Cancer Treat Res Commun. 2026 Mar 2;47:101162. doi: 10.1016/j.ctarc.2026.101162. Online ahead of print.

ABSTRACT

BACKGROUND: The collection of clinical data and patient tumor specimens in institutional repositories is essential to accelerate translational research in lung cancer, linking laboratory findings with patient outcomes. These resources allow investigators to explore tumor heterogeneity, analyze therapeutic profiles and, more recently, generate patient-derived models of cancer. Given the plethora of therapies in clinical use or under investigation, it is critical to establish tissue collection programs that support the identification of predictive biomarkers of drug sensitivity to define patient subgroups that may benefit from tailored therapeutic strategies. However, access to high-quality viable specimens remains limited.

METHODS: We established a multidisciplinary program -the Lung Cancer Tissue Collection (LCTC) study- to prospectively collect viable human specimens and clinical data. Samples can be collected post-diagnosis and at multiple treatment time points, preserving material for future studies.

RESULTS: In the first 24 months of the LCTC study, we enrolled 158 patients and collected over 700 specimens from patients with lung cancer. EGFR and KRAS mutations were the most frequently identified oncogenic drivers, mirroring frequencies reported in public datasets. We achieved a 60 % success rate in cryopreservation -measured by the proportion of patient-derived organoids growing after tissue thawing and processing- highlighting the feasibility of our program.

CONCLUSIONS: The LCTC biobank captures the molecular and clinical diversity of lung cancer, providing a clinically annotated resource of viable tissue and longitudinal blood specimens. This platform enables patient-derived modeling and multi-omic and functional studies to investigate tumor biology, treatment response, and resistance, supporting biomarker discovery and precision medicine.

PMID:41797251 | DOI:10.1016/j.ctarc.2026.101162

Chin Med J (Engl). 2025 Nov 28;138(24):3332-50. doi: 10.1097/CM9.0000000000003922. Online ahead of print.

ABSTRACT

Personalized medicine for gastric cancer continues to face numerous challenges, primarily due to the complexity of clinical decision making and the difficulty of integrating multimodal data. Artificial intelligence (AI), with its powerful capabilities in feature learning and pattern recognition, is emerging as a key technology to overcome these barriers. It provides critical support in areas such as early screening, histological subtyping, prediction of treatment response, and prognostic risk stratification. This review examines the application of AI in diagnosing and treating gastric cancer, with particular attention to the current mainstream AI methodologies, including feature engineering and deep learning and the rapidly evolving pretrained foundation models and multimodal large models. With the integration of medical images, digital pathology, multiomics data, and structured clinical information, AI systems are increasingly effective at capturing tumor heterogeneity and supporting complex clinical decisions in real time. On the one hand, task-specific models have demonstrated excellent performance in subtyping, staging, and prognosis assessment. On the other hand, the rise of foundation models and general-purpose large models is redefining the limits of AI in cross-task transfer, complex reasoning, and human-machine interaction. These technologies hold promise in addressing key obstacles such as data scarcity, modality heterogeneity, and fragmented clinical workflows, offering a feasible path toward a unified and efficient AI-driven diagnostic and therapeutic system for gastric cancer. As technological maturity progresses alongside the development of robust safety and ethical frameworks, AI is expected to evolve from a static auxiliary interpretation tool into an intelligent decision-making platform capable of semantic understanding, dynamic feedback, and multidisciplinary collaboration-therefore playing a pivotal role across the full spectrum of precision medicine in gastric cancer.

PMID:41400327 | PMC:PMC12721780 | DOI:10.1097/CM9.0000000000003922