Transl Lung Cancer Res. 2025 Sep 30;14(9):4112-4130. doi: 10.21037/tlcr-2025-350. Epub 2025 Sep 18.

ABSTRACT

Lung squamous cell carcinoma (LUSC), a predominant type of lung cancer, is marked by an unfavorable prognosis and limited therapeutic options. Unlike lung adenocarcinoma (LUAD), LUSC exhibits few driver mutations, resulting in minimal benefits from targeted therapies for these patients. Despite the transformative effects of immunotherapy on patient outcomes, only a subset of patients achieving durable responses. This heterogeneity in treatment outcomes is increasingly attributed to the complex feature of the tumor immune microenvironment (TIME) in LUSC. The TIME of LUSC is a highly dynamic ecosystem composed of diverse immune cell populations and stromal components that collectively foster an immune-evasive niche. Recent breakthroughs in multi-omics technologies, particularly single-cell RNA sequencing (scRNA-seq) and spatial omics, have provided unprecedented resolution in dissecting the cellular and molecular architecture of the TIME in LUSC. These technologies have enabled the identification of distinct immune cells and their spatial interactions with the tumor, shedding light on the mechanisms underlying immune evasion and resistance to immunotherapy. Building on these advancements, this review establishes a new classification of the TIME which may guide patient stratification and personalized immunotherapy. And we comprehensively offer a detailed examination of the principal characteristics and regulatory mechanisms of the TIME, highlighting potential immunotherapeutic strategies tailored to this distinct immunological context.

PMID:41133013 | PMC:PMC12541881 | DOI:10.21037/tlcr-2025-350

Transl Lung Cancer Res. 2025 Sep 30;14(9):3645-3658. doi: 10.21037/tlcr-2025-603. Epub 2025 Sep 25.

ABSTRACT

BACKGROUND: Lung cancer poses a major public health challenge, accounting for the highest cancer-related mortality worldwide. This study aimed to identify non-invasive biomarkers for the early detection of non-small cell lung cancer (NSCLC) risk.

METHODS: We randomly selected 150 incident NSCLC cases during follow-up from the Korean Cancer Prevention Study-II. Controls (n=150) were matched to cases by age, gender, and the time of blood collection. Non-targeted metabolite screening by ultra-high-performance liquid chromatography (UHPLC)/mass spectrometry (MS) was conducted on the pre-diagnostic biological samples. The 11 reported lung cancer-associated single-nucleotide polymorphisms (SNPs) in Koreans were extracted from DNA genotyping data of the study population. Metabolite markers related to NSCLC risk were identified through clustering using hierarchical density-based spatial clustering of applications with noise. The associations between smoking, dietary factors, and NSCLC were also examined.

RESULTS: Six discriminative serum metabolites were identified as having an association with NSCLC incidence. Notably, the relationship between specific metabolite levels and NSCLC risk differed by rs7086803 genotype. Smoking status and occupational exposures appear to influence specific metabolite profiles, while dietary vegetable intake may modulate the risk of NSCLC among smokers.

CONCLUSIONS: The meaningful biomarkers revealed in the current research could be used to enhance the predictive ability for NSCLC risk. Furthermore, we suggest that the protective role of dietary vegetables against NSCLC may be attenuated or absent in smokers.

PMID:41133005 | PMC:PMC12541849 | DOI:10.21037/tlcr-2025-603

Biochim Biophys Acta Rev Cancer. 2025 Oct 23:189484. doi: 10.1016/j.bbcan.2025.189484. Online ahead of print.

ABSTRACT

The five-year survival rate for Pancreatic Ductal Adenocarcinoma (PDAC) remains below 10 %, primarily due to the limited efficacy of conventional chemotherapy and immune checkpoint inhibitors against its triple-immune-sequestered, low-TMB tumor microenvironment(TME). This situation has been furter exacerbated by the stagnation of traditional vaccine development, driven by inefficient antigen screening and high tumor heterogeneity. Artificial intelligence (AI) exhibits remarkable advantages in the design of pancreatic ductal adenocarcinoma (PDAC) vaccines. It can integrate multi - omics data to efficiently unearth cryptic neoantigens from low - tumor mutation burden (TMB) samples, significantly enhancing the screening efficiency. Through dynamic modeling, AI can rationally plan the timing of combined vaccine therapies, effectively reducing the degree of T - cell exhaustion. By leveraging the digital twin model, AI can remarkably improve the matching accuracy between antigens and human leukocyte antigen (HLA). Additionally, it can construct a monitoring system to provide early warnings of antigen loss risks, thus gaining adjustment time for clinical treatments.This review aims to accomplish three primary objectives: demonstrate AI's potential in breaking the therapeutic impasse to overcome manufacturing-related treatment delays for 25-30 % of patients; further delineates the logical progression of AI from concept to clinical application; thereby provides a translational framework to bridge the gap between research and patient benefit.

PMID:41138796 | DOI:10.1016/j.bbcan.2025.189484