Identifying cooperative genes causing cancer progression with dynamic causal inference

Cancer progression is driven by complex gene interactions, which are not fully understood due to the dynamic and multifaceted nature of the disease. Traditional methods for identifying genes driving cancer typically focus on detecting mutational events, often neglecting other genetic alterations and the temporal dynamics of the cancer development. This study introduces a novel approach that expands our understanding of cancer by identifying cooperative networks of cancer drivers through dynamic causal inference, complementing classical mutation-based methods.

We developed a data-driven causal inference method that integrates a temporal dimension to gene expression data through pseudotime analysis. By modelling cancer progression as a dynamic system, the method evaluates gene interactions and their causal relationships using causal kinetic models (CKMs). This approach enables detecting cancer driver genes regardless of their mutational status. The approach was applied to both single cell and bulk sequencing datasets of breast cancer to identify and rank cancer driver genes based on their stability on the causal model.

The proposed method effectively identifies and ranks cancer driver genes by considering their dynamic interactions during cancer progression. It systematically identified cancer driver genes in both categories: those with single nucleotide variants (SNVs) and those with other alterations. The method demonstrated a significant overlap with known cancer driver genes from the Cancer Gene Census, validating its effectiveness. Additionally, the method uncovered novel driver genes that act cooperatively to influence cancer development, offering a more comprehensive view of the genetic mechanisms underlying cancer. Our approach, along with the scripts for the experiments and datasets used, can be found at https://github.com/AndresMCB/DynamicCancerDriverKM