,L Petronio,Andrew Tang,Cong Zhu,Li Wang,Danny Rosenberg,Vamsi Mangena,Jennifer A. Roth,Kwanghun Chung,Rakesh K. Jain,Clary B. Clish,Matthew G. Vander Heiden,Todd R. Golub
出处
期刊:Nature
[Nature Portfolio]
日期:2020-12-09
卷期号:588 (7837): 331-336
被引量:352
标识
DOI:10.1038/s41586-020-2969-2
摘要
Abstract Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical owing to the complexity of in vivo models. Here we introduce an in vivo barcoding strategy that is capable of determining the metastatic potential of human cancer cell lines in mouse xenografts at scale. We validated the robustness, scalability and reproducibility of the method and applied it to 500 cell lines 1,2 spanning 21 types of solid tumour. We created a first-generation metastasis map (MetMap) that reveals organ-specific patterns of metastasis, enabling these patterns to be associated with clinical and genomic features. We demonstrate the utility of MetMap by investigating the molecular basis of breast cancers capable of metastasizing to the brain—a principal cause of death in patients with this type of cancer. Breast cancers capable of metastasizing to the brain showed evidence of altered lipid metabolism. Perturbation of lipid metabolism in these cells curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a resource to support metastasis research.
科研通智能强力驱动
Strongly Powered by AbleSci AI