 |
| About Bioline | All Journals | Testimonials | Membership | News |
|
||||||
|
||||||
Journal of Cancer Research and Therapeutics, Vol. 7, No. 4, October-December, 2011, pp. 393-398 Review Article Lymphomas: Its gene expression profiling Manjul Tiwari Department of Oral Pathology and Microbiology, School of Dental Sciences, Sharda University, Uttar Pradesh, India Code Number: cr11109 DOI: 10.4103/0973-1482.91998 Abstract To find gene expression patterns in disease and biological processes the use of DNA microarrays has already begun to have a significant impact on modern medicine. Gene expression profiling have impact on hematological malignancies including from prognosis to its therapeutic regimens. DNA microarrays have led to the discovery of better prognostic tools and the mechanisms including molecular of lymphoma have clarified, with a help of cell cycle and DNA damage pathways that are responsible for tumor cell proliferation and different clinical outcomes. While in future it is hope that important discoveries will be done in leukemias, lymphomas, and many other cancer subtypes using gene expression profiling. Keywords: ALL, CLL, gene profiling, lymphoma, malignancy, malignant lymphoma Introduction With the development of gene expression analysis at the genomic scale comes the possibility of accurately stratifying cancer patients by the molecular characteristics of their tumors and the development of individualized, tumor-specific therapy. We currently use histological examination of cancer specimens, clinical characteristics of patients, and, more recently, genetic information to classify tumors into pathological entities and to stratify cancer patients into treatment paradigms. For example, the current diagnosis of follicular lymphoma (FL) brings together morphological aspects of the tumor cell infiltrate (atypical follicular structures), immunophenotyping of the tumor cells, and cytogenetics. [1],[2] Gene expression profiling provides the potential to gain a deeper understanding of the complex biological and molecular basis of lymphomas, facilitating discovery of new drug targets and new therapeutic regimens. In addition, while the most basic goals of diagnosis in the clinical setting are to clarify a clear prognosis and a therapeutic plan for both the patient and the care provider both, our current standard of diagnosis does not reliably yield either. Within diagnostic categories there can be great variation among patients, both in their response to therapeutic regimens and their overall survival. This situation provides the opportunity for molecular diagnostics to be developed, which may help to provide clear definitions of various forms of cancer, and allow more accurate diagnosis and more applicable therapy.[1],[2],[3] While high-throughput genomic techniques are applicable to all facets of human pathology, lymphomas will be the specific focus of this article. The use in clinical practice is not the only goal of gene expression profiling. Careful characterization of the patterns of gene expression of cancer cells versus normal cells can clarify the cells of origin of these conditions and the pathways that are altered during neoplastic transformation.[1] Expression of Gene Profiling[1] Gene expression profiling is based on the use of DNA microarrays to assess the level of gene expression at a given time in a population of cells. There are two main forms of microarrays.[1],[2] The first involves the use of robotic equipment to spot complementary DNAs (cDNAs) onto glass slides coated to bind DNA effectively.[1],[3],[4] The cDNAs are attached to the slides in carefully planned grids, which allows accurate analysis of sample binding patterns after experiments take place. In order to use these arrays, the cell sample of choice is lysed, mRNA is extracted and reverse transcribed to cDNA which is fluorescently labeled. These fluorescently labeled probes are allowed to anneal to the cDNA microarray, and the degree of hybridization is assessed by fluorescent microscopy.[1],[3] This platform was created before DNA microarrays were widely available commercially. A second type of DNA microarray is that provided by Affymetrix. This technique uses multiple representative oligonucleotides for each gene of interest which are synthesized directly onto silicon wafers. In addition, for each oligonucleotide there is a "mismatch'' probe arrayed on the chip as well, with a slightly altered sequence. Samples are prepared in a similar manner as for the cDNA arrays, although there is one round of RNA amplification added in the Affymetrix protocol. Pairing of sample cDNA to the perfect match oligonucleotide alone (with no binding to the mismatch sequence) is considered evidence of gene expression in that sample.[1],[3],[5],[6] Analysis of Expression of Genes[1] The quantity of data generated using these techniques required the development of new analytical techniques to find the patterns and alterations of relevant pathways among all the less important changes in gene expression (noise). There are two main paradigms of analysis that have emerged. The first is based on the idea that the answer should come from the data itself, without any input from the researcher, which is known as an unsupervised approach. [1] The preferred method of analysis in this case is often hierarchical clustering. [1],[6],[7] This technique uses an algorithm to cluster together genes with correlated expression or samples with similar gene expression patterns (or both) simply based upon the intrinsic relationships among gene expression patterns. [1] The method of analysis is based on the concept that the data should follow known biological principles and is generally regarded as supervised analysis. [1],[8] In this case, a model is designed to distinguish between a chosen number of groups rather than being allowed to discover as many groups as it finds in the data. [1],[9],[10] Once genes are clustered, based on similarity in gene expression, we can identify gene expression signatures that represent sets of genes all of which participate in a biological process or characterize a given cell type. [1],[11], [12],[13] In addition, this allows us to derive biological meaning from the genomic data by identifying relevant cell types and important pathways in the lymphoma samples. [1],[11],[12] Recognition of these gene signatures allows a better understanding of the biological processes and pathways involved in a given sample, and fosters development of prognosis prediction algorithms. Representative genes from each of the prognostically informative signatures can be combined into prognostic predictors that can be used to classify patients into prognostic and therapeutic groups. [1] Gene Expression Profiling in Leukemia and Lymphoma[1] Diffuse large B-cell lymphoma (DLBCL)[1] DLBCL is the most common lymphoma in adults. [1],[13],[14] While many therapeutic regimens have been attempted, the previous standard of care, CHOP (an anthracycline-based chemotherapeutic regimen) was able to cure DLBCL in only 35-40% of patients. [1],[13],[14] Attempts to improve survival with multiple alternate regimes did not make significant changes in that success rate. [1],[14],[15] However, the addition of Rituximab (a monoclonal anti-CD20 antibody) to the traditional CHOP regimen did lead to a significant increase in survival, [1],[15],[16] although prognosis for any individual patient remains challenging to predict. It was therefore hypothesized that the failure to improve DLBCL cure rates may reflect the existence of multiple subgroups of patients, each with a slightly different pathogenic mechanism. This would imply that one particular therapeutic regimen may not be equally effective among different DLBCL subgroups. A number of methods exist for stratifying DLBCL patients in order to predict outcome, including the currently used International Prognostic Index (IPI) which focuses on clinical parameters that include: age of the patient, Eastern Cooperative Oncology Group (ECOG) performance status, tumor stage, lactate dehydrogenase level, and the number of sites of extranodal disease. [1],[16],[17] However, the IPI is not able to stratify patients into different therapeutic regimens effectively. [1] The microarray data allowed the diagnosis of DLBCL to go far beyond simply stratifying patients into three subgroups. A molecular predictor of survival after chemotherapy for DLBCL was created based on outcome data correlation with gene expression patterns. [1],[11],[18],[19] The goal of this analysis was the identification of genes correlated with outcome, based on a Cox proportional hazards model. Genes were classified into so-called gene expression signatures that had been previously defined; genes belonging to the germinal center B-cell signature, lymph node signature and MHC class II signature were associated with good outcome in DLBCL patients, while the proliferation signature was associated with the poor outcome. As expected, the expression of the germinal center B-cell signature was high in the GCB DLBCL subgroup, whereas the proliferation signature was generally more highly expressed in the ABC DLBCL subgroup. At the average, the MHC class II signature was similarly expressed in all three DLBCL groups. For the development of a gene expression based outcome predictor, 17 genes were selected which were highly variable in expression and which represented the expression level of the respective signatures. Overall, the molecular predictor was found to be independent of the IPI. [1] Using Affymetrix arrays, Shipp and colleagues also studied DLBCL patients and developed a gene expression outcome predictor that included, among other genes, the expression of NOR1, PDE4B, and PKCβ, which are all involved in apoptotic pathways. [1],[20],[21] The distinction between GCB and ABC DLBCL was also evident in this dataset and the DLBCL subgroups had different survival. [1],[8],[9] Interestingly, Shipp et al., had showed in their dataset that PDE4β was over expressed in samples from DLBCL patients with poor prognosis. They put forward a hypothesis that the resulting inhibition of cAMP would prevent apoptosis in these cells. Subsequently, PDE4β was confirmed as a target in poor prognosis DLBCLs and the apoptotic pathway blocked in DLBCL was found to be dependent on the PI3K/AKT pathway. [1],[21],[22] A very recent study from the same group has yielded three DLBCL subgroups by microarray profiling, with one characterized by a higher level host response, including immune cell infiltration of the tumors. [1],[22],[23] The importance of the tumor microenvironment in DLBCL has also recently been shown in FL, [1],[23],[24] which may foreshadow more emphasis on the interaction between the lymphoma cells and the host environment in lymphoid neoplasms. The studies by both the Shipp and Staudt groups have led to the identification of potential therapeutic targets in different subgroups of DLBCL. The Staudt group proposed the NFKB pathway as a target for ABC DLBCL. [1],[17],[18] The Shipp group is interested in finding inhibitors of PDE4β and the PI3K/AKT pathway as well as PKCβ, and clinical trials with a PKC-β inhibitor are ongoing. B-cell chronic lymphocytic leukemia (B-CLL)[1] B-CLL is the most common leukemia in the Western hemisphere. [1],[24],[25] However, while B-CLL is often diagnosed at an early stage of the disease, patients can either develop indolent disease or may suffer an acute, precipitous decline. In order to find prognostic markers that identified which outcome was most likely for each patient. Initially, cytogenetic differences were studied to find prognostic markers in B-CLL, and 17p or 11q deletions were suggested to be predictors of poor outcome. [1],[25],[26] In two independent studies, however, the presence of somatic mutations in the immunoglobulin heavy chain variable regions (IgVn) of the tumor cells was found to be a predictor of better patient outcome. [1],[24],[27] While this finding was a landmark discovery, it is not suitable as a practical clinical test since it is expensive and time consuming to generate IgVn sequences on all B-CLL patients, and many clinical laboratories may not have the capacity to do this test on a routine basis. In addition, it is unclear what threshold to use for differentiating IgVn -mutated from IgVn-unmutated B-CLL cases (thresholds from 96-98% have been used). [1],[25] CD38 was suggested as a surrogate marker for the IgVn mutation status; however, while CD38 expression is of prognostic significance, it failed to be confirmed as a useful surrogate marker for the IgVn mutation status in two large studies. [1],[25],[28],[29] The finding of IgVn somatic mutation levels having prognostic value might have implied that there were truly two different subtypes of B-CLL, each with their own progenitor cell, with a pregerminal B-cell in the case of IgVn-unmutated B-CLL and a postgerminal B-cell in the case of IgVH-mutated B-CLL. However, two separate DNA microarray studies [1],[7],[29],[30] showed clearly that IgVH-mutated and -unmutated B-CLLs share a homogenous gene expression signature that allows B-CLL as a whole to be distinguished from other leukemias and lymphomas. Thus, B-CLL, regardless of IgVn somatic mutation frequency, has a distinct transcriptional profile and therefore seems to constitute one single disease. [1] The IgVn mutation status therefore remained an important prognostic factor, and many groups began using the DNA microarray data to find a gene expression pattern that could serve as a proxy. While B-CLL has a generally homogenous gene expression signature across IgVn-mutated and -unmutated samples, the data from gene expression studies also showed a small number of genes that had different expression levels between patients with mutated and unmutated IgVn genes. [1],[7],[8] The best correlate for an unmutated IgVn status was found to be ZAP-70, a tyrosine kinase previously only known for its role in T cell receptor signaling. Interestingly, ZAP-70 was shown to be expressed at negligible levels in IgVn-mutated B-CLL cells, while it was expressed at significant levels in IgVn-unmutated B-CLL cells. [1],[7],[30],[31] A large study in the UK further validated the correlation between ZAP-70 expression and the IgVn mutation status. [1],[32],[33] Other groups have validated ZAP-70 as a prognostic indicator as well, [1],[33] and recently ZAP-70 has been shown to be a more useful predictor of need for therapy in B-CLL than the IgVn mutation status. [1],[34] In order to facilitate transfer from bench to bedside, both RT-PCR and immunohistochemical methods were developed to measure ZAP-70 levels. The use of immunohistochemistry allows proxy assessment of the IgVn status without separating out the tumor cells, by using a concomitant stain for the B-cell marker CD 19. The most promising clinical application, however, may be the measurement of ZAP-70 expression by flow cytometry analysis, since this technique is widely used in the standard work-up procedure of B-CLL samples in many laboratories. [1],[33],[34] It has become clear recently that DNA damage response pathways play a role in response of B-CLL cells to therapy. There is an intriguing subset of B-CLL cases that have a shared alteration in their response to DNA damage, specifically due to ATM and p53 mutations. [1],[35],[36],[37] As expected, with defects in DNA damage repair, these B-CLL cells have a different response to ionizing radiation than B-CLL samples with unmutated DNA damage repair genes. [38] Specifically, B-CLL cells with mutations in p53 and ATM failed to upregulate proapoptotic target genes (like FAS and TRAIL-receptor 2) suggesting that restoration of these mutated genes in the subset of patients with p53 and ATM mutations may have therapeutic effects. [1] Mantle cell lymphoma (MCL)[1] MCL is a mature B-cell lymphoma that makes up 6% of all B-cell non-Hodgkin's lymphomas (B-NHLs). [1] MCL has a median patient survival of 3 to 4 years, and while survival is heterogeneous, there is generally an aggressive clinical course with poor response to chemotherapy. The classic translocation associated with this condition is the t(11;l4)(ql3;q32) which leads to cyclin Dl over expression and effects at the Gl/S checkpoint of the cell cycle. [1],[4],[36] MCL is also associated with ATM inactivation and p53 mutations. Previous attempts to stratify MCL patients have identified characteristics associated with poor survival including a blastic morphological variant, increased tumor cell proliferation, INK4a/ARF locus deletion, and p53 mutation or protein over expression, but none of these biological features have been used to successfully stratify MCL patients into therapeutic categories. [1],[37],[38] In a large gene expression profiling study, DNA microarrays of MCL patient samples were used to derive molecular prognostic information. [1],[4],[35],[36],[37],[38] In this study, gene expression patterns of cyclin Dl-positive MCL specimens were compared to other B-NHL subsets and a large set of genes were derived that is characteristically expressed at high levels in MCL. Moreover, a small subset of MCL-like lymphoma cases were studied that show morphologic and immunophenotypic characteristics, but lack expression of cyclin Dl. [1] A substantial proportion of these cases showed an expression profile identical to cyclin Dl-positive MCL cases and, therefore, these cases may represent a small subgroup of bona fide MCL that lack expression of cyclin Dl. [1] Several molecular features were associated with increased proliferation in MCL cells. First, higher levels of Cyclin D1 expression were found to be associated with an increase of tumor cell proliferation. MCLs can express different cyclin Dl mRNA isoforms that differ in the lengths of their 3' untranslated regions (UTRs). The 4.5 kb version may have reduced stability due to a longer UTR and the presence of an RNA destabilizing element in this region. A shorter isoform of 1.7kb may be more stable due to lack of this destabilizing element. MCL cases with abundant expression of the short cyclin Dl isoform had higher levels of overall cyclin Dl mRNA and, therefore, an increased rate of proliferation. ATM deletions were also found in this set of MCL patients, but neither had a strong correlation with survival or proliferation. [1] Interestingly, mathematical models including the level of cyclin Dl expression or the INK4a/ARF deletion status alone or in combination did not perform as well in predicting survival than the proliferation signature based outcome model. Thus, the gene expression based model may capture additional oncogenic events in MCL cells that are presently not known and serve as a global integrator of molecular alterations in MCL. [1] Follicular lymphoma (FL)[1] FL is the second most common form of B-NHLs. [1] FL is often associated with the chromosomal translocation t(l4;18) that causes BCL-2 dysregulation, leading to reduced apoptosis. Clinically, there is a variable progression of the disease, from aggressive lymphoma to an indolent condition with intermittent episodes. At the present time, there are no robust biological markers available that predict the clinical course of FL patients at the time of diagnosis. In order to search for predictive markers and to identify the molecular basis of the biological and clinical heterogeneity of FL, gene expression analysis and survival signature analysis were undertaken, using RNA extracted from almost 200 FL patient samples. [1],[23] Samples were split into training and validation sets, and a statistical survival model was developed using only the training set. In particular, a Cox proportional hazards model was used to find genes associated with survival in the patient population. Hierarchical clustering was then applied to group single genes that were associated with favorable and poor survival into gene expression signatures, [1],[12],[13] and two of these signatures were found to have "statistical synergy": One associated with good prognosis (immune response 1) and one associated with poor prognosis (immune response 2). Interestingly, both signatures were derived from nonmalignant bystander cells in the lymph node specimens and not from the tumor cells themselves. [1] The signature associated with a more favorable clinical course (immune response 1) contains genes associated with subsets of T cells (e.g., CD8B1, ITK, and STAT4). However, the presence of this signature is not simply due to the number of infiltrating T cells in the tumor sample, since a number of pan-T cell markers were not part of this signature. The signature associated with poor prognosis (immune response 2) contains genes associated with macrophages and dendritic cells (e.g., TLR5, LGMN, and C3AR1). This model therefore predicts that the relative levels of subsets of infiltrating cells are of prognostic significance in FL, which is intriguing for understanding the pathogenesis of this condition. Future studies in FL will likely place emphasis on investigating the interaction between the neoplastic B-cells and the nonmalignant bystander cells which appears to be of importance for the biological and clinical behavior of this lymphoma subtype. [1] Gene Expression Profiling in Diagnosis, Prognosis, and Therapeutic Choices Over the past 6 years, gene expression profiling has been used to create molecular profiles of various cancer subtypes. Lymphoid malignancies, especially the more common subtypes of B-NHL, have been well-studied and gene expression profiling data have yielded many new insights into these conditions. The first goal was to clarify whether these diseases, with historically diverse prognostic outcomes, are truly single conditions or diseases with multiple subgroups that might each benefit from individualized therapy. [1] Another important goal of gene expression profiling is the identification of differentially implicated oncogenic pathways in the newly discovered subsets of lymphoid malignancies that could be targeted for future drug development. In this regard, ABC DLBCL was found to have constitutive NFKB expression and subsets of DLBCL patients are characterized by activation of PKCp; exciting clinical trials, in which these potent oncogenic pathways are targeted by specific inhibitors, are ongoing. [1] The benefit of molecular diagnostic studies and, in particular, of gene expression profiling will have to be tested in future multicenter clinical trials. Once sufficient data have been collected to define molecular signatures that allow the stratification of patients into subgroups with prognostic and therapeutic implications, gene expression may make its way into the mainstream of clinical practice as an adjunct diagnostic tool. There is clearly much research left to be done both in lymphoid malignancies and beyond, but the goal is clear. [1] Acknowledgement I want to give my sincere gratitude and acknowledgement to Simone Mocellin, Sarah E. Henrickson, Elena M. Hartmann, German Ott, Andreas Rosenwald, Susanna Mandruzzato, Carlo Riccardo Rossi, David W. Petersen and Ernest S. Kawasakin without this article cannot be prepared. References
Copyright 2011 - Journal of Cancer Research and Therapeutics |
| |||||||||
