J Adv Res. 2025 Jun 11:S2090-1232(25)00427-8. doi: 10.1016/j.jare.2025.06.017. Online ahead of print.

ABSTRACT

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies.

OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data.

METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes.

RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts.

CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

PMID:40513660 | DOI:10.1016/j.jare.2025.06.017

Front Pharmacol. 2025 Feb 19;16:1540477. doi: 10.3389/fphar.2025.1540477. eCollection 2025.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) remains a major cause of cancer-related mortality worldwide, with high heterogeneity and poor prognosis. Epigenetic dysregulation plays a crucial role in LUAD progression, yet its potential in molecular classification and therapeutic prediction remains largely unexplored.

METHODS: We performed an integrated multi-omics analysis of 432 LUAD patients from TCGA and 398 patients from GEO datasets. Using consensus clustering and random survival forest (RSF) algorithms, we established an epigenetic-based molecular classification system and constructed a prognostic model. The model's performance was validated in multiple independent cohorts, and its biological implications were investigated through comprehensive functional analyses.

RESULTS: We identified two distinct molecular subtypes (CS1 and CS2) with significant differences in epigenetic modification patterns, immune microenvironment, and clinical outcomes (P = 0.005). The RSF-based prognostic model demonstrated robust performance in both training (TCGA-LUAD) and validation (GSE72094) cohorts, with time-dependent AUC values ranging from 0.625 to 0.694. Low-risk patients exhibited enhanced immune cell infiltration, particularly CD8⁺ T cells and M1 macrophages, and showed better responses to immune checkpoint inhibitors. Drug sensitivity analysis revealed subtype-specific therapeutic vulnerabilities, with low-risk patients showing higher sensitivity to conventional chemotherapy and targeted therapy.

CONCLUSION: Our study establishes a novel epigenetic-based classification system and predictive model for LUAD, providing valuable insights into patient stratification and personalized treatment selection. The model's ability to predict immunotherapy response and drug sensitivity offers practical guidance for clinical decision-making, potentially improving patient outcomes through precision medicine approaches.

PMID:40046740 | PMC:PMC11879945 | DOI:10.3389/fphar.2025.1540477