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. 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. 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 + TME) 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. Our study identified new prognostic categories of DLBCL, providing an easy-to-apply gene panel that powerfully predicts patients’ 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.

Classical Hodgkin’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 microenvironment. 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.

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.

Tumor-infiltrating lymphocytes (TILs) are a valuable indicator of the immune microenvironment that plays the central role in new anticancer drugs. TILs have a strong prognostic role in triple negative breast cancer (TNBC). Little is known about the interaction with the androgen receptor (AR) and forkhead box A1 (FOXA1). We analyzed the relationships between TIL levels, AR, and FOXA1 expression and their clinical significance in TNBC patients. Further, we investigated their interaction with other biomarkers like programmed cell death ligand-1 (PD-L1), breast cancer type 1 susceptibility protein (BRCA1), poly (ADP-Ribose) polymerase 1 (PARP1), and Na+/H+ exchanger regulatory factor 1 (NHERF1). The expression of the proteins was evaluated by immunohistochemistry in 124 TNBC samples. TILs were performed adhering to International TILs Working Group 2014 criteria. Cox proportional hazards models were also used to identify risk factors associated with poor prognosis. Multivariate analysis identified TILs as independent prognostic factor of disease free survival (DFS; p = 0.045). A Kaplan–Meyer analysis revealed that the patients with high TILs had a better DFS compared to patients with low TILs (p = 0.037), and the phenotypes TILs−/AR+ and TILs−/FOXA1− had a worse DFS (p = 0.032, p = 0.001 respectively). AR was associated with FOXA1 expression (p = 0.007), and the tumors FOXA1+ presented low levels of TILs (p = 0.028). A poor DFS was observed for AR+/FOXA1+ tumors compared to other TNBCs (p = 0.0117). Low TILs score was associated with poor patients’ survival, and TILs level in combination with AR or FOXA1 expression affected patient’s clinical outcome. In addition, AR+/FOXA1+ phenotype identified a specific subgroup of TNBC patients with poor prognosis. These data may suggest new ways of therapeutic intervention to support current treatments.

Breast cancer (BC) cells secrete soluble factors that accelerate osteoclast (OC) differentiation, leading to the formation of osteolytic bone metastases. In the BOLERO-2 trial, BC patients with bone involvement who received Everolimus had a delayed tumor progression in the skeleton as a result of direct OC suppression through the inhibition of mTOR, in addition to the general suppressor effect on the cancer cells. Here, we explored the effect of Everolimus, as mTOR inhibitor, on the pro-OC paracrine activity of BC cells. Both MDA-MB-231 and MCF-7 BC cell lines were incubated with sub-lethal amounts of Everolimus, and their conditioned supernatants were assessed for their capacity to differentiate OCs from PBMC from healthy donors, as well as to interfere with their bone resorbing activity shown on calcium phosphate slices. We also measured the mRNA levels of major pro-OC factors in Everolimus-treated BC cells and their secreted levels by ELISA, and evaluated by immunoblotting the phosphorylation of transcription factors enrolled by pathways cooperating with the mTOR inhibition. Finally, the in vivo pro-OC activity of these cells was assessed in SCID mice after intra-tibial injections. We found that Everolimus significantly inhibited the differentiation of OCs and their in vitro bone-resorbing activity, and also found decreases of both mRNA and secreted pro-OC factors such as M-CSF, IL-6, and IL-1β, whose lower ELISA levels paralleled the defective phosphorylation of NFkB pathway effectors. Moreover, when intra-tibially injected in SCID mice, Everolimus-treated BC cells produced smaller bone metastases than the untreated cells. mTOR inhibition in BC cells leads to a suppression of their paracrine pro-OC activity by interfering with the NFkB pathway; this effect may also account for the delayed progression of bone metastatic disease observed in the BOLERO-2 trial.

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.