Skip to main content

Advertisement

SpringerLink
Log in

Gene arrays in lymphoma: Where will they fit in?

  • Published:
Current Hematologic Malignancy Reports Aims and scope Submit manuscript

Abstract

Molecular diagnostics for lymphoid malignancies has undergone substantial technical evolution during the past two decades, moving from labor-intensive investigations of individual abnormalities to high-throughput genome-wide analyses. Accordingly, its role has expanded to new fields such as monitoring of minimal residual disease and, more recently, outcome prediction in specific lymphoma subtypes. One novel technology that has had a major impact on the molecular diagnosis of lymphoid malignancies is gene expression profiling by DNA microarrays. It has provided robust and distinct molecular signatures for the most common types of lymphomas and has identified novel subsets that would not be identified by conventional methods. It also has led to the construction of molecularly defined prognostic models in these lymphoma subtypes and to a better understanding of the molecular mechanisms of lymphomagenesis. This development will undoubtedly transform diagnostic medicine in the near future and lead us into an era when tumor diagnosis will incorporate the information of critical molecular abnormalities that will have significant impact on disease outcome in each individual tumor sample. Future treatments are likely to be founded on effective, individualized, and mechanism-based therapies with the least toxicity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References and Recommended Reading

  1. Jaffe ES, Harris NL, Stein H, Vardiman JW, eds: World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of Haematopoietic and Lymphoid Tissues. Lyon, France: IARC Press; 2001.

    Google Scholar 

  2. Willis TG, Dyer MJ: The role of immunoglobulin translocations in the pathogenesis of B-cell malignancies. Blood 2000, 96:808–822.

    PubMed  CAS  Google Scholar 

  3. Pasqualucci L, Migliazza A, Basso K, et al.: Mutations of the BCL6 proto-oncogene disrupt its negative autoregulation in diffuse large B-cell lymphoma. Blood 2003, 101:2914–2923.

    Article  PubMed  CAS  Google Scholar 

  4. Pasqualucci L, Neumeister P, Goossens T, et al.: Hypermutation of multiple proto-oncogenes in B-cell diffuse large-cell lymphomas. Nature 2001, 412:341–346.

    Article  PubMed  CAS  Google Scholar 

  5. Greiner TC, Moynihan MJ, Chan WC, et al.: p53 mutations in mantle cell lymphoma are associated with variant cytology and predict a poor prognosis. Blood 1996, 87:4302–4310.

    PubMed  CAS  Google Scholar 

  6. Pinyol M, Hernandez L, Cazorla M, et al.: Deletions and loss of expression of p16INK4a and p21Waf1 genes are associated with aggressive variants of mantle cell lymphomas. Blood 1997, 89:272–280.

    PubMed  CAS  Google Scholar 

  7. Pinyol M, Cobo F, Bea S, et al.: p16(INK4a) gene inactivation by deletions, mutations, and hypermethylation is associated with transformed and aggressive variants of non-Hodgkin’s lymphomas. Blood 1998, 91:2977–2984.

    PubMed  CAS  Google Scholar 

  8. Gronbaek K, de Nully Brown P, Moller MB, et al.: Concurrent disruption of p16INK4a and the ARF-p53 pathway predicts poor prognosis in aggressive non-Hodgkin’s lymphoma. Leukemia 2000, 14:1727–1735.

    Article  PubMed  CAS  Google Scholar 

  9. Dalla-Favera R, Migliazza A, Chang CC, et al.: Molecular pathogenesis of B cell malignancy: the role of BCL-6. Curr Top Microbiol Immunol 1999, 246:257–263; discussion 263–265.

    PubMed  CAS  Google Scholar 

  10. National Cancer Institute sponsored study of classifications of non-Hodgkin’s lymphomas: summary and description of a working formulation for clinical usage. The Non-Hodgkin’s Lymphoma Pathologic Classification Project. Cancer 1982, 49:2112–2135.

  11. Harris NL, Jaffe ES, Stein H, et al.: A revised European-American classification of lymphoid neoplasms: a proposal from the International Lymphoma Study Group. Blood 1994, 84:1361–1392.

    PubMed  CAS  Google Scholar 

  12. A predictive model for aggressive non-Hodgkin’s lymphoma. The International Non-Hodgkin’s Lymphoma Prognostic Factors Project. N Engl J Med 1993, 329:987–994.

  13. Salles G, Shipp MA, Coiffier B: Chemotherapy of non-Hodgkin’s aggressive lymphomas. Semin Hematol 1994, 31:46–69.

    PubMed  CAS  Google Scholar 

  14. Shipp MA: Prognostic factors in aggressive non-Hodgkin’s lymphoma: Who has "high-risk" disease? Blood 1994, 83:1165–1173.

    PubMed  CAS  Google Scholar 

  15. Armitage JO: Treatment of non-Hodgkin’s lymphoma. N Engl J Med 1993, 328:1023–1030.

    Article  PubMed  CAS  Google Scholar 

  16. Alizadeh AA, Eisen MB, Davis RE, et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403:503–511. The first microarray study performed in lymphoid malignancies. This study identified two distinct subgroups of DLBCL using a supervised approach.

    Article  PubMed  CAS  Google Scholar 

  17. Wright G, Tan B, Rosenwald A, et al.: A gene expressionbased method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A 2003, 100:9991–9996.

    Article  PubMed  CAS  Google Scholar 

  18. Rosenwald A, Wright G, Chan WC, et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002, 346:1937–1947. dy of 240 patients with diffuse large B-cell lymphoma, which confirmed the initial findings (reference 16••) and identified a model of 17 genes as molecular predictor of clinical outcome.

    Article  PubMed  Google Scholar 

  19. Lossos IS, Alizadeh AA, Eisen MB, et al.: Ongoing immunoglobulin somatic mutation in germinal center B cell-like but not in activated B cell-like diffuse large cell lymphomas. Proc Natl Acad Sci U S A 2000, 97:10209–10213.

    Article  PubMed  CAS  Google Scholar 

  20. Iqbal J, Sanger WG, Horsman DE, et al.: BCL2 translocation defines a unique tumor subset within the germinal center B-cell-like diffuse large B-cell lymphoma. Am J Pathol 2004, 165:159–166.

    PubMed  CAS  Google Scholar 

  21. Huang JZ, Sanger WG, Greiner TC, et al.: The t(14;18) defines a unique subset of diffuse large B-cell lymphoma with a germinal center B-cell gene expression profile. Blood 2002, 99:2285–2290.

    Article  PubMed  CAS  Google Scholar 

  22. Barth TF, Leithauser F, Joos S, et al.: Mediastinal (thymic) large B-cell lymphoma: Where do we stand? [review]. Lancet Oncol 2002, 3:229–234.

    Article  PubMed  Google Scholar 

  23. Savage KJ, Monti S, Kutok JL, et al.: The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical Hodgkin lymphoma. Blood 2003, 102:3871–3879. of the two studies identifying a unique gene expression profile that can distinguish primary mediastinal large-B-cell lymphoma from other diffuse large-B-cell lymphomas, including those that happen to involve the anterior mediastinum.

    Article  PubMed  CAS  Google Scholar 

  24. Rosenwald A, Wright G, Leroy K, et al.: Molecular diagnosis of primary mediastinal B cell lymphoma identifies a clinically favorable subgroup of diffuse large B cell lymphoma related to Hodgkin lymphoma. J Exp Med 2003, 198:851–862. of the two studies identifying a unique gene expression profile that can distinguish primary mediastinal large-B-cell lymphoma from other diffuse large-B-cell lymphomas, including those that happen to involve the anterior mediastinum.

    Article  PubMed  CAS  Google Scholar 

  25. Campo E, Raffeld M, Jaffe ES: Mantle-cell lymphoma. Semin Hematol 1999, 36:115–127.

    PubMed  CAS  Google Scholar 

  26. Rosenwald A, Wright G, Wiestner A, et al.: The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 2003, 3:185–197. A study of 101 patients with mantle cell lymphoma (MCL), in which the authors identified a unique gene expression signature, illustrated the role of proliferative gene expression signature in predicting the survival of patients with MCL and identified several cases of cyclin D1-negative MCL.

    Article  PubMed  CAS  Google Scholar 

  27. Fu K, Weisenburger DD, Greiner TC, et al.: Cyclin D1-negative mantle cell lymphoma: a clinicopathologic study based on gene expression profiling. Blood 2005, 106:4315–4321.

    Article  PubMed  CAS  Google Scholar 

  28. 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 2002, 8:68–74. A study of 58 cases of diffuse large-B-cell lymphoma using oligonucleotide microarrays, which identified a model of 13 genes as a molecular predictor of clinical outcome.

    Article  PubMed  CAS  Google Scholar 

  29. Rimsza LM, Roberts RA, Miller TP, et al.: Loss of MHC class II gene and protein expression in diffuse large B-cell lymphoma is related to decreased tumor immunosurveillance and poor patient survival regardless of other prognostic factors: a follow-up study from the Leukemia and Lymphoma Molecular Profiling Project. Blood 2004, 103:4251–4258.

    Article  PubMed  CAS  Google Scholar 

  30. Monti S, Savage KJ, Kutok JL, et al.: Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. Blood 2005, 105:1851–1861.

    Article  PubMed  CAS  Google Scholar 

  31. Dave SS, Wright G, Tan B, et al.: Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 2004, 351:2159–2169. A study of 192 patients with follicular lymphoma, which identified a model based on molecular features of tumor-infiltrating immune cells as molecular predictor of clinical outcome.

    Article  PubMed  CAS  Google Scholar 

  32. Davis RE, Brown KD, Siebenlist U, Staudt LM: Constitutive nuclear factor kappaB activity is required for survival of activated B cell-like diffuse large B cell lymphoma cells. J Exp Med 2001, 194:1861–1874.

    Article  PubMed  CAS  Google Scholar 

  33. Dave SS, Wright G, Tan B, et al.: LymphDx: a custom microarray for molecular diagnosis and prognosis in non-Hodgkin lymphoma. Blood 2004, 104:201a.

    Google Scholar 

  34. Lossos IS, Czerwinski DK, Alizadeh AA, et al.: Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med 2004, 350:1828–1837.

    Article  PubMed  CAS  Google Scholar 

  35. Hans CP, Weisenburger DD, Greiner TC, et al.: Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray. Blood 2004, 103:275–282.

    Article  PubMed  CAS  Google Scholar 

  36. Barrans SL, Fenton JA, Banham A, et al.: Strong expression of FOXP1 identifies a distinct subset of diffuse large B-cell lymphoma (DLBCL) patients with poor outcome. Blood 2004, 104:2933–2935.

    Article  PubMed  CAS  Google Scholar 

  37. Elias L, Portlock CS, Rosenberg SA: Combination chemotherapy of diffuse histiocytic lymphoma with cyclophosphamide, adriamycin, vincristine and prednisone (CHOP). Cancer 1978, 42:1705–1710.

    Article  PubMed  CAS  Google Scholar 

  38. Shipp MA, Yeap BY, Harrington DP, et al.: The m-BACOD combination chemotherapy regimen in large-cell lymphoma: analysis of the completed trial and comparison with the M-BACOD regimen. J Clin Oncol 1990, 8:84–93.

    PubMed  CAS  Google Scholar 

  39. Klimo P, Connors JM: MOPP/ABV hybrid program: combination chemotherapy based on early introduction of seven effective drugs for advanced Hodgkin’s disease. J Clin Oncol 1985, 3:1174–1182.

    PubMed  CAS  Google Scholar 

  40. Klimo P, Connors JM: MACOP-B chemotherapy for the treatment of diffuse large-cell lymphoma. Ann Intern Med 1985, 102:596–602.

    PubMed  CAS  Google Scholar 

  41. Fisher RI, Gaynor ER, Dahlberg S, et al.: Comparison of a standard regimen (CHOP) with three intensive chemotherapy regimens for advanced non-Hodgkin’s lymphoma. N Engl J Med 1993, 328:1002–1006.

    Article  PubMed  CAS  Google Scholar 

  42. A clinical evaluation of the International Lymphoma Study Group classification of non-Hodgkin’s lymphoma. The Non-Hodgkin’s Lymphoma Classification Project. Blood 1997, 89:3909–3918.

  43. Rosenwald A, Alizadeh AA, Widhopf G, et al.: Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia. J Exp Med 2001, 194:1639–1647.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Pathology and Microbiology, University of Nebraska Medical Center, 68198-3135, Omaha, NE, USA

    Kai Fu MD, PhD

Authors
  1. Javeed Iqbal PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco d’Amore MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinglong Hu MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wing C. Chan MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai Fu MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Fu MD, PhD.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Iqbal, J., d’Amore, F., Hu, Q. et al. Gene arrays in lymphoma: Where will they fit in?. Curr Hematol Malig Rep 1, 129–136 (2006). https://doi.org/10.1007/s11899-006-0024-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11899-006-0024-5

Keywords

Search

Navigation