Researchers at the University of Liège have now confronted this question head-on in a review published in Trends in Molecular Medicine, arguing that the field’s long search for a single decisive biomarker may have been looking for the wrong kind of answer. Michael Herfs, an oncologist at the GIGA Institute in Liège, along with colleagues Diane Bruyère and Romane Geuzaine, contends that no viral, immune or molecular marker identified to date can reliably separate self-resolving infections from those destined to progress. Their analysis suggests the problem is not a shortage of candidate markers but a fundamental misunderstanding of what kind of biological system HPV-driven carcinogenesis actually is.

The core of the difficulty is that HPV-associated cancer development is not a linear process with a single molecular switch. It is a dynamic, multi-layered phenomenon in which the outcome depends on the interplay of viral characteristics, the host’s immune response, genetic and epigenetic alterations, the local microbiota, hormonal influences and broader environmental factors. On top of these interacting variables sit stochastic events, most notably the accumulation of mutations driven by virus-induced genomic instability. Two patients infected with the same viral type can follow radically different trajectories because the determinants of progression are distributed across biological scales rather than concentrated in one measurable signal.

This complexity helps explain why decades of biomarker research have produced incremental improvements rather than a decisive breakthrough. Viral load measurements, E6 and E7 oncogene expression, methylation patterns, immune cell profiling and a host of other candidate indicators have each shown promise in isolated studies, yet none has achieved the predictive accuracy needed to transform clinical decision-making. Herfs crystallizes the field’s predicament in a striking observation: perhaps the main challenge is no longer to discover a new biomarker, but to understand how all the biological mechanisms interact to determine the outcome of an HPV infection. The framing represents a conceptual shift from reductionist marker-hunting toward systems-level interpretation.

It is precisely this shift that recent technological developments have made feasible. High-throughput molecular analysis platforms can now generate vast datasets capturing gene expression, epigenetic modification, viral integration sites and immune signatures simultaneously, while artificial intelligence and machine learning approaches offer the computational tools to integrate these heterogeneous data streams into coherent predictive models. Rather than asking whether a single marker predicts progression, integrative models can weigh dozens or hundreds of features at once, potentially capturing the multi-factorial nature of carcinogenesis that has defeated simpler approaches.

The Liège team sees particular promise in using these models to distinguish between the possible trajectories of an infection or a low-grade precancerous lesion: spontaneous regression, persistence without progression, advancement to a more serious lesion, or evolution toward invasive cancer. Bruyère, a researcher at the Laboratory of Experimental Pathology, explains that artificial intelligence and integrative models could help clarify the diversity of biological trajectories observed in patients and ultimately enable a more personalised form of risk assessment. In practical terms, this would mean moving beyond the binary categorizations of current screening toward a graduated risk profile that reflects the actual biology of each individual infection.

The clinical stakes of such a capability are considerable. Current management of HPV-positive screening results and low-grade lesions relies on HPV testing, cytology and histopathological analysis, supplemented by monitoring protocols that are only weakly personalised. This one-size-fits-all approach generates a well-documented burden of unnecessary colposcopies, repeat biopsies and anxiety-inducing follow-up for patients whose infections would have resolved regardless, while simultaneously risking under-surveillance in the minority whose lesions are genuinely dangerous. A validated predictive framework could tailor follow-up intensity to actual risk, reducing overtreatment and its associated costs and harms while concentrating clinical attention where it is most needed.

However, the researchers are careful to temper expectations with an acknowledgment of the current gap between promise and practice. These new models have not yet been sufficiently validated for routine clinical use, and the path from an encouraging computational result to a deployable diagnostic involves rigorous external validation, standardization of input data, regulatory approval and demonstration of clinical utility across diverse populations. The history of biomarker research in oncology offers cautionary examples of tools that performed impressively in retrospective datasets but faltered in prospective clinical testing. The Liège authors position integrative AI approaches as the most promising avenue for improving risk stratification, not as a finished solution.

Their broader conclusion reframes the research agenda for the field. Rather than continuing to pursue the elusive single indicator that will cleanly predict progression, the future lies in a better overall understanding of the biology of HPV infections as integrated biological systems. This perspective aligns with wider trends in cancer research, where multi-omics profiling and machine learning are increasingly applied to problems whose complexity resists traditional reductionist methods. For HPV specifically, the approach could eventually unify viral genomics, host immunology, epigenetics and microbiome data into models that reflect the true determinants of infection outcome.

If that vision matures, the practical consequences could extend well beyond cervical cancer prevention. Persistent oncogenic HPV infection is also implicated in a rising proportion of oropharyngeal cancers and in anogenital malignancies, and improved risk prediction would benefit screening and surveillance strategies across all of these sites. For now, patients and clinicians must continue working with the imperfect tools of cytology, HPV genotyping and histopathology, but the Liège analysis offers a clear-eyed map of where the field must go next: away from the search for a molecular oracle and toward a genuinely integrative understanding of how viral and host biology combine to decide, infection by infection, whether the outcome is clearance or cancer.

Subject of Research: Prediction of progression risk in oncogenic human papillomavirus infection using integrative biomarkers and artificial intelligence

Article Title: Human papillomavirus: Can we finally predict which infections will progress to cancer?

Article References: Human papillomavirus: Can we finally predict which infections will progress to cancer?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: HPV, human papillomavirus, cancer risk prediction, biomarkers, artificial intelligence, cervical cancer, carcinogenesis, risk stratification, molecular medicine, precancerous lesions, University of Liège, personalised medicine

Colorectal cancer is the third most frequently diagnosed cancer worldwide, with an estimated 1.8 million new cases each year. Randomized controlled trials have already shown that aspirin can reduce colorectal cancer incidence in certain populations, and the United States Preventive Services Task Force has recommended low-dose aspirin for primary prevention in adults aged 50 to 59 who carry a ten-year cardiovascular disease risk of at least 10 percent. Yet questions of long-term adherence, contraindications, and the sheer scale of the disease mean screening and surveillance will remain essential. That is why researchers are increasingly interested in novel biomarkers that could flag cancer risk earlier and more precisely, and epigenetic aging measures are among the most promising candidates.

Epigenetic clocks are mathematical models that estimate biological age from patterns of DNA methylation, the chemical tags that attach to DNA and switch genes on or off without altering the underlying sequence. The best-known clocks, developed by Steven Horvath and Gregory Hannum in 2013, capture aging signals that are largely independent of cell division. But cancer is intimately a disease of uncontrolled mitosis, and that is where a newer family of biomarkers comes in. Mitotic clocks, including EpiTOC, EpiTOC2, and MiAge, are designed to estimate the cumulative number of cell divisions a tissue has undergone. EpiTOC tracks methylation gains at CpG sites within Polycomb group target genes that are typically unmethylated in fetal tissue but accumulate methylation with age, effectively counting stem cell replication errors. EpiTOC2 goes further, estimating the total number of stem cell divisions in a tissue, while MiAge represents the lifetime tally of cell divisions and has been found universally accelerated across thirteen cancer types, including colon adenocarcinoma.

Previous work on this same cohort, published in 2020 by Noreen and colleagues, reported that long-term aspirin users showed deceleration of conventional epigenetic age measures, specifically the Hannum, Horvath, and PhenoAge metrics, in the proximal colon but not the distal colon. What that study could not answer was whether aspirin’s effect extended to cell proliferation itself. Because the widely used clocks capture aging signals independent of mitosis, it was possible that aspirin’s relationship with epigenetic aging was entirely proliferation-independent. The new study, led by Jamaji C. Nwanaji-Enwerem of Emory University and the University of California, Berkeley, together with Chijioke Nze and Andres Cardenas, set out to close that gap by applying three mitotic clocks to the same decade-spanning dataset.

The data came from a publicly available NCBI Gene Expression Omnibus dataset comprising DNA methylation profiles from healthy colon mucosa biopsies collected at two timepoints, baseline and a ten-year follow-up. The researchers analyzed 112 samples from 28 healthy Polish women, 11 of whom never used aspirin and 17 of whom were long-term users, defined as taking aspirin at least twice per week for at least two years. Three participants who crossed over from non-use to long-term use during the study were excluded to keep the longitudinal comparison clean. Each participant had provided both proximal, or cecal, and distal, or sigmoid, colon biopsies at both timepoints, allowing the team to examine whether effects differed along the length of the colon. Genome-wide methylation was profiled using the Illumina Infinium MethylationEPIC BeadChip, and the mitotic clocks were computed from the normalized methylation values using published R code.

The statistical approach was deliberately careful. Because the mitotic clocks correlate strongly with chronological age, the researchers calculated age-independent intrinsic rates by dividing each clock value by the participant’s age, then derived residuals from linear mixed effects models adjusted for body mass index, polyp status, and DNA methylation batch, with a random intercept for each participant to account for repeated measures. They then used paired Wilcoxon signed rank tests to compare baseline and follow-up values within each aspirin group. All three clocks correlated significantly with chronological age, with coefficients ranging from 0.41 to 0.63, and the clocks correlated strongly with one another, with r values above 0.79 in both colon regions and an especially tight relationship between EpiTOC and EpiTOC2 exceeding 0.99.

The headline result was unambiguous. In the proximal colon of long-term aspirin users, the intrinsic rates of all three mitotic clocks decelerated significantly over the ten-year period: EpiTOC by a median of 0.0004 DNAm units, EpiTOC2 by roughly 16 cell divisions, and MiAge by about 3 cell divisions, with P values of 0.008, 0.009, and 0.002 respectively. No such deceleration was observed in non-users. In the distal colon, only MiAge showed a significant deceleration among long-term users. When the researchers compared the ten-year changes between the two groups directly, long-term users showed more negative median differences in the proximal colon for all three clocks, though these between-group comparisons did not reach statistical significance, likely a consequence of the small sample size and the unpaired nature of that analysis.

The findings dovetail neatly with the earlier report of aspirin-linked deceleration of Horvath, Hannum, and PhenoAge measures in the proximal colon, and they suggest that cell proliferation may indeed be part of the mechanism by which aspirin influences epigenetic aging. The proximal-distal split is itself intriguing. The right and left colon differ in embryonic origin and in the molecular pathways that drive tumor initiation: mismatch repair mutations are more common in right-sided tumors, while chromosomal instability dominates on the left. Some chemopreventive interventions, including aspirin, appear more robust in the proximal colon. The authors argue that future epigenetic aging studies of colorectal cancer should routinely report colon location-specific results rather than pooling the two regions.

Beyond the aspirin story, the paper issues a broader call to standardize a young and somewhat fragmented field. The Horvath and PhenoAge clocks were built from roughly 8,000 and 10,000 samples respectively, whereas EpiTOC was developed from just over 650 samples, and MiAge and EpiTOC2 from around 4,000 and 3,000. Differences in training data may explain why different clocks show different sensitivity to biological processes, a pattern the authors have observed elsewhere, including in a study of simulated long-duration space travel where only PhenoAge deceleration remained statistically significant. They recommend that future colorectal cancer epigenetic studies include, at minimum, the traditional Hannum, Horvath, and PhenoAge measures alongside the mitotic clocks MiAge, EpiTOC, and EpiTOC2, to allow the field to converge on which markers best capture cancer-relevant biology and to enable meaningful meta-analyses.

The authors also urge caution on the social dimensions of epigenetic aging. Racial disparities in colorectal cancer are well documented, and one recent study reported epigenetic age acceleration in the right colons of African Americans alongside deceleration in European American peers. Yet when Devall and colleagues analyzed differentially methylated positions by race, 18.4 percent overlapped with markers of ancestry, and lifestyle and social factors were largely absent from the discussion. Given that epigenetic aging has been linked to diet, racial discrimination, neighborhood deprivation, and exposure to violence, the researchers recommend that future studies explicitly distinguish the effects of racism and social environment from ancestry itself. The study carries clear limitations: it was observational, involved only Polish women, lacked aspirin dose information, and adjusted for a limited set of covariates, so causality cannot be established, and immune-mediated mechanisms remain possible. Still, the convergence of three independent mitotic clocks on the same signal in the proximal colon of long-term aspirin users is a compelling hint that a century-old drug may be quietly slowing the cellular clockwork of the gut, and it sets a template for the larger, more diverse, and better standardized studies that must now follow.

Subject of Research: Epigenetic mitotic clock analysis of long-term aspirin use and colorectal cancer risk in healthy colon mucosa

Article Title: Long-term aspirin use and epigenetic mitotic clocks for cancer risk prediction: findings in healthy colon mucosa and recommendations for future epigenetic aging studies

Article References: Nwanaji-Enwerem, J. C., Nze, C., & Cardenas, A. (2021). Long-term aspirin use and epigenetic mitotic clocks for cancer risk prediction: findings in healthy colon mucosa and recommendations for future epigenetic aging studies. Epigenetics Communications, 1(1), Article 5. https://doi.org/10.1186/s43682-021-00004-4

Image Credits: AI Generated

DOI: 10.1186/s43682-021-00004-4

Keywords: aspirin, epigenetic clocks, mitotic clocks, colorectal cancer, DNA methylation, EpiTOC, EpiTOC2, MiAge, colon mucosa, chemoprevention, epigenetic aging, cancer risk prediction