Abstract
Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing smaller subgroups with similar genetic makeup (i.e. patient-like-me subgroups) using a decision tree structure and analyzing multiple trees to identify the SGAs that play a significant role in regulating downstream gene expression patterns at the subgroup and individual levels. This is distinct from population-based approaches, which tend to evaluate the influence of an SGA for the entire population, thereby likely missing low-frequency SGAs that may well explain a small subgroup of cancer patients. Also importantly, IBI-DT can efficiently identify cancer drivers that may have functional interactions. We applied IBI-DT to identify cancer drivers regulating the downstream differential gene expression in cancer patients and compared it to the standard, population-based method of expression quantitative trait loci analysis. Our results show that IBI-DT performs well in identifying both important cancer drivers, especially the low-frequency drivers, and their interactions, allowing for a better understanding of the cancer signaling pathways.
Recommended Citation
M. A. Rahman et al., "IBI-DT: A Novel Approach Combining Individualized Bayesian Inference and Decision Tree for Identifying Cancer Drivers and their Interactions," Briefings in Bioinformatics, vol. 26, no. 5, article no. bbaf463, Oxford University Press, Sep 2025.
The definitive version is available at https://doi.org/10.1093/bib/bbaf463
Department(s)
Engineering Management and Systems Engineering
Second Department
Biological Sciences
Publication Status
Open Access
Keywords and Phrases
cancer driver; decision tree; genetic interactions; individualized Bayesian inference; somatic genome alterations
International Standard Serial Number (ISSN)
1477-4054; 1467-5463
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 Oxford University Press, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Sep 2025
PubMed ID
40975833
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Computational Biology Commons, Data Science Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Statistics and Probability Commons, Systems Biology Commons
Comments
National Heart, Lung, and Blood Institute, Grant K01HL161538