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2014, Número 4

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Biotecnol Apl 2014; 31 (4)


Reacción en cadena de la polimerasa cuantitativa de la expresión génica en el desarrollo preclínico y clínico de medicamentos anticancerosos

Mainet-González D

Idioma: Español
Referencias bibliográficas: 62
Paginas: 258-267
Archivo PDF: 334.36 Kb.


RESUMEN

En la aparición del cáncer influyen factores físicos, químicos y biológicos que alteran el genoma de los individuos. Esta enfermedad afecta la morbilidad y la mortalidad en países desarrollados y en Cuba. El proceso de desarrollo de medicamentos es largo, costoso y con un índice de fallo mayor en cáncer que en otras enfermedades. Por ello se continúan investigando diagnosticadores con elevada confiabilidad, para conocer el posible éxito de nuevos candidatos terapéuticos, desde las etapas más tempranas del desarrollo preclínico y clínico. Así, la decisión de emplearlo o desecharlo no se tomaría en las etapas finales del desarrollo, cuando se han gastado varios recursos. Los diagnosticadores genómicos son evaluadores indirectos de la respuesta a medicamentos, menos costosos y más fáciles para determinar y específicos que los diagnosticadores proteómicos en el cáncer. La reacción en cadena de la polimerasa cuantitativa se ha empleado para determinar la expresión de ácidos ribonucleicos codificantes y la confirmación y validación de diagnosticadores genómicos. En este artículo se discuten su alta sensibilidad y especificidad, las posibilidades de automatización, y su empleo independiente o complementario a tecnologías genómicas de alto flujo, para la obtención de medicamentos anticancerosos en países en desarrollo. La reacción en cadena de la polimerasa puede aplicarse con una adecuada relación beneficio-costo en distintas etapas de este proceso, y en la confección de bases de datos farmacológicas y toxicológicas más confiables. Ello favorecerá el conocimiento de esta enfermedad para la generación de medicamentos más seguros y eficaces.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. Globetech Media. Los diagnósticos acompañantes favorecen la medicina personalizada. LabMedica. 2008 [cited 2013 Nov 27];25:4. Available from: http:// mydigitalpublication.com/display_article. php?id=46132

  2. Ministerio de Salud Pública. Anuario Estadístico de Salud, Cuba, 2012 [Internet]. Habana: Dirección Nacional de Registros Médicos y Estadísticas de Salud, Minsap; 2013 [cited 2013 Oct 17]. Available from: http://files.sld.cu/dne/ files/2013/04/anuario_2012.pdf

  3. Pearce HL, Blanchard KL, Slapak CA. Failure modes in anticancer drug discovery and development. In: Neidle S, editor. Cancer Drug Design and Discovery. London: Elsevier Inc; 2008. p. 424-35.

  4. Wang J, Urban L. The impact of early ADME profiling on drug discovery and development strategy. Drug Discovery World. 2004;5:73-86.

  5. Furones-Mourelle JA. Bases científicas para el desarrollo y la utilización de medicamentos. En: Morón-Rodríguez FJ, editor. Farmacología General. La Habana: Editorial Ciencias Médicas; 2002. p. 9-21.

  6. Ross JS, Schenkein DP, Kashala O, Linette GP, Stec J, Symmans WF, et al. Pharmacogenomics. Adv Anat Pathol. 2004;11(4):211-20.

  7. Mocellin S, Rossi CR, Pilati P, Nitti D, Marincola FM. Quantitative real-time PCR: a powerful ally in cancer research. Trends Mol Med. 2003;9(5):189-95.

  8. Workman P, Collins I. Modern cancer drug discovery: integrating targets, technologies and treatments. In: Neidle S, editor. Cancer Drug Design and Discovery. London: Elsevier Inc; 2008. p. 3-38.

  9. Jia HL, Ye QH, Qin LX, Budhu A, Forgues M, Chen Y, et al. Gene expression profiling reveals potential biomarkers of human hepatocellular carcinoma. Clin Cancer Res. 2007;13(4):1133-9.

  10. Preziosi P. Science, pharmacoeconomics and ethics in drug R&D: a sustainable future scenario? Nat Rev Drug Discov. 2004;3(6):521-6.

  11. Dix R. Experimental medicine: Developing biomarkers in early discovery to bridge preclinical and clinical development. Drug Discovery World. 2004;5:56-60.

  12. Katz DA. Overview of Pharmacogenetics. In: Enna SJ, Williams M, Frechette R, Kenakin T, McGonigle P, Ruggeri B, editors. Curr Protoc Pharmacol. New Jersey: John Wiley & Sons, Inc.; 2007. p. 6.10.1-6.10.24.

  13. Freeman WM, Bixler GV, Brucklacher RM, Lin CM, Patel KM, VanGuilder HD, et al. A multistep validation process of biomarkers for preclinical drug development. Pharmacogenomics J. 2010;10(5):385-95.

  14. DeCristofaro M, Daniels KK. Toxicogenomics in Biomarker Discovery. In: Mendrick DL, Mattes WB, editors. Methods in Molecular Biology, vol. 460: Essential Concepts in Toxicogenomics. Totowa: Humana Press; 2008. p. 185-95.

  15. Schulz WA. An introduction to human cancers. In: Schulz WA, editor. Molecular Biology of Human Cancers: An advanced Student’s Textbook. Dordrecht: Springer Science + Business Media, Inc; 2005. p. 1-23.

  16. Levitzki A, Klein S. Signal transduction therapy of cancer. Mol Aspects Med. 2010;31(4):287-329.

  17. Ocak S, Sos ML, Thomas RK, Massion PP. High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications. Eur Respir J. 2009;34(2):489-506.

  18. Petak I, Schwab R, Orfi L, Kopper L, Keri G. Integrating molecular diagnostics into anticancer drug discovery. Nat Rev Drug Discov. 2010;9(7):523-35.

  19. Kamb A, Wee S, Lengauer C. Why is cancer drug discovery so difficult? Nat Rev Drug Discov. 2007;6(2):115-20.

  20. Krauss G. Structure and Function of Signal Pathways. In: Krauss G, editor. Biochemistry of Signal Transduction and Regulation. 3rd ed. Weinheim: Wiley -VCH Verlag GmbH & Co; 2003. p. 115-130.

  21. Granner DK. Hormone Action and Signal Transduction. In: Rodwell VW, Murray RK, Davis JC, Mayes PA, editors. Harper’s Illustrated Biochemistry. 26th ed. New York: Lange Medical Books/McGraw-Hill; 2003. p. 456-73.

  22. Schulz WA. Cancer pathways. In: Schulz WA, editor. Molecular Biology of Human Cancers: An advanced Student’s Textbook. Dordrech: Springer Science+Business Media, Inc; 2005. p. 113-144.

  23. Joyce C. Quantitative RT-PCR: A Review of Current Methodologies. In: O'Connell J, editor. Methods in Molecular Biology, vol. 193: RT-PCR Protocols. New Jersey: Humana Press Inc; 2002. p. 83-91.

  24. Goodsaid F. Quantitative Real Time Polymerase Chain Reaction in Drug Development Research. Drug Development Res. 2004;62(2):151-8.

  25. Malek A, Tchernitsa O. Evaluation of targets for ovarian cancer gene silencing therapy: In vitro and in vivo approaches. In: Min WP, Ichim T, editors. RNA Interference, Methods in Molecular Biology, vol. 623. Dordhrecht: Springer Science+Business Media; 2010. p. 423-37.

  26. Schlecht NF, Kulaga S, Robitaille J, Ferreira S, Santos M, Miyamura RA, et al. Persistent human papillomavirus infection as a predictor of cervical intraepithelial neoplasia. JAMA. 2001;286(24):3106-14

  27. Soto Y, Kourí V, Martínez PA, Correa C, Torres G, Goicolea A, et al. Normalización de un sistema de reacción en cadena de la polimerasa en tiempo real para la cuantificación de papilomavirus humano de alto riesgo oncogénico. VacciMonitor. 2012;21(1):30-37.

  28. Smith SM, Murray DW. An Overview of MicroRNA Methods: Expression Profiling and Target Identification. In: Espina V, Liotta LA, editors. Molecular Profiling: Methods and Protocols, Methods in Molecular Biology, vol. 823. Dordrecht: Springer Science + Business Media; 2012. p. 119-39.

  29. Taylor CF. Mutation scanning using high-resolution melting. Biochem Soc Trans. 2009;37(Pt 2):433-7.

  30. Ginzinger DG, Godfrey TE, Nigro J, Moore DH, 2nd, Suzuki S, Pallavicini MG, et al. Measurement of DNA copy number at microsatellite loci using quantitative PCR analysis. Cancer Res. 2000;60(19):5405-9.

  31. Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res. 2001;61(8):3225-9.

  32. Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426-37.

  33. Balcells I, Cirera S, Busk PK. Specific and sensitive quantitative RT-PCR of miRNAs with DNA primers. BMC Biotechnology. 2011;11:70.

  34. Day PJ. Miniaturized PCR systems for cancer diagnosis. Biochem Soc Trans. 2009;37(Pt 2):424-6.

  35. Qiagen kit handbooks and user manuals. Critical Factors for Successful Real-Time PCR. 2010 [cited 2013 Nov 27]. Available from: http://www.genequantification.de/qiagen-qpcr-sampleassay-tech-guide-2010.pdf

  36. Fathman CG, Soares L, Chan SM, Utz PJ. An array of possibilities for the study of autoimmunity. Nature. 2005;435(7042):605-11.

  37. Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonak J, Lind K, et al. The real-time polymerase chain reaction. Mol Aspects Med. 2006;27(2-3):95-125.

  38. Xi L, Nicastri DG, El-Hefnawy T, Hughes SJ, Luketich JD, Godfrey TE. Optimal markers for real-time quantitative reverse transcription PCR detection of circulating tumor cells from melanoma, breast, colon, esophageal, head and neck, and lung cancers. Clin Chem. 2007;53(7):1206-15.

  39. Heckmann LH, Sorensen PB, Krogh PH, Sorensen JG. NORMA-Gene: a simple and robust method for qPCR normalization based on target gene data. BMC Bioinformatics. 2011;12:250.

  40. Sims AH. Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us? J Clin Pathol. 2009;62(10):879-85.

  41. Debey S, Schoenbeck U, Hellmich M, Gathof BS, Pillai R, Zander T, et al. Comparison of different isolation techniques prior gene expression profiling of blood derived cells: impact on physiological responses, on overall expression and the role of different cell types. Pharmacogenomics J. 2004;4(3):193-207.

  42. Nolan T, Hands RE, Bustin SA. Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006;1(3):1559-82.

  43. Life Technologies Inc. Ambion´s Technical Bulletin #159. Working with RNA. 2010 [cited 2013 Nov 27]. Available from: http://www.invitrogen.com/site/us/en/ home/References/Ambion-Tech-Support/ nuclease-enzymes/general-articles/working-with-rna.html

  44. Ho-Pun-Cheung A, Bascoul-Mollevi C, Assenat E, Boissiere-Michot F, Bibeau F, Cellier D, et al. Reverse transcriptionquantitative polymerase chain reaction: description of a RIN-based algorithm for accurate data normalization. BMC Mol Biol. 2009;10:31.

  45. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6(4):279-84.

  46. Murphy J, Bustin SA. Reliability of realtime reverse-transcription PCR in clinical diagnostics: gold standard or substandard? Expert Rev Mol Diagn. 2009;9(2):187-97.

  47. Yuan JS, Reed A, Chen F, Stewart CN, Jr. Statistical analysis of real-time PCR data. BMC Bioinformatics. 2006;7:85

  48. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611-22.

  49. Wang Y, Barbacioru C, Keys D, Brzoska P, Chen C, Li K, et al. Real-Time polymerase chain reaction gene expression assays in biomarker discovery and validation. In: Wang F, editor. Methods in Pharmacology and Toxicology: Biomarker Methods in Drug Discovery and Development. Totowa: Humana Press; 2010. p. 63-86.

  50. Dignam J, Bregant J, Paik S. Statistical Considerations in Assessing Molecular Markers for Cancer Prognosis and Treatment Efficacy. In: Looney SW, editor. Methods in Molecular Biology: Bioestatistical Methods. New Jersey: Humana Press; 2002. p.169- 89.

  51. Sarker D, Workman P. Pharmacodynamic biomarkers for molecular cancer therapeutics. Adv Cancer Res. 2007;96:213-68.

  52. Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011;364(12):1144-53.

  53. Nishizuka S, Charboneau L, Young L, Major S, Reinhold WC, Waltham M, et al. Proteomic profiling of the NCI-60 cancer cell lines using new high-density reversephase lysate microarrays. Proc Natl Acad Sci USA. 2003;100(24):14229-34.

  54. Kitano H. Perspectives on Systems Biology. New Generation Computing 2000;18:199-216.

  55. Lopez-Lazaro M. A new view of carcinogenesis and an alternative approach to cancer therapy. Mol Med. 2010;16(3- 4):144-53.

  56. Huggett J, Green C, Zumla A. Nucleic acid detection and quantification in the developing world. Biochem Soc Trans. 2009;37(Pt 2):419-23.

  57. Wang H, Huang S, Shou J, Su EW, Onyia JE, Liao B, et al. Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data. BMC Genomics. 2006;7:166.

  58. Kammula US, Marincola FM, Rosenberg SA. Real-time quantitative polymerase chain reaction assessment of immune reactivity in melanoma patients after tumor peptide vaccination. J Natl Cancer Inst. 2000;92(16):1336-44.

  59. Panelli MC, Wang E, Monsurro V, Marincola FM. The role of quantitative PCR for the immune monitoring of cancer patients. Expert Opin Biol Ther. 2002;2(5):557-64.

  60. Zitvogel L, Kepp O, Kroemer G. Immune parameters affecting the efficacy of chemotherapeutic regimens. Nat Rev Clin Oncol. 2011;8(3):151-60.

  61. Whiteside TL. Immune responses to cancer: are they potential biomarkers of prognosis? Front Oncol. 2013;3:107.

  62. Borad M J, Von Hoff DD. Chapter 3: Clinical trial designs for more rapid proofof-principle and approval. In: Neidle S, ed. Cancer Drug Design and Discovery. London: Elsevier Inc. 2008, p. 53-87.




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