Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance
Autoři:
Hanane Omichessan aff001; Gianluca Severi aff001; Vittorio Perduca aff001
Působiště autorů:
CESP (UMR INSERM 1018), Université Paris-Saclay, UPSud, UVSQ, Villejuif, France
aff001; Gustave Roussy, Villejuif, France
aff002; Cancer Epidemiology Centre, Cancer Council Victoria, and Centre for Epidemiology and Biostatistics, Melbourne School for Population and Global Health, The University of Melbourne, Melbourne, Australia
aff003; Laboratoire de Mathématiques Appliquées à Paris 5—MAP5 (UMR CNRS 8145), Université Paris Descartes, Université de Paris, Paris, France
aff004
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0221235
Souhrn
Mutational signatures refer to patterns in the occurrence of somatic mutations that might be uniquely ascribed to particular mutational process. Tumour mutation catalogues can reveal mutational signatures but are often consistent with the mutation spectra produced by a variety of mutagens. To date, after the analysis of tens of thousands of exomes and genomes from about 40 different cancer types, tens of mutational signatures characterized by a unique probability profile across the 96 trinucleotide-based mutation types have been identified, validated and catalogued. At the same time, several concurrent methods have been developed for either the quantification of the contribution of catalogued signatures in a given cancer sequence or the identification of new signatures from a sample of cancer sequences. A review of existing computational tools has been recently published to guide researchers and practitioners through their mutational signature analyses, but other tools have been introduced since its publication and, a systematic evaluation and comparison of the performance of such tools is still lacking. In order to fill this gap, we have carried out an empirical evaluation of the main packages available to date, using both real and simulated data. Among other results, our empirical study shows that the identification of signatures is more difficult for cancers characterized by multiple signatures each having a small contribution. This work suggests that detection methods based on probabilistic models, especially EMu and bayesNMF, have in general better performance than NMF-based methods.
Zdroje
1. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458: 719–724. doi: 10.1038/nature07943 19360079
2. Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR. Deciphering Signatures of Mutational Processes Operative in Human Cancer. Cell Reports. 2013;3: 246–259. doi: 10.1016/j.celrep.2012.12.008 23318258
3. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Research. 2017;45: D777–D783. doi: 10.1093/nar/gkw1121 27899578
4. Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Ng AW, Boot A, et al. The Repertoire of Mutational Signatures in Human Cancer. bioRxiv. 2018; 322859. doi: 10.1101/322859
5. Mayakonda A, Koeffler HP. Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv. 2016; 052662. doi: 10.1101/052662
6. Huang P-J, Chiu L-Y, Lee C-C, Yeh Y-M, Huang K-Y, Chiu C-H, et al. mSignatureDB: a database for deciphering mutational signatures in human cancers. Nucleic Acids Research. 2018;46: D964–D970. doi: 10.1093/nar/gkx1133 29145625
7. Baez-Ortega A, Gori K. Computational approaches for discovery of mutational signatures in cancer. Briefings in Bioinformatics. 2017; doi: 10.1093/bib/bbx082 28968631
8. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401: 788. doi: 10.1038/44565 10548103
9. Fischer A, Illingworth CJ, Campbell PJ, Mustonen V. EMu: probabilistic inference of mutational processes and their localization in the cancer genome. Genome Biology. 2013;14: R39. doi: 10.1186/gb-2013-14-4-r39 23628380
10. Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, ICGC PedBrain, Alexandrov LB, Nik-Zainal S, et al. Signatures of mutational processes in human cancer. Nature. 2013;500: 415–421. doi: 10.1038/nature12477 23945592
11. Letouzé E, Shinde J, Renault V, Couchy G, Blanc J-F, Tubacher E, et al. Mutational signatures reveal the dynamic interplay of risk factors and cellular processes during liver tumorigenesis. Nature Communications. 2017;8. doi: 10.1038/s41467-017-01358-x 29101368
12. Kim SY, Jung S-H, Kim MS, Han M-R, Park H-C, Jung ES, et al. Genomic profiles of a hepatoblastoma from a patient with Beckwith-Wiedemann syndrome with uniparental disomy on chromosome 11p15 and germline mutation of APC and PALB2. Oncotarget. 2017;8. doi: 10.18632/oncotarget.20515 29190888
13. Han M-R, Shin S, Park H-C, Kim MS, Lee SH, Jung SH, et al. Mutational signatures and chromosome alteration profiles of squamous cell carcinomas of the vulva. Experimental & Molecular Medicine. 2018;50: e442. doi: 10.1038/emm.2017.265 29422544
14. Rosales RA, Drummond RD, Valieris R, Dias-Neto E, da Silva IT. signeR: an empirical Bayesian approach to mutational signature discovery. Bioinformatics. 2017;33: 8–16. doi: 10.1093/bioinformatics/btw572 27591080
15. Gehring JS, Fischer B, Lawrence M, Huber W. SomaticSignatures: inferring mutational signatures from single-nucleotide variants: Fig 1. Bioinformatics. 2015; btv408. doi: 10.1093/bioinformatics/btv408 26163694
16. Shiraishi Y, Tremmel G, Miyano S, Stephens M. A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures. Marchini J, editor. PLOS Genetics. 2015;11: e1005657. doi: 10.1371/journal.pgen.1005657 26630308
17. Kim J, Mouw KW, Polak P, Braunstein LZ, Kamburov A, Tiao G, et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nature Genetics. 2016;48: 600–606. doi: 10.1038/ng.3557 27111033
18. Kasar S, Kim J, Improgo R, Tiao G, Polak P, Haradhvala N, et al. Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nature Communications. 2015;6. doi: 10.1038/ncomms9866 26638776
19. Tan VYF, Fevotte C. Automatic Relevance Determination in Nonnegative Matrix Factorization with the /spl beta/-Divergence. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;35: 1592–1605. doi: 10.1109/TPAMI.2012.240 23681989
20. Fantini D, Huang S, Asara JM, Bagchi S, Raychaudhuri P. Chromatin association of XRCC5/6 in the absence of DNA damage depends on the XPE gene product DDB2. Tansey WP, editor. Molecular Biology of the Cell. 2017;28: 192–200. doi: 10.1091/mbc.E16-08-0573 28035050
21. Carlson J, Li JZ, Zöllner S. Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets. BMC Genomics. 2018;19. doi: 10.1186/s12864-018-5264-y 30486787
22. Ramazzotti D, Lal A, Liu K, Tibshirani R, Sidow A. De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures. bioRxiv. 2018; doi: 10.1101/384834
23. Rosenthal R, McGranahan N, Herrero J, Taylor BS, Swanton C. deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biology. 2016;17. doi: 10.1186/s13059-016-0893-4 26899170
24. Lynch AG. Decomposition of mutational context signatures using quadratic programming methods. F1000Research. 2016;5: 1253. doi: 10.12688/f1000research.8918.1
25. Huang X, Wojtowicz D, Przytycka TM. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics. 2018;34: 330–337. doi: 10.1093/bioinformatics/btx604 29028923
26. Blokzijl F, Janssen R, van Boxtel R, Cuppen E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Medicine. 2018;10. doi: 10.1186/s13073-018-0539-0 29695279
27. Huebschmann D, Gu Z, Schelsner M. YAPSA: Yet Another Package for Signature Analysis R package version 1.6.0. R package version 1.6.0. 1 Jan 2015.
28. Krüger S, Piro RM. Identification of mutational signatures active in individual tumors. doi: 10.1186/s12859-019-2688-6 30999866
29. Gori K, Baez-Ortega A. sigfit: flexible Bayesian inference of mutational signatures. bioRxiv. 2018; doi: 10.1101/372896
30. Ardin M, Cahais V, Castells X, Bouaoun L, Byrnes G, Herceg Z, et al. MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes. BMC Bioinformatics. 2016;17. doi: 10.1186/s12859-016-1011-z 27091472
31. Goncearenco A, Rager SL, Li M, Sang Q-X, Rogozin IB, Panchenko AR. Exploring background mutational processes to decipher cancer genetic heterogeneity. Nucleic Acids Research. 2017;45: W514–W522. doi: 10.1093/nar/gkx367 28472504
32. Lee J, Lee AJ, Lee J-K, Park J, Kwon Y, Park S, et al. Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures. Nucleic Acids Research. 2018;46: W102–W108. doi: 10.1093/nar/gky406 29790943
33. Díaz-Gay M, Vila-Casadesús M, Franch-Expósito S, Hernández-Illán E, Lozano JJ, Castellví-Bel S. Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples. BMC Bioinformatics. 2018;19. doi: 10.1186/s12859-018-2234-y 29898651
34. Liao X, Meyer MC. coneproj: An R Package for the Primal or Dual Cone Projections with Routines for Constrained Regression. Journal of Statistical Software. 2014;61. doi: 10.18637/jss.v061.i12
35. Fan Y, Xi L, Hughes DST, Zhang J, Zhang J, Futreal PA, et al. MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biology. 2016;17. doi: 10.1186/s13059-016-1029-6 27557938
36. Zou X, Owusu M, Harris R, Jackson SP, Loizou JI, Nik-Zainal S. Validating the concept of mutational signatures with isogenic cell models. Nature Communications. 2018;9. doi: 10.1038/s41467-018-04052-8 29717121
37. Perduca V, Omichessan H, Baglietto L, Severi G. Mutational and epigenetic signatures in cancer tissue linked to environmental exposures and lifestyle: Current Opinion in Oncology. 2018;30: 61–67. doi: 10.1097/CCO.0000000000000418 29076965
38. Perduca V, Alexandrov LB, Kelly-Irving M, Delpierre C, Omichessan H, Little MP, et al. Stem cell replication, somatic mutations and role of randomness in the development of cancer. Eur J Epidemiol. 2019;34: 439–445. doi: 10.1007/s10654-018-0477-6 30623292
39. Kucab JE, Zou X, Morganella S, Joel M, Nanda AS, Nagy E, et al. A Compendium of Mutational Signatures of Environmental Agents. Cell. 2019;177: 821–836.e16. doi: 10.1016/j.cell.2019.03.001 30982602
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