|dc.description.abstract||In the last decade unprecedented improvement in cure rates and overall survival was achieved in diffuse large B-cell Lymphoma (DLBCL) through the introduction of rituximab and anthracyclin-based chemotherapy (R-CHOP) as first line treatment. However, 40% of patients are refractory or relapse after R-CHOP and are hardly salvaged. To date, only age, International Prognostic Index (IPI) stratification and genetic aberrations defining gray-zone lymphomas have been used in clinical trials to select high-risk patients for more aggressive regimens. However, these prognostic features do not take into account the full biological heterogeneity of DLBCL. This reflects our limited knowledge on comprehensive prognostication in this group of disorders and supports our choice to investigate old and new prognostic factors for DLBCL in this thesis.
Molecular characterization is generating opportunities for personalized therapy in poor-risk DLBCL. In order for targeted therapies to succeed in this disease, reliable and reproducible strategies that adequately segregate patients into distinct molecular groups are needed. While gene expression profiling (GEP) is the gold standard method, there is presently a lack of standardized methodology for array analysis, which can lead to variable results. The lack of a routine methodology for GEP has led investigators to develop immunohistochemistry (IHC) based approaches for the molecular classification in DLBCL. In fact, the Hans algorithm is being used to identify non-GCB DLBCLs in clinical trials offering NF-kB targeting agents to patients with this subtype. By performing a systematic comparison of nine IHC algorithms for molecular classification in a new large dataset of diagnostic DLBCL, we document an extremely low concordance across all classifiers (<21%) when classifying each individual patient, and a lack of outcome impact of all strategies, demonstrating that IHC is not a reliable alternative to molecular-based methods to be used for clinical decisions in DLBCL.
GEP studies also suggested that the microenvironment could provide prognostic biomarkers in DLBCL in the R-CHOP era. Most authors have focused on the use of IHC to enumerate and functionally characterize the microenvironment in DLBCL. In our second study, by comparing two methods of semi-automated analysis for IHC staining
of the microenvironment, we demonstrate that the computerized results are highly reproducible, add the required robustness to IHC studies and should be used in the future instead of manual analysis. By applying comprehensive statistical analysis we propose that CD3 and FoxP3 should be validated as predictors of response to R-CHOP in clinical trials.
Whereas a number of mechanisms by which cancer cells influence macrophage function have been described, currently there is very limited understanding of the macrophage polarisation status and effector function in human DLBCL. In our third study we analysed the GEP of macrophages sorted from human DLBCL samples. Unsupervised hierarchical clustering does not resolve DLBCL macrophage samples from reactive macrophage samples, indicating that macrophage heterogeneity in DLBCL should be considered. 202 genes are differentially expressed in DLBCL relative to controls. Functional annotation supports that these genes are macrophage-specific. We demonstrate that DLBCL macrophages have a bidirectional M1 and M2 functional activation, challenging the concept, widespread in the literature, that macrophages in tumours have a predominant M2 transcriptome.
In our fifth study we used a two-cell co-culture model in an attempt to demonstrate that DLBCL cells influence macrophage transcriptome and proteome. The heterogeneity of the results, which precludes the confirmation of our hypothesis, is fully discussed.
In our last study we tease out the DLBCL macrophage GEP heterogeneity and propose IFN- as a culprit B-cell derived molecule influencing macrophage activation status. Finally, using immunofluorescence we demonstrate that both M1 and M2 proteins are expressed in DLBCL macrophages.||en_US