Somatic mutations in early onset luminal breast cancer

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art_ENCINAS_Somatic_mutations_in_early_onset_luminal_breast_cancer_2018.PDF (4.04 MB)
Citações na Scopus
24
Tipo de produção
article
Data de publicação
2018
DOI
Scopus
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
IMPACT JOURNALS LLC
Autores
SABELNYKOVA, V. Y.
LYRA, E. C. de
Citação
ONCOTARGET, v.9, n.32, p.22460-22479, 2018
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Breast cancer arising in very young patients may be biologically distinct; however, these tumors have been less well studied. We characterized a group of very young patients (≤ 35 years) for BRCA germline mutation and for somatic mutations in luminal (HER2 negative) breast cancer. Thirteen of 79 unselected very young patients were BRCA1/2 germline mutation carriers. Of the non-BRCA tumors, eight with luminal subtype (HER2 negative) were submitted for whole exome sequencing and integrated with 29 luminal samples from the COSMIC database or previous literature for analysis. We identified C to T single nucleotide variants (SNVs) as the most common base-change. A median of six candidate driver genes was mutated by SNVs in each sample and the most frequently mutated genes were PIK3CA, GATA3, TP53 and MAP2K4. Potential cancer drivers affected in the present non-BRCA tumors include GRHL2, PIK3AP1, CACNA1E, SEMA6D, SMURF2, RSBN1 and MTHFD2. Sixteen out of 37 luminal tumors (43%) harbored SNVs in DNA repair genes, such as ATR, BAP1, ERCC6, FANCD2, FANCL, MLH1, MUTYH, PALB2, POLD1, POLE, RAD9A, RAD51 and TP53, and 54% presented pathogenic mutations (frameshift or nonsense) in at least one gene involved in gene transcription. The differential biology of luminal early-age onset breast cancer needs a deeper genomic investigation. © Encinas et al.
Palavras-chave
Breast cancer, Germline mutation, Luminal subtype, Somatic mutation, Young patients
URI
https://observatorio.fm.usp.br/handle/OPI/31031
Referências
  1. (2015), http://www.fosp.saude.sp.gov.br/htmL/tabnet.htmL
  2. Siegel, R.L., Miller, K.D., Jemal, A., Cancer Statistics, 2017 (2017) CA Cancer J Clin, 67, pp. 7-30. , https://doi.org/10.3322/caac.21387
  3. Bleyer, A., Barr, R., Hayes-Lattin, B., Thomas, D., Ellis, C., Anderson, B., The distinctive biology of cancer in adolescents and young adults (2008) Nat Rev Cancer, 8, pp. 288-298. , https://doi.org/10.1038/nrc2349
  4. Lalloo, F., Varley, J., Moran, A., Ellis, D., O'dair, L., Pharoah, P., Antoniou, A., Evans, D.G., BRCA1, BRCA2 and TP53 mutations in very early-onset breast cancer with associated risks to relatives (2006) Eur J Cancer, 42, pp. 1143-1150. , https://doi.org/10.1016/j.ejca.2005.11.032
  5. Loizidou, M., Marcou, Y., Anastasiadou, V., Newbold, R., Hadjisavvas, A., Kyriacou, K., Contribution of BRCA1 and BRCA2 germline mutations to the incidence of early-onset breast cancer in Cyprus (2007) Clin Genet, 71, pp. 165-170. , https://doi.org/10.1111/j.1399-0004.2007.00747.x
  6. Haffty, B.G., Choi, D.H., Goyal, S., Silber, A., Ranieri, K., Matloff, E., Lee, M.H., Moran, M.S., Breast cancer in young women (YBC): prevalence of BRCA1/2 mutations and risk of secondary malignancies across diverse racial groups (2009) Ann Oncol, 20, pp. 1653-1659. , https://doi.org/10.1093/annonc/mdp051
  7. Carraro, D.M., Koike Folgueira, M.A., Garcia Lisboa, B.C., Ribeiro Olivieri, E.H., Vitorino Krepischi, A.C., de Carvalho, A.F., de Carvalho Mota, L.D., Soares, F.A., Comprehensive Analysis of BRCA1, BRCA2 and TP53 Germline Mutation and Tumor Characterization: A Portrait of Early-Onset Breast Cancer in Brazil (2013) PLoS One, 8. , https://doi.org/10.1371/journal.pone.0057581
  8. Kurian, A.W., BRCA1 and BRCA2 mutations across race and ethnicity: distribution and clinical implications (2010) Curr Opin Obstet Gynecol, 22, pp. 72-78. , https://doi.org/10.1097/GCO.0b013e328332dca3
  9. Churpek, J.E., Walsh, T., Zheng, Y., Moton, Z., Thornton, A.M., Lee, M.K., Casadei, S., Liu, F., Inherited predisposition to breast cancer among African American women (2015) Breast Cancer Res Treat, 149, pp. 31-39. , https://doi.org/10.1007/s10549-014-3195-0
  10. Cancello, G., Maisonneuve, P., Rotmensz, N., Viale, G., Mastropasqua, M.G., Pruneri, G., Veronesi, P., Ghisini, R., Prognosis and adjuvant treatment effects in selected breast cancer subtypes of very young women ( < 35 years) with operable breast cancer (2010) Ann Oncol, 21, pp. 1974-1981. , https://doi.org/10.1093/annonc/mdq072
  11. Azim, H.A., Jr., Michiels, S., Bedard, P.L., Singhal, S.K., Criscitiello, C., Ignatiadis, M., Haibe-Kains, B., Loi, S., Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling (2012) Clin Cancer Res, 18, pp. 1341-1351. , https://doi.org/10.1158/1078-0432.CCR-11-2599
  12. Loibl, S., Jackisch, C., Lederer, B., Untch, M., Paepke, S., Kümmel, S., Schneeweiss, A., Denkert, C., Outcome after neoadjuvant chemotherapy in young breast cancer patients: a pooled analysis of individual patient data from eight prospectively randomized controlled trials (2015) Breast Cancer Res Treat, 152, pp. 377-387. , https://doi.org/10.1007/s10549-015-3479-z
  13. Cancello, G., Maisonneuve, P., Mazza, M., Montagna, E., Rotmensz, N., Viale, G., Pruneri, G., Colleoni, M., Pathological features and survival outcomes of very young patients with early breast cancer: how much is ""very young""? (2013) Breast, 22, pp. 1046-1051. , https://doi.org/10.1016/j.breast.2013.08.006
  14. Stephens, P.J., McBride, D.J., Lin, M.L., Varela, I., Pleasance, E.D., Simpson, J.T., Stebbings, L.A., Latimer, C., Complex landscapes of somatic rearrangement in human breast cancer genomes (2009) Nature, 462, pp. 1005-1010. , https://doi.org/10.1038/nature08645
  15. Comprehensive molecular portraits of human breast tumours (2012) Nature, 490, pp. 61-70. , https://doi.org/10.1038/nature11412
  16. Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Smid, M., Landscape of somatic mutations in 560 breast cancer whole-genome sequences (2016) Nature, 534, pp. 47-54. , https://doi.org/10.1038/nature17676
  17. Stephens, P.J., Tarpey, P.S., Davies, H., Van Loo, P., Greenman, C., Wedge, D.C., Nik-Zainal, S., Beare, D., The landscape of cancer genes and mutational processes in breast cancer (2012) Nature, 486, pp. 400-404. , https://doi.org/10.1038/nature11017
  18. Banerji, S., Cibulskis, K., Rangel-Escareno, C., Brown, K.K., Carter, S.L., Frederick, A.M., Lawrence, M.S., Peng, S., Sequence analysis of mutations and translocations across breast cancer subtypes (2012) Nature, 486, pp. 405-409. , https://doi.org/10.1038/nature11154
  19. Futreal, P.A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., Stratton, M.R., A census of human cancer genes (2004) Nat Rev Cancer, 4, pp. 177-183. , https://doi.org/10.1038/nrc1299
  20. Lánczky, A., Nagy, A., Bottai, G., Munkácsy, G., Szabó, A., Santarpia, L., Gyorffy, B., miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients (2016) Breast Cancer Res Treat, 160, pp. 439-446. , https://doi.org/10.1007/s10549-016-4013-7
  21. Abbott, K.L., Nyre, E.T., Abrahante, J., Ho, Y.Y., Isaksson Vogel, R., Starr, T.K., The Candidate Cancer Gene Database: a database of cancer driver genes from forward genetic screens in mice (2015) Nucleic Acids Res, 43, pp. D844-D848. , https://doi.org/10.1093/nar/gku770
  22. Chen, H.J., Wei, Z., Sun, J., Bhattacharya, A., Savage, D.J., Serda, R., Mackeyev, Y., Huang, E., A recellularized human colon model identifies cancer driver genes (2016) Nat Biotechnol, 34, pp. 845-851. , https://doi.org/10.1038/nbt.3586
  23. Dennis, G., Jr., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., Lempicki, R.A., DAVID: Database for Annotation, Visualization, and Integrated Discovery (2003) Genome Biol, 4, p. 3. , https://doi.org/10.1186/gb-2003-4-5-p3
  24. Curtin, N.J., DNA repair dysregulation from cancer driver to therapeutic target (2012) Nat Rev Cancer, 12, pp. 801-817. , https://doi.org/10.1038/nrc3399
  25. Chen, J., Xu, H., Aronow, B.J., Jegga, A.G., Improved human disease candidate gene prioritization using mouse phenotype (2007) BMC Bioinformatics, 8, p. 392. , https://doi.org/10.1186/1471-2105-8-392
  26. Pena, S.D., Di Pietro, G., Fuchshuber-Moraes, M., Genro, J.P., Hutz, M.H., Kehdy Fde, S., Kohlrausch, F., Ojopi, E.B., The Genomic Ancestry of Individuals from Different Geographical Regions of Brazil Is More Uniform Than Expected (2011) PLoS One, 6. , https://doi.org/10.1371/journal.pone.0017063
  27. Ellis, M.J., Ding, L., Shen, D., Luo, J., Suman, V.J., Wallis, J.W., Van Tine, B.A., Crowder, R., Whole-genome analysis informs breast cancer response to aromatase inhibition (2012) Nature, 486, pp. 353-360. , https://doi.org/10.1038/nature11143
  28. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A., Behjati, S., Biankin, A.V., Bignell, G.R., Butler, A.P., Signatures of mutational processes in human cancer (2013) Nature, 500, pp. 415-421. , https://doi.org/10.1038/nature12477
  29. Guo, Y., Updegraff, B.L., Park, S., Durakoglugil, D., Cruz, V.H., Maddux, S., Hwang, T.H., O'Donnell, K.A., Comprehensive Ex Vivo Transposon Mutagenesis Identifies Genes That Promote Growth Factor Independence and Leukemogenesis (2016) Cancer Res, 76, pp. 773-786. , https://doi.org/10.1158/0008-5472.CAN-15-1697
  30. Cieply, B., Farris, J., Denvir, J., Ford, H.L., Frisch, S.M., Epithelial-mesenchymal transition and tumor suppression are controlled by a reciprocal feedback loop between ZEB1 and Grainyhead-like-2 (2013) Cancer Res, 73, pp. 6299-6309. , https://doi.org/10.1158/0008-5472.CAN-12-4082
  31. North, W.G., Gao, G., Memoli, V.A., Pang, R.H., Lynch, L., Breast cancer expresses functional NMDA receptors (2010) Breast Cancer Res Treat, 122, pp. 307-314. , https://doi.org/10.1007/s10549-009-0556-1
  32. Blank, M., Tang, Y., Yamashita, M., Burkett, S.S., Cheng, S.Y., Zhang, Y.E., A tumor suppressor function of Smurf2 associated with controlling chromatin landscape and genome stability through RNF20 (2012) Nat Med, 18, pp. 227-234. , https://doi.org/10.1038/nm.2596
  33. Chandhoke, A.S., Chanda, A., Karve, K., Deng, L., Bonni, S., The PIAS3-Smurf2 sumoylation pathway suppresses breast cancer organoid invasiveness (2017) Oncotarget, 8, pp. 21001-21014. , https://doi.org/10.18632/oncotarget.15471
  34. Politopoulos, I., Gibson, J., Tapper, W., Ennis, S., Eccles, D., Collins, A., Composite likelihood-based meta-analysis of breast cancer association studies (2011) J Hum Genet, 56, pp. 377-382. , https://doi.org/10.1038/jhg.2011.23
  35. Abu-Jamous, B., Buffa, F.M., Harris, A.L., Nandi, A.K., In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer (2017) Mol Cancer, 16, p. 105. , https://doi.org/10.1186/s12943-017-0673-0
  36. Phan, N.N., Wang, C.Y., Chen, C.F., Sun, Z., Lai, M.D., Lin, Y.C., Voltage-gated calcium channels: novel targets for cancer therapy (2017) Oncol Lett, 14, pp. 2059-2074. , https://doi.org/10.3892/ol.2017.6457
  37. Weinreb, I., Piscuoglio, S., Martelotto, L.G., Waggott, D., Ng, C.K., Perez-Ordonez, B., Harding, N.J., Marchio, C., Hotspot activating PRKD1 somatic mutations in polymorphous lowgrade adenocarcinomas of the salivary glands (2014) Nat Genet, 46, pp. 1166-1169. , https://doi.org/10.1038/ng.3096
  38. Kim, Y., Park, E.Y., Chang, E., Kang, H.G., Koo, Y., Lee, E.J., Ko, J.Y., Park, J.H., A novel miR-34a target, protein kinase D1, stimulates cancer stemness and drug resistance through GSK3/β-catenin signaling in breast cancer (2016) Oncotarget, 7, pp. 14791-14802. , https://doi.org/10.18632/oncotarget.7443
  39. (2017), https://www.ncbi.nlm.nih.gov/gene/5424, (POLD)1-(GENE)-(NCBI) []
  40. Briggs, S., Tomlinson, I., Germline and somatic polymerase e and d mutations define a new class of hypermutated colorectal and endometrial cancers (2013) J Pathol, 230, pp. 148-153. , https://doi.org/10.1002/path.4185
  41. Tzeng, S.T., Tsai, M.H., Chen, C.L., Lee, J.X., Jao, T.M., Yu, S.L., Yen, S.J., Yang, Y.C., NDST4 Is a Novel Candidate Tumor Suppressor Gene at Chromosome 4q26 and Its Genetic Loss Predicts Adverse Prognosis in Colorectal Cancer (2013) PLoS One, 8. , https://doi.org/10.1371/journal.pone.0067040
  42. Peng, H.Y., Yu, Q.F., Shen, W., Guo, C.M., Li, Z., Zhou, X.Y., Zhou, N.J., Gao, D., Knockdown of ELMO3 Suppresses Growth, Invasion and Metastasis of Colorectal Cancer (2016) Int J Mol Sci, 17, p. 2119. , https://doi.org/10.3390/ijms17122119
  43. Kadletz, L., Heiduschka, G., Wiebringhaus, R., Gurnhofer, E., Kotowski, U., Haymerle, G., Brunner, M., Kenner, L., ELMO3 expression indicates a poor prognosis in head and neck squamous cell carcinoma-a short report (2017) Cell Oncol (Dordr), 40, pp. 193-198. , https://doi.org/10.1007/s13402-016-0310-8
  44. Fan, W., Yang, H., Xue, H., Sun, Y., Zhang, J., ELMO3 is a novel biomarker for diagnosis and prognosis of non-small cell lung cancer (2015) Int J Clin Exp Pathol, 8, pp. 5503-5508. , http://www.ncbi.nlm.nih.gov/pubmed/26191257
  45. Tedeschi, P.M., Scotto, K.W., Kerrigan, J., Bertino, J.R., MTHFD2-a new twist? (2016) Oncotarget, 7, pp. 7368-7369. , https://doi.org/10.18632/oncotarget.7147
  46. Liu, F., Liu, Y., He, C., Tao, L., He, X., Song, H., Zhang, G., Increased MTHFD2 expression is associated with poor prognosis in breast cancer (2014) Tumour Biol, 35, pp. 8685-8690. , https://doi.org/10.1007/s13277-014-2111-x
  47. Moriarity, B.S., Otto, G.M., Rahrmann, E.P., Rathe, S.K., Wolf, N.K., Weg, M.T., Manlove, L.A., Sarver, A.L., A Sleeping Beauty forward genetic screen identifies new genes and pathways driving osteosarcoma development and metastasis (2015) Nat Genet, 47, pp. 615-624. , https://doi.org/10.1038/ng.3293
  48. Chen, D., Li, Y., Wang, L., Jiao, K., SEMA6D Expression and Patient Survival in Breast Invasive Carcinoma (2015) Int J Breast Cancer, 2015. , https://doi.org/10.1155/2015/539721
  49. Azim, H.A., Jr., Nguyen, B., Brohée, S., Zoppoli, G., Sotiriou, C., Genomic aberrations in young and elderly breast cancer patients (2015) BMC Med, 13, p. 266. , https://doi.org/10.1186/s12916-015-0504-3
  50. Di Leva, G., Piovan, C., Gasparini, P., Ngankeu, A., Taccioli, C., Briskin, D., Cheung, D.G., Iorio, M.V., Estrogen Mediated-Activation of miR-191/425 Cluster Modulates Tumorigenicity of Breast Cancer Cells Depending on Estrogen Receptor Status. Clurman BE, editor (2013) PLoS Genet, 9. , https://doi.org/10.1371/journal.pgen.1003311
  51. Bong, I.P., Ng, C.C., Fakiruddin, S.K., Lim, M.N., Zakaria, Z., Small interfering RNA-mediated silencing of nicotinamide phosphoribosyltransferase (NAMPT) and lysosomal trafficking regulator (LYST) induce growth inhibition and apoptosis in human multiple myeloma cells: A preliminary study (2016) Bosn J Basic Med Sci, 16, pp. 268-275. , https://doi.org/10.17305/bjbms.2016.1568
  52. Smith, T.R., Liu-Mares, W., Van Emburgh, B.O., Levine, E.A., Allen, G.O., Hill, J.W., Reis, I.M., Hu, J.J., Genetic polymorphisms of multiple DNA repair pathways impact age at diagnosis and TP53 mutations in breast cancer (2011) Carcinogenesis, 32, pp. 1354-1360. , https://doi.org/10.1093/carcin/bgr117
  53. Wolff, A.C., Hammond, M.E., Hicks, D.G., Dowsett, M., McShane, L.M., Allison, K.H., Allred, D.C., Mangu, P.B., Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update (2013) J Clin Oncol, 31, pp. 3997-4013. , https://doi.org/10.1200/JCO.2013.50.9984
  54. Arnold, N., Gross, E., Schwarz-Boeger, U., Pfisterer, J., Jonat, W., Kiechle, M., A highly sensitive, fast, and economical technique for mutation analysis in hereditary breast and ovarian cancers (1999) Hum Mutat, 14, pp. 333-339. , https://doi.org/10.1002/(SICI)1098-1004(199910)14:4<333::AIDHUMU9>3.0.CO;2-C
  55. Friedman, L.S., Ostermeyer, E.A., Szabo, C.I., Dowd, P., Lynch, E.D., Rowell, S.E., King, M.C., Confirmation of BRCA1 by analysis of germline mutations linked to breast and ovarian cancer in ten families (1994) Nat Genet, 8, pp. 399-404. , https://doi.org/10.1038/ng1294-399
  56. Wagner, T.M., Hirtenlehner, K., Shen, P., Moeslinger, R., Muhr, D., Fleischmann, E., Concin, H., Ropp, E., Global sequence diversity of BRCA2: analysis of 71 breast cancer families and 95 control individuals of worldwide populations (1999) Hum Mol Genet, 8, pp. 413-423. , http://www.ncbi.nlm.nih.gov/pubmed/9971877https://doi.org/10.1093/hmg/8.3.413
  57. Szabo, C., Masiello, A., Ryan, J.F., Brody, L.C., The breast cancer information core: database design, structure, and scope (2000) Hum Mutat, 16, pp. 123-131. , https://doi.org/10.1002/1098-1004(200008)16:2<123::AID-HUMU4>3.0.CO;2-Y
  58. Fokkema, I.F., Taschner, P.E., Schaafsma, G.C., Celli, J., Laros, J.F., den Dunnen, J.T., LOVD v.2.0: the next generation in gene variant databases (2011) Hum Mutat, 32, pp. 557-563. , https://doi.org/10.1002/humu.21438
  59. Béroud, C., Collod-Béroud, G., Boileau, C., Soussi, T., Junien, C., UMD (Universal mutation database): a generic software to build and analyze locus-specific databases (2000) Hum Mutat, 15, pp. 86-94. , https://doi.org/10.1002/(SICI)1098-1004(200001)15:1<86::AID-HUMU16>3.0.CO;2-4
  60. Caputo, S., Benboudjema, L., Sinilnikova, O., Rouleau, E., Béroud, C., Lidereau, R., Description and analysis of genetic variants in French hereditary breast and ovarian cancer families recorded in the UMD-BRCA1/BRCA2 databases (2012) Nucleic Acids Res, 40, pp. D992-D1002. , https://doi.org/10.1093/nar/gkr1160
  61. Landrum, M.J., Lee, J.M., Riley, G.R., Jang, W., Rubinstein, W.S., Church, D.M., Maglott, D.R., ClinVar: public archive of relationships among sequence variation and human phenotype (2014) Nucleic Acids Res, 42, pp. D980-D985. , https://doi.org/10.1093/nar/gkt1113
  62. Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., Sunyaev, S.R., A method and server for predicting damaging missense mutations (2010) Nat Methods, 7, pp. 248-249. , https://doi.org/10.1038/nmeth0410-248
  63. Ng, P.C., Henikoff, S., Predicting deleterious amino acid substitutions (2001) Genome Res, 11, pp. 863-874. , https://doi.org/10.1101/gr.176601
  64. Tavtigian, S.V., Deffenbaugh, A.M., Yin, L., Judkins, T., Scholl, T., Samollow, P.B., de Silva, D., Thomas, A., Comprehensive statistical study of 452 BRCA1 missense substitutions with classification of eight recurrent substitutions as neutral (2006) J Med Genet, 43, pp. 295-305. , https://doi.org/10.1136/jmg.2005.033878
  65. Mathe, E., Olivier, M., Kato, S., Ishioka, C., Hainaut, P., Tavtigian, S.V., Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods (2006) Nucleic Acids Res, 34, pp. 1317-1325. , https://doi.org/10.1093/nar/gkj518
  66. Choi, Y., Sims, G.E., Murphy, S., Miller, J.R., Chan, A.P., Predicting the Functional Effect of Amino Acid Substitutions and Indels. de Brevern AG, editor (2012) PLoS One, 7. , https://doi.org/10.1371/journal.pone.0046688
  67. Desmet, F.O., Hamroun, D., Lalande, M., Collod-Béroud, G., Claustres, M., Béroud, C., Human Splicing Finder: an online bioinformatics tool to predict splicing signals (2009) Nucleic Acids Res, 37, pp. e67-e67. , https://doi.org/10.1093/nar/gkp215
  68. Abecasis, G.R., Auton, A., Brooks, L.D., DePristo, M.A., Durbin, R.M., Handsaker, R.E., Kang, H.M., McVean, G.A., An integrated map of genetic variation from 1,092 human genomes (2012) Nature, 491, pp. 56-65. , https://doi.org/10.1038/nature11632
  69. (2016) ExAC Browser, , http://exac.broadinstitute.org/variant/9-139413097-T-G
  70. Lek, M., Karczewski, K.J., Minikel, E.V., Samocha, K.E., Banks, E., Fennell, T., O'Donnell-Luria, A.H., Kosmicki, J.A., Analysis of protein-coding genetic variation in 60,706 humans (2016) Nature, 536, pp. 285-291. , https://doi.org/10.1038/nature19057
  71. National Center for Biotechnology Information, U.S (2004) National Library of Medicine, database, , https://pubchem.ncbi.nlm.nih.gov/
  72. (2014), http://evs.gs.washington.edu/EVS/
  73. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W.W., Rehm, H.L., Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015) Genet Med, 17, pp. 405-424. , https://doi.org/10.1038/gim.2015.30
  74. Li, H., Durbin, R., Fast and accurate short read alignment with Burrows-Wheeler transform (2009) Bioinformatics, 25, pp. 1754-1760. , https://doi.org/10.1093/bioinformatics/btp324
  75. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., DePristo, M.A., The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data (2010) Genome Res, 20, pp. 1297-1303. , https://doi.org/10.1101/gr.107524.110
  76. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Durbin, R., The Sequence Alignment/Map format and SAMtools (2009) Bioinformatics, 25, pp. 2078-2079. , https://doi.org/10.1093/bioinformatics/btp352
  77. Larson, D.E., Harris, C.C., Chen, K., Koboldt, D.C., Abbott, T.E., Dooling, D.J., Ley, T.J., Ding, L., SomaticSniper: identification of somatic point mutations in whole genome sequencing data (2012) Bioinformatics, 28, pp. 311-317. , https://doi.org/10.1093/bioinformatics/btr665
  78. Forbes, S.A., Beare, D., Boutselakis, H., Bamford, S., Bindal, N., Tate, J., Cole, C.G., Kok, C.Y., COSMIC: somatic cancer genetics at high-resolution (2017) Nucleic Acids Res, 45, pp. D777-D783. , https://doi.org/10.1093/nar/gkw1121
  79. Wang, K., Li, M., Hakonarson, H., ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data (2010) Nucleic Acids Res, 38, pp. e164-e164. , https://doi.org/10.1093/nar/gkq603
  80. Li, H., Tabix: fast retrieval of sequence features from generic TAB-delimited files (2011) Bioinformatics, 27, pp. 718-719. , https://doi.org/10.1093/bioinformatics/btq671
  81. Fuentes Fajardo, K.V., Adams, D., Mason, C.E., Sincan, M., Tifft, C., Toro, C., Boerkoel, C.F., Markello, T., Detecting false-positive signals in exome sequencing (2012) Hum Mutat, 33, pp. 609-613. , https://doi.org/10.1002/humu.22033
  82. Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., Sirotkin, K., dbSNP: the NCBI database of genetic variation (2001) Nucleic Acids Res, 29, pp. 308-311. , http://www.ncbi.nlm.nih.gov/pubmed/11125122https://doi.org/10.1093/nar/29.1.308
  83. Gerstein, M.B., Kundaje, A., Hariharan, M., Landt, S.G., Yan, K.K., Cheng, C., Mu, X.J., Abyzov, A., Architecture of the human regulatory network derived from ENCODE data (2012) Nature, 489, pp. 91-100. , https://doi.org/10.1038/nature11245
  84. Fraser, M., Sabelnykova, V.Y., Yamaguchi, T.N., Heisler, L.E., Livingstone, J., Huang, V., Shiah, Y.J., Prokopec, S.D., Genomic hallmarks of localized, non-indolent prostate cancer (2017) Nature, 541, pp. 359-364. , https://doi.org/10.1038/nature20788
  85. Cingolani, P., Platts, A., Wang, L., Coon, M., Nguyen, T., Wang, L., Land, S.J., Ruden, D.M., A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118
  86. iso-2
  87. iso-3 (2012) Fly (Austin), 6, pp. 80-92. , https://doi.org/10.4161/fly.19695
  88. P'ng, C., Green, J., Chong, L.C., Waggott, D., Prokopec, S.D., Shamsi, M., Nguyen, F., Starmans, M.H.W., BPG: Seamless, Automated and Interactive Visualization of Scientific Data (2017) bioRxiv, , https://doi.org/10.1101/156067
  89. Shihab, H.A., Gough, J., Cooper, D.N., Stenson, P.D., Barker, G.L., Edwards, K.J., Day, I.N., Gaunt, T.R., Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models (2013) Hum Mutat, 34, pp. 57-65. , https://doi.org/10.1002/humu.22225
  90. Douville, C., Carter, H., Kim, R., Niknafs, N., Diekhans, M., Stenson, P.D., Cooper, D.N., Karchin, R., CRAVAT: cancer-related analysis of variants toolkit (2013) Bioinformatics, 29, pp. 647-648. , https://doi.org/10.1093/bioinformatics/btt017

Coleções
Artigos e Materiais de Revistas Científicas - FM/MDR
Artigos e Materiais de Revistas Científicas - HC/ICESP
Artigos e Materiais de Revistas Científicas - HC/InRad
Artigos e Materiais de Revistas Científicas - LIM/24
Artigos e Materiais de Revistas Científicas - ODS/03