Finding driver mutations in cancer: Elucidating the role of background mutational processes

Abstract

Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation in patients remains one of the most reliable markers of mutation driver status. However, some mutations are more likely to occur than others due to differences in background mutation rates arising from various forms of infidelity of DNA replication and repair machinery, endogenous, and exogenous mutagens. We calculated nucleotide and codon mutability to study the contribution of background processes in shaping the observed mutational spectrum in cancer. We developed and tested probabilistic pan-cancer and cancer-specific models that adjust the number of mutation recurrences in patients by background mutability in order to find mutations which may be under selection in cancer. We showed that mutations with higher mutability values had higher observed recurrence frequency, especially in tumor suppressor genes. This trend was prominent for nonsense and silent mutations or mutations with neutral functional impact. In oncogenes, however, highly recurring mutations were characterized by relatively low mutability, resulting in an inversed U-shaped trend. Mutations not yet observed in any tumor had relatively low mutability values, indicating that background mutability might limit mutation occurrence. We compiled a dataset of missense mutations from 58 genes with experimentally validated functional and transforming impacts from various studies. We found that mutability of driver mutations was lower than that of passengers and consequently adjusting mutation recurrence frequency by mutability significantly improved ranking of mutations and driver mutation prediction. Even though no training on existing data was involved, our approach performed similarly or better to the state-of-the-art methods.

PremPS: Predicting the Effects of Single Mutations on Protein Stability

“…Therefore, we further checked the capability of PremPS on the identification of functionally important mutations. A previously proposed experimental dataset including 1139 non-neutral/deleterious and 4137 neutral/benign mutations was used here to evaluate the performance of PremPS in distinguishing non-neutral from neutral mutations (66). Among them 1037 non-neutral and 1159 neutral mutations (named as F2196 dataset) could be mapped to the corresponding protein 3D structures and allowed to calculate the unfolding free energy changes ( Figure S6).…”

An overview of precision oncology basket and umbrella trials for clinicians

“…For instance, it is common for cancers to have multiple genetic mutations; however, it is important to note that only some of these may be driver mutations for the carcinogenic process, and most mutations are passenger mutations that do not affect the underlying carcinogenic process . Intervention strategies, of course, should be targeting driver mutations, but it can be difficult to differentiate driver mutations from passenger mutations . Careful consideration of the preclinical evidence and underlying biological models informing the targeted intervention strategies will need to be made for critical appraisals of both basket and umbrella trials.…”

“…59 Intervention strategies, of course, should be targeting driver mutations, but it can be difficult to differentiate driver mutations from passenger mutations. 59,60 Careful consideration of the preclinical evidence and underlying biological models informing the targeted intervention strategies will need to be made for critical appraisals of both basket and umbrella trials.…”

Dimensionality and Structure in Cancer Genomics: A Statistical Learning Perspective

“…Identifying driver genes (or driver sites within genes) among the extensive backdrop mutation in tumours is notoriously difficult. Selection pressures produce subtle and often non-obvious patterns of mutation density between neutral and non-neutral genes as well as distinct signatures for oncogenes and tumour suppressors [21]. Neglecting these difficulties for now, suppose we wish to infer some phenotype Y (again for simplicity we assume that this is continuous and single-valued).…”