Paper Contents
Abstract
AbstractHuman papillomavirus (HPV) integration into the host genome is a pivotal event in cervical carcinogenesis, yet the precise genomic hotspots and their prognostic significance remain incompletely characterized. We present a machine- learning framework that detects HPV integration signatures from sequencing and genome- annotation data, identifies recurrent integration hotspots, and links these events to clinical outcomes. Our approach first transforms raw integration breakpoints into structured features capturing genomic context local gene annotations, chromatin state proxies, repeat elements, and microhomology patterns then applies unsupervised clustering to discover hotspot regions and supervised models to predict patient prognosis. Feature importance and model-agnostic explainability methods are used to interpret biological drivers behind high-risk integrations. When applied to multi-cohort integration datasets, the framework robustly recapitulated known integration loci and revealed novel hotspot candidates enriched near oncogenes and regulatory elements. Integrations in a subset of hotspots correlated with reduced progression-free survival after adjusting for clinical covariates. Overall, this pipeline provides a reproducible, interpretable way to turn integration maps into testable biological hypotheses and potential prognostic biomarkers, facilitating targeted follow- up experimental validation and ultimately contributing to precision risk stratification in cervical cancer.Keywords: Cervical Cancer; Human Papillomavirus; HPV Integration; Genomic Instability; Prognostic Biomarkers1.INTRODUCTION Cervical cancer remains a major global health burden, and infection with high-risk human papillomaviruses (HPVs) is the principal etiologic factor. While persistent viral infection is necessary, the mechanism by which HPV drives malignant transformation is multifactorial. One important mechanism is physical insertion of viral DNA into the host genome.One important mechanism is physical insertion of viral DNA into the host genome. Integration can disrupt or dysregulate host genes, alter chromatin architecture, and generate fusion transcripts all of which may accelerate oncogenic processes. However, not every integration event contributes equally to tumor biology: many are likely passenger events, while a smaller subset occur at genomic loci that meaningfully alter cell behavior. Distinguishing driver hotspots from background noise is therefore critical for understanding pathogenesis and for identifying clinically actionable biomarkers. High-throughput sequencing and targeted enrichment approaches now provide large catalogs of HPV integration coordinates across tumor cohorts. These datasets are heterogeneous: they vary in coverage, experimental protocol, and clinical annotation, and integration breakpoints are often imprecise at the nucleotide level. Moreover, genomic context is complex integration sites are influenced by gene density, repetitive sequences, fragile sites, and three-dimensional chromatin folding. These complexities make manual curation slow and subjective and limit straightforward statistical approaches. Machine learning (ML) offers a path forward by integrating diverse genomic features and learning patterns that distinguish recurrent, biologically relevant integration events from random insertions. An effective ML pipeline for HPV integration analysis must address several challenges: (1) robustly represent the local genomic environment around breakpoints, (2) handle uncertainty and heterogeneity in breakpoint calls, (3) discover recurrent hotspots without imposing overly strict positional constraints, and (4) provide interpretable outputs that can be linked to biological mechanisms and clinical outcomes. Importantly, interpretability is essential if predictions are to be used as biomarkers or to guide laboratory validation. In this work we develop a comprehensive ML framework that transforms raw integration calls into rich feature vectors capturing sequence composition, local gene and regulatory annotations, repeat element overlaps, predicted effects on coding sequence, and surrogate measures of chromatin accessibility and replication timing where available.We use a two- stage strategy: unsupervised clustering and density-based hotspot detection to locate recurrent integration regions across samples, followed by supervised modeling to associate hotspot membership and feature combinations with clinical end points such as progression-free survival. To ensure biological transparency, we apply model-agnostic explanation tools that rank the genomic and viral features most predictive of hotspot-associated poor prognosis. Key contributions of our framework are: (1) a flexible feature engineering approach that integrates multi-modal genomic signals around integration breakpoints; (2) an unsupervised hotspot discovery algorithm resilient to breakpoint imprecision; (3) predictive models that link integration signatures to patient outcomes while adjusting for clinical covariates; and (4) an interpretability layer that converts model outputs into testable biological hypotheses. We validate the approach on publicly accessible integration cohorts and show that it recovers known driver loci and also nominates novel hotspots enriched for nearby oncogenes and regulatory elements. Finally, we discuss how this pipeline can be incorporated into translational workflows for example, to prioritize integrations for functional assays or to add an orthogonal layer to existing prognostic models in cervical cancer. Figure 1: Cervical Cancer Stage
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Copyright © 2025 R. Aarthi. This is an open access article distributed under the Creative Commons Attribution License.
