微软研究院Hoifung Poon小组的一项最新研究揭示了多模态人工智能为肿瘤微环境建模生成虚拟种群。相关论文发表在2025年12月9日出版的《细胞》杂志上。
研究组提出了GigaTIME，这是一个通过桥接细胞形态和状态进行种群尺度时间建模的多模态AI框架。GigaTIME学习了一个跨模态翻译者，通过训练4000万个细胞，在21种蛋白质中使用配对的H&E和mIF数据，从苏木精和伊红（H&E）载片生成虚拟的mIF图像。该课题组人员将GigaTIME应用于来自美国七个州的51家医院和1000多家诊所的14256名患者，生成了299376张虚拟mIF幻灯片，涵盖24种癌症类型和306种亚型。这个虚拟人群发现了1234个具有统计学意义的关联，这些关联涉及蛋白质、生物标志物、分期和生存。由于缺乏mIF数据，这种分析以前是不可行的。对10200例TCGA患者的独立验证进一步证实了他们的发现。
研究人员表示，肿瘤免疫微环境（TIME）对肿瘤进展和免疫治疗反应有重要影响。多路免疫荧光（multiple immunofluorescence, mIF）是一种功能强大的TIME解码成像方式，但其应用受到高成本和低通量的限制。
附：英文原文
Title: Multimodal AI generates virtual population for tumor microenvironment modeling
Author: Jeya Maria Jose Valanarasu, Hanwen Xu, Naoto Usuyama, Chanwoo Kim, Cliff Wong, Peniel Argaw, Racheli Ben Shimol, Angela Crabtree, Kevin Matlock, Alexandra Q. Bartlett, Jaspreet Bagga, Yu Gu, Sheng Zhang, Tristan Naumann, Bernard A. Fox, Bill Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, Hoifung Poon
Issue&Volume: 2025-12-09
Abstract: The tumor immune microenvironment (TIME) critically impacts cancer progression and immunotherapy response. Multiplex immunofluorescence (mIF) is a powerful imaging modality for deciphering TIME, but its applicability is limited by high cost and low throughput. We propose GigaTIME, a multimodal AI framework for population-scale TIME modeling by bridging cell morphology and states. GigaTIME learns a cross-modal translator to generate virtual mIF images from hematoxylin and eosin (H&E) slides by training on 40 million cells with paired H&E and mIF data across 21 proteins. We applied GigaTIME to 14,256 patients from 51 hospitals and over 1,000 clinics across seven US states in Providence Health, generating 299,376 virtual mIF slides spanning 24 cancer types and 306 subtypes. This virtual population uncovered 1,234 statistically significant associations linking proteins, biomarkers, staging, and survival. Such analyses were previously infeasible due to the scarcity of mIF data. Independent validation on 10,200 TCGA patients further corroborated our findings.
DOI: 10.1016/j.cell.2025.11.016
Source: https://www.cell.com/cell/abstract/S0092-8674(25)01312-1