美国斯坦福大学Aaron M. Newman团队提出了空间生态型肿瘤微环境的非侵入性分析。2026年5月6日出版的《自然》发表了这项成果。

课题组研究人员提出了一个机器学习框架，用于空间依赖性细胞状态和多细胞生态系统的多分析分析，称为空间生态型（SEs）。通过对10积分从不同的人类癌和黑素瘤中，课题组发现了9个具有广泛保守性的SEs，每个都具有独特的生物学、地理空间特征和临床结果关联，其中一些与免疫治疗反应有关。值得注意的是，SEs可以通过DNA甲基化分析来区分，并且可以从无浆细胞DNA （cfDNA）主题深度学习中恢复。在近100例黑色素瘤患者的cfDNA中，SE水平与免疫治疗反应表现出显著的相关性。他们的数据揭示了TME组织的基本单位，并展示了分析固体和液体TME的多模式平台，这对改善风险分层和治疗个性化具有重要意义。

研究人员表示，肿瘤微环境（TME）中的多细胞程序驱动癌症发病机制和对治疗的反应，但在临床鉴定和描述方面仍然具有挑战性。

附：英文原文

Title: Non-invasive profiling of the tumour microenvironment with spatial ecotypes

Author: Zhang, Wubing, Brown, Erin L., Usmani, Abul, Earland, Noah, Kang, Minji, Olelewe, Chibuzor, Viswanathan, Anushka, Chauhan, Pradeep S., Steen, Chlo B., Jeon, Hyun Soo, Avagyan, Susanna, Alahi, Irfan, Semenkovich, Nicholas P., Schwab, Janella C., Sachs, Chloe M., Qaium, Faridi, Harris, Peter K., Cai, Qingyuan, Gentles, Andrew J., Knight, James, Graham, Rondell P., Bacchiocchi, Antonietta, Lucas, Peter C., Fields, Ryan C., Sznol, Mario, Halaban, Ruth, Chen, David Y., Chaudhuri, Aadel A., Newman, Aaron M.

Issue&Volume: 2026-05-06

Abstract: Multicellular programs in the tumour microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically1,2,3. Here, we present a machine-learning framework for multi-analyte profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). By integrating over 10million single-cell and spot-level spatial transcriptomes from diverse human carcinomas and melanomas, we identified nine SEs with broad conservation, each of which has unique biology, geospatial features and clinical outcome associations, including several linked to immunotherapy response. Notably, SEs were distinguishable by DNA methylation profiling and were recoverable from plasma cell-free DNA (cfDNA) using deep learning. In cfDNA from nearly 100 patients with melanoma, SE levels exhibited striking associations with immunotherapy response. Our data reveal fundamental units of TME organization and demonstrate a multimodal platform for profiling solid and liquid TMEs, with implications for improved risk stratification and therapy personalization.

DOI: 10.1038/s41586-026-10452-4

Source: https://www.nature.com/articles/s41586-026-10452-4