Explore the vast range of titles available.

Background Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Results Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Conclusion Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

PurposeThree dimensional (3D) “in vitro” models are progressively being applied to investigate tumor cell biology and the interaction of cancer cells with tumor microenvironment under conditions more similar and realistic to “in vivo” behavior than standard bidimensional (2D) cultures.MethodsIn the last years, different methods have been developed to create spheroids and organoids and each technique has advantages and limitations also based on individual needs and cell types used. This review offers an overview of methodologies used for 3D systems: scaffold-free and scaffold-based methods up to bioreactors and organ-on-chip models.ResultsThe principal goal for researchers is to select the 3D system that best suits their needs and that reflects the tumor model they want to study. A large chapter is dedicated to the application of these models to lymphomas’ study, a neoplasm still little explored in the 3D field.ConclusionThese innovative and advanced models may represent new tools for cancer research and pre-clinical studies of new therapies in the perspective of precision medicine.

Multivariate circular observations, i.e. points on a torus arise frequently in fields where instruments such as compass, protractor, weather vane, sextant or theodolite are used. Multivariate wrapped models are often appropriate to describe data points scattered on p-dimensional torus. However, the statistical inference based on such models is quite complicated since each contribution in the log-likelihood function involves an infinite sum of indices in Zp, where p is the dimension of the data. To overcome this problem, for moderate dimension p, we propose two estimation procedures based on Expectation-Maximisation and Classification Expectation-Maximisation algorithms. We study the performance of the proposed techniques on a Monte Carlo simulation and further illustrate the advantages of the new procedures on three real-world data sets.

Peripheral T-cell lymphoma not otherwise specified represents a diagnostic category comprising clinically, histologically, and molecularly heterogeneous neoplasms that are poorly understood. The genetic landscape of peripheral T-cell lymphoma not otherwise specified remains largely undefined, only a few sequencing studies having been conducted so far. In order to improve our understanding of the genetics of this neoplasm, we performed whole exome sequencing along with RNA-sequencing in a discovery set of 21 cases. According to whole exome sequencing results and mutations previously reported in other peripheral T-cell lymphomas, 137 genes were sequenced by a targeted deep approach in 71 tumor samples. In addition to epigenetic modifiers implicated in all subtypes of T-cell neoplasm ( TET2, DNMT3A, KMT2D, KMT2C, SETD2 ), recurrent mutations of the FAT1 tumor suppressor gene were for the first time recorded in 39% of cases. Mutations of the tumor suppressor genes LATS1, STK3, ATM, TP53 , and TP63 were also observed, although at a lower frequency. Patients with FAT1 mutations showed inferior overall survival compared to those with wild-type FAT1 . Although peripheral T-cell lymphoma not otherwise specified remains a broad category also on molecular grounds, the present study highlights that FAT1 mutations occur in a significant proportion of cases, being provided with both pathogenetic and prognostic impact.

Cutaneous T cell lymphomas (CTCLs), encompassing mycosis fungoides (MF) and Sézary syndrome (SS), present a complex landscape influenced by cytokines and cellular responses. In this work, the intricate relationship between these inflammatory proteins and disease pathogenesis is examined, focusing on what is known at the clinical and therapeutic levels regarding the most well-known inflammatory mediators. An in-depth look is given to their possible alterations caused by novel immunomodulatory drugs and how they may alter disease progression. From this narrative review of the actual scientific landscape, Interferon-gamma (IFN-γ) emerges as a central player, demonstrating a dual role in both promoting and inhibiting cancer immunity, but the work navigates through all the major interleukins known in inflammatory environments. Immunotherapeutic perspectives are elucidated, highlighting the crucial role of the cutaneous microenvironment in shaping dysfunctional cell trafficking, antitumor immunity, and angiogenesis in MF, showcasing advancements in understanding and targeting the immune phenotype in CTCL. In summary, this manuscript aims to comprehensively explore the multifaceted aspects of CTCL, from the immunopathogenesis and cytokine dynamics centred around TNF-α and IFN-γ to evolving therapeutic modalities. Including all the major known and studied cytokines in this analysis broadens our understanding of the intricate interplay influencing CTCL, paving the way for improved management of this complex lymphoma.

Standard chemotherapies for diffuse large B-cell lymphoma (DLBCL), based on the induction of exogenous DNA damage and oxidative stress, are often less effective in the presence of increased MYC and BCL-2 levels, especially in the case of double hit (DH) lymphomas harboring rearrangements of the MYC and BCL-2 oncogenes, which enrich for a patient’s population characterized by refractoriness to anthracycline-based chemotherapy. Here we hypothesized that adaptive mechanisms to MYC-induced replicative and oxidative stress, consisting in DNA damage response (DDR) activation and BCL-2 overexpression, could represent the biologic basis of the poor prognosis and chemoresistance observed in MYC/BCL-2-positive lymphoma. We first integrated targeted gene expression profiling (T-GEP), fluorescence in situ hybridization (FISH) analysis, and characterization of replicative and oxidative stress biomarkers in two independent DLBCL cohorts. The presence of oxidative DNA damage biomarkers identified a poor prognosis double expresser (DE)-DLBCL subset, characterized by relatively higher BCL-2 gene expression levels and enrichment for DH lymphomas. Based on these findings, we tested therapeutic strategies based on combined DDR and BCL-2 inhibition, confirming efficacy and synergistic interactions in in vitro and in vivo DH-DLBCL models. These data provide the rationale for precision-therapy strategies based on combined DDR and BCL-2 inhibition in DH or DE-DLBCL.

Background: Dermoscopy, a non-invasive diagnostic technique, is being increasingly used to evaluate cutaneous T-cell lymphomas such as mycosis fungoides (MF) and Sézary syndrome (SS). However, its diagnostic accuracy and role in staging remain underexplored. Objective: This study aimed to assess the dermoscopic patterns in MF and SS, correlating the findings with the disease stage and lesion type to evaluate dermoscopy’s diagnostic utility. Methods: A retrospective, monocentric analysis was conducted on patients with histologically confirmed MF or SS. Dermoscopic images were evaluated for vascular patterns, pigmentation, scaling, and keratin plugs. The statistical analysis assessed the correlations between these dermoscopic features and the TNMB staging and lesion type. A literature review was also performed to contextualize the findings, focusing on studies describing dermoscopic features in MF based on retrospective, prospective, and cross-sectional data. Results: The study included 30 patients with histologically confirmed MF or SS (19 males and 11 females; mean age: 64.5 years). The dermoscopic evaluation revealed that all the lesions were pigment-free, with vascular structures as the predominant feature. Linear vessels (40%) and serpentine vessels (13.3%) were the most frequently observed, along with dotted vessels (36.7%) and clods (10%). The vessel distribution was diffuse (40%) or perifollicular (36.7%), with a predominant red (56.7%) or orange (40%) background. Scaling was present in 76.7% of cases, either diffuse (40%) or perifollicular (36.7%), and keratin plugs were detected in 40% of the lesions. No statistically significant correlations were found between dermoscopic features and the TNMB stage or lesion type (p > 0.05). A cluster analysis identified two patient groups with differing vascular and scaling features but no clear association with disease stage. The literature review identified studies that commonly reported features in MF dermoscopy, including fine, short linear vessels and an orange-yellow background, particularly in early-stage MF. Spermatozoa-like structures have been marked as highly specific for diagnosing MF. Some studies also suggested a transition in vascular morphology from linear vessels in early disease to branched vessels and ulceration in advanced stages. Conclusions: Our results showed some vascular patterns have some potential but lack sensitivity for staging MF and SS. The terminology used and the reproducibility of our results compared to those reported in the literature showed little consistency, with none of our cases showing spermatozoa-like structures. Moreover, the same issues with the use of non-reproducible terminology were noted across the studies because it is not standardized and due to different incongruent dermoscopic patterns. More significant prospective studies with standardized descriptors and larger groups are needed to refine its diagnostic and staging utility.

Merkel cell carcinoma is an aggressive neuroendocrine skin tumor, for which several non-conclusive prognostic factors of adverse clinical behavior have been reported. As promoter methylation of the immune checkpoint receptor CD279/PD-1/PDCD1 (m PDCD1 ) has been shown to be a prognostic factor in different cancers, we investigated its role in Merkel cell carcinoma. m PDCD1 was assessed retrospectively in a cohort of 69 Merkel cell carcinoma patients from the University of Bologna, University of Turin and University of Insubria. Kaplan-Meier curves and log-rank tests were calculated for all variables. To assess the influence of m PDCD1 , the Cox proportional hazards model and different Royston-Parmar models were evaluated. High PDCD1 methylation (m PDCD1 high ) was associated with a higher overall mortality at both the univariate analysis (log rank test: χ 2  = 5.17, p  = 0.023; permutation test: p  = 0.023) and the multivariate analysis (HR = 2.111, p  = 0.042). The other variables associated with a higher overall mortality at the multivariate analysis were clinical stage III-IV (HR = 2.357, p  = 0.008), size > 2 cm (HR = 2.248, p  = 0.031) and Merkel cell polyomavirus (HR = 0.397, p  = 0.015). Further, m PDCD1 high was strongly associated with older age (81 vs 76 years, p  = 0.042), absence of immune cells (92.6%, p  < 0.001), no expression of PD-L1 by immune cells (70.4%, p  = 0.041) and by both immune and tumor cells (70.4%, p  = 0.001). m PDCD1 is a valid prognostic parameter in patients affected by Merkel cell carcinoma. In addition, it could provide an estimate of the global PD-1/PD-L1 expression with potentially relevant implications from a therapeutic point of view.

Mycosis fungoides (MF) represents the most common type of cutaneous lymphoma. In the majority of patients, the disease has a slow evolution and a protracted course; however, a subset of patients shows poor oncologic outcomes. Unfortunately, there are no reliable prognostic markers for MF, and the currently available treatments are only effective in a minority of patients. This study aimed to evaluate the expression and clinical significance of PARP-1 and CAF-1/p60 in MF. Sixty-four MF representatives of the different stages of disease were assessed by immunohistochemistry for PARP-1 and CAF-1/p60. The association of PARP-1 and CAF-1/p60 with the MF stage and outcome was assessed by using Fisher’s exact test and Kaplan-Meier survival analysis with the Log-rank test; a p value < 0.05 was considered significant. PARP-1 was overexpressed in 57.9% of MF and was significantly associated with a MF stage > II (p = 0.034) but not with the risk of death (p = 0.237). CAF-1/p60 was overexpressed in 26.8% of MF and was significantly associated with decreased overall survival (p < 0.001) but not with the MF stage (p = 1). A significant association was found between PARP-1 overexpression and CAF-1/p60 overexpression (p = 0.0025). Simultaneous overexpression of PARP-1 and CAF-1/p60 was significantly associated with decreased overall survival (p < 0.001), although less strongly than CAF-1/p60 alone (χ2 = 14.916 vs 21.729, respectively). In MF, PARP-1 is overexpressed in advanced stages, while CAF-1/p60 is overexpressed in the cases with shorter overall survival, appearing as a significant prognostic marker. A role for PARP-1 inhibitors and anti-CAF-1/p60 targeted therapy may be reasonably hypothesized in MF.

In this work, we deal with a robust fitting of a wrapped normal model to multivariate circular data. Robust estimation is supposed to mitigate the adverse effects of outliers on inference. Furthermore, the use of a proper robust method leads to the definition of effective outlier detection rules. Robust fitting is achieved by a suitable modification of a classification-expectation-maximization algorithm that has been developed to perform a maximum likelihood estimation of the parameters of a multivariate wrapped normal distribution. The modification concerns the use of complete-data estimating equations that involve a set of data dependent weights aimed to downweight the effect of possible outliers. Several robust techniques are considered to define weights. The finite sample behavior of the resulting proposed methods is investigated by some numerical studies and real data examples.