Search

Background: Progressive multifocal leukoencephalopathy (PML) is a rare and fatal opportunistic viral demyelinating infectious disease of the central nervous system (CNS). There are various clinical presenting symptoms for the disease. Case presentation: This paper presents a clinical case of PML in a patient with B-Chronic lymphocytic leukemia (B-CLL), previously treated with Chlorambucil, later complicated later with COVID-19 and mucormycosis. Conclusion: PML can develop in the setting of cellular immune dysfunction. Late diagnosis of this disease based on nonspecific symptoms is common, therefore when we face a neurological complication in a CLL or immunocompromised patient, we should consider PML infection. A remarkable feature of this case is the possible triggering effect of COVID-19 vaccination for emergence of PML as the disease can be asymptomatic or sub-clinical before diagnosis. [ABSTRACT FROM AUTHOR]

Background: The assessment of TP53 mutational status is becoming a routine clinical practice for chronic lymphocytic leukemia patients (CLL). A broad spectrum of molecular techniques has been employed so far, including both direct Sanger sequencing and next generation sequencing. Oxford Nanopore Technologies recently released the MinION an USB-interfaced sequencer. In this paper we report our experience, with the MinION technology for the detection of the TP53 gene mutation in CLL patients. Twelve CLL patients at diagnosis were included in this study. All except one patient showed the TP53 gene deletion in Fluorescence in situ hybridization experiments. Patients were investigated for TP53 mutation by Sanger and by MinION sequencing. Analysis by Sanger was performed according with the IARC protocol. Analysis by MinION was performed adopting a strategy based on long template PCR, read error correction, and post variant calling filtering. Results: Due to the high error rate of nanopore technology, sequence data were both used directly and before correction with two different in silico methods: ALEC and nanocorrect. A mean error rate of 15 % was detected before correction that was reduced to 4-5 % after correction. Analysis by Sanger sequencing was able to detect four patients mutated for TP53. MinION analysis detected one more mutated patient previously not detected from Sanger. Conclusion: In our hands, the Nanopore technology shows correlation with Sanger sequencing but more sensitive, manageable and less expensive, and therefore has proven to be a useful tool for TP53 gene mutation detection. [ABSTRACT FROM AUTHOR]

Background: Somatic Hypermutation (SHM) refers to the introduction of mutations within rearranged V(D)J genes, a process that increases the diversity of Immunoglobulins (IGs). The analysis of SHM has offered critical insight into the physiology and pathology of B cells, leading to strong prognostication markers for clinical outcome in chronic lymphocytic leukaemia (CLL), the most frequent adult B-cell malignancy. In this paper we present a methodology for integrating multiple immunogenetic and clinocobiological data sources in order to extract features and create high quality datasets for SHM analysis in IG receptors of CLL patients. This dataset is used as the basis for a higher level integration procedure, inspired form social choice theory. This is applied in the Towards Analysis, our attempt to investigate the potential ontogenetic transformation of genes belonging to specific stereotyped CLL subsets towards other genes or gene families, through SHM. Results: The data integration process, followed by feature extraction, resulted in the generation of a dataset containing information about mutations occurring through SHM. The Towards analysis performed on the integrated dataset applying voting techniques, revealed the distinct behaviour of subset #201 compared to other subsets, as regards SHM related movements among gene clans, both in allele-conserved and non-conserved gene areas. With respect to movement between genes, a high percentage movement towards pseudo genes was found in all CLL subsets. Conclusions: This data integration and feature extraction process can set the basis for exploratory analysis or a fully automated computational data mining approach on many as yet unanswered, clinically relevant biological questions. [ABSTRACT FROM AUTHOR]

RIP140 is a transcriptional coregulator, (also known as NRIP1), which finely tunes the activity of various transcription factors and plays very important physiological roles. Noticeably, the RIP140 gene has been implicated in the control of energy expenditure, behavior, cognition, mammary gland development and intestinal homeostasis. RIP140 is also involved in the regulation of various oncogenic signaling pathways and participates in the development and progression of solid tumors. During the past years, several papers have reported evidences linking RIP140 to hematologic malignancies. Among them, two recent studies with correlative data suggested that gene expression signatures including RIP140 can predict survival in chronic lymphocytic leukemia (CLL). This review aims to summarize the literature dealing with the expression of RIP140 in CLL and to explore the potential impact of this factor on transcription pathways which play key roles in this pathology. [ABSTRACT FROM AUTHOR]

Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgV[sub H]) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgV[sub H] status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgV[sub H] mutational status which can accurately predict the survival outcome are yet to be discovered. Results: In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the coexpression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgV[sub H] mutation status from the ZAP70 co-expression network. Conclusions: We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgV[sub H] mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information. [ABSTRACT FROM AUTHOR]