Novel ratio-metric features enable the identification of new driver genes across cancer types

Malvika Sudhakar,Raghunathan Rengaswamy,Karthik Raman

doi:10.1038/s41598-021-04015-y

Malvika Sudhakar, Raghunathan Rengaswamy + Show 1 more

Open Access

https://doi.org/10.1038/s41598-021-04015-y

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Abstract

An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG.

Highlights

An emergent area of cancer genomics is the identification of driver genes
Novel features used for classification of tumour suppressor genes (TSGs) and OGs
We note that TSGs can be predicted with higher accuracy than OGs; it is probable that the features are biased at capturing information regarding TSGs better than OGs

Summary

Introduction

An emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. Driver genes are effectively those genes that harbour mutations that provide them with a selective advantage to divide and grow unchecked These driver genes help the cells bypass the cell cycle checkpoints to divide in an uncontrolled fashion and give added functionality, such as bypassing the immune system[8,9] and angiogenesis[10,11], which lead to their persistence in the body. Driver genes that contain a lower frequency of mutations are challenging to identify[12] because, most likely, these genes work in combination with other genes to confer a selective advantage to the cell, or mutations occur at functionally important locations

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