美国斯坦福大学Ash A. Alizadeh、Aaron M. Newman等研究人员合作揭示弥漫性大B细胞淋巴瘤的肿瘤细胞状态和生态系统。2021年9月30日，《癌细胞》杂志在线发表了这项成果。

研究人员开发了EcoTyper，一个整合了转录组解构和单细胞RNA测序的机器学习框架，用于描述临床上相关的弥漫性大B细胞淋巴瘤（DLBCL）细胞状态和生态系统。使用这种方法，研究人员确定了恶性B细胞的五种细胞状态，它们在预后关联和分化状态上有所不同。研究人员还确定了组成肿瘤微环境（TME）的其他12个谱系细胞状态的显著变化，并在定型的生态系统中形成细胞状态的相互作用。

虽然原发细胞亚型有不同的TME组成，但DLBCL生态系统捕捉到了现有亚型中的临床异质性，并超出了原发细胞和基因型的范畴。这些结果在系统层面解决了DLBCL微环境问题，并确定了治疗靶向的机会（https://ecotyper.stanford.edu/lymphoma）。

据介绍，DLBCL的生物异质性部分由原发细胞亚型和相关基因组病变驱动，但也由TME中的不同细胞类型和细胞状态驱动。然而，大规模地剖析这些细胞状态及其临床相关性仍然具有挑战性。

附：英文原文

Title: The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma

Author: Chloé B. Steen, Bogdan A. Luca, Mohammad S. Esfahani, Armon Azizi, Brian J. Sworder, Barzin Y. Nabet, David M. Kurtz, Chih Long Liu, Farnaz Khameneh, Ranjana H. Advani, Yasodha Natkunam, June H. Myklebust, Maximilian Diehn, Andrew J. Gentles, Aaron M. Newman, Ash A. Alizadeh

Issue&Volume: 2021-09-30

Abstract: Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly drivenby cell-of-origin subtypes and associated genomic lesions, but also by diverse celltypes and cell states in the tumor microenvironment (TME). However, dissecting thesecell states and their clinical relevance at scale remains challenging. Here, we implementedEcoTyper, a machine-learning framework integrating transcriptome deconvolution andsingle-cell RNA sequencing, to characterize clinically relevant DLBCL cell statesand ecosystems. Using this approach, we identified five cell states of malignant Bcells that vary in prognostic associations and differentiation status. We also identifiedstriking variation in cell states for 12 other lineages comprising the TME and formingcell state interactions in stereotyped ecosystems. While cell-of-origin subtypes havedistinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existingsubtypes and extend beyond cell-of-origin and genotypic classes. These results resolvethe DLBCL microenvironment at systems-level resolution and identify opportunitiesfor therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).

DOI: 10.1016/j.ccell.2021.08.011

Source: https://www.cell.com/cancer-cell/fulltext/S1535-6108(21)00451-7