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 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.
Ndjekadom et al. (Thu,) studied this question.