Estimating the predictive power of silent mutations on cancer classification and prognosis

Tal Gutman,Omri Efroni,Tamir Tuller,Guy Goren

doi:10.1038/s41525-021-00229-1

Abstract

In recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

Highlights

The rapid developments of New Generation Sequencing (NGS)technologies and acceleration of computational abilities over the past few years have led to the availability of extensive genomic information[1,2,3,4,5]
Genomic and clinical data of 9915 patients across 33 cancer types were obtained from The Cancer Genome Atlas (TCGA)[43]
To ensure that the improvement gained from adding silent features to nonsilent features is not mainly due to the increase in the total mutational burden that occurs because of the addition, we examined how the increase in total mutational burden is correlated with the improvement in the F1 scores of the different cancer types (Supplementary Fig. 2)

Summary

Introduction

Technologies and acceleration of computational abilities over the past few years have led to the availability of extensive genomic information[1,2,3,4,5]. Multiple research utilizing these high-dimensional data establish cancer as a group of highly heterogeneous genomic diseases, characterized by large inter-tumor and intra-tumor diversities[6,7,8]. Common genetic features were repeatedly identified among patients of different cancer types and significant diversities were found among patients diagnosed with the same cancer type[9,10] These findings highlight the need for personalized, gene-targeted cancer treatments. There are still numerous obstacles to overcome in order to fully unravel the cancer genomic landscape

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Conclusion