Background: Predicting transcription factor (TF) interactions in biological systems presents a major computational challenge critical for deciphering regulatory logic. This is particularly important in contexts like aggressive brain tumors (e.g., Glioblastoma, GBM), where tumor subtype-specific regulatory networks orchestrate therapeutic resistance. Advanced computational modeling is needed to accurately predict cooperative TF-DNA binding in specific cellular contexts.This study details an enhanced computational tool to model cooperative binding of multiple TFs to target regulatory regions (promoters and enhancers).

Materials and methods: We significantly enhanced the Composite Module Analyst (CMA) algorithm, a method originally designed to predict TF binding compositions to promoter regions. The primary computational enhancement involves a redesigned fitness function incorporating gene expression levels (RNA-seq) and modeling factor-factor interaction dynamics. The new CMA was trained on a specific GBM dataset to identify tumor subtype-specific TF composite modules, which serves as a proof-of-concept application for its ability to correlate computational predictions with RNA-seq data.

Results: Our enhanced CMA demonstrated a marked ability for identifying cooperating TFs binding to their composite sites. Application of this robust model to the GBM subtype data successfully revealed distinct sets of key TFs (e.g., SP100, TP53, MITF, MeCP2) that are active in different tumor microenvironment contexts.

Conclusions: This work presents an advanced computational tool for modeling complex transcription regulatory landscapes. Our model provides a novel, mechanistic approach to identifying key cooperating TFs in subtype-specific regulatory networks . This advancement is applicable for dissecting the transcriptional logic of any disease context and highlights the identified cooperating TFs.

Co-authors: Alexander Kel, Jochen Prehn, Mohannad Dabbour