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Two main variants of Richter syndrome (RS) are recognized, namely, the diffuse large B-cell lymphoma (DLBCL) and the Hodgkin’s lymphoma (HL) variant. Clonal relationship, defined as an identity of the immunoglobulin heavy chain variable (IGHV) region sequence between chronic lymphocytic leukemia (CLL) and RS clones, characterizes patients with a poor prognosis. Due to method sensitivity, this categorization is performed without considering the possibility of small-size ancillary clones, sharing the same phenotype with the preexisting predominant CLL clone, but with different IGHV rearrangements. Here we describe and molecularly profile the peculiar case of a patient with a CLL-like monoclonal B-cell lymphocytosis (MBL), who sequentially developed a DLBCL, which occurred concomitantly to progression of MBL to CLL, and a subsequent HL. Based on standard IGHV clonality analysis, DLBCL was considered clonally unrelated to the concomitantly expanded CLL clone and treated as a de novo lymphoma, achieving a persistent response. Three years later, the patient further developed a clonally unrelated HL, refractory to bendamustine, which was successfully treated with brentuximab vedotin and radiotherapy, and later with pembrolizumab. We retrospectively performed additional molecular testing, by applying next-generation sequencing (NGS) of immunoglobulin repertoire (Ig-rep) techniques and a more sensitive allele-specific oligonucleotide-droplet digital PCR (ASO-ddPCR) strategy, in order to quantitatively investigate the presence of the rearranged IGHV genes in tumor specimens collected during the disease course. In this highly complex case, the application of modern and sensitive molecular technologies uncovered that DLBCL, initially considered as a de novo lymphoma, was instead the result of the transformation of a preexisting ancillary B-cell clone, which was already present at the time of first MBL diagnosis. A similar approach was also applied on the HL sample, showing its clonal unrelatedness to the previous MBL and DLBCL.

Simple Summary Mantle cell lymphoma (MCL) is an aggressive lymphoid tumour with a poor prognosis. There exist no routine biomarkers for the early prediction of relapse. Our study compared the potential of radiomics-based machine learning and 3D deep learning models as non-invasive biomarkers to risk-stratify MCL patients, thus promoting precision imaging in clinical oncology. Abstract Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL., publishedVersion

Anaplastic large-cell lymphoma (ALCL) is a clinical and biological heterogeneous disease that includes systemic anaplastic lymphoma kinase (ALK)-positive and ALK-negative entities. To discover biomarkers and/or genes involved in ALK-negative ALCL pathogenesis, we applied the cancer outlier profile analysis algorithm to a gene expression profiling data set including 249 cases of T-cell non-Hodgkin lymphoma and normal T cells. Ectopic coexpression of ERBB4 and COL29A1 genes was detected in 24% of ALK-negative ALCL patients. RNA sequencing and 5' RNA ligase-mediated rapid amplification of complementary DNA ends identified 2 novel ERBB4-truncated transcripts displaying intronic transcription start sites. By luciferase assays, we defined that the expression of ERBB4-aberrant transcripts is promoted by endogenous intronic long terminal repeats. ERBB4 expression was confirmed at the protein level by western blot analysis and immunohistochemistry. Lastly, we demonstrated that ERBB4-truncated forms show oncogenic potentials and that ERBB4 pharmacologic inhibition partially controls ALCL cell growth and disease progression in an ERBB4-positive patient-derived tumorgraft model. In conclusion, we identified a new subclass of ALK-negative ALCL characterized by aberrant expression of ERBB4-truncated transcripts carrying intronic 5' untranslated regions. © 2016 by The American Society of Hematology.

Patients with Systemic Mastocytosis (SM) need a highly sensitive diagnostic test for D816V detection of the KIT receptor gene.Along with histology/cytology and flow cytometry evaluation, bone marrow (BM) from 110 consecutive adult patients referred with a suspicion of SM to Multidisciplinary Outpatient Clinic for Mastocytosis in Verona were tested both by Amplification Refractory Mutation System Reverse Transcriptase quantitative real time Polymerase Chain Reaction (ARMS-RT-qPCR) and RT-PCR. +. Restriction Fragment Length Polymorphism (RFLP) followed by Denaturing-High Performance Liquid Chromatography (D-HPLC) and Sanger sequencing.ARMS-RT-qPCR identified D816V mutation in 77 patients, corresponding to 100% of cases showing CD25+ mast cells (MCs) whereas RT-PCR+RFLP/D-HPLC+sequencing revealed D816V mutations in 47 patients.According to the 2008 WHO criteria 75 SM, 1 Cutaneous Mastocytosis (CM), 1 monoclonal MC activation syndrome (MMAS), and 1 SM Associated with Haematologic Non-Mast Cell Disorder (SM-AHNMD) were diagnosed. Seventeen out 75 SM patients (23%) would have not satisfied sufficient WHO criteria on the basis of the sole RT-PCR+RFLP: these patients had significantly lower serum tryptase levels and amount of CD25+ MCs.Therefore, ARMS-RT-qPCR might result particularly useful, in patients that do not fulfil major BM histological criterion, for the recognition of indolent SM with a very low MC burden.