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

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.

[1]  F. Cleton Evolution of Cancer , 1991, British Journal of Cancer.

[2]  Bruce Stillman,et al.  The p150 and p60 subunits of chromatin assemblyfactor I: A molecular link between newly synthesized histories and DNA replication , 1995, Cell.

[3]  David Sidransky,et al.  Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. , 2002, Cancer research.

[4]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[5]  D. Berdnik,et al.  Mitotic activation of the kinase Aurora-A requires its binding partner Bora. , 2006, Developmental cell.

[6]  T. Taniguchi,et al.  Proteasome function is required for DNA damage response and fanconi anemia pathway activation. , 2007, Cancer research.

[7]  J. Rinn,et al.  Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression , 2009, Proceedings of the National Academy of Sciences.

[8]  Gary D Bader,et al.  International network of cancer genome projects , 2010, Nature.

[9]  John T. Wei,et al.  Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1, an unannotated lincRNA implicated in disease progression , 2011, Nature Biotechnology.

[10]  A. Gonzalez-Perez,et al.  Functional impact bias reveals cancer drivers , 2012, Nucleic acids research.

[11]  Matthew B. Callaway,et al.  MuSiC: Identifying mutational significance in cancer genomes , 2012, Genome research.

[12]  Peter J. Campbell,et al.  Evolution of the cancer genome , 2012, Nature Reviews Genetics.

[13]  Gary D Bader,et al.  Computational approaches to identify functional genetic variants in cancer genomes , 2013, Nature Methods.

[14]  F. D'armiento,et al.  Cancer-Associated CD43 Glycoforms as Target of Immunotherapy , 2013, Molecular Cancer Therapeutics.

[15]  David T. W. Jones,et al.  Signatures of mutational processes in human cancer , 2013, Nature.

[16]  D. Schadendorf,et al.  TERT Promoter Mutations in Familial and Sporadic Melanoma , 2013, Science.

[17]  S. Szymczak,et al.  Chromosomal Aneuploidy Affects the Global Proteome Equilibrium of Colorectal Cancer Cells , 2014, Analytical cellular pathology.

[18]  P. Stephens,et al.  Targeted next-generation sequencing of head and neck squamous cell carcinoma identifies novel genetic alterations in HPV+ and HPV- tumors , 2013, Genome Medicine.

[19]  Ye Tian,et al.  Expression of p114RhoGEF predicts lymph node metastasis and poor survival of squamous-cell lung carcinoma patients , 2013, Tumor Biology.

[20]  Jan Gorodkin,et al.  RNAsnp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNPs , 2013, Human mutation.

[21]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer-associated genes , 2013 .

[22]  David Tamborero,et al.  OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes , 2013, Bioinform..

[23]  Peter F. Stadler,et al.  RNAsnp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNPs , 2013, Human Mutation.

[24]  S. Diederichs,et al.  MALAT1 — a paradigm for long noncoding RNA function in cancer , 2013, Journal of Molecular Medicine.

[25]  Gary D Bader,et al.  Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers , 2013 .

[26]  Chris Sander,et al.  Comprehensive Analysis of Long Non-Coding RNAs in Ovarian Cancer Reveals Global Patterns and Targeted DNA Amplification , 2013, PloS one.

[27]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[28]  Shibing Deng,et al.  Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer , 2014, Nature Genetics.

[29]  C. Sander,et al.  Genome-wide analysis of non-coding regulatory mutations in cancer , 2014, Nature Genetics.

[30]  E. Larsson,et al.  Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types , 2014, Nature Genetics.

[31]  S. Gabriel,et al.  Discovery and saturation analysis of cancer genes across 21 tumor types , 2014, Nature.

[32]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[33]  Steven J. M. Jones,et al.  Comprehensive molecular profiling of lung adenocarcinoma , 2014, Nature.

[34]  Adam Godzik,et al.  e-Driver: a novel method to identify protein regions driving cancer , 2014, Bioinform..

[35]  M. Albà,et al.  Long non-coding RNAs as a source of new peptides , 2014, eLife.

[36]  Donavan T. Cheng,et al.  Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. , 2015, The Journal of molecular diagnostics : JMD.

[37]  Paz Polak,et al.  Cell-of-origin chromatin organization shapes the mutational landscape of cancer , 2015, Nature.

[38]  Brent S. Pedersen,et al.  Signatures of accelerated somatic evolution in gene promoters in multiple cancer types , 2015, Nucleic acids research.

[39]  Involvement of p29/SYF2/fSAP29/NTC31 in the progression of NSCLC via modulating cell proliferation. , 2015, Pathology, research and practice.

[40]  M. Gerstein,et al.  LARVA: an integrative framework for large-scale analysis of recurrent variants in noncoding annotations , 2015, Nucleic acids research.

[41]  M. Stratton,et al.  High burden and pervasive positive selection of somatic mutations in normal human skin , 2015, Science.

[42]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[43]  Radhakrishnan Sabarinathan,et al.  Nucleotide excision repair is impaired by binding of transcription factors to DNA , 2015, Nature.

[44]  Michael P Snyder,et al.  Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations , 2015, Nature Genetics.

[45]  D. Schadendorf,et al.  Highly Recurrent TERT Promoter Mutations in Human Melanoma , 2022 .