Computational Tools for Identifying Cancer Driver Genes and Mutations: A Comprehensive Review

Abstract: Understanding the genetic basis of cancer requires the accurate identification of driver genes and driver mutations, those alterations that promote tumorigenesis, while distinguishing them from neutral, or passenger, mutations. This review provides a comprehensive overview of computational strategies developed to detect and prioritise cancer drivers at both the gene and mutation levels. The review systematically classifies and compares more than 20 widely used tools, highlighting differences in their conceptual foundations, including sequence-based, structure-based, statistical, machine learning, and network/pathway-based methods. These tools leverage diverse types of data, including mutation frequency and evolutionary conservation, as well as gene expression profiles and interaction networks, to assess the functional relevance of somatic alterations. By integrating complementary approaches, researchers can enhance the sensitivity and specificity of driver prediction, particularly in cases involving rare or heterogeneous mutations. This review aims to serve as a practical guide for researchers and clinicians seeking to apply or evaluate current methods for cancer driver identification.