ABSTRACT: Diffuse large B-cell lymphoma (DLBCL) has striking clinical and molecular variability. Although a more precise identification of the multiple determinants of this variability is still under investigation, there is a consensus that high-clinical-risk DLBCL cases require a risk-adapted therapy, since intensification of chemotherapy with autologous stem-cell transplantation (ASCT) has been shown to improve the prognosis for high-risk patients in randomised clinical trials. In spite of this, the protocols used for these patients have a high morbidity, associated with ASCT and the use of multiple drugs. This makes it important to identify patients that may take benefit from risk-adapted therapies, through the recognition of biological markers that provide information about both the tumoral cells and the microenvironment. Unfortunately, many of the studies so far performed have relied on heterogeneous series of patients, staged or treated with different protocols. For instance, some of the variability in DLBCL arises from the fact that this diagnosis is applied to de novo and secondary tumours, nodal and extranodal, irrespective of clinical stage, patient age and associated infections. Additionally, DLBCL includes some specific variants, such as mediastinal DLBCL and T/HRBCL, with specific prognostic parameters. This could prevent the identification of potential predictive biomarkers, because the results of many studies show that the search for predictive biomarkers should be promoted in the context of samples of clinically homogeneous patients enrolled in clinical trials. A further source of variability is the dependence of some predictive markers on specific therapeutic approaches, as is the case for the Bcl6 expression in DLBCL, since Bcl-6+ cases have been shown not to benefit from the addition of R to CHOP. Here we have analysed a series of high-clinical-risk DLBCLs by a two-stage approach, first identifying functional signatures by expression analysis, then analysing surrogate biomarkers using tissue microarrays (TMAs). This eclectic approach could reveal new aspects of the relationship between the neoplastic cells and the microenvironment, leading to the identification of previously unknown prognostic markers. At the same time, the use of functional signatures to analyse expression-profiling data avoids the poor reproducibility of the data obtained from gene-by-gene analysis, and benefits from the existence of a growing body of data concerning the major pathways deregulated in DLBCL. To avoid the bias of semiquantitative scoring, in this study we have quantified the markers included in the multivariate analysis. Keywords: new biological variables, risk-adapted therapies
