An Enriched Multigen Low-Density Array To Predict Outcome in Advanced Hodgkin Lymphoma Paraffin-Embedded Samples.

Despite the major advances in the treatment of classical Hodgkin Lymphoma (cHL) patients, around 30% to 40% of cases in advanced stages may relapse or die as result of the disease, and current markers to predict prognosis are rather unreliable. The identification of molecular events and biological processes associated with treatment failure are essential to develop new predictive tools. We used gene expression data from 29 samples of advanced cHL patients and HL-derived cell lines in order to identify transcriptional patterns from both tumoral cells and cell microenvironment. Student t-test was used to detect genes differentially overexpressed in cell lines and in tumor samples, thus creating two databases that report for genes expressed by the tumor HRS cells and genes expressed by the microenvironment. Using Gene Set Enrichment analysis (GSEA) we identified specific gene sets enriched in both databases in patients with favorable and unfavorable outcome, respectively. To validate these pathways we designed a novel Taqman low-density array (LDA) to examine the expression of the most relevant genes in 60 formalin-fixed, paraffin embedded (FFPE) tissue samples, and correlated the results with treatment outcome. Functional pathways related to unfavorable outcome significantly enriched in the HRS cells included the regulation of the G2/M checkpoint of the cell cycle, S phase and G1/S transition, chaperons, histone modification and other signaling pathways with an important representation of the MAPK pathway. On the other hand, genes reporting for specific T-cell populations (T-cytotoxic and T-regulatory cells) and macrophage activation were found to be overexpressed in the microenvironment. The final model presents a balanced representation of these genes, including also genes encoding factors implicated in drug resistance (RRM2, TYMS and TOP2A). RNA extracted from FFPE sections yielded analyzable data for 80% of samples. LDA analysis of the genes included in the model confirmed the feasibility of this approach, and the capacity for identifying cases with increased risk of failure.LDA provides an effective technique for analyzing gene expression in FFPE tissues, and it can be used for clinical prediction in diagnostic samples, using a selection of genes identified after GSEA analysis of the initial molecular signatures. This novel Taqman LDA will be used to develop a new molecular predictor of the outcome of patients with advanced cHL.