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Advances in single-cell and spatial multi-omics for deciphering the mechanisms of pan-organ metastasis in breast cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yang Qu · Juan Sun · Zihao Wang · Qiang Sun · Yidong Zhou

15 September 2026 at 18:00

Biochim Biophys Acta Rev Cancer. 2026 Sep 15:189717. doi: 10.1016/j.bbcan.2026.189717. Online ahead of print.

ABSTRACT

Breast cancer deaths are mainly caused by metastasis to distant organs, not by the primary tumor. Bone, lung, liver, and brain are the most common metastatic sites, each showing different clinical behaviors and treatment responses-a pattern often called metastatic organotropism. Bulk omics can provide tissue-level information, but they fall short in identifying rare metastasis-initiating clones or capturing how tumor cells adapt to distinct organ microenvironments. With recent progress in single-cell sequencing, multi-omics integration, and spatial profiling, it is now possible to study metastasis at much finer cellular and spatial resolution. In this review, we synthesize current evidence from two complementary perspectives. First, we summarize pan-organ programs associated with metastatic competence, including partial epithelial-mesenchymal transition, lineage plasticity, stem-like states, stress tolerance, metabolic flexibility, immune evasion, and stromal-vascular remodeling. Second, we discuss how these programs are reshaped by organ-specific microenvironments: osteolytic and mixed bone remodeling and marrow dormancy in bone, inflammatory vascular niches in lung, tolerogenic antigen presentation and hepatic metabolism in liver, and blood-brain/blood-tumor barrier constraints, glial crosstalk, neuronal interactions, and lipid-metabolic adaptation in brain. We also highlight how CTC/CTM profiling, spatial mapping, and longitudinal integration refine the understanding of dissemination, dormancy, colonization, outgrowth, and treatment resistance. Although these approaches hold translational promise, most remain at the discovery or early validation stage and require assay simplification, prospective testing, and cross-center standardization. Overall, single-cell and spatial multi-omics are reframing breast cancer metastasis as a dynamic, multi-stage, and tissue-shaped process, providing a foundation for future biomarker development and mechanism-guided therapeutic strategies.

PMID:42744123 | DOI:10.1016/j.bbcan.2026.189717

CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment

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

By: Maoyuan Shao · Yutong Gao · Xinyang Huang · Chuang Zhu · Lijuan Sun · Guoshun Nan

4 March 2026 at 13:00

arXiv:2603.02557v1 Announce Type: cross Abstract: Vision-language models like CLIP have achieved remarkable progress in cross-modal representation learning, yet suffer from systematic misclassifications among visually and semantically similar categories. We observe that such confusion patterns are not random but persistently occur between specific category pairs, revealing the model's intrinsic bias and limited fine-grained discriminative ability. To address this, we propose CAPT, a Confusion-Aware Prompt Tuning framework that enables models to learn from their own misalignment. Specifically, we construct a Confusion Bank to explicitly model stable confusion relationships across categories and misclassified samples. On this basis, we introduce a Semantic Confusion Miner (SEM) to capture global inter-class confusion through semantic difference and commonality prompts, and a Sample Confusion Miner (SAM) to retrieve representative misclassified instances from the bank and capture sample-level cues through a Diff-Manner Adapter that integrates global and local contexts. To further unify confusion information across different granularities, a Multi-Granularity Difference Expert (MGDE) module is designed to jointly leverage semantic- and sample-level experts for more robust confusion-aware reasoning. Extensive experiments on 11 benchmark datasets demonstrate that our method significantly reduces confusion-induced errors while enhancing the discriminability and generalization of both base and novel classes, successfully resolving 50.72 percent of confusable sample pairs. Code will be released at https://github.com/greatest-gourmet/CAPT.