Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations.

Abstract

Simple SummaryGenes dictate the grounds of life by comprising molecular bases which encode proteins. A mutation represents a gene modification that may influence the protein function. Cancer occurs when the mutation triggers uncontrolled cellular growth. Judging by the cancer expansion, mutations labelled as drivers confer a growth advantage, while passengers do not contribute to this augmentation. The aim of this study is methodological, which assesses the usefulness of a classification method for distinguishing between driver and passenger mutations. Based on 51 molecular characteristics of mutations and genes, including 3 novel features, multiple machine learning algorithms were used to determine whether these characteristics biologically represent the driver mutations and how they impact the classification procedure. To test the ability of the present methodology, the same steps were applied to an independent dataset. The results showed that both gene and mutation level characteristics are representative of the driver mutations, and the proposed approach achieved more than 80% accuracy in finding the true type of mutation. The evidence suggests that machine learning methods can be used to gain knowledge from mutational data seeking to deliver more targeted cancer treatment.Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein–protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein–protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.