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Despite recent advances, Multiple Myeloma (MM) remains an incurable disease with apparent heterogeneity that may explain patients’ variable clinical outcomes. While the phenotypic, (epi)genetic, and molecular characteristics of myeloma cells have been thoroughly examined, there is limited information regarding the role of the bone marrow (BM) microenvironment in the natural history of the disease. In the present study, we performed deep phenotyping of 32 distinct immune cell subsets in a cohort of 94 MM patients to reveal unique immune profiles in both BM and peripheral blood (PB) that characterize distinct prognostic groups, responses to induction treatment, and minimal residual disease (MRD) status. Our data show that PB cells do not reflect the BM microenvironment and that the two sites should be studied independently. Adverse ISS stage and high-risk cytogenetics were correlated with distinct immune profiles; most importantly, BM signatures comprised decreased tumor-associated macrophages (TAMs) and erythroblasts, whereas the unique Treg signatures in PB could discriminate those patients achieving complete remission after VRd induction therapy. Moreover, MRD negative status was correlated with a more experienced CD4- and CD8-mediated immunity phenotype in both BM and PB, thus highlighting a critical role of by-stander cells linked to MRD biology.

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An in silico pathway analysis was performed in order to improve current knowledge on the molecular drivers of cervical cancer and detect potential targets for treatment. Three publicly available Affymetrix gene expression data-sets (GSE5787, GSE7803, GSE9750) were retrieved, vouching for a total of 9 cervical cancer cell lines (CCCLs), 39 normal cervical samples, 7 CIN3 samples and 111 cervical cancer samples (CCSs). Predication analysis of microarrays was performed in the Affymetrix sets to identify cervical cancer biomarkers. To select cancer cell-specific genes the CCSs were compared to the CCCLs. Validated genes were submitted to a gene set enrichment analysis (GSEA) and Expression2Kinases (E2K). In the CCSs a total of 1,547 probe sets were identified that were overexpressed (FDR < 0.1). Comparing to CCCLs 560 probe sets (481 unique genes) had a cancer cell-specific expression profile, and 315 of these genes (65%) were validated. GSEA identified 5 cancer hallmarks enriched in CCSs (P < 0.01 and FDR < 0.25) showing that deregulation of the cell cycle is a major component of cervical cancer biology. E2K identified a protein-protein interaction (PPI) network of 162 nodes (including 20 drugable kinases) and 1626 edges. This PPI-network consists of 5 signaling modules associated with MYC signaling (Module 1), cell cycle deregulation (Module 2), TGFβ-signaling (Module 3), MAPK signaling (Module 4) and chromatin modeling (Module 5). Potential targets for treatment which could be identified were CDK1, CDK2, ABL1, ATM, AKT1, MAPK1, MAPK3 among others. The present study identified important driver pathways in cervical carcinogenesis which should be assessed for their potential therapeutic drugability.

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Combined treatment with somatostatin and terlipressin does not exert an additive portal hypotensive effect in cirrhotic patients as compared to terlipressin alone, whereas somatostatin alone may impair systemic hemodynamics. Compared with somatostatin, terlipressin exerts a more beneficial effect on renal sodium excretion in patients with or without ascites.

Multiple myeloma (MM) remains incurable despite the abundance of novel drugs. As it has been previously shown, preclinical 2D models fail to predict disease progression due to their inability to simulate the microenvironment of the bone marrow. In this review, we focus on 3D models and present all currently available ex vivo MM models that fulfil certain criteria, such as development of complex 3D environments using patients’ cells and ability to test different drugs in order to assess personalized MM treatment efficacy of various regimens and combinations. We selected models representing the top-notch ex vivo platforms and evaluated them in terms of cost, time-span, and feasibility of the method. Finally, we propose where such a model can be more informative in a patient’s treatment timeline. Overall, advanced 3D preclinical models are very promising as they may eventually offer the opportunity to precisely select the optimal personalized treatment for each MM patient.

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