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OncodriveFML：一种识别具有癌症驱动突变的编码和非编码区域的通用框架。

OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations.

作者信息

Mularoni Loris, Sabarinathan Radhakrishnan, Deu-Pons Jordi, Gonzalez-Perez Abel, López-Bigas Núria

机构信息

Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain.

Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain.

出版信息

Genome Biol. 2016 Jun 16;17(1):128. doi: 10.1186/s13059-016-0994-0.

DOI:10.1186/s13059-016-0994-0
PMID:27311963
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4910259/
Abstract

Distinguishing the driver mutations from somatic mutations in a tumor genome is one of the major challenges of cancer research. This challenge is more acute and far from solved for non-coding mutations. Here we present OncodriveFML, a method designed to analyze the pattern of somatic mutations across tumors in both coding and non-coding genomic regions to identify signals of positive selection, and therefore, their involvement in tumorigenesis. We describe the method and illustrate its usefulness to identify protein-coding genes, promoters, untranslated regions, intronic splice regions, and lncRNAs-containing driver mutations in several malignancies.

摘要

区分肿瘤基因组中的驱动突变和体细胞突变是癌症研究的主要挑战之一。对于非编码突变而言，这一挑战更为严峻且远未得到解决。在此，我们介绍OncodriveFML，这是一种旨在分析编码和非编码基因组区域中肿瘤体细胞突变模式以识别正选择信号，进而确定其在肿瘤发生中作用的方法。我们描述了该方法，并举例说明了它在识别多种恶性肿瘤中含驱动突变的蛋白质编码基因、启动子、非翻译区、内含子剪接区域和长链非编码RNA方面的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/ea7706e94c37/13059_2016_994_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/d34f4656c085/13059_2016_994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/ca764433bf8e/13059_2016_994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/816e31039f31/13059_2016_994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/64e9a9bacc49/13059_2016_994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/ea7706e94c37/13059_2016_994_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/d34f4656c085/13059_2016_994_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/ca764433bf8e/13059_2016_994_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/816e31039f31/13059_2016_994_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/64e9a9bacc49/13059_2016_994_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a5d/4910259/ea7706e94c37/13059_2016_994_Fig5_HTML.jpg

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本文引用的文献

1
Nucleotide excision repair is impaired by binding of transcription factors to DNA.核苷酸切除修复会因转录因子与 DNA 的结合而受损。
Nature. 2016 Apr 14;532(7598):264-7. doi: 10.1038/nature17661.
2
Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations.跨癌症类型显著突变区域的鉴定凸显了功能分子改变的丰富图景。
Nat Genet. 2016 Feb;48(2):117-25. doi: 10.1038/ng.3471. Epub 2015 Dec 21.
3
LARVA: an integrative framework for large-scale analysis of recurrent variants in noncoding annotations.
基于基因表达微阵列分析识别出的核心驱动基因集揭示了其在泛癌中的潜在驱动机制。
NPJ Precis Oncol. 2025 Aug 9;9(1):278. doi: 10.1038/s41698-025-01060-y.
4
Whole-genome sequencing reveals three follicular lymphoma subtypes with distinct cell of origin and patient outcomes.全基因组测序揭示了三种滤泡性淋巴瘤亚型，它们具有不同的细胞起源和患者预后。
Cell Rep Med. 2025 Aug 19;6(8):102278. doi: 10.1016/j.xcrm.2025.102278. Epub 2025 Aug 7.
5
A comprehensive database for identifying and interpreting ctDNA driver genes and variants in cancer.一个用于识别和解释癌症中ctDNA驱动基因及变异的综合数据库。
Sci Data. 2025 Jul 16;12(1):1244. doi: 10.1038/s41597-025-05550-3.
6
Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer.高级别浆液性卵巢癌中结构多样性的不同轨迹影响患者生存。
Nat Commun. 2025 Jul 1;16(1):5586. doi: 10.1038/s41467-025-60655-y.
7
Analysis of IDH1 and IDH2 mutations as causes of the hypermethylator phenotype in colorectal cancer.分析异柠檬酸脱氢酶1（IDH1）和异柠檬酸脱氢酶2（IDH2）突变作为结直肠癌高甲基化表型的原因。
J Pathol. 2025 Jun 22. doi: 10.1002/path.6446.
8
Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.整合肿瘤和普通人群的基因突变谱与基因表达拓扑网络以鉴定新型癌症驱动基因。
Hum Genet. 2025 Jun 14. doi: 10.1007/s00439-025-02755-9.
9
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Genome Biol. 2025 Jun 13;26(1):165. doi: 10.1186/s13059-025-03557-y.
10
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Int J Mol Sci. 2025 May 19;26(10):4865. doi: 10.3390/ijms26104865.
LARVA：非编码注释中复发性变异大规模分析的综合框架。
Nucleic Acids Res. 2015 Sep 30;43(17):8123-34. doi: 10.1093/nar/gkv803. Epub 2015 Aug 24.
4
Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin.肿瘤进化。正常人类皮肤中体细胞突变的高负担和普遍正向选择。
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5
Signatures of accelerated somatic evolution in gene promoters in multiple cancer types.多种癌症类型中基因启动子区域加速体细胞进化的特征
Nucleic Acids Res. 2015 Jun 23;43(11):5307-17. doi: 10.1093/nar/gkv419. Epub 2015 May 1.
6
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10
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