Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-025-01108-9Applications of digital pathology in clinical oncology have largely depended on the requirement for labeled data and model re-training. A study now presents PRET, a training-free framework with robust performance for pan-cancer diagnosis that adapts pathology foundation models to diverse tasks at inference stage, from screening and subtyping tasks to segmentation and metastasis detection tasks.

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01138-xLeptomeningeal metastases are a devastating complication of solid tumors; how this niche becomes pro-metastatic remains poorly understood. A study now reveals that tumor cells prime a pre-metastatic niche within the leptomeninges via exosome-derived 5-HIAA, triggering vascular leakiness and accumulation of disseminated tumor cells.

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01145-yHuang, Hou, Yang et al. demonstrate that leptomeningeal metastatic cells favor the formation of a premetastatic niche by remodeling the choroid plexus vasculature through the serotonin metabolite 5-hydroxyindoleacetic acid, which signals into endothelial cells through the aryl hydrocarbon receptor.

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01141-2Li et al. present PRET, a few-shot system for pan-cancer detection not requiring model fine-tuning, validated it in multicenter datasets and found that it outperformed existing approaches across tasks and pathologists in lymph node metastasis detection.

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01142-1Izar and colleagues review the insights into cancer biology gained via single-cell analyses and spatial profiling and overview the challenges and opportunities associated with the implementation of these approaches to guide clinical discovery.