A DNA Methylation–based Computational Framework for Tumour–microenvironment State Inference and Molecular Stratification

Abstract Cervical cancer remains a persistent yet preventable threat to women’s health worldwide, with a disproportionate burden borne by women in low- and middle-income countries. In sub-Saharan Africa, including South Africa, it continues to rank among the leading causes of cancer-related morbidity and mortality despite the availability of screening, vaccination, and treatment strategies. Structural inequities in healthcare access, late-stage diagnosis, and the prevalence of biologically aggressive disease contribute to poor outcomes, underscoring the need for molecularly informed and context-sensitive precision medicine approaches. A central biological challenge in cervical cancer management is pronounced intra-tumour heterogeneity (ITH), arising from the coexistence of multiple tumour subclones shaped by genetic variation, epigenetic regulation, and dynamic tumour microenvironment (TME) pressures. This heterogeneity drives tumour adaptation, immune evasion, therapeutic resistance, and disease recurrence, complicating clinical decision-making and limiting the durability of standard treatments. These challenges are further intensified by persistent human papillomavirus (HPV) infection and, in many settings, HIV co-infection, which together impose distinct immune and stromal programmes that fundamentally shape tumour behaviour. Advances in computational biology and analytical programming have enabled the large-scale analysis of patient-derived omics data, including genomic, epigenomic, transcriptomic, and proteomic profiles, often through machine learning–based classification, clustering, and predictive modelling frameworks. However, despite the widespread availability of multi-omics datasets through resources such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), their translation into clinically meaningful tumour stratification and experimentally actionable insight for cervical cancer remains limited. A key barrier is the lack of integrated, biologically grounded frameworks capable of reconstructing tumour–microenvironment interaction states directly from bulk patient data and systematically linking these inferred states to representative and experimentally tractable in vitro model systems. As a consequence, commonly used cervical cancer models frequently fail to capture critical immune and stromal dimensions, contributing to poor translatability of preclinical findings. To address this gap, we developed a DNA methylation–based computational framework for data-driven tumour stratification and tumour–microenvironment state inference. The framework integrates epigenomic feature restriction, joint tumour–TME modelling, and machine learning–based state reconstruction to infer biologically meaningful tumour microenvironment states directly from patient methylation profiles. It is designed not merely as a clustering pipeline, but as a generalizable epigenetic state inference engine that connects patient tumour states to experimentally controllable in vitro systems, with a specific focus on cervical cancer cell lines as the primary translational models. By explicitly modelling tumour-intrinsic, microenvironmental, and host-associated regulatory programmes—including those influenced by HIV infection—this framework enables the systematic selection and evaluation of cell line models that more faithfully recapitulate patient tumour biology. It advances precision oncology by providing a reproducible and interpretable approach to methylation-driven tumour stratification and cell line alignment in cervical cancer, with broader applicability to other immune-modulated malignancies and underserved disease contexts.