Suppr超能文献

癌症基因组中剪接相关序列中体细胞突变的缺失。

Depletion of somatic mutations in splicing-associated sequences in cancer genomes.

机构信息

The Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY, UK.

Institute for Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.

出版信息

Genome Biol. 2017 Nov 7;18(1):213. doi: 10.1186/s13059-017-1337-5.

Abstract

BACKGROUND

An important goal of cancer genomics is to identify systematically cancer-causing mutations. A common approach is to identify sites with high ratios of non-synonymous to synonymous mutations; however, if synonymous mutations are under purifying selection, this methodology leads to identification of false-positive mutations. Here, using synonymous somatic mutations (SSMs) identified in over 4000 tumours across 15 different cancer types, we sought to test this assumption by focusing on coding regions required for splicing.

RESULTS

Exon flanks, which are enriched for sequences required for splicing fidelity, have ~ 17% lower SSM density compared to exonic cores, even after excluding canonical splice sites. While it is impossible to eliminate a mutation bias of unknown cause, multiple lines of evidence support a purifying selection model above a mutational bias explanation. The flank/core difference is not explained by skewed nucleotide content, replication timing, nucleosome occupancy or deficiency in mismatch repair. The depletion is not seen in tumour suppressors, consistent with their role in positive tumour selection, but is otherwise observed in cancer-associated and non-cancer genes, both essential and non-essential. Consistent with a role in splicing modulation, exonic splice enhancers have a lower SSM density before and after controlling for nucleotide composition; moreover, flanks at the 5' end of the exons have significantly lower SSM density than at the 3' end.

CONCLUSIONS

These results suggest that the observable mutational spectrum of cancer genomes is not simply a product of various mutational processes and positive selection, but might also be shaped by negative selection.

摘要

背景

癌症基因组学的一个重要目标是系统地识别致癌突变。一种常见的方法是识别非同义突变与同义突变比值较高的位点;然而,如果同义突变受到纯化选择的影响,这种方法会导致假阳性突变的识别。在这里,我们使用在超过 4000 个肿瘤中鉴定的同义体细胞突变(SSM),通过关注剪接所需的编码区域来检验这一假设。

结果

exon flanks(exon 侧翼)富含剪接保真所需的序列,与exon cores(exon 核心)相比,SSM 密度低约 17%,即使排除了经典剪接位点。虽然无法消除未知原因的突变偏差,但多条证据支持纯化选择模型而非突变偏差解释。侧翼/核心差异不能用核苷酸含量偏斜、复制时相、核小体占有率或错配修复缺陷来解释。肿瘤抑制基因中没有这种耗尽现象,这与它们在正向肿瘤选择中的作用一致,但在其他癌症相关和非癌症基因中,无论是必需基因还是非必需基因,都观察到了这种现象。exon 剪接增强子在控制核苷酸组成前后的 SSM 密度较低,这与它们在剪接调节中的作用一致;此外,exon 5' 侧翼的 SSM 密度明显低于 3' 侧翼。

结论

这些结果表明,可观察到的癌症基因组突变谱不仅仅是各种突变过程和正向选择的产物,还可能受到负向选择的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a971/5678748/1dd508a437e8/13059_2017_1337_Fig1_HTML.jpg

相似文献

1
Depletion of somatic mutations in splicing-associated sequences in cancer genomes.
Genome Biol. 2017 Nov 7;18(1):213. doi: 10.1186/s13059-017-1337-5.
3
Purifying Selection on Exonic Splice Enhancers in Intronless Genes.
Mol Biol Evol. 2016 Jun;33(6):1396-418. doi: 10.1093/molbev/msw018. Epub 2016 Jan 23.
4
Positive selection acting on splicing motifs reflects compensatory evolution.
Genome Res. 2008 Apr;18(4):533-43. doi: 10.1101/gr.070268.107. Epub 2008 Jan 18.
5
Computational analysis of splicing errors and mutations in human transcripts.
BMC Genomics. 2008 Jan 14;9:13. doi: 10.1186/1471-2164-9-13.
6
Genomic features defining exonic variants that modulate splicing.
Genome Biol. 2010;11(2):R20. doi: 10.1186/gb-2010-11-2-r20. Epub 2010 Feb 16.
7
Massive computational identification of somatic variants in exonic splicing enhancers using The Cancer Genome Atlas.
Cancer Med. 2019 Dec;8(17):7372-7384. doi: 10.1002/cam4.2619. Epub 2019 Oct 21.
9
Mutational bias and the protein code shape the evolution of splicing enhancers.
Nat Commun. 2020 Jun 5;11(1):2845. doi: 10.1038/s41467-020-16673-z.

引用本文的文献

1
Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation.
Nucleic Acids Res. 2024 May 8;52(8):4393-4408. doi: 10.1093/nar/gkae252.
2
SUVA: splicing site usage variation analysis from RNA-seq data reveals highly conserved complex splicing biomarkers in liver cancer.
RNA Biol. 2021 Oct 15;18(sup1):157-171. doi: 10.1080/15476286.2021.1940037. Epub 2021 Jun 21.
4
Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution.
Nat Genet. 2020 Mar;52(3):283-293. doi: 10.1038/s41588-020-0584-7. Epub 2020 Mar 5.
5
Estimating growth patterns and driver effects in tumor evolution from individual samples.
Nat Commun. 2020 Feb 5;11(1):732. doi: 10.1038/s41467-020-14407-9.
6
A pan-cancer analysis of synonymous mutations.
Nat Commun. 2019 Jun 12;10(1):2569. doi: 10.1038/s41467-019-10489-2.

本文引用的文献

1
Pathogenic variants that alter protein code often disrupt splicing.
Nat Genet. 2017 Jun;49(6):848-855. doi: 10.1038/ng.3837. Epub 2017 Apr 17.
2
Both Maintenance and Avoidance of RNA-Binding Protein Interactions Constrain Coding Sequence Evolution.
Mol Biol Evol. 2017 May 1;34(5):1110-1126. doi: 10.1093/molbev/msx061.
6
Landscape of somatic mutations in 560 breast cancer whole-genome sequences.
Nature. 2016 Jun 2;534(7605):47-54. doi: 10.1038/nature17676. Epub 2016 May 2.
7
Purifying Selection on Exonic Splice Enhancers in Intronless Genes.
Mol Biol Evol. 2016 Jun;33(6):1396-418. doi: 10.1093/molbev/msw018. Epub 2016 Jan 23.
8
Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools.
PLoS Genet. 2016 Jan 13;12(1):e1005756. doi: 10.1371/journal.pgen.1005756. eCollection 2016 Jan.
9
Determinants of the Usage of Splice-Associated cis-Motifs Predict the Distribution of Human Pathogenic SNPs.
Mol Biol Evol. 2016 Feb;33(2):518-29. doi: 10.1093/molbev/msv251. Epub 2015 Nov 5.
10
Gene essentiality and synthetic lethality in haploid human cells.
Science. 2015 Nov 27;350(6264):1092-6. doi: 10.1126/science.aac7557. Epub 2015 Oct 15.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验