Patient-specific integration of RNA expression, methylation-derived replication-timing proxies, and histomorphology for pancreatic cancer subtyping.

Network biology uses gene correlations to characterize biological pathways. Linear Interpolation to Obtain Network Estimates for Single Samples (LIONESS) enables individualized gene networks, but the influence of methylation derived replication timing and tissue morphology on these networks remains unclear. We integrated replication timing proxies, RNA expression, and morphological embeddings into individualized LIONESS networks for basal like and classical pancreatic ductal adenocarcinoma using data from The Cancer Genome Atlas (TCGA). Networks and gene modules were constructed within training partitions and evaluated in held out patients using nested cross validation. The RNA expression module model achieved a mean held out area under the receiver operating characteristic curve of 0.815, comparable to the fixed Moffitt logistic regression baseline. Module selection produced reproducible RNA modules, with 18 of 29 selected genes retained in every fold, and replication timing and RNA modules, with 15 of 24 genes retained across all folds. Patient specific LIONESS edge scores did not improve subtype classification. However, replication timing and RNA integration increased global network density from 0.27 to 0.34, mean degree from 11 to 14, and transitivity from 0.64 to 0.68. Morphology and RNA integration increased density from 0.31 to 0.85, mean degree from 14 to 38, and transitivity from 0.66 to 0.92. These findings show that multimodal LIONESS networks can identify reproducible gene modules and characterize modality associated changes in biological connectivity without requiring direct mapping between separate modality specific networks.