Dynamic cancer drivers: a causal approach for cancer driver discovery based on bio-pathological trajectories. (19th September 2022)

• Record Type: Journal Article
• Authors: Cifuentes-Bernal, Andres M; Pham, Vu V H; Li, Xiaomei; Liu, Lin; Li, Jiuyong; Duy Le, Thuc
• Is Part Of: Briefings in functional genomics. Volume 21:Number 6(2022)
• Page Start: 455
• Page End: 465
• Publication Date: 2022-09-19
• Subjects: Driver genes -- dynamics -- causality -- pseudotime -- causal impact -- cancer; Genomics -- Methodology -- Periodicals; Genomics -- Technological innovations -- Periodicals; 572.86072
• DOI: 10.1093/bfgp/elac030
• Languages: English
• ISSNs: 2041-2649

Abstract

Abstract: The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver