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Spatial evolution of a cachexia-promoting microenvironment in pancreatic cancer

Cell. 2026 Sep 29:S0092-8674(26)01081-0. doi: 10.1016/j.cell.2026.09.012. Online ahead of print.

ABSTRACT

Cachexia is a major cause of morbidity in pancreatic cancer, but the cellular circuitry linking tumor progression to systemic wasting remains incompletely understood. Integrating single-cell RNA sequencing, Xenium spatial transcriptomics, multiplex immunohistochemistry, bulk transcriptomics, and functional studies across human non-cachexia, pre-cachexia, and cachexia samples, together with mouse models, we define a cachexia-associated microenvironmental niche composed of SEMA4A+ tumor cells, AQP9+ macrophages, and LOXL2+ cancer-associated fibroblasts. Mechanistically, SEMA4A-associated signaling promotes bone morphogenetic protein-2 (BMP2)-dependent acquisition of an AQP9-associated macrophage phenotype, and macrophage-derived CXCL8 activates LOXL2+ fibroblasts. LOXL2+ fibroblasts reciprocally enhance tumor cell FOSL1/SEMA4A signaling through exosomal N-glycosylated LOXL2. Spatial analyses demonstrate progressive enrichment of this niche with cachexia severity and association with postoperative development of cachexia in previously non-cachectic patients. These findings provide a framework linking local tumor ecosystem dynamics to cachexia progression.

PMID:42810340 | DOI:10.1016/j.cell.2026.09.012

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AgentHijack: Visual Patch Attacks on Multimodal Computer-Use Agents

arXiv:2609.09212v1 Announce Type: cross Abstract: This paper presents an end-to-end evaluation framework for image-triggered command injection against computer-use agents (CUAs). The goal is to test whether a local visual patch can induce verifiable environmental consequences along the full chain of screenshot input, VLM generation, action parsing, and environment execution. We train and deploy patches on author-controlled GitHub Pages pages and a locally deployed CSDN clone, and evaluate them in real environments across five open-source or publicly available GUI-agent or vision-language-model (VLM) backends. Our experiment aggregates 600 instance-level online cases, with T-ASR, TAPR, and E2E-ASR reaching 84.5%, 47.0%, and 20.3%, respectively. Trajectory analysis further shows that in some successful cases the agent first executes a malicious terminal command and then continues the original benign task. These results indicate that optimized local visual signals can affect not only VLM outputs but also propagate through the execution pipeline of open CUAs and create real environmental risk.
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