Identifying cooperative genes causing cancer progression with dynamic causal inference

Cancer progression is driven by complex gene interactions, often overlooked due to the dynamic nature of the disease. Traditional inference methods mainly identify driver genes through mutational events, neglecting other genetic alterations and temporal dynamics. This study introduces a novel approach to understanding cancer by identifying cooperative networks of cancer drivers using dynamic causal inference, complementing classic mutation-based strategies. We developed a data-driven causal inference framework integrating temporal analysis of gene expression data by using pseudotime analysis. By modelling cancer as a dynamic system, it evaluates causal relationships among genes using causal kinetic models, detecting cancer driver genes regardless of mutational status. Applied to both single-cell and bulk sequencing datasets of breast cancer, the framework identified and ranked driver genes based on their stability within the causal model. Our framework effectively identifies driver genes by accounting for dynamic interactions during cancer progression, capturing genes with single nucleotide variants and other alterations. It showed significant overlap with known driver genes from the Cancer Gene Census, validating its effectiveness. Moreover, it uncovered novel cooperative driver genes, offering a more comprehensive view of cancer’s genetic mechanisms. Research data and code are stored on GitHub (https://github.com/AndresMCB/DynamicCancerDriverKM) and archived on Zenodo (https://doi.org/10.5281/zenodo.17363895).