Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

Mona Nourbakhsh, Kristine Degn, Astrid Brix Saksager, Matteo Tiberti, Elena Papaleo*

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

38 Downloads (Pure)


The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Original languageEnglish
Article numberbbad519
JournalBriefings in Bioinformatics
Issue number2
Number of pages16
Publication statusPublished - 2024


  • Cancer
  • Computational research
  • Driver genes
  • Driver mutations
  • Pathogenicity
  • Predictive tools
  • Protein structural analysis


Dive into the research topics of 'Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks'. Together they form a unique fingerprint.

Cite this