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Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine
Benjamin J Raphael, Jason R Dobson, Layla Oesper and 1 more
Genome Medicine · 2014 · Review
Abstract
High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.
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Thomas RK Baker AC Debiasi RM Winckler W Laframboise T Lin WM Wang M Feng W Zander T MacConaill L MacConnaill LE Lee JC Nicoletti R Hatton C Goyette M Girard L Majmudar K Ziaugra L Wong KK Gabriel S Beroukhim R Peyton M Barretina J Dutt A Emery C Greulich H Shah K Sasaki H Gazdar A Minna J High-throughput oncogene mutation profiling in human cancerNature Genetics2007 39347351
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Kim ES Herbst RS Wistuba II Lee JJ Blumenschein GR Tsao A Stewart DJ Hicks ME Erasmus J Gupta S Alden CM Liu S Tang X Khuri FR Tran HT Johnson BE Heymach JV Mao L Fossella F Kies MS Papadimitrakopoulou V Davis SE Lippman SM Hong WK The BATTLE trial: personalizing therapy for lung cancerCancer Discov2011 14453
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Huang FW Hodis E Xu MJ Kryukov GV Chin L Garraway LA Highly recurrent TERT promoter mutations in human melanomaScience2013 339957959
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Dunham I Kundaje A Aldred SF Collins PJ Davis CA Doyle F Epstein CB Frietze S Harrow J Kaul R Khatun J Lajoie BR Landt SG Lee BK Pauli F Rosenbloom KR Sabo P Safi A Sanyal A Shoresh N Simon JM Song L Trinklein ND Altshuler RC Birney E Brown JB Cheng C Djebali S Dong X Ernst J An integrated encyclopedia of DNA elements in the human genomeNature2012 4895774
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Not in our library10.1038/nmeth.2651
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Weinstein JN Collisson EA Mills GB Shaw KRM Ozenberger BA Ellrott K Shmulevich I Sander C Stuart JM Cancer Genome Atlas Research NetworkThe Cancer Genome Atlas Pan-Cancer analysis projectNat Genet2013 4511131120
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Kandoth C McLellan MD Vandin F Ye K Niu B Lu C Xie M Zhang Q McMichael JF Wyczalkowski MA Leiserson MDM Miller CA Welch JS Walter MJ Wendl MC Ley TJ Wilson RK Raphael BJ Ding L Mutational landscape and significance across 12 major cancer typesNature2013 502333339
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Not in our library10.1038/ng.2762
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Zack TI Schumacher SE Carter SL Cherniack AD Saksena G Tabak B Lawrence MS Zhang CZ Wala J Mermel CH Sougnez C Gabriel SB Hernandez B Shen H Laird PW Getz G Meyerson M Beroukhim R Pan-cancer patterns of somatic copy number alterationNat Genet2013 4511341140
Not in our library10.1038/ng.2760
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Vaske CJ Benz SC Sanborn JZ Earl D Szeto C Zhu J Haussler D Stuart JM Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGMBioinformatics2010 26i237i245
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Paull EO Carlin DE Niepel M Sorger PK Haussler D Stuart JM Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)Bioinformatics2013 2927572764
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Roth A Ding J Morin R Crisan A Ha G Giuliany R Bashashati A Hirst M Turashvili G Oloumi A Marra MA Aparicio S Shah SP JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing dataBioinformatics2012 28907913
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Xi R Luquette J Hadjipanayis A Kim TM Park PJ BIC-seq: a fast algorithm for detection of copy number alterations based on high-throughput sequencing dataGenome Biol2010 11O10
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Escaramís G Tornador C Bassaganyas L Rabionet R Tubio JMC Martínez-Fundichely A Cáceres M Gut M Ossowski S Estivill X PeSV-Fisher: identification of somatic and non-somatic structural variants using next generation sequencing dataPLoS One2013 8e63377
Not in our library10.1371/journal.pone.0063377
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Drier Y Lawrence MS Carter SL Stewart C Gabriel SB Lander ES Meyerson M Beroukhim R Getz G Somatic rearrangements across cancer reveal classes of samples with distinct patterns of DNA breakage and rearrangement-induced hypermutabilityGenome Res2013 23228235
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Hua X Xu H Yang Y Zhu J Liu P Lu Y DrGaP: a powerful tool for identifying driver genes and pathways in cancer sequencing studiesAm J Hum Genet2013 93439451
Not in our library10.1016/j.ajhg.2013.07.003
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Cited by papers we hold 5
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases · 2020No notice
- A novel network regularized matrix decomposition method to detect mutated cancer genes in tumour samples with inter-patient heterogeneity · 2017Not checked
- CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer · 2015Corrected
- Systematic pan-cancer analysis of tumour purity · 2015Corrected
- Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing · 2014Not checked
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