Pathology Oncology Research

, Volume 8, Issue 4, pp 231–240

New molecular methods for classification, diagnosis and therapy prediction of hematological malignancies

  • Ágnes Zvara
  • László Hackler
  • B. Zsolt Nagy
  • Tamás Micsik
  • László G. Puskás


Normal functions of the cell are based on the precise regulation of various genes. If this strict regulation and the hierarchy of genes becomes upset due to flaws in this system, the result will be cellular dysfunction which eventually may lead to carcinogenic transformation. Two basic challenges of the classification of cancers are the discovery of new molecular markers characteristic to defined disease groups and the classification of already diagnosed or new cases into existing groups. This precise classification may open the door to tailored treatment or project the expected outcome of the disease. Today there is unlimited access available to the databases containing sequences and localization of the genes within the confines of Human Genome project. It provides significant help for the discovery of chromosome abnormalities and systematic analysis of gene expression patterns. This is important not only to understand normal functions of the cells, but it also contributes to the identification of new genes that are characteristic to given disease groups as markers and that are potential drug targets. Until the second half of the twentieth century the study of the function and regulation of genes was based on step-by-step investigation of individual genes. Regarding the fact, that the genomes of an increasing number of organisms have become known in whole or in part, numerous new techniques have been developed that facilitated the systematic analysis of gene functions. The aim of this study is to summarize the new, molecular based possibilities for classification, diagnosis and prognosis of hematological malignancies, as well as to summarize the main results of these areas.


molecular methods DNA-chip oncohematology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adorján P, Distler J, Lipscher E, et al: Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic Acids Res 30:e21, 2002.PubMedCrossRefGoogle Scholar
  2. 2.
    Albala JS and Humphrey-Smith I: Array-based proteomics: high-throughput expression and purification of IMAGE consortium cDNA clones. Curr Opin Mol Ther 680–684, 1999.Google Scholar
  3. 3.
    Alizadeh AA, Ross DT Perou CM, et al: Towards a novel classification of human malignancies based on gene expression patterns. J Pathol 195: 41–52, 2001.PubMedCrossRefGoogle Scholar
  4. 4.
    Alwine JC, Kemp DJ, Stark GR: Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci USA 74: 5350–5354, 1977.PubMedCrossRefGoogle Scholar
  5. 5.
    Appelbaum FR: Molecular Diagnosis and Clinical Decisions in Adult Acute Leukemia, Seminars in Hematology 36: 401–410, 1999.PubMedGoogle Scholar
  6. 6.
    Arico M, Valsecchi MG, Camitta B et al: Outcome of treatment in children with Philadelphia chromosome-positive acute lymphoblastic leukemia. N Engl J Med 342: 998–1006, 2000.PubMedCrossRefGoogle Scholar
  7. 7.
    Aubele M, Auer G, Braselmann H et al: Chromosomal imbalances are associated with metastasis-free survival in breast cancer patients. Anal Cell Pathol 24: 77–87, 2002.PubMedGoogle Scholar
  8. 8.
    Bittner M, Meltzer P, Chen Y et al: Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406: 536–540, 2000.PubMedCrossRefGoogle Scholar
  9. 9.
    Blohm DH, Guiseppi-Elie A: New developments in microarray technology. Curr Opin Biotechnol 12: 41–47, 2001.PubMedCrossRefGoogle Scholar
  10. 10.
    Bruckert P, Kappler R, Scherthan H et al: Double minutes and c-MYC amplification in acute myelogenous leukemia: Are they prognostic factors? Cancer Genet Cytogenet 120: 73–79, 2000.PubMedCrossRefGoogle Scholar
  11. 11.
    Cahill DJ: Protein arrays: a high-throughput solution for proteomics research? Proteomics: A Trends Guide, 47–51, 2000.Google Scholar
  12. 12.
    Chan MF, Liang G andJones PA: Relationship between transcription and DNA methylation. Curr Top Microbiol Immunol 249: 75–86, 2000.PubMedGoogle Scholar
  13. 13.
    Chen KT, Lin JD, Chao TC et al: Identifying differentially expressed genes associated with metastasis of follicular thyroid cancer by cDNA expression array. Thyroid 11: 41–46, 2001.PubMedCrossRefGoogle Scholar
  14. 14.
    Clark J, Edwards S, John M et al: Identification of amplified and expressed genes in breast cancer by comparative hybridization onto microarrays of randomly selected cDNA clones. Genes Chromosomes Cancer 34: 104–114, 2002.PubMedCrossRefGoogle Scholar
  15. 15.
    Costello JF, Fruhwald MC, Smiraglia DJ et al: Aberrant CpGisland methylation has non-random and tumour-type-specific patterns. Nature Genet 24: 132–138, 2000.PubMedCrossRefGoogle Scholar
  16. 16.
    De Risi J, Penland L, Brown P et al: Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genet 14: 457–460, 1996.CrossRefGoogle Scholar
  17. 17.
    Dunican DS, McWilliam P, Tighe O et al: Gene expression differences between the microsatellite instability (MIN) and chromosomal instability (CIN) phenotypes in colorectal cancer revealed by high-density cDNA array hybridization. Oncogene 21: 3253–3257, 2002.PubMedCrossRefGoogle Scholar
  18. 18.
    Eads CA, Danenberg KD, Kawakami K et al: MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 28–c32, 2000.Google Scholar
  19. 19.
    Emili AQ andCagney G: Large-scale functional analysis using peptide or protein arrays. Nat. Biotechnol 18: 393–397, 2000.PubMedCrossRefGoogle Scholar
  20. 20.
    Ernst T, Hergenhahn M, Kenzelmann M et al: Decrease and gain of gene expression are equally discriminatory markers for prostate carcinoma: a gene expression analysis on total and microdissected prostate tissue. Am J Pathol 160: 2169–2180, 2002.PubMedGoogle Scholar
  21. 21.
    Esteller M, Corn PG, Baylin SB et al: A gene hypermethylation profile of human cancer. Cancer Res 61: 3225–3229, 2001.PubMedGoogle Scholar
  22. 22.
    Ferrando AA, Look AT: Clinical implications of recurring chromosomal and associated molecular abnormalities in acute lymphoblastic leukemia. Semin Hematol 37: 381–95, 2000.PubMedCrossRefGoogle Scholar
  23. 23.
    Ferrando AA, Neuberg DS, Staunton J, et al: Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell, 1: 75–87, 2002.PubMedCrossRefGoogle Scholar
  24. 24.
    Furák JI, Troján T, Szõke L et al: Development of Brain Metastasis 5 Years Before the Appearance of the Primary Lung Cancer: Messenger Metachronous Metastasis. Annals Thoracic Surg (in press).Google Scholar
  25. 25.
    Gitan RS, Shi H, Chen CM et al: Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis. Genome Res 12: 158–164, 2001.CrossRefGoogle Scholar
  26. 26.
    Gilliland DG: Molecular Genetics of Human Leukemia. Leukemia 12: S7-S12, 1998.PubMedGoogle Scholar
  27. 27.
    Golub TR: The Genetics of AML: An Update in Proceedings of the American Society of Hematology, pp. 102–111, 1999.Google Scholar
  28. 28.
    Golub TR, Slonim DK, Tamayo P et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286: 531–537, 1999.PubMedCrossRefGoogle Scholar
  29. 29.
    Haab BB Dunham MJ, Brown PO: Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions. Genome Biol 2: Research 0004.1-0004. 13, 2001.Google Scholar
  30. 30.
    Hanash SM, Madoz-Gurpide J, Misek DE: Identification of novel targets for cancer therapy using expression proteomics. Leukemia 16: 478–485, 2002.PubMedCrossRefGoogle Scholar
  31. 31.
    Hedenfalk I, Duggan D, Chen Y et al: Gene-expression profiles in hereditary breast cancer. N Engl J Med 344: 539–548, 2001.PubMedCrossRefGoogle Scholar
  32. 32.
    Hedrick SM, Cohen DI, Nielsen EA et al: Isolation of cDNA clones encoding T cell-specific membrane-associated proteins. Nature 308: 149–153, 1984.PubMedCrossRefGoogle Scholar
  33. 33.
    Hodgson G, Hager JH, Volik S et al: Genome scanning with array CGH delineates regional alterations in mouse islet carcinomas. Nat Genet 29: 459–464, 2001.PubMedCrossRefGoogle Scholar
  34. 34.
    Houldsworth J, Chaganti RS: Comparative genomic hybridization: an overview. Am J Pathol 145: 1253–1260, 1994.PubMedGoogle Scholar
  35. 35.
    Huang TH-M, Perry MR andLaux DE: Methylation profiling of CpG islands in human breast cancer cells. Hum Mol Genet 8: 459–470, 1999.PubMedCrossRefGoogle Scholar
  36. 36.
    Huang Y, Prasad M, Lemon WJ et al: Gene expression in papillary thyroid carcinoma reveals highly consistent profiles. Proc Natl Acad Sci USA 98: 15044–15049, 2001.PubMedCrossRefGoogle Scholar
  37. 37.
    Jiang Y, Harlocker SL, Molesh DA et al: Discovery of differentially expressed genes in human breast cancer using subtracted cDNA libraries and cDNA microarrays. Oncogene 21: 2270–2282, 2002.PubMedCrossRefGoogle Scholar
  38. 38.
    Jones PA: DNA methylation errors and cancer. Cancer Res 65: 2463–2467, 1996.Google Scholar
  39. 39.
    Kanerva J, Niini T, Vettenranta K et al: Loss at 12p detected by comparative genomic hybridization (CGH): association with TEL-AML1 fusion and favorable prognostic features in childhood acute lymphoblastic leukemia (ALL). A multi-institutional study. Med Pediatr Oncol 37: 419–425, 2001.PubMedCrossRefGoogle Scholar
  40. 40.
    Kitajka K, Puskás LG, Zvara Á et al: The role of n-3 polyunsaturated fatty acids in brain: Modulation of rat brain gene expression by dietary n-3 fatty acids. Proc Natl Acad Sci USA 99, 2619–2624, 2002.PubMedCrossRefGoogle Scholar
  41. 41.
    Kopper L, Tímár J: Gene expression profiles in the diagnosis and prognosis of cancer. Magy Onkol 46:3–9, 2002.PubMedGoogle Scholar
  42. 42.
    Kozian DH, Kirschbaum BJ: Comparative gene-expression analysis. Trends Biotechnol 17: 73–78, 1999.PubMedCrossRefGoogle Scholar
  43. 43.
    Kroll T, Odyvanova L, Clement JH et al: Molecular characterization of breast cancer cell lines by expression profiling. J Cancer Res Clin Oncol 128: 125–134, 2002.PubMedCrossRefGoogle Scholar
  44. 44.
    Liang P, Pardee A et al: Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science 257: 967–971, 1992.PubMedCrossRefGoogle Scholar
  45. 45.
    Lin YM, Furukawa Y, Tsunoda T et al: Molecular diagnosis of colorectal tumors by expression profiles of 50 genes expressed differentially in adenomas and carcinomas. Oncogene 21: 4120–4128, 2002.PubMedCrossRefGoogle Scholar
  46. 46.
    Lisitsyn N, Wigler M et al: Cloning the differences between two complex genomes. Science 259: 946–951, 1993.PubMedCrossRefGoogle Scholar
  47. 47.
    Luo JH, Yu YP, Cieply K et al: Gene expression analysis of prostate cancers. Mol Carcinog 33: 25–35, 2002.PubMedCrossRefGoogle Scholar
  48. 48.
    Mitelman F, Mertens F, Johansson B: A breakpoint map of recurrent chromosomal rearrangements in human neoplasia. Nat Genet Spec No: 417–474, 1997.Google Scholar
  49. 49.
    Novak U, Oppliger Leibundgut E, Hager J et al: A high-resolution allelotype of B-cell chronic lymphocytic leukemia (BCLL). Blood 100: 1787–1794, 2002.PubMedGoogle Scholar
  50. 50.
    Pinkel D, Segraves R, Sudar D et al: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet 20: 207–11, 1998.PubMedCrossRefGoogle Scholar
  51. 51.
    Prashar Y, Weissman S et al: Analysis of differential gene expression by display of 3’ end restriction fragments of cDNAs. Proc Nat Acad Sci USA 93: 659–663, 1996.PubMedCrossRefGoogle Scholar
  52. 52.
    Pui CH, Campana D, Evans WE: Childhood acute lymphoblastic leukaemia current status and future perspectives. Lancet Oncol 10: 597–607, 2001.CrossRefGoogle Scholar
  53. 53.
    Puskás LG, Zvara Á, Hackler JrL et al: RNA amplification results in reproducible microarray data with slight ratio biases. Biotechniques 32: 1330–1342, 2002.PubMedGoogle Scholar
  54. 54.
    Puskás LG, Zvara Á, Hackler JrL et al: Production of bulk amounts of universal RNA for DNA-microarrays. Biotechniques 33: 898–900, 902, 904, 2002.PubMedGoogle Scholar
  55. 55.
    Ramaswamy S, Tamayo P, Rifkin R et al: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 98: 15149–15154, 2001.PubMedCrossRefGoogle Scholar
  56. 56.
    Rubnitz JE andPui C-H: Leukemias, inPrinciples of Molecular Medicine, pp. 233–239. (J.L. Jameson ed.) Humana Press, NJ, 1998.Google Scholar
  57. 57.
    Schlossman SF, Chess L, Humphreys RE et al: Distribution of Ia-like molecules on the surface of normal and leukemic human cells. Proc Natl Acad Sci USA 73: 1288–1292, 1976.PubMedCrossRefGoogle Scholar
  58. 58.
    Schwarze SR, DePrimo SE, Grabert LM et al: Novel pathways associated with bypassing cellular senescence in human prostate epithelial cells. J Biol Chem 277: 14877–14883, 2002.PubMedCrossRefGoogle Scholar
  59. 59.
    Shipp MA, Ross KN, Tamayo P et al: Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 8: 68–74, 2002.PubMedCrossRefGoogle Scholar
  60. 60.
    Shou J, Soriano R, Hayward SW et al: Expression profiling of a human cell line model of prostatic cancer reveals a direct involvement of interferon signaling in prostate tumor progression. Proc Natl Acad Sci USA 99: 2830–2835, 2002.PubMedCrossRefGoogle Scholar
  61. 61.
    Sidransky D: Emerging molecular markers of cancer. Nature Rev Cancer 2: 210–219, 2002.CrossRefGoogle Scholar
  62. 62.
    Stremmel C, Wein A, Hohenberger W et al: DNA microarrays: a new diagnostic tool and its implications in colorectal cancer. Int J Colorectal Dis 17: 131–136, 2002.PubMedCrossRefGoogle Scholar
  63. 63.
    Suzuki H, Gabrielson E, Chen W et al: A genomic screen for genes upregulated by demethylation and histone deacetylase inhibition in human colorectal cancer. Nat Genet 31: 141–149, 2002.PubMedCrossRefGoogle Scholar
  64. 64.
    Swaroop A, Xu JZ, Agarwal N et al.: A simple and efficient cDNA library subtraction procedure: isolation of human retinaspecific cDNA clones. Nucleic Acids Res 19: 1954, 1991.PubMedCrossRefGoogle Scholar
  65. 65.
    Sweetser DA, Chen CS, Blomberg AA et al: Loss of heterozygosity in childhood de novo acute myelogenous leukemia. Blood 98: 1188–94, 2001.PubMedCrossRefGoogle Scholar
  66. 66.
    Takano T, Hasegawa Y, Matsuzuka F et al: Gene expression profiles in thyroid carcinomas Br J Cancer 83: 1495–502, 2000.PubMedCrossRefGoogle Scholar
  67. 67.
    Toyota M, Ho C, Ahuja N et al: Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification. Cancer Res 59: 2307–2312, 1999.PubMedGoogle Scholar
  68. 68.
    Tsukasaki K, Krebs J, Nagai K et al: Comparative genomic hybridization analysis in adult T-cell leukemia/lymphoma: correlation with clinical course. Blood 97: 3875–3881, 2001.PubMedCrossRefGoogle Scholar
  69. 69.
    Yeoh EJ, Ross ME, Shurtleff SAA et al: Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1: 133–143, 2002.PubMedCrossRefGoogle Scholar
  70. 70.
    Velculescu VE, Zhang L, Vogelstein B et al: Serial analysis of gene expression. Science 270: 464–467, 1995.CrossRefGoogle Scholar
  71. 71.
    Vogeli-Lange R, Burckert N, Boiler T et al: Rapid selectionand classification of positive clones generated by mRNA differential display. Nucleic Acids Res 24: 1385–1386, 1996.PubMedCrossRefGoogle Scholar
  72. 72.
    Weinberger SR, Morris TS, Pawlak M: Recent trends in protein biochip technology. Pharmacogenomics 1: 395–416, 2000.PubMedCrossRefGoogle Scholar
  73. 73.
    Wieser R: Rearrangements of chromosome band 3q21 in myeloid leukemia. Leuk Lymphoma 43: 59–65, 2002.PubMedCrossRefGoogle Scholar
  74. 74.
    Wilhelm M, Veltman JA, Olshen AB et al: Array-based comparative genomic hybridization for the differential diagnosis of renal cell cancer. Cancer Res 62: 957–960, 2002.PubMedGoogle Scholar
  75. 75.
    de Wit NJ, Burtscher HJ, Weidle UH et al: Differentially expressed genes identified in human melanoma cell lines with different metastatic behaviour using high density oligonucleotide arrays. Melanoma Res 12: 57–69, 2002.PubMedCrossRefGoogle Scholar
  76. 76.
    Wreesmann VB, Ghossein RA, Patel SG et al: Genome-wide appraisal of thyroid cancer progression. Am J Pathol 161: 1549–1556, 2002.PubMedGoogle Scholar

Copyright information

© Arányi Lajos Foundation 2002

Authors and Affiliations

  • Ágnes Zvara
    • 1
  • László Hackler
    • 1
  • B. Zsolt Nagy
    • 1
  • Tamás Micsik
    • 1
  • László G. Puskás
    • 1
  1. 1.Laboratory of Functional Genomics, Biological Research CenterHungarian Academy of SciencesSzegedHungary

Personalised recommendations