Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Abstract

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

“…In the case of whole genome/exome data, the MultiQC report was generated based on the results from tools such as FastQC and SnpEff. Two cancer-related data analysis tools, NBDriver ( 29 ), which uses a machine learning approach to identify the context of mutations, and cTaG ( 30 ), a tool to predict whether a given gene is a tumor suppressor (TSG) or an oncogene (OG), were also incorporated in the final pipeline (Figure 2 ).…”

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

“…In the case of whole genome/exome data, the MultiQC report was generated based on the results from tools such as FastQC and SnpEff. Two cancer-related data analysis tools, NBDriver [31], which uses a machine learning approach to identify the context of mutations, and cTaG [32], a tool to predict whether a given gene is a tumor suppressor (TSG) or an oncogene (OG), were also incorporated in the final pipeline(Figure 2) .…”

“…Despite the dramatic advances in developing predictive algorithms to differentiate between driver and passenger mutations, very few have concentrated on utilizing the local sequence context as potential features for further analysis. To capture this information, we built a robust machine learning model called NBDriver [31], which uses raw nucleotide sequences surrounding cancer-causing mutations as features to build machine learning models.…”

Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes

“…Various computational methods exist for identifying driver genes. Tools classify either mutations as driver events ( Mao et al, 2013 ; Tokheim and Karchin, 2019 ; Banerjee et al, 2021 ), or the genes mutated as driver genes ( Tokheim et al, 2016 ). Driver mutation prediction relies on the functional impact or neighborhood sequence.…”