Non-Hodgkin's lymphoma in the microarray era

Minimal gene selection for classification and diagnosis prediction based on gene expression profile

Background:

Up to date different methods have been used in order to dimensions reduction, classification, clustering and prediction of cancers based on gene expression profiling. The aim of this study is extracting most significant genes and classifying of Diffuse Large B-cell Lymphoma (DLBCL) patients on the basis of their gene expression profiles.

Materials and Methods:

We studied 40 DLBCL patients and 4026 genes. We utilized Artificial Neural Network (ANN) for classification of patients in two groups: Germinal center and Activated like. As we were faced with low number of patients (40) and numerous genes (4026), we tried to deploy one optimum network and achieve to minimum error. Moreover we used signal to noise (S/N) ratio as a main tool for dimension reduction. We tried to select suitable training data and so to train just one network instead of 26 networks. Finally, we extracted two most significant genes.

Result:

In this study two most significant genes based on their S/N ratios were selected. After selection of suitable training samples, the training and testing error were 0 and 7% respectively.

Conclusion:

We have shown that the use of two most significant genes based on their S/N ratios and selection of suitable training samples can lead to classify DLBCL patients with a rather good result. Actually with the aid of mentioned methods we could compensate lack of enough number of patients, improve accuracy of classifying and reduce complication of computations and so running time.

14-3-3zeta mediates resistance of diffuse large B cell lymphoma to an anthracycline-based chemotherapeutic regimen

Patients presenting with diffuse large B cell lymphoma (DLBCL) are treated with a standard anthracycline-based chemotherapeutic mixture consisting of cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP). Half of DLBCL patients will develop chemo-refractory tumors due to the emergence of CHOP-resistant DLBCL cells. We isolated DLBCL cells that were resistant to CHOP as a model system to investigate the molecular basis of CHOP resistance. Resistant cells emerged from CHOP-sensitive DLBCL populations after repeated cycles of on-off exposure to stepwise increased dosages of CHOP. A proteomic analysis of CHOP-sensitive and -resistant DLBCL cells identified the zeta isoform of the 14-3-3 family as a differentially expressed protein. CHOP-sensitive cells showed reduced expression of 14-3-3zeta protein in the presence of high-dose CHOP relative to control cells. In contrast, CHOP-resistant cells expressed markedly higher levels of 14-3-3zeta regardless the presence of high-dose CHOP. Because 14-3-3zeta is known to exert anti-apoptotic influences and chemoresistance in lung, colon, and prostate carcinoma, we hypothesized that 14-3-3zeta promotes survival of DLBCL cells in CHOP. In support of our hypothesis, knockdown of 14-3-3zeta by small interfering RNA restored the sensitivity of resistant DLBCL to CHOP-induce apoptosis. In addition, 14-3-3zeta expression was highly up-regulated in a resected DLBCL lymph node relative to a normal lymph node by Western blot analysis. Furthermore, more than half of 35 DLBCL tissues showed elevated 14-3-3zeta expression relative to normal lymph tissue by immunohistochemical analysis. Our study implicates 14-3-3zeta in the pathogenesis of DLBCL and suggests a promising combination strategy with a 14-3-3 inhibitor for the treatment of refractory DLBCL.