AI could help predict which HPV infections progress to cancer
For several decades, researchers have sought to identify biomarkers capable of accurately predicting the outcome of an infection with a high-risk oncogenic HPV type. Despite significant advances, no viral, immune-related or molecular marker can currently and reliably predict which infections will regress spontaneously and which will progress to more severe disease.
Researchers have struggled to find reliable biomarkers for predicting the progression of high-risk HPV infections to cancer. This difficulty arises from the intricate and evolving nature of HPV-associated carcinogenesis, influenced by various factors such as viral properties, immune response, genetics, microbiome, hormones, and environmental elements.
Diane Bruyère, Romane Geuzaine, and Michael Herfs, from GIGA (University of Liège), believe that a single biomarker may have reached its limits. Recent advancements in high-throughput molecular profiling and artificial intelligence now offer new avenues. These technologies allow for the simultaneous analysis of extensive biological data to create more comprehensive predictive models.
The aim is to distinguish between the potential paths of an HPV infection or a low-grade precancerous lesion, including spontaneous regression, persistence, or progression to severe cancer.
While these integrative approaches are promising for enhancing risk stratification, they have not yet been fully validated for routine clinical use. Currently, patient management involves HPV testing, cytology, histopathology, and relatively nonpersonalized follow-ups. The researchers emphasize the future lies in a deeper understanding of HPV infection biology rather than the search for a single predictive marker.
Ultimately, more precise individual risk assessments could enable tailored patient follow-up, reduce unnecessary interventions, and improve management of HPV infections.
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