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A compendium of mutational cancer driver genes

Abstract

A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.

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Fig. 1: Signals of positive selection identify driver genes.
Fig. 2: Application of the IntOGen pipeline to datasets of tumour mutations.
Fig. 3: Schematic representation of the Integrative OncoGenomics (IntOGen) pipeline.
Fig. 4: A snapshot of the compendium of mutational driver genes.
Fig. 5: Distribution of the prevalence of driver genes across cancer types in the compendium.
Fig. 6: Interpreting the mutational patterns of driver genes.
Fig. 7: Recurrent cancer driver domains and mutational clusters.
Fig. 8: Linear clusters detected in tumour suppressors and oncogenes in the compendium.
Fig. 9: The past, present and future of cancer genomics.

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Acknowledgements

First and foremost, the authors acknowledge the contribution of patients with cancer, their families and a myriad of medical doctors and cancer genomics researchers who laboriously gather, process and sequence tens of thousands of tumour samples. Without them, the compendium of mutational driver genes would not be possible. They are also greatly indebted to generations of researchers who laid the foundations of cancer genomics, generated and shared data, and developed methods for driver identification. N.L-B. acknowledges funding from the European Research Council (consolidator grant 682398), the Spanish Ministry of Economy and Competitiveness (SAF2015-66084-R, European Regional Development Fund) and the Asociación Española Contra el Cáncer (GC16173697BIGA). C.A.-P. is supported by a “la Caixa” Foundation (ID 100010434) fellowship (LCF/BQ/ES18/11670011). H.K. and J.B. are supported by CONTRA innovative training network European Union Horizon 2020 grant MSCA-ITN-2017-766030. O.P. is supported by a Barcelona Institute of Science and Technology Ph.D. fellowship supported by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the Barcelona Institute of Science and Technology, which is a recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (Government of Spain) and is supported by CERCA (Generalitat de Catalunya). The results shown here are in whole or in part based on data generated by the TCGA Research Network, the Pan-Cancer Analysis of Whole Genomes, cBioPortal, the Hartwig Medical Foundation, the International Cancer Genome Consortium, St Jude Children’s Research Hospital, PedcBioPortal, TARGET projects, the BEAT AML study and several other studies scattered throughout the scientific literature. Finally, the authors state the specific contributions of different authors to the development of IntOGen. IntOGen pipeline conceptualization: F.M-J., F.M, A.G-P. and N.L-B. Combination approach development: F.M. and F.M-J. Reimplementation of driver identification methods: C.A-P., L.M. and F.M-J. Downstream analyses: F.M-J., F.M., O.P., H.K., J.B. and C.A-P. Analysis and discussion of the snapshot of the compendium: F.M-J., F.M., O.P., A.G-P. and N.L-B. Data collection and annotation: I.S., F.M-J. and L.M. IntOGen pipeline development and maintenance: J.D-P., F.M-J., L.M. and I.R-S. IntOGen website development and maintenance: I.R-S. and F.M-J. Project supervision: A.G-P. and N.L-B.

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F.M-J., F.M, A.G-P., N.L-B., C.A-P., L.M., O.P., H.K., J.B., I.S., J.D-P. and I.R-S. researched data for the article. F.M-J., A. G-P. and N.L-B. contributed to discussion of the content. F.M-J., A. G-P. and N.L-B. wrote the article. F.M-J., F.M, A.G-P., N.L-B., C.A-P., O.P., H.K., J.B., I.R-S, L.M. and I.S. reviewed and/or edited the manuscript before submission.

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Correspondence to Abel Gonzalez-Perez or Nuria Lopez-Bigas.

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Supplementary information

Glossary

Positional cloning

Technique for molecular cloning of all genetic material in a chromosomal locus with the aim of identifying genes.

Retrotransposition

Use of DNA retrotransposons to introduce pieces of foreign DNA into a genome with different research aims, such as transgenesis and insertional mutagenesis.

Sanger sequencing

Method of DNA sequencing developed by Sanger and colleagues in the 1970s which implements an in vitro DNA replication with selective incorporation of chain-terminating dideoxynucleotides.

Next-generation sequencing

(NGS). Also known as massively parallel sequencing, a group of high-throughput methods of DNA sequencing based on the concept of massively parallel processing.

Non-B-DNA structures

Local structures of chromosomal DNA that deviate (frequently in a transient manner) from the Watson–Crick double helix; they include stem–loop structures involving one or both DNA strands and G-quadruplexes.

Synonymous mutations

Single-nucleotide variants that cause a change of codon for a synonymous one.

Hypermutator phenotype

Tumours with abnormally high mutation burden in comparison with other samples of the same cohort (for example, more than three times the interquartile range above the median of the distribution), usually as a result of defective DNA repair mechanisms.

Nonsense mutations

Single-nucleotide variants that cause the change of a stop codon for an amino acid-coding codon.

Missense mutations

Single-nucleotide variants that cause the change of an amino acid in a protein sequence.

Wilms tumours

A rare type of kidney cancer that affects mostly children.

Paralogues

Genes within the same genome that have evolved from a common ancestor.

Degrons

Short sequences (4–10 amino acids) within a protein that are specifically recognized and bound by enzymes responsible for the conjugation of ubiquitin moieties.

Nonsense-mediated decay

Surveillance mechanism charged with the elimination of mRNA transcripts with premature stop codons.

Cis-regulatory regions

DNA sequences involved in the regulation of the expression of genes, such as transcription factor binding sites that may be found in promoters or enhancers.

Clonal haematopoiesis

Ageing-related clonal expansion of specific haematopoietic stem cells (HSCs) or other early blood cell progenitors which contributes to the appearance of genetically distinct subpopulations of blood cells.

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Martínez-Jiménez, F., Muiños, F., Sentís, I. et al. A compendium of mutational cancer driver genes. Nat Rev Cancer 20, 555–572 (2020). https://doi.org/10.1038/s41568-020-0290-x

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