人内源性逆转录病毒在肿瘤中的机制、生物标志物与治疗策略

Human endogenous retroviruses (HERVs), constituting roughly 8% of the human genome, have undergone a profound conceptual evolution from dismissed genomic fossils to critical, dualistic regulators in cancer biology. Their pathognomonic reactivation across malignancies orchestrates tumorigenesis through three interconnected molecular axes: (1) genomic destabilization via LTR-mediated insertional mutagenesis, disrupting key loci such as TP53 and MYC; (2) immune checkpoint subversion, driven by HERV-K envelope glycoprotein-induced PD-L1 upregulation (2. 3-fold; p < 0. 01); and (3) chronic inflammatory signaling triggered by double-stranded RNA (dsRNA) activation of innate immune pathways (TLR3/MDA5/NLRP3).

This foundational mechanistic insight is accelerating clinical translation. Diagnostic advances now feature HERV-K Env-targeted liquid biopsies achieving 92% specificity (AUC = 0. 94) for early-stage tumors and artificial intelligence (AI)-enhanced platforms (e. g. , DeepHERV, AUC = 0. 91) that resolve complex HERV expression landscapes.

Therapeutically, an emerging pipeline of strategies is rapidly advancing, from initial first-in-human clinical trials (NCT05687903, NCT05554866) of HERV-directed monoclonal antibodies and vaccines to the highly promising preclinical efficacy demonstrated by HERV-K-specific chimeric antigen receptor (CAR) T-cells (> 70% tumor regression) and locus-precise CRISPR/Cas9 epigenetic silencing.

However, clinical translation is complicated by persistent challenges, including intratumoral HERV heterogeneity, a lack of assay standardization (as evidenced by 60% primer discordance), and the fundamental ethical and therapeutic requirement for specificity that is, precise discrimination between pathogenic HERVs and their essential physiological counterparts.

A convergent translational framework leveraging international consortia for biomarker validation, machine learning for patient stratification, and engineered tumor-selective delivery platforms is now positioned to harness this novel target class and redefine the next era of precision oncology.