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Integration of DNA Microarray with Clinical and Genomic Data

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Microarray Data Analysis

Abstract

DNA microarrays have been widely employed to understand cancer development. This technology is able to measure expression levels of a large numbers of genes or to genotype multiple regions of a genome in a massively parallel experiment. In addition, the detection of methylation patterns and gene copy number variations are also performed. Clinicians began to apply these findings in personalized medicine for the selection of cancer therapy according to the individual’s cancer genomic profile. Because cancer is a complex disease it is of great value to integrate microarray data with genomic and clinical data. Here, we presented an overview of DNA microarray technology and discuss about benefits and challenging of microarray data integration.

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References

  1. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921

    Article  CAS  Google Scholar 

  2. Couch FJ, Wang X, McGuffog L et al (2013) Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet 9(3):e1003212. https://doi.org/10.1371/journal.pgen.1003212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Barrett JH, Iles MM, Harland M, Taylor JC, Aitken JF, Andresen PA, Akslen LA, Armstrong BK, Avril MF, Azizi E, Bakker B, Bergman W, Bianchi-Scarrà G, Bressac-de Paillerets B, Calista D, Cannon-Albright LA, Corda E, Cust AE, Dębniak T, Duffy D, Dunning AM, Easton DF, Friedman E, Galan P, Ghiorzo P, Giles GG, Hansson J, Hocevar M, Höiom V, Hopper JL, Ingvar C, Janssen B, Jenkins MA, Jönsson G, Kefford RF, Landi G, Landi MT, Lang J, Lubiński J, Mackie R, Malvehy J, Martin NG, Molven A, Montgomery GW, van Nieuwpoort FA, Novakovic S, Olsson H, Pastorino L, Puig S, Puig-Butille JA, Randerson-Moor J, Snowden H, Tuominen R, Van Belle P, van der Stoep N, Whiteman DC, Zelenika D, Han J, Fang S, Lee JE, Wei Q, Lathrop GM, Gillanders EM, Brown KM, Goldstein AM, Kanetsky PA, Mann GJ, Macgregor S, Elder DE, Amos CI, Hayward NK, Gruis NA, Demenais F, Bishop JA, Bishop DT, GenoMEL Consortium (2011) Genome-wide association study identifies three new melanoma susceptibility loci. Nat Genet 43(11):1108–1113. https://doi.org/10.1038/ng.959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Purdue MP, Johansson M, Zelenika D, Toro JR, Scelo G, Moore LE, Prokhortchouk E, Wu X, Kiemeney LA, Gaborieau V, Jacobs KB, Chow WH, Zaridze D, Matveev V, Lubinski J, Trubicka J, Szeszenia-Dabrowska N, Lissowska J, Rudnai P, Fabianova E, Bucur A, Bencko V, Foretova L, Janout V, Boffetta P, Colt JS, Davis FG, Schwartz KL, Banks RE, Selby PJ, Harnden P, Berg CD, Hsing AW, Grubb RL 3rd, Boeing H, Vineis P, Clavel-Chapelon F, Palli D, Tumino R, Krogh V, Panico S, Duell EJ, Quirós JR, Sanchez MJ, Navarro C, Ardanaz E, Dorronsoro M, Khaw KT, Allen NE, Bueno-de-Mesquita HB, Peeters PH, Trichopoulos D, Linseisen J, Ljungberg B, Overvad K, Tjønneland A, Romieu I, Riboli E, Mukeria A, Shangina O, Stevens VL, Thun MJ, Diver WR, Gapstur SM, Pharoah PD, Easton DF, Albanes D, Weinstein SJ, Virtamo J, Vatten L, Hveem K, Njølstad I, Tell GS, Stoltenberg C, Kumar R, Koppova K, Cussenot O, Benhamou S, Oosterwijk E, Vermeulen SH, Aben KK, van der Marel SL, Ye Y, Wood CG, Pu X, Mazur AM, Boulygina ES, Chekanov NN, Foglio M, Lechner D, Gut I, Heath S, Blanche H, Hutchinson A, Thomas G, Wang Z, Yeager M, Fraumeni JF Jr, Skryabin KG, JD MK, Rothman N, Chanock SJ, Lathrop M, Brennan P (2011) Genome-wide association study of renal cell carcinoma identifies two susceptibility loci on 2p21 and 11q13.3. Nat Genet 43(1):60–65. https://doi.org/10.1038/ng.723; Epub 2010 Dec 5

    Article  CAS  PubMed  Google Scholar 

  5. Guo M, Yue W, Samuels DC, Yu H, He J, Zhao YY, Guo Y (2019) Quality and concordance of genotyping array data of 12,064 samples from 5840 cancer patients. Genomics 111(4):950–957. https://doi.org/10.1016/j.ygeno.2018.06.001

    Article  CAS  PubMed  Google Scholar 

  6. Michels E, De Preter K, Van Roy N et al (2007) Detection of DNA copy number alterations in cancer by array comparative genomic hybridization. Genet Med 9:574–584. https://doi.org/10.1097/GIM.0b013e318145b25b

    Article  CAS  PubMed  Google Scholar 

  7. Li S, Tollefsbol TO (2021) DNA methylation methods: global DNA methylation and methylomic analyses. Methods 187:28–43. https://doi.org/10.1016/j.ymeth.2020.10.002

    Article  CAS  PubMed  Google Scholar 

  8. Han HY, Mou JT, Jiang WP, Zhai XM, Deng K (2021) Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer. Biosci Rep 41(3):BSR20204394. https://doi.org/10.1042/BSR20204394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang B, Qu XL, Chen Y (2019) Identification of the potential prognostic genes of human melanoma. J Cell Physiol 234(6):9810–9815. https://doi.org/10.1002/jcp.27668

    Article  CAS  PubMed  Google Scholar 

  10. Chu PY, Wang SM, Chen PM, Tang FY, Chiang EI (2020) Expression of MTDH and IL-10 is an independent predictor of worse prognosis in ER-negative or PR-negative breast cancer patients. J Clin Med 9(10):3153. https://doi.org/10.3390/jcm9103153

    Article  CAS  PubMed Central  Google Scholar 

  11. Borisov N, Sorokin M, Tkachev V, Garazha A, Buzdin A (2020) Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments. BMC Med Genet 13(Suppl 8):111. https://doi.org/10.1186/s12920-020-00759-0

    Article  CAS  Google Scholar 

  12. Shipp MA, Ross KN, Tamayo P et al (2002) Diffuse large B-cell lymphoma outcome predicted by gene-expression profiling and supervised machine learning. Nat Med 8:68–74

    Article  CAS  Google Scholar 

  13. Perou C, Sørlie T, Eisen M et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752. https://doi.org/10.1038/35021093

    Article  CAS  PubMed  Google Scholar 

  14. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lønning PE, Børresen-Dale AL (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98(19):10869–10874. https://doi.org/10.1073/pnas.191367098

    Article  PubMed  PubMed Central  Google Scholar 

  15. van ‘t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536

    Article  Google Scholar 

  16. Portela A, Esteller M (2010) Epigenetic modifications and human disease. Nat Biotechnol 28:1057–1068. https://doi.org/10.1038/nbt.1685

    Article  CAS  PubMed  Google Scholar 

  17. Wu Y, Sarkissyan M, Vadgama JV (2015) Epigenetics in breast and prostate cancer. Methods Mol Biol 1238:425–466. https://doi.org/10.1007/978-1-4939-1804-1_23

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pfeifer GP (2018) Defining driver DNA methylation changes in human cancer. Int J Mol Sci 19(4):1166. https://doi.org/10.3390/ijms19041166

    Article  CAS  PubMed Central  Google Scholar 

  19. Xiang R, Fu T (2020) Gastrointestinal adenocarcinoma analysis identifies promoter methylation-based cancer subtypes and signatures. Sci Rep 10:21234. https://doi.org/10.1038/s41598-020-78228-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lujambio A et al (2007) Genetic unmasking of an epigenetically silenced microRNA in human cancer cells. Cancer Res 67:1424–1429

    Article  CAS  Google Scholar 

  21. Oltra SS, Peña-Chilet M, Vidal-Tomas V et al (2018) Methylation deregulation of miRNA promoters identifies miR124-2 as a survival biomarker in breast cancer in very young women. Sci Rep 8:14373. https://doi.org/10.1038/s41598-018-32393-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zarrei M, MacDonald J, Merico D et al (2015) A copy number variation map of the human genome. Nat Rev Genet 16:172–183. https://doi.org/10.1038/nrg3871

    Article  CAS  PubMed  Google Scholar 

  23. Zhang L, Feizi N, Chi C, Hu P (2018) Association analysis of somatic copy number alteration burden with breast cancer survival. Front Genet 9:421. https://doi.org/10.3389/fgene.2018.00421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kumaran M, Cass CE, Graham K et al (2017) Germline copy number variations are associated with breast cancer risk and prognosis. Sci Rep 7:14621. https://doi.org/10.1038/s41598-017-14799-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rocca MS, Benna C, Mocellin S et al (2019) E2F1 germline copy number variations and melanoma susceptibility. J Transl Med 17:181. https://doi.org/10.1186/s12967-019-1933-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Scionti F, Di Martino MT, Pensabene L, Bruni V, Concolino D (2018) The Cytoscan HD Array in the diagnosis of neurodevelopmental disorders. High Throughput 7(3):28. https://doi.org/10.3390/ht7030028

    Article  CAS  PubMed Central  Google Scholar 

  27. Zhang X, Sjöblom T (2021) Targeting loss of heterozygosity: a novel paradigm for cancer therapy. Pharmaceuticals 14:57. https://doi.org/10.3390/ph14010057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nichols CA, Gibson WJ, Brown MS et al (2020) Loss of heterozygosity of essential genes represents a widespread class of potential cancer vulnerabilities. Nat Commun 11:2517. https://doi.org/10.1038/s41467-020-16399-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Arbitrio M, Di Martino MT, Scionti F, Barbieri V, Pensabene L, Tagliaferri P (2018) Pharmacogenomic profiling of ADME gene variants: current challenges and validation perspectives. High Throughput 7:40. https://doi.org/10.3390/ht7040040

    Article  CAS  PubMed Central  Google Scholar 

  30. Low SK, Takahashi A, Mushiroda T, Kubo M (2014) Genome-wide association study: a useful tool to identify common genetic variants associated with drug toxicity and efficacy in cancer pharmacogenomics. Clin Cancer Res 20(10):2541–2552. https://doi.org/10.1158/1078-0432.CCR-13-2755

    Article  CAS  PubMed  Google Scholar 

  31. Arbitrio M, Scionti F, Di Martino MT, Caracciolo D, Pensabene L, Tassone P, Tagliaferri P (2021) Pharmacogenomics biomarker discovery and validation for translation in clinical practice. Clin Transl Sci 14(1):113–119. https://doi.org/10.1111/cts.12869

    Article  PubMed  Google Scholar 

  32. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J, Vingron M (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 29(4):365–371. https://doi.org/10.1038/ng1201-365

    Article  CAS  PubMed  Google Scholar 

  33. Rack KA, van den Berg E, Haferlach C et al (2019) European recommendations and quality assurance for cytogenomic analysis of haematological neoplasms. Leukemia 33:1851–1867. https://doi.org/10.1038/s41375-019-0378-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhou Y, Frings O, Branca R et al (2017) microRNAs with AAGUGC seed motif constitute an integral part of an oncogenic signaling network. Oncogene 36:731–745. https://doi.org/10.1038/onc.2016.242

    Article  CAS  PubMed  Google Scholar 

  35. Han N, Song YK, Burckart GJ, Ji E, Kim IW, Oh JM (2017) Regulation of pharmacogene expression by microRNA in the Cancer genome atlas (TCGA) research network. Biomol Ther (Seoul) 25(5):482–489. https://doi.org/10.4062/biomolther.2017.122

    Article  CAS  Google Scholar 

  36. Settino M, Arbitrio M, Scionti F, Caracciolo D, Agapito G, Tassone P, Tagliaferri P, Di Martino MT, Cannataro M (2021) Identifying Prognostic Markers for Multiple Myeloma through integration and analysis of MMRF-CoMMpass data. J Comput Sci 51:101346. https://doi.org/10.1016/j.jocs.2021.101346

    Article  Google Scholar 

  37. Das T, Andrieux G, Ahmed M, Chakraborty S (2020) Integration of Online Omics-Data Resources for Cancer Research. Front Genet 11:578345. https://doi.org/10.3389/fgene.2020.578345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Maria Teresa Di Martino .

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Scionti, F. et al. (2022). Integration of DNA Microarray with Clinical and Genomic Data. In: Agapito, G. (eds) Microarray Data Analysis. Methods in Molecular Biology, vol 2401. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1839-4_15

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  • DOI: https://doi.org/10.1007/978-1-0716-1839-4_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1838-7

  • Online ISBN: 978-1-0716-1839-4

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