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Comprehensive assessment of computational algorithms in predicting cancer driver mutations.

Abstract：

BACKGROUND:The initiation and subsequent evolution of cancer are largely driven by a relatively small number of somatic mutations with critical functional impacts, so-called driver mutations. Identifying driver mutations in a patient's tumor cells is a central task in the era of precision cancer medicine. Over the decade, many computational algorithms have been developed to predict the effects of missense single-nucleotide variants, and they are frequently employed to prioritize mutation candidates. These algorithms employ diverse molecular features to build predictive models, and while some algorithms are cancer-specific, others are not. However, the relative performance of these algorithms has not been rigorously assessed. RESULTS:We construct five complementary benchmark datasets: mutation clustering patterns in the protein 3D structures, literature annotation based on OncoKB, TP53 mutations based on their effects on target-gene transactivation, effects of cancer mutations on tumor formation in xenograft experiments, and functional annotation based on in vitro cell viability assays we developed including a new dataset of ~ 200 mutations. We evaluate the performance of 33 algorithms and found that CHASM, CTAT-cancer, DEOGEN2, and PrimateAI show consistently better performance than the other algorithms. Moreover, cancer-specific algorithms show much better performance than those designed for a general purpose. CONCLUSIONS:Our study is a comprehensive assessment of the performance of different algorithms in predicting cancer driver mutations and provides deep insights into the best practice of computationally prioritizing cancer mutation candidates for end-users and for the future development of new algorithms.

journal_name

Genome Biol

journal_title

Genome biology

authors

Chen H,Li J,Wang Y,Ng PK,Tsang YH,Shaw KR,Mills GB,Liang H

doi

10.1186/s13059-020-01954-z

subject

Has Abstract

pub_date

2020-02-20 00:00:00

pages

43

issue

1

eissn

1474-7596

issn

1474-760X

pii

10.1186/s13059-020-01954-z

journal_volume

21

pub_type

杂志文章

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