CellTFusion: a transcriptional regulatory network framework for the identification of functional multicellular states from bulk RNA-seq data

Abstract

Bulk RNA-seq remains the most accessible transcriptomic platform for tumor microenvironment (TME) characterization, yet existing computational approaches treat cell type abundance, pathway activity, and transcription factor (TF) activity as independent sources of information, missing the coordinated regulatory programs that define functional multicellular states. Here we introduce CellTFusion, a framework that integrates cell type deconvolution with transcriptional regulatory network analysis from bulk RNA-seq data to identify functional multicellular groups. CellTFusion produces a mixture representation of the TME in which each patient is described as a weighted combination of states, each capturing a distinct coordinated hallmark program. Applied to melanoma and bladder cancer cohorts, CellTFusion identified recurrent TME programs with opposing associations with immunotherapy responses that only emerged through joint multivariate modeling, and demonstrated superior cross-cohort transferability compared to established TME characterization tools.