Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
Nat Commun. 2021 Jan 4;12(1):22. doi: 10.1038/s41467-020-20294-x.
Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.
细胞系是癌症临床前研究的重要工具，但它们在多大程度上能代表患者肿瘤样本仍不清楚。肿瘤和细胞系转录谱的直接比较受到多种因素的影响，包括肿瘤样本中正常细胞的存在情况不同。因此，我们开发了一种无监督的对齐方法（Celligner），并将其应用于整合几个大规模的细胞系和肿瘤 RNA-Seq 数据集。尽管我们的方法可以将大多数细胞系与相同癌症类型的肿瘤样本对齐，但它也揭示了细胞系之间肿瘤相似性的巨大差异。通过这种方法，我们从不同的谱系中鉴定出数百种具有更间质和未分化转录状态的细胞系，并且表现出明显不同的化学和遗传依赖性。Celligner 可用于指导选择更接近患者肿瘤的细胞系，并提高从细胞系中获得的见解的临床转化。
