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DNA Microarrays in Lymphoid Malignancies

DNA Microarrays in Lymphoid Malignancies

In the introduction to his book, The Order of Things, the French structuralist philosopher Michel Foucault posed the question: "When we establish a considered classification, when we say that a cat and a dog resemble each other less than two greyhounds do, on what grounds are we able to establish the validity of this classification with complete certainty?" He went on: "On what table, according to what grid of identities, similarities, analogies do we sort out so many different and similar things." Individualized Diagnosis
As Rosenwald summarizes in his article, the grid that has been recently used with success in studying lymphoma types and subtypes and holds promise for future clinical application is the DNA microarray. The reason that DNA microarrays-by virtue of analyzing the expression levels of thousands of genes-are such powerful tools is simply the following basic notion (as expressed by the 18th century French naturalist Buffon): "The more we increase the number of divisions in the production of nature, the closer we shall approach to the truth, since nothing really exists in nature except the individual." In his article, Rosenwald illustrates how lymphoma diagnosis can be "individualized" and what the clinical potential of doing so could be. But in the words of Foucault, "we have to critically evaluate the grounds on which we are able to establish the validity of a more individualized lymphoma classification." Prior to the 1970s, the diagnosis and classification of lymphoma was based purely on histologic examination.[ 1] Later, with the advent of monoclonal antibodies specific for a multitude of lymphoid antigens and the application of these antibodies in immunophenotyping, lymphoma diagnosis could be more refined. During the early days of molecular diagnostics of lymphoid malignancies, the ability to establish a diagnosis of neoplastic disease by demonstrating the clonal nature of the lymphoid population represented another quantum leap. In subsequent years, a vast amount of information has accumulated on the molecular pathology of various lymphoid malignancies. These developments ultimately led to the recognition of biological entities, reflected in the Revised European- American Classification of Lymphoid Neoplasms[2] and updated in the recent World Health Organization lymphoma classification.[3] Further biologic variables of these established lymphoma entities have already been recognized, but as discussed by Rosenwald, microarray analysis can help to identify these variables more exhaustively, objectively, and reproducibly. This brings us one step closer to an "individualized" diagnosis. In addition, and importantly, aberrant molecular pathways can be revealed in lymphomas, opening the door to more specific treatment. Indeed, establishing a direct link between precise diagnosis and treatment should be the ultimate goal. Gene Expression Profiling
DNA microarray technology is possible largely due to the structural genomic foundation established by several large-scale human genome projects. It allows the study of genome- wide gene expression profiles in physiologic and disease processes. The technology has been employed to study a wide variety of human tumors with the expectation that it will engender a better understanding of the molecular mechanisms underlying the behavior of a tumor. Various microarray platforms have been used for studying gene expression profiles. The studies conducted by Alizadeh, Rosenwald, and coworkers employed a cDNA microarray, the Lymphochip. Major concerns about the use of this technology include considerations of how readily we can compare studies across different platforms and how such comparisons can be facilitated. In this regard, it is important that sufficient information on experimental design and data processing be available so that the experimental data can be independently analyzed by other investigators. A crossplatform comparison of data is a challenging endeavor, and one example is the recently published comparison of two sets of microarray data on diffuse large B-cell lymphoma (DLBCL)-one based on the Affymetrix platform and the other, on the Lymphochip platform.[ 4] It is interesting that the germinal center B-cell and activated B-cell subsets of DLBCL could be defined based on Affymetrix data, and furthermore, that the subsets had different overall survival rates. Molecular Prognosticators
While the molecular prognosticators defined by the studies of Rosenwald et al can be used in addition to or alternatively to the International Prognostic Index and have been validated in the studies by these investigators, several unresolved issues remain. The number of array elements on the Lymphochip, although substantial, represents only a fraction of the transcriptome. Will the predictors change substantially if the study is repeated using an array with a more complete representation of the transcriptome? As mentioned above, a molecular prognosticator will be of value only as long as it reflects the variables of the biologic response to specific treatments. The molecular prognosticator for DLBCL established by Rosenwald et al was based on a series of patients treated with anthracycline-based multiagent chemotherapy.[5] Currently, patients with DLBCL are often treated with regimens containing rituximab (Rituxan), which may significantly alter their response and survival. Will the molecular prognosticator based on samples from patients treated with anthracycline-based chemotherapy be valid for patients treated with current modalities? Although gene expression profiling is a powerful technique, additional information is useful in supervising the discovery process. Clinical information was used to supervise the discovery of prognosticators, and immunoglobulin variable region gene mutation information was essential to finding differentially expressed genes in mutated and nonmutated chronic lymphocytic leukemia cases.[6,7] It is likely that certain unique genetic abnormalities within a tumor subset may also be used to guide similar discovery processes. The integration of global genetic and gene expression data will certainly be highly synergistic in future investigations. Conclusions
Nonetheless, Rosenwald's conclusion is well founded. Gene expression profiling will have a dramatic impact on the diagnosis of lymphoma, adding many variables to the grid by which diagnoses are made. In the foreseeable future, the initial characterization of a lymphoma case may be performed on a single platform, which could be either a diagnostic DNAminichip, an antibody panel, or a realtime reverse transciptase-polymerase chain reaction plate, and, no doubt, these instruments will be refined further. The ultimate goal is to treat patients with tailored therapy, based on targeting aberrant biologic pathways detected in the individual tumor.

Disclosures

The author(s) have no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.

References

1. Rappaport H: Tumors of the hematopoietic system, fascicle 8, first series. Atlas of Tumor Pathology. Washington, DC, Armed Forces Institute of Pathology, 1966.
2. 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 84:1361-1392, 1994.
3. Jaffe ES, Harris NL, Stein H, et al (eds): WHO Classification of Tumours; Pathology and Genetics of Tumours of Hematopoietic and Lymphoid Tissues. Lyon, France, IARC Press, 2001.
4. Wright G, Tan B, Rosenwald A, et al: A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A 100:9991-9996, 2003.
5. 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 346:1937-1947, 2002.
6. 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 194:1639-1647, 2001.
7. Klein U, Tu Y, Stolovitzky GA, et al: Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells. J Exp Med 194:1625-1638, 2001.
 
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