Predicting Methylation-Based Replication Timing from Whole Slide Images

Replication timing is a costly but powerful tool for characterizing cellular mechanisms that underlie chromatin organization, cancer epigenetics, and genomic instability. Genome-wide replication timing profiles reflect the temporal order of DNA synthesis during S phase and are closely linked to chromatin accessibility, transcriptional activity, and proliferative state. Prior work has demonstrated a robust inverse relationship between replication timing and DNA methylation at the domain scale, enabling methylation-based proxies to approximate replication timing in large cohorts where direct experimental measurement is impractical. Given the tight coupling between replication timing, chromatin structure, and cellular phenotype, we hypothesized that histologic morphology encodes information consistent with replication timing states. To test this hypothesis, we implemented Vision Transformer (ViT) architectures to predict replication timing proxies from whole-slide histopathology images. Patch-level embeddings were extracted using a pretrained ViT and aggregated through attention-based multiple instance learning to predict sample-level replication timing. In parallel, CellViT models were employed to perform cell-level prediction, enabling direct comparison between patch-based and cellular representations. Across both modeling strategies, statistically significant correlations were observed between image-derived features and methylation-based replication timing proxies. Patch-level models achieved correlations of approximately 46%, while cell-level models consistently reached correlations exceeding 50% on the validation cohort. Prediction error remained stable across folds, with mean absolute error and mean squared error values ranging between 0.4 and 0.6. These results demonstrate that replication timing-associated epigenomic states are reflected in tissue morphology and can be inferred using deep learning models applied to routine histopathology. This work establishes a feasible, noninvasive framework for replicosomic inference from whole-slide images and supports future efforts toward spatially resolved replication timing analysis and integrative modeling of replicative stress in cancer.