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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Analysis of Novel Variants Associated with Three Human Ovarian Cancer Cell Lines

Author(s): Venugopala Reddy Mekala, Jan-Gowth Chang and Ka-Lok Ng*

Volume 17, Issue 4, 2022

Published on: 13 April, 2022

Page: [380 - 392] Pages: 13

DOI: 10.2174/1574893617666220224105106

Price: $65

Abstract

Background: Identification of mutations is of great significance in cancer research, as it can contribute to the development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Furthermore, it has been observed that the accumulation of genetic mutations can lead to cancer.

Objective: We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients.

Methods: We utilized cell line whole-exome sequencing data, 188 patients’ whole-exome sequencing data, and in vitro experiments to verify predicted variant results. We established an effective analysis workflow for the discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants, (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions.

Results: Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. A total of 13 missense mutations were found to affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one variant was uncertain.

Conclusion: It was confirmed that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote the progression and development of ovarian cancer.

Keywords: Ovarian cancer, cell lines, next-generation sequencing, whole-exome sequencing, structural variants, missense variant.

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[1]
Bernards R, Jaffee E, Joyce JA, et al. A roadmap for the next decade in cancer research. Nat Can 2020; 1: 12-7.
[http://dx.doi.org/10.1038/s43018-019-0015-9]
[2]
Griffiths JF. An introduction to genetic analysis. New York, USA: Macmillan 2005.
[3]
Eichler EE. Genetic variation, comparative genomics, and the diagnosis of disease. N Engl J Med 2019; 381(1): 64-74.
[http://dx.doi.org/10.1056/NEJMra1809315] [PMID: 31269367]
[4]
Padma VV. An overview of targeted cancer therapy. Biomedicine (Taipei) 2015; 5(4): 19.
[http://dx.doi.org/10.7603/s40681-015-0019-4] [PMID: 26613930]
[5]
Al-Tassan NA, Whiffin N, Hosking FJ, et al. A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Sci Rep 2015; 5: 10442.
[http://dx.doi.org/10.1038/srep10442] [PMID: 25990418]
[6]
Pande M, Spitz MR, Wu X, Gorlov IP, Chen WV, Amos CI. Novel genetic variants in the chromosome 5p15.33 region associate with lung cancer risk. Carcinogenesis 2011; 32(10): 1493-9.
[http://dx.doi.org/10.1093/carcin/bgr136] [PMID: 21771723]
[7]
Yovinska M, Kaneva R, Dimova I. Conference: Interactive e- Posters Eur J Hum Genet 2020; 28(Suppl. 1): 141-797.
[PMID: 33262485]
[8]
Yang MD, Lin KC, Lu MC, et al. Contribution of matrix metalloproteinases-1 genotypes to gastric cancer susceptibility in Taiwan. Biomedicine (Taipei) 2017; 7(2): 10.
[http://dx.doi.org/10.1051/bmdcn/2017070203] [PMID: 28612708]
[9]
Koczkowska M, Krawczynska N, Stukan M, et al. Spectrum and prevalence of pathogenic variants in ovarian cancer susceptibility genes in a group of 333 patients. Cancers (Basel) 2018; 10(11): 442.
[http://dx.doi.org/10.3390/cancers10110442] [PMID: 30441849]
[10]
Pavanello M, Chan IH, Ariff A, Pharoah PD, Gayther SA, Ramus SJ. Rare germline genetic variants and the risks of epithelial ovarian can-cer. Cancers (Basel) 2020; 12(10): 3046.
[http://dx.doi.org/10.3390/cancers12103046] [PMID: 33086730]
[11]
Zimmerman L, Zelichov O, Aizenmann A, Barbash Z, Vidne M, Tarcic G. A novel system for functional determination of variants of uncertain significance using deep convolutional neural networks. Sci Rep 2020; 10(1): 4192.
[http://dx.doi.org/10.1038/s41598-020-61173-1] [PMID: 32144301]
[12]
Katharopoulos E, Di Iorgi N, Fernandez-Alvarez P, et al. Characterization of two novel variants of the steroidogenic acute regulatory pro-tein identified in a girl with classic lipoid congenital adrenal hyperplasia. Int J Mol Sci 2020; 21(17): 6185.
[http://dx.doi.org/10.3390/ijms21176185] [PMID: 32867102]
[13]
Caiola E, Broggini M, Marabese M. Genetic markers for prediction of treatment outcomes in ovarian cancer. Pharmacogenomics J 2014; 14(5): 401-10.
[http://dx.doi.org/10.1038/tpj.2014.32] [PMID: 25001881]
[14]
Pinto R, Assis J, Nogueira A, et al. Pharmacogenomics in epithelial ovarian cancer first-line treatment outcome: validation of GWAS-associated NRG3 rs1649942 and BRE rs7572644 variants in an independent cohort. Pharmacogenomics J 2019; 19(1): 25-32.
[http://dx.doi.org/10.1038/s41397-018-0056-y] [PMID: 30287910]
[15]
Kaufman B, Shapira-Frommer R, Schmutzler RK, et al. Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation. J Clin Oncol 2015; 33(3): 244-50.
[http://dx.doi.org/10.1200/JCO.2014.56.2728] [PMID: 25366685]
[16]
Richards CS, Bale S, Bellissimo DB, et al. ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet Med 2008; 10(4): 294-300.
[http://dx.doi.org/10.1097/GIM.0b013e31816b5cae] [PMID: 18414213]
[17]
Tavtigian SV, Greenblatt MS, Harrison SM, et al. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med 2018; 20(9): 1054-60.
[http://dx.doi.org/10.1038/gim.2017.210] [PMID: 29300386]
[18]
Plon SE, Eccles DM, Easton D, et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 2008; 29(11): 1282-91.
[http://dx.doi.org/10.1002/humu.20880] [PMID: 18951446]
[19]
Otto R, Sers C, Leser U. Robust in-silico identification of cancer cell lines based on next generation sequencing. Oncotarget 2017; 8(21): 34310-20.
[http://dx.doi.org/10.18632/oncotarget.16110] [PMID: 28415721]
[20]
Zhang L, Gao J, Liu H, et al. Pathogenic variants identified by whole-exome sequencing in 43 patients with epilepsy. Hum Genomics 2020; 14(1): 44.
[http://dx.doi.org/10.1186/s40246-020-00294-0] [PMID: 33287870]
[21]
Tetreault M, Bareke E, Nadaf J, Alirezaie N, Majewski J. Whole-exome sequencing as a diagnostic tool: Current challenges and future opportunities. Expert Rev Mol Diagn 2015; 15(6): 749-60.
[http://dx.doi.org/10.1586/14737159.2015.1039516] [PMID: 25959410]
[22]
Oliver GR, Hart SN, Klee EW. Bioinformatics for clinical next generation sequencing. Clin Chem 2015; 61(1): 124-35.
[http://dx.doi.org/10.1373/clinchem.2014.224360] [PMID: 25451870]
[23]
Retterer K, Juusola J, Cho MT, et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med 2016; 18(7): 696-704.
[http://dx.doi.org/10.1038/gim.2015.148] [PMID: 26633542]
[24]
Sharma SV, Haber DA, Settleman J. Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat Rev Cancer 2010; 10(4): 241-53.
[http://dx.doi.org/10.1038/nrc2820] [PMID: 20300105]
[25]
Justice AE, Karaderi T, Highland HM, et al. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet 2019; 51(3): 452-69.
[http://dx.doi.org/10.1038/s41588-018-0334-2] [PMID: 30778226]
[26]
Chen Y-F, Yuan G-F, Liao C-C. Connecting industry and the Bioresource Collection and Research Center (BCRC) in Taiwan. Microbiol Aust 2006; 27(1): 36-7.
[http://dx.doi.org/10.1071/MA06036]
[27]
den Dunnen JT, Dalgleish R, Maglott DR, et al. HGVS recommendations for the description of sequence variants: 2016 update. Hum Mutat 2016; 37(6): 564-9.
[http://dx.doi.org/10.1002/humu.22981] [PMID: 26931183]
[28]
Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature 2015; 526(7571): 68-74.
[http://dx.doi.org/10.1038/nature15393] [PMID: 26432245]
[29]
Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016; 536(7616): 285-91.
[http://dx.doi.org/10.1038/nature19057] [PMID: 27535533]
[30]
Whiffin N, Minikel E, Walsh R, et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med 2017; 19(10): 1151-8.
[http://dx.doi.org/10.1038/gim.2017.26] [PMID: 28518168]
[31]
Meira LAA, Máximo VR, Fazenda ÁL, da Conceição AF. Acc-Motif: Accelerated network motif detection. IEEE/ACM Trans Comput Biol Bioinformatics 2014; 11(5): 853-62.
[http://dx.doi.org/10.1109/TCBB.2014.2321150] [PMID: 26356858]
[32]
Stenson PD, Mort M, Ball EV, Shaw K, Phillips A, Cooper DN. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet 2014; 133(1): 1-9.
[http://dx.doi.org/10.1007/s00439-013-1358-4] [PMID: 24077912]
[33]
Forbes SA, Beare D, Gunasekaran P, et al. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 2015; 43(Database issue): D805-11.
[http://dx.doi.org/10.1093/nar/gku1075] [PMID: 25355519]
[34]
Landrum MJ, Lee JM, Benson M, et al. ClinVar: Public archive of interpretations of clinically relevant variants. Nucleic Acids Res 2016; 44(D1): D862-8.
[http://dx.doi.org/10.1093/nar/gkv1222] [PMID: 26582918]
[35]
Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res 2015; 43(Database issue): D789-98.
[http://dx.doi.org/10.1093/nar/gku1205] [PMID: 25428349]
[36]
Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 2001; 29(1): 308-11.
[http://dx.doi.org/10.1093/nar/29.1.308] [PMID: 11125122]
[37]
Shihab HA, Gough J, Cooper DN, et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat 2013; 34(1): 57-65.
[http://dx.doi.org/10.1002/humu.22225] [PMID: 23033316]
[38]
Choi Y, Chan AP. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 2015; 31(16): 2745-7.
[http://dx.doi.org/10.1093/bioinformatics/btv195] [PMID: 25851949]
[39]
Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human ge-nome. Nucleic Acids Res 2019; 47(D1): D886-94.
[http://dx.doi.org/10.1093/nar/gky1016] [PMID: 30371827]
[40]
Franc V, Sonnenburg S. Optimized cutting plane algorithm for large-scale risk minimization. J Mach Learn Res 2009; 10(10): 2157-92.
[41]
Ewing B, Green P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 1998; 8(3): 186-94.
[http://dx.doi.org/10.1101/gr.8.3.186] [PMID: 9521922]
[42]
Repana D, Nulsen J, Dressler L, et al. The Network of Cancer Genes (NCG): A comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens. Genome Biol 2019; 20(1): 1.
[http://dx.doi.org/10.1186/s13059-018-1612-0] [PMID: 30606230]
[43]
Martínez-Jiménez F, Muiños F, Sentís I, et al. A compendium of mutational cancer driver genes. Nat Rev Cancer 2020; 20(10): 555-72.
[http://dx.doi.org/10.1038/s41568-020-0290-x] [PMID: 32778778]
[44]
Dennis G Jr, Sherman BT, Hosack DA, et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol 2003; 4(5): 3.
[http://dx.doi.org/10.1186/gb-2003-4-5-p3] [PMID: 12734009]
[45]
Stephenson JD, Laskowski RA, Nightingale A, Hurles ME, Thornton JM. VarMap: A web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations. Bioinformatics 2019; 35(22): 4854-6.
[http://dx.doi.org/10.1093/bioinformatics/btz482] [PMID: 31192369]
[46]
Boutet E. Uniprotkb/swiss-prot. In: Plant bioinformatics. Springer 2007; pp. 89-112.
[http://dx.doi.org/10.1007/978-1-59745-535-0_4]
[47]
Ng PC, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res 2001; 11(5): 863-74.
[http://dx.doi.org/10.1101/gr.176601] [PMID: 11337480]
[48]
Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen‐2 Curr Protoc Hum Genet. 2013. Chapter 7:Unit7.20
[http://dx.doi.org/10.1002/0471142905.hg0720s76] [PMID: 23315928]
[49]
Ul Alam MN. Computational assessment of somatic and germline mutations of p16INK4a: Structural insights and implications in disease. Inform Med Unlocked 2019; 17: 100208.
[http://dx.doi.org/10.1016/j.imu.2019.100208]
[50]
Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 2003; 31(13): 3812-4.
[http://dx.doi.org/10.1093/nar/gkg509] [PMID: 12824425]
[51]
Pucci F, Bernaerts KV, Kwasigroch JM, Rooman M. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics 2018; 34(21): 3659-65.
[http://dx.doi.org/10.1093/bioinformatics/bty348] [PMID: 29718106]
[52]
Sugita Y, Kitao A. Dependence of protein stability on the structure of the denatured state: Free energy calculations of I56V mutation in human lysozyme. Biophys J 1998; 75(5): 2178-87.
[http://dx.doi.org/10.1016/S0006-3495(98)77661-1] [PMID: 9788912]
[53]
Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: A web server for the estimation of protein stability changes upon muta-tion and sequence optimality. BMC Bioinformatics 2011; 12(1): 151.
[http://dx.doi.org/10.1186/1471-2105-12-151] [PMID: 21569468]
[54]
Pires DE, Ascher DB, Blundell TL. DUET: A server for predicting effects of mutations on protein stability using an integrated computa-tional approach Nucleic Acids Res 2014. 42(Web Server issue): W314-9.
[http://dx.doi.org/10.1093/nar/gku411] [PMID: 24829462]
[55]
Laimer J, Hofer H, Fritz M, Wegenkittl S, Lackner P. MAESTRO--multi agent stability prediction upon point mutations. BMC Bioinformatics 2015; 16(1): 116.
[http://dx.doi.org/10.1186/s12859-015-0548-6] [PMID: 25885774]
[56]
Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005. 33(Web Server issue)(Suppl. 2): W306-10.
[http://dx.doi.org/10.1093/nar/gki375] [PMID: 15980478]
[57]
Parthiban V, Gromiha MM, Schomburg D. CUPSAT: prediction of protein stability upon point mutations Nucleic Acids Res 2006. 34(Web Server issue)(Suppl. 2): W239-42.
[http://dx.doi.org/10.1093/nar/gkl190] [PMID: 16845001]
[58]
Chen C-W, Lin J, Chu Y-W. iStable: off-the-shelf predictor integration for predicting protein stability changes. In: BMC Bioinformatics. Springer 2013; 14: pp. (2)1-14.
[59]
Laskowski RA, Stephenson JD, Sillitoe I, Orengo CA, Thornton JM. VarSite: Disease variants and protein structure. Protein Sci 2020; 29(1): 111-9.
[http://dx.doi.org/10.1002/pro.3746] [PMID: 31606900]
[60]
Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013; 41(Database issue): D955-61.
[PMID: 23180760]
[61]
Chandrashekar DS, Bashel B, Balasubramanya SAH, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and surviv-al analyses. Neoplasia 2017; 19(8): 649-58.
[http://dx.doi.org/10.1016/j.neo.2017.05.002] [PMID: 28732212]
[62]
Kobayashi Y, Yang S, Nykamp K, Garcia J, Lincoln SE, Topper SE. Pathogenic variant burden in the ExAC database: An empirical ap-proach to evaluating population data for clinical variant interpretation. Genome Med 2017; 9(1): 13.
[http://dx.doi.org/10.1186/s13073-017-0403-7] [PMID: 28166811]
[63]
Kanchi KL, Johnson KJ, Lu C, et al. Integrated analysis of germline and somatic variants in ovarian cancer. Nat Commun 2014; 5(1): 3156.
[http://dx.doi.org/10.1038/ncomms4156] [PMID: 24448499]
[64]
Bashashati A, Ha G, Tone A, et al. Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J Pathol 2013; 231(1): 21-34.
[http://dx.doi.org/10.1002/path.4230] [PMID: 23780408]
[65]
Lee J-Y, Yoon JK, Kim B, et al. Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing. BMC Cancer 2015; 15(1): 85.
[http://dx.doi.org/10.1186/s12885-015-1077-4] [PMID: 25881093]
[66]
Li C, Bonazzoli E, Bellone S, et al. Mutational landscape of primary, metastatic, and recurrent ovarian cancer reveals c-MYC gains as potential target for BET inhibitors. Proc Natl Acad Sci USA 2019; 116(2): 619-24.
[http://dx.doi.org/10.1073/pnas.1814027116] [PMID: 30584090]
[67]
Zhang Y, Shi X, Zhang J, et al. A comprehensive analysis of somatic alterations in Chinese ovarian cancer patients. Sci Rep 2021; 11(1): 387.
[http://dx.doi.org/10.1038/s41598-020-79694-0] [PMID: 33432021]
[68]
Chen K, Ma H, Li L, et al. Genome-wide association study identifies new susceptibility loci for epithelial ovarian cancer in Han Chinese women. Nat Commun 2014; 5(1): 4682.
[http://dx.doi.org/10.1038/ncomms5682] [PMID: 25134534]
[69]
Huang K-l, Mashl RJ, Wu Y, et al. Pathogenic germline variants in 10,389 adult cancers. Cell 2018; 173(2): 355-370.e14.
[http://dx.doi.org/10.1016/j.cell.2018.03.039] [PMID: 29625052]
[70]
Kang S, Yu YL, Cho SY, Park SY. Prevalence of pathogenic variants in actionable genes in advanced ovarian cancer: A next-generation sequencing analysis of a nationwide registry study. Eur J Cancer 2020; 141: 185-92.
[http://dx.doi.org/10.1016/j.ejca.2020.09.036] [PMID: 33166861]
[71]
Choi MC, Hwang S, Kim S, et al. Clinical impact of somatic variants in homologous recombination repair-related genes in ovarian high-grade serous carcinoma. Cancer Res Treat 2020; 52(2): 634-44.
[http://dx.doi.org/10.4143/crt.2019.207] [PMID: 32019284]
[72]
Kim SI, Lee JW, Lee M, et al. Genomic landscape of ovarian clear cell carcinoma via whole exome sequencing. Gynecol Oncol 2018; 148(2): 375-82.
[http://dx.doi.org/10.1016/j.ygyno.2017.12.005] [PMID: 29233531]

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