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From the invasive front to organotropic pre-metastatic niches: spatial immune regulatory networks governing cholangiocarcinoma dissemination and metastasis-intercepting immunotherapy

By: Longhao Zhang · Kai Zhang · Zhihong Chen · Xin Lu

4 September 2026 at 18:00

Front Immunol. 2026 Aug 20;17:1919864. doi: 10.3389/fimmu.2026.1919864. eCollection 2026.

ABSTRACT

Cholangiocarcinoma is an aggressive biliary tract malignancy in which metastatic relapse and primary or acquired resistance to immunotherapy remain major causes of mortality. Although immune checkpoint inhibitors have improved first-line treatment for advanced biliary tract cancer, most patients do not achieve durable benefit, indicating that immune failure is not explained by a single checkpoint pathway. In this Review, we propose a spatial immune-regulatory continuum for cholangiocarcinoma dissemination. Most direct single-cell and spatial evidence currently derives from intrahepatic cholangiocarcinoma, and its applicability to perihilar and distal disease remains to be established. This continuum begins in the tumor core and invasive front, where malignant cells, cancer-associated fibroblasts, tumor-associated macrophages, endothelial and lymphatic cells, regulatory T cells, immature neutrophils and excluded or dysfunctional cytotoxic T cells form a pro-invasive ecosystem. It then extends through extracellular vesicles, soluble mediators and lymphovascular routes that may educate organotropic pre-metastatic niches. Finally, lymph node, lung, liver, peritoneal and bone microenvironments provide organ-specific extracellular matrix, myeloid and stromal programs that enable immune evasion and metastatic colonization. By integrating clinical evidence, multi-omics studies, single-cell and spatial transcriptomics, extracellular vesicle biology, pre-metastatic niche concepts and emerging therapeutic strategies, we argue that cholangiocarcinoma metastasis should be targeted before overt dissemination whenever possible. In this Review, "metastasis-intercepting immunotherapy" is used as an author-defined conceptual framework for strategies intended to prevent or disrupt the immune-stromal conditions that enable dissemination and colonization, rather than merely shrink established metastatic lesions. Metastasis-intercepting immunotherapy will likely require rational combinations that reprogram the invasive front, restore dendritic-cell-mediated antigen presentation, block tumor-stroma-myeloid circuits, disrupt EV-mediated communication that may contribute to niche formation and select patients using spatial biomarkers rather than bulk immune markers alone.

PMID:42694469 | PMC:PMC13539491 | DOI:10.3389/fimmu.2026.1919864

Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

cs.AI, q-bio.NC updates on arXiv.org

By: Louis Blankemeier · Ashwin Kumar · Joseph Paul Cohen · Jiaming Liu · Longchao Liu · Dave Van Veen · Syed Jamal Safdar Gardezi · Hongkun Yu · Magdalini Paschali · Zhihong Chen · Jean-Benoit Delbrouck · Eduardo Reis · Robbie Holland · Cesar Truyts · Christian Bluethgen · Yufu Wu · Long Lian · Malte Engmann Kjeldskov Jensen · Sophie Ostmeier · Maya Varma · Jeya Maria Jose Valanarasu · Zhongnan Fang · Zepeng Huo · Zaid Nabulsi · Diego Ardila · Wei-Hung Weng · Edson Amaro Junior · Neera Ahuja · Jason Fries · Nigam H. Shah · Greg Zaharchuk · Marc Willis · Adam Yala · Andrew Johnston · Robert D. Boutin · Andrew Wentland · Curtis P. Langlotz · Jason Hom · Sergios Gatidis · Akshay S. Chaudhari

5 March 2026 at 13:00

arXiv:2406.06512v2 Announce Type: replace-cross Abstract: The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images and radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. Here to overcome these shortcomings for abdominal CT interpretation, we introduce Merlin, a 3D VLM that learns from volumetric CT scans, electronic health record data and radiology reports. This approach is enabled by a multistage pretraining framework that does not require additional manual annotations. We trained Merlin using a high-quality clinical dataset of paired CT scans (>6 million images from 15,331 CT scans), diagnosis codes (>1.8 million codes) and radiology reports (>6 million tokens). We comprehensively evaluated Merlin on 6 task types and 752 individual tasks that covered diagnostic, prognostic and quality-related tasks. The non-adapted (off-the-shelf) tasks included zero-shot classification of findings (30 findings), phenotype classification (692 phenotypes) and zero-shot cross-modal retrieval (image-to-findings and image-to-impression). The model-adapted tasks included 5-year chronic disease prediction (6 diseases), radiology report generation and 3D semantic segmentation (20 organs). We validated Merlin at scale, with internal testing on 5,137 CT scans and external testing on 44,098 CT scans from 3 independent sites and 2 public datasets. The results demonstrated high generalization across institutions and anatomies. Merlin outperformed 2D VLMs, CT foundation models and off-the-shelf radiology models. We also release our trained models, code, and dataset, available at: https://github.com/StanfordMIMI/Merlin.