Our study developed a multiomics-driven transformer model that combines mammography, MRI, transcriptomic and proteomic data to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. A total of 2105 patients from 10 institutions were included for model training and validation. The model achieved an AUC of 0.939 in the training cohort (n = 658) and 0.830-0.867 across three independent validation cohorts (n = 282, 971 and 194, respectively), outperforming conventional ultrasound examination. Grad-CAM visualizations highlighted the tumor edges and surrounding tissue, consistent with clinical and pathological findings. In a cohort of 194 patients, multiomics analyses linked the model output to gene and protein signatures involved in immune modulation, cytoskeletal remodeling, and epithelial-to-mesenchymal transition. Critically, the major enriched pathways identified through model-stratified analysis were independently replicated in a parallel non-model-driven analysis using ALN status, demonstrating that these signatures reflect tumor biology. Network analysis revealed gene clusters related to DNA replication and immune pathways, providing biological insights into the model's decisions. These findings suggest that the stacking model holds promise as a noninvasive decision-support tool that may complement, rather than replace, current clinical staging practices. However, integration into clinical workflows requires prospective validation.
Xiaodong Liu, Fan Li, Ye Xiang et al.· npj Precision Oncology· 0 citations
Metabolic reprogramming constitutes a fundamental hallmark of malignancy, orchestrating a hostile tumor microenvironment (TME) that severely compromises anti-tumor immunity. Despite the transformative success of immune checkpoint blockade and adoptive cell therapies, clinical efficacy is frequently curtailed by the metabolic barriers imposed by the TME. This review systematically elucidates the complex metabolic interplay between tumor cells and infiltrating T cells, highlighting two defining mechanisms driving immune evasion: the competitive sequestration of essential nutrients and the accumulation of immunosuppressive oncometabolites. We detail how the depletion of glucose and critical amino acids (glutamine, arginine, methionine, etc.) imposes a state of “metabolic siege” on T cells, impairing their bioenergetics and effector functions. Concurrently, we explore how accumulated metabolites—such as lactate, succinate, 2-hydroxyglutarate, kynurenine, and lipids—function as non-canonical signaling molecules to subvert immune surveillance via epigenetic remodeling and oxidative stress. Furthermore, we synthesize emerging therapeutic strategies designed to dismantle this metabolic barrier, including targeting metabolic enzymes (IDO1 and FASN) and transporters, repurposing metabolic waste, and genetically engineering T cells with enhanced metabolic fitness and resilience. By integrating the latest insights into the “metabolism–epigenetics–immunity” axis, this review provides a theoretical foundation for developing next-generation immunotherapies that target metabolic vulnerabilities to overcome resistance in cancer treatment.
Jia-Ning Zhang, Zi-Mei Tang, Yiran Wang et al.· Cells· 0 citations
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