PO.BCS01.06 · 生物信息与计算
从像素到空间微环境：H&E切片上AI驱动的CAF-Epi生态位预测
From pixels to spatial microenvironments: AI-powered CAF-Epi niche prediction on H&E slides
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
常规H&E切片仍然是临床实践中最易获取且最快速的诊断材料，尤其对于胃肠道癌症。然而，尽管H&E能有效区分癌症与非癌症，但它提供的信息不足以预测治疗反应。空间生态位可被概念化为多细胞微解剖单元——由细胞邻接、相互作用和协调信号传导的特征性模式所定义。利用跨多阶段食管鳞状细胞癌（ESCC）的空间转录组学，我们识别出一个CAF-上皮（CAF-Epi）生态位，其中癌相关成纤维细胞和上皮细胞协同保护肿瘤细胞免受免疫监视。在一个新辅助免疫治疗队列中，我们发现源自该生态位的CAF-Epi评分能够稳健地区分应答者与非应答者并对长期生存进行分层，表明这一功能性生态位——而非细胞比例或形态的细微变化——更直接地决定治疗结局。为了在常规组织病理学上实现可扩展的CAF-Epi评估，我们开发了SHEEN-CAF（基于空间H&E的CAF-Epi生态位模型），这是一个直接从H&E切片识别ST定义的CAF-Epi生态位的深度学习模型。我们组建了一个配对的H&E-ST图谱，包含198例多阶段ESCC标本，总计960万个细胞标注和275,753个CAF-Epi标记的FOV。SHEEN-CAF学习多尺度形态学表征并重建全切片CAF-Epi分布。在测试集中，它实现了0.91的AUC，跨中心准确率为87-94%。在一个具有配对H&E和多重免疫荧光的独立外部验证队列中，SHEEN-CAF衍生的CAF-Epi评分与免疫荧光定义的CAF-Epi信号相关（r=0.81，P<0.01），并区分高与低CAF-Epi负荷病灶（AUC=0.89）。在一个接受免疫检查点阻断治疗的ESCC队列中，非应答者显示出显著高于应答者的CAF-Epi评分（P<0.05），将常规H&E上的CAF-Epi负荷与免疫治疗耐药联系起来。总之，SHEEN-CAF将CAF-Epi生态位负荷——此前只能用先进的空间组学测量——转化为一种仅从常规诊断H&E切片衍生的自动化、定量生物标志物。由于无需额外组织或测序，它提供了一种高效且可扩展的空间微环境测量方法，有望改进ESCC中的免疫治疗反应评估和风险分层。
查看英文原文 English abstract
Routine H&E slides remain the most accessible and rapid diagnostic material in clinical practice, particularly for gastrointestinal cancers. However, although H&E effectively distinguishes cancer from non-cancer, it provides insufficient information to predict treatment response. Spatial niches can be conceptualized as multicellular microanatomical units-defined by characteristic patterns of cellular adjacency, interaction and coordinated signaling. Using spatial transcriptomics across multistage esophageal squamous cell carcinoma (ESCC), we identified a CAF-epithelial (CAF-Epi) niche in which cancer-associated fibroblasts and epithelial cells cooperatively shield tumor cells from immune surveillance. In a neoadjuvant immunotherapy cohort, we found CAF-Epi score derived from this niche robustly discriminated responders from non-responders and stratified long-term survival, indicating that this functional niche, rather than subtle changes in cell proportions or morphology, more directly governs treatment outcome. To enable scalable CAF-Epi assessment on routine histopathology, we developed SHEEN-CAF (Spatial H&E-based CAF-Epi niche model), a deep-learning model that identifies ST-defined CAF-Epi niches directly from H&E slides. We assembled a paired H&E-ST atlas of 198 multistage ESCC specimens, totaling 9.6 million cell annotations and 275,753 CAF-Epi-labeled FOVs. SHEEN-CAF learns multi-scale morphological representations and reconstructs whole-slide CAF-Epi distributions. In the test set, it achieved an AUC of 0.91 with cross-center accuracies of 87-94%. In an independent external validation cohort with paired H&E and multiplex immunofluorescence, SHEEN-CAF-derived CAF-Epi scores correlated with immunofluorescence-defined CAF-Epi signals (r = 0.81, P < 0.01) and discriminated high- versus low-CAF-Epi burden lesions (AUC = 0.89). In an ESCC cohort treated with immune checkpoint blockade, non-responders exhibited significantly higher CAF-Epi scores than responders (P < 0.05), linking CAF-Epi burden on routine H&E to immunotherapy resistance. In summary, SHEEN-CAF converts CAF-Epi niche burden-previously measurable only with advanced spatial-omics-into an automated, quantitative biomarker derived solely from routine diagnostic H&E slides. By requiring no additional tissue or sequencing, it provides an efficient and scalable measure of the spatial microenvironment, with potential to refine immunotherapy response assessment and risk stratification in ESCC.
利益披露 Disclosure
Z. You, None..
Q. Li, None..
J. Zhu, None..
G. Cheng, None..
Y. Shen, None..
C. Wu, None.