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Whole-genome landscapes of 1,364 breast cancers

Nature, Published online: 03 December 2025; doi:10.1038/s41586-025-09812-3

Whole-genome and transcriptome analysis of 1,364 cases of breast cancer from South Korea broadens our understanding of breast cancer biology and reveals genomic features that connect tumour biology with treatment responses and clinical outcomes.
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Exosome-Mediated RUNX3 DNA Delivery for Lung Cancer Therapy

ACS Appl Mater Interfaces. 2025 Dec 1. doi: 10.1021/acsami.5c15987. Online ahead of print.

ABSTRACT

Gene therapy represents a promising strategy for treating lung cancer, with the potential to inhibit the proliferation of cancerous cells and induce apoptosis. However, current gene therapy for lung cancer encounters challenges with delivery, targeting, and safety, such as off-target effects, immune responses, and the necessity for better delivery methods. Here, we introduce gene therapy using the key regulator in lung adenocarcinoma, runt-related transcription factor 3 (RUNX3), within exosomes (Exos), which are known for their biocompatibility and ability to selectively target cancer cells. We packaged the RUNX3 plasmid DNA into human exosomes (hExo-Rs), designed to target and induce apoptosis in cancer cells, resulting in a viability decrease to 43.3%. Normal fibroblasts remained viable at 96.0%, confirming the safety of hExo-Rs for future therapies. We delivered hExo-Rs to cancer spheroids, examined their effects, and found that cytokines from treated cells promote M1 macrophage polarization, emphasizing their potential for immunotherapy. We developed a hydrogel platform for the targeted 14-day release of RUNX3 pDNA by attaching hExo-Rs to gelatin using microbial transglutaminase, which enables the selective decrease in cancer cell viability and confirms apoptosis. Our demonstration of RUNX3 gene therapy with Exos presents selective anticancer effectiveness and the promise of clinical use through localized, sustained release using the hydrogel.

PMID:41325015 | DOI:10.1021/acsami.5c15987

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A Multi-faceted Analysis of Cognitive Abilities: Evaluating Prompt Methods with Large Language Models on the CONSORT Checklist

arXiv:2510.19139v1 Announce Type: new Abstract: Despite the rapid expansion of Large Language Models (LLMs) in healthcare, the ability of these systems to assess clinical trial reporting according to CONSORT standards remains unclear, particularly with respect to their cognitive and reasoning strategies. This study applies a behavioral and metacognitive analytic approach with expert-validated data, systematically comparing two representative LLMs under three prompt conditions. Clear differences emerged in how the models approached various CONSORT items, and prompt types, including shifts in reasoning style, explicit uncertainty, and alternative interpretations shaped response patterns. Our results highlight the current limitations of these systems in clinical compliance automation and underscore the importance of understanding their cognitive adaptations and strategic behavior in developing more explainable and reliable medical AI.
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