自然封面：人类乳腺癌脑转移的花瓣图
众所周知，大多数癌症患者并非死于癌症原发部位，而是死于癌症转移部位，例如迅速致命的脑转移。既往研究已经发现，HER2阳性乳腺癌细胞系HCC1954多见于脑转移，而三阴性乳腺癌细胞系MDA-MB-231多见于颅外转移。不过，由于体内模型的复杂性，全面完整的癌症转移研究始终无法实现，我们对于癌症转移过程生物学机制的认识仍是东鳞西爪，存在大量盲区。
2020年12月10日，全球自然科学三大旗舰期刊之一英国《自然》正刊封面推荐了美国麻省理工学院、布罗德研究所、哈佛大学医学院、麻省总医院、达纳法伯癌症研究所的研究报告，介绍了一种体内识别码策略，能够按比例确定人类癌细胞系在小鼠异种移植模型中的转移能力。
该研究验证了该方法的可靠性、可扩展性和可重复性，并将其用于21种实体肿瘤的503种细胞系，首次创建了全面完整的转移图谱，犹如封面所示的花瓣，不同的花瓣代表了不同器官的转移能力和转移模式，及其与临床特征和基因组特征的相关性。例如，人类三阴性乳腺癌细胞系MDA-MB-231向脑、骨、肺、肝、肾转移的能力都很强，尤其肝转移。
该研究通过分析乳腺癌能够转移至大脑的分子机制，证实了转移图谱的实用性，而脑转移是此类癌症患者的主要死因。
该研究将被编码的人类癌细胞系注入小鼠异种移植模型，可同时绘制多个细胞系的转移能力，并且发现：乳腺癌脑转移细胞的脂质代谢已经发生变化，而这些细胞的脂质代谢紊乱可抑制脑转移。
因此，该研究结果表明，乳腺癌细胞脂质代谢紊乱有望成为治疗乳腺癌脑转移的新靶点，并且证明转移图谱有助于全面完整的癌症转移研究。
相关链接
Nature. 2020 Dec 10;588(7837):331-336.
A metastasis map of human cancer cell lines.
Xin Jin, Zelalem Demere, Karthik Nair, Ahmed Ali, Gino B. Ferraro, Ted Natoli, Amy Deik, Lia Petronio, Andrew A. Tang, Cong Zhu, Li Wang, Danny Rosenberg, Vamsi Mangena, Jennifer Roth, Kwanghun Chung, Rakesh K. Jain, Clary B. Clish, Matthew G. Vander Heiden, Todd R. Golub.
Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
A method in which pooled barcoded human cancer cell lines are injected into a mouse xenograft model enables simultaneous mapping of the metastatic potential of multiple cell lines, and shows that breast cancer cells that metastasize to the brain have altered lipid metabolism.
Most deaths from cancer are related to tumours spreading to secondary sites in the body through metastasis, yet there are significant gaps in our knowledge of the underlying biology of this process. In this week's issue Todd Golub and his colleagues report the MetMap, a barcoding system that they have used to determine the metastatic potential of human cancer cell lines. The system is based on an analysis of some 500 cell lines representing 21 solid cancer types. From their analyses, the researchers created petal plots, as illustrated on the cover, that relate to the metastatic pattern of the cancer cells. The team used the map to assess breast cancers that metastasize to the brain, finding that this process was linked to changes in lipid metabolism that could be a target for future therapies.
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 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.
KEYWORDS: Breast cancer; Cancer genomics; Cancer models; Metastasis
DOI: 10.1038/s41586-020-2969-2