Cancer driver mutations: predictions and reality

Abstract

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Keywords: cancer; cancer biomarkers; driver mutation prediction methods; driver mutations; mutational motifs; mutational signatures.

Figure 1.

Driver and passenger mutations in human cancer. A: Detection of somatic mutations in human cancers requires the extraction of DNA from tumor and normal samples, DNA sequencing, alignment of paired tumor-normal sequences to the human reference genome for subsequent mutation calling. B: Cancerogenesis is a multi-stage process, and cells accumulate a myriad of somatic mutations throughout their life. Driver mutations lead to a variety of genetic and epigenetic alterations beneficial to cancer cells. C: A proportion of driver mutations per cancer type; all mutations are shown in gray radial bars, colored radial bars correspond to driver mutations for each cancer type. D: In certain cancer types, patients harboring driver mutations are characterized by better or worse survival compared to patients without drivers suggesting clinical importance of driver mutation identification.

Figure 2.

Schematic representation of mutational signature and mutational motif approaches to predict driver mutations. Computational methods are used to identify mutational processes represented in the form of mutational signatures or mutational motifs in cancer genomes. By analyzing mutational signatures/motifs, driver mutations can be predicted.

Figure 3.

Probabilistic statistical framework to predict driver mutations. To identify a status of mutation, these methods compare observed mutational frequencies in a given cohort of patients to the expected background mutability. Afterwards, DNA mutations (or amino acid substitutions) are ranked according to their scores.

Figure 4.

Deciphering phenotypic and functional effects of mutations to identify their driver status. A: Effects of driver missense mutations on protein stability and binding. Driver mutations might have higher destabilizing (or in some cases over-stabilizing) effects compared to other mutations in a protein. B: Distribution of functional impact scores. Driver mutation might have higher impact on function compared to other mutations in a protein. Red bars depict driver mutations. C: Lossof-function driver mutations disrupt the function of a protein (e.g., altering a protein binding site), whereas gain-of-function driver mutations may lead to the increased functional activity of a protein or confer a new function (e.g., gaining a novel post-transcriptional modification (PTM) site).

Figure 5.

Supervised machine learning workflow for driver mutation identification involves several steps. First, various features are selected and preprocessed from the input data. Next, a suitable algorithm is chosen to train the prediction model on the preprocessed labelled input data using features of interest. After training process, the algorithm can make prediction of driver mutations on new data. Finally, the results are validated and interpreted to elucidate a status of mutations.