Hodgkin lymphoma: A special microenvironment

Classical Hodgkn’s lymphoma (cHL) is one of the most particular lymphomas for the few tumor cells surrounded by an inflammatory microenvironment. Reed-Sternberg (RS) and Hodgkin (H) cells reprogram and evade antitumor mechanisms of the normal cells present in the microenvi-ronment. The cells of microenvironment are essential for growth and survival of the RS/H cells and are recruited through the effect of cytokines/chemokines. We summarize recent advances in gene expression profiling (GEP) analysis applied to study microenvironment component in cHL. We also describe the main therapies that target not only the neoplastic cells but also the cellular components of the background

The Tumor Microenvironment of DLBCL in the Computational Era

Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the \u201ceffacement model\u201d resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and recent clinical trials revitalized the interest in this field, prompting the use of new tools to dissect DLBCL composition and reveal novel prognostic association. Here we revisited major controversies related to TME in DLBCL, focusing on the use of bioinformatics to mine transcriptomic data and provide new insights to be translated into the clinical setting

NR1H3 (LXRα) is associated with pro-inflammatory macrophages, predicts survival and suggests potential therapeutic rationales in diffuse large b-cell lymphoma

The role of macrophages (Mo) and their prognostic impact in diffuse large B-cell lymphomas (DLBCL) remain controversial. By regulating the lipid metabolism, Liver-X-Receptors (LXRs) control Mo polarization/inflammatory response, and their pharmacological modulation is under clinical investigation to treat human cancers, including lymphomas. Herein, we surveyed the role of LXRs in DLBCL for prognostic purposes. Comparing bulk tumors with purified malignant and normal B-cells, we found an intriguing association of NR1H3, encoding for the LXR-α isoform, with the tumor microenvironment (TME). CIBERSORTx-based purification on large DLBCL datasets revealed a high expression of the receptor transcript in M1-like pro-inflammatory Mo. By determining an expression cut-off of NR1H3, we used digital measurement to validate its prognostic capacity on two large independent on-trial and real-world cohorts. Independently of classical prognosticators, NR1H3high patients displayed longer survival compared with NR1H3low cases and a high-resolution Mo GEP dissection suggested a remarkable transcriptional divergence between subgroups. Overall, our findings indicate NR1H3 as a Mo-related biomarker identifying patients at higher risk and prompt future preclinical studies investigating its mouldability for therapeutic purposes

Dissection of DLBCL Microenvironment Provides a Gene Expression-Based Predictor of Survival Applicable to Formalin-Fixed Paraffin-Embedded Tissue

Background Gene expression profiling (GEP) studies recognized a prognostic role for tumor microenvironment (TME) in diffuse large B-cell lymphoma (DLBCL), but the routinely adoption of prognostic stromal signatures remains limited. Patients and methods Here, we applied the computational method CIBERSORT to generate a 1028-gene matrix incorporating signatures of 17 immune and stromal cytotypes. Then, we carried out a deconvolution on publicly available GEP data of 482 untreated DLBCLs to reveal associations between clinical outcomes and proportions of putative tumor-infiltrating cell types. Forty-five genes related to peculiar prognostic cytotypes were selected and their expression digitally quantified by NanoString technology on a validation set of 175 formalin-fixed, paraffin-embedded DLBCLs from two randomized trials. Data from an unsupervised clustering analysis were used to build a model of clustering assignment, whose prognostic value was also assessed on an independent cohort of 40 cases. All tissue samples consisted of pretreatment biopsies of advanced-stage DLBCLs treated by comparable R-CHOP/R-CHOP-like regimens. Results In silico analysis demonstrated that higher proportion of myofibroblasts (MFs), dendritic cells, and CD4+ T cells correlated with better outcomes and the expression of genes in our panel is associated with a risk of overall and progression-free survival. In a multivariate Cox model, the microenvironment genes retained high prognostic performance independently of the cell-of-origin (COO), and integration of the two prognosticators (COO\u2009+\u2009TME) improved survival prediction in both validation set and independent cohort. Moreover, the major contribution of MF-related genes to the panel and Gene Set Enrichment Analysis suggested a strong influence of extracellular matrix determinants in DLBCL biology. Conclusions Our study identified new prognostic categories of DLBCL, providing an easy-to-apply gene panel that powerfully predicts patients\u2019 survival. Moreover, owing to its relationship with specific stromal and immune components, the panel may acquire a predictive relevance in clinical trials exploring new drugs with known impact on TME

Physiology of incretins and loss of incretin effect in type 2 diabetes and obesity.

Abstract An important role in the regulation of glucose homeostasis is played by incretins, which are gut-derived hormones released in response to nutrient ingestion. In humans, the major incretin hormones are glucagon-like peptide (GLP)-1 and glucose-dependent insulinotropic polypeptide (GIP), and together they fully account for the incretin effect (that is, higher insulin release in response to an oral glucose challenge compared to an equal intravenous glucose load). Studies have shown that GLP-1 and GIP levels and actions may be perturbed in disease states, and the loss of incretin effect is likely to contribute importantly to the postprandial hyperglycaemia in type 2 diabetes. However, the specific cause-effect relationship between disease and incretins is still unclear. This review focuses on several key studies elucidating the association of defective incretin action with obesity and T2DM and the effects of metformin and other anti-diabetic agents on the incretin system

Hierarchical clustering analysis identifies metastatic colorectal cancers patients with more aggressive phenotype

A large percentage of metastatic colorectal cancer (mCRC) patients presents metastasis at the time of diagnosis. In the last years, great efforts have been made in the treatment of these patients with the identification of different phenotypes playing a key role in the definition of new systemic therapies. Unsupervised hierarchical clustering analysis (HCA) was performed considering the clinicopathological characteristics of 51 mCRCs. Using immunohistochemistry on tissue microarrays, we assessed the expression of beta-catenin, NHERF1, RASSF1A, TWIST1, HIF-1 alpha proteins in tumors and paired liver metastases. We also analyzed RASSF1A methylation status on the samples of the same patients. HCA distinguished Group 1 and Group 2 characterized by different clinicopathological features. Group 1 was characterized by higher number of positive lymph nodes (p=0.0139), poorly differentiated grade (p<0.0001) and high extent of tumor spread (p=0.0053) showing a more aggressive phenotype compared to Group 2. In both Groups, we found a common "basal" condition with a higher level of nuclear TWIST1 (p<0.0001) and cytoplasmic beta-catenin (p<0.0001) in tumors than in paired liver metastases. Furthermore, the Group 1 was also characterized by RASSF1A hypermethylation (p<0.0001) and nuclear HIF-1a overexpression (p=0.0354) in paired liver metastases than in tumors.In conclusion, HCA identifies mCRC patients with a more aggressive phenotype. Moroever, our results support the important contribution to the progression of the disease of RASSF1A methylation and the oncogenic role of HIF-1a in these patients. These evidences, should provide relevant information concerning the biology of this tumor and, as a consequence, potential new systemic therapeutic approaches

A new ensemble method for detecting anomalies in gene expression matrices

One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies