Transl Oncol. 2026 Jan 6;64:102660. doi: 10.1016/j.tranon.2025.102660. Online ahead of print.

ABSTRACT

Lung cancer remains the leading cause of cancer incidence and mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for about 85% of cases. The low rate of early diagnosis and the high rate of occult metastases limit the survival benefits of conventional treatments. The current TNM staging system fails to fully reflect tumor heterogeneity or the dynamic molecular evolution of the disease, thus affecting the prediction of recurrence and the prognostic stratification. Some recent advances in minimal residual disease (MRD) detection, such as ultra-sensitive liquid biopsy technologies, have largely overcome the limitations of traditional imaging and offered a transformative approach for continuous, precision-based management of lung cancer. This review systematically summarized the technological evolution of MRD detection and highlighted its clinical significance in guiding adaptive therapy for NSCLC, including treatment escalation, de-escalation, and the emerging concept of precision-guided drug holidays. Moreover, the authors comprehensively discussed the "Four-Dimensional TNMB Staging System," which incorporates continuous molecular monitoring to address the static limitations of conventional staging and enhance the accuracy of prognostic stratification. Although ongoing challenges, such as the lack of standardized interpretation criteria and limited detection sensitivity, the combinations with the third-generation liquid biopsy platforms, multi-omics analyses, and multi-center prospective validation studies are expected to advance the clinical implementation of MRD-guided strategies. The paradigm change will enable the transition of NSCLC management from conventional standardized models to a precision-guided, closed-loop system of "monitoring-intervention-remonitoring," establishing a solid theoretical and practical foundation for comprehensive, molecularly driven management strategies.

PMID:41496417 | DOI:10.1016/j.tranon.2025.102660

Front Immunol. 2024 Dec 6;15:1517312. doi: 10.3389/fimmu.2024.1517312. eCollection 2024.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and the development of accurate predictive models for prognosis and drug sensitivity remains challenging.

METHODS: We integrated laboratory data and public cohorts to conduct a multi-omics analysis of HCC, which included bulk RNA sequencing, proteomic analysis, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics sequencing (ST-seq), and genome sequencing. We constructed a tumor purity (TP) and tumor microenvironment (TME) prognostic risk model. Proteomic analysis validated the TP-TME-related signatures. Joint analysis of scRNA-seq and ST-seq revealed characteristic clusters associated with TP high-risk subtypes, and immunohistochemistry confirmed the expression of key genes. We conducted functional enrichment analysis, transcription factor activity inference, cell-cell interaction, drug efficacy analysis, and mutation information analysis to identify a novel subtype of HCC.

RESULTS: Our analyses constructed a robust HCC prognostic risk prediction model. The patients with TP-TME high-risk subtypes predominantly exhibit hypoxia and activation of the Wnt/beta-catenin, Notch, and TGF-beta signaling pathways. Furthermore, we identified a novel subtype, XPO1+Epithelial. This subtype expresses signatures of the TP risk subtype and aligns with the biological behavior of high-risk patients. Additional analyses revealed that XPO1+Epithelial is influenced primarily by fibroblasts via ligand-receptor interactions, such as FN1-(ITGAV+ITGB1), and constitute a significant component of the TP-TME subtype. Moreover, XPO1+Epithelial interact with monocytes/macrophages, T/NK cells, and endothelial cells through ligand-receptor pairs, including MIF-(CD74+CXCR4), MIF-(CD74+CD44), and VEGFA-VEGFR1R2, respectively, thereby promoting the recruitment of immune-suppressive cells and angiogenesis. The ST-seq cohort treated with Tyrosine Kinase Inhibitors (TKIs) and Programmed Cell Death Protein 1 (PD-1) presented elevated levels of TP and TME risk subtype signature genes, as well as XPO1+Epithelial, T-cell, and endothelial cell infiltration in the treatment response group. Drug sensitivity analyses indicated that TP-TME high-risk subtypes, including sorafenib and pembrolizumab, were associated with sensitivity to multiple drugs. Further exploratory analyses revealed that CTLA4, PDCD1, and the cancer antigens MSLN, MUC1, EPCAM, and PROM1 presented significantly increase expression levels in the high-risk subtype group.

CONCLUSIONS: This study constructed a robust prognostic model for HCC and identified novel subgroups at the single-cell level, potentially assisting in the assessment of prognostic risk for HCC patients and facilitating personalized drug therapy.

PMID:39712016 | PMC:PMC11659151 | DOI:10.3389/fimmu.2024.1517312