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Open access Aug 2026

Multi‐Omics Integration Identifies a CDH3‐Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors

ABSTRACT Thymic epithelial tumors (TETs) are rare and heterogeneous malignancies whose aggressive epithelial states and microenvironmental organization remain poorly defined. Here, we integrated single‐cell RNA sequencing, spatial transcriptomics, multiplex immunofluorescence, bulk transcriptomics, functional assays, xenograft validation, and computational pathology to characterize malignant epithelial heterogeneity in TETs. We identified a CDH3‐associated malignant epithelial state located at the origin of malignant‐state trajectories and enriched for stem‐like and EMT‐related features. Spatial transcriptomics and multiplex immunofluorescence showed that CDH3+ tumor cells preferentially localized within an M2 macrophage‐rich immunosuppressive niche, while cell–cell communication analyses nominated CCN2–LRP1 as a candidate epithelial–myeloid crosstalk axis. A 68‐gene CDH3‐associated signature stratified TCGA‐THYM into biologically distinct subgroups with differences in survival, histology, genomic instability, and immune contexture. Patient‐derived thymic carcinoma organoids showed elevated CDH3 expression, and CDH3 silencing suppressed thymic carcinoma cell proliferation, migration, invasion, EMT/PI3K–Akt‐related signaling, and macrophage‐associated crosstalk. Candidate inhibitors showed antitumor activity in xenograft models. We also established a deep learning pathology model that captured CDH3‐associated morphology from routine H&E slides and predicted patient outcome. Together, these findings define CDH3 as a biomarker and therapeutic target linking malignant epithelial plasticity to immunosuppressive niche formation and adverse clinical behavior in TETs.

Yuntao Feng, Jing-Yu Chen, Lang Xia et al. · 0 citations
Aug 2026

The Multimodal Pretraining Framework CarHE Predicts Spatial Transcriptomics in Tumors from Routine Pathology Images.

Spatial transcriptomic analyses provide spatially resolved gene expression data that can provide insights into complex biological processes. However, current spatial transcriptomics approaches remain financially prohibitive and restricted in resolution, scalability, and gene coverage, limiting broader adoption for large-scale studies. Here, we developed CarHE (contrastive alignment of gene expression for hematoxylin and eosin images), a multimodal pretraining framework that infers high-dimensional spatial transcriptomic profiles from routine H&E-stained slides. By using contrastive learning to align cell type-specific transcriptomic information with histological features, CarHE achieved high prediction accuracy across evaluated datasets and spatial transcriptomics platforms. CarHE approximated spatially organized pathological microenvironment features consistent with tertiary lymphoid structure (TLS)-associated regions in breast cancer, lung cancer, melanoma, and clear cell renal cell carcinoma. Additionally, CarHE inferred approximated 3D spatial transcriptomic context from 2D images, providing more informative neighborhood context than 2D visualization. In a cohort of 880 lung cancer patients, CarHE-derived features were associated with disease-free survival and outperformed current approaches. Overall, CarHE provides a cost-effective and scalable framework for H&E-based spatial inference, supporting further validation toward translational research applications.

Jiawei Zou, Kai Xiao, Zexi Chen et al. · 0 citations

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