生物
弥漫性大B细胞淋巴瘤
转录组
细胞
肿瘤微环境
淋巴瘤
癌症研究
计算生物学
遗传学
肿瘤细胞
免疫学
基因
基因表达
作者
Chloé B. Steen,Bogdan A. Luca,Mohammad Shahrokh Esfahani,Armon Azizi,Brian Sworder,Barzin Y. Nabet,David M. Kurtz,Chih Long Liu,Farnaz Khameneh,Ranjana H. Advani,Yasodha Natkunam,June Helen Myklebust,Maximilian Diehn,Andrew J. Gentles,Aaron M. Newman,Ash A. Alizadeh
出处
期刊:Cancer Cell
[Elsevier]
日期:2021-10-01
卷期号:39 (10): 1422-1437.e10
被引量:101
标识
DOI:10.1016/j.ccell.2021.08.011
摘要
Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).
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