Clustering of Expression Data in Chronic Lymphocytic Leukemia Reveals New Molecular Subdivisions

B cell receptor signaling proteins as biomarkers for progression of CLL requiring first-line therapy

The molecular landscape of chronic lymphocytic leukemia (CLL) has been extensively characterized, and various potent prognostic biomarkers were discovered. The genetic composition of the B-cell receptor (BCR) immunoglobulin (IG) was shown to be especially powerful for discerning indolent from aggressive disease at diagnosis. Classification based on the IG heavy chain variable gene (IGHV) somatic hypermutation status is routinely applied. Additionally, BCR IGH stereotypy has been implicated to improve risk stratification, through characterization of subsets with consistent clinical profiles. Despite these advances, it remains challenging to predict when CLL progresses to requiring first-line therapy, thus emphasizing the need for further refinement of prognostic indicators. Signaling pathways downstream of the BCR are essential in CLL pathogenesis, and dysregulated components within these pathways impact disease progression. Considering not only genomics but the entirety of factors shaping BCR signaling activity, this review offers insights in the disease for better prognostic assessment of CLL.

Novel genes exhibiting DNA methylation alterations in Korean patients with chronic lymphocytic leukaemia: a methyl-CpG-binding domain sequencing study

Chronic lymphocytic leukaemia (CLL) exhibits differences between Asians and Caucasians in terms of incidence rate, age at onset, immunophenotype, and genetic profile. We performed genome-wide methylation profiling of CLL in an Asian cohort for the first time. Eight Korean patients without somatic immunoglobulin heavy chain gene hypermutations underwent methyl-CpG-binding domain sequencing (MBD-seq), as did five control subjects. Gene Ontology, pathway analysis, and network-based prioritization of differentially methylated genes were also performed. More regions were hypomethylated (2,062 windows) than were hypermethylated (777 windows). Promoters contained the highest proportion of differentially methylated regions (0.08%), while distal intergenic and intron regions contained the largest number of differentially methylated regions. Protein-coding genes were the most abundant, followed by long noncoding and short noncoding genes. The most significantly over-represented signalling pathways in the differentially methylated gene list included immune/cancer-related pathways and B-cell receptor signalling. Among the top 10 hub genes identified via network-based prioritization, four (UBC, GRB2, CREBBP, and GAB2) had no known relevance to CLL, while the other six (STAT3, PTPN6, SYK, STAT5B, XPO1, and ABL1) have previously been linked to CLL in Caucasians. As such, our analysis identified four novel candidate genes of potential significance to Asian patients with CLL.

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.

Altered expression of metabolic pathways in CLL detected by unlabelled quantitative mass spectrometry analysis

Chronic lymphocytic leukaemia (CLL) remains the most common incurable malignancy of B cells in the western world. Patient outcomes are heterogeneous and can be difficult to predict with current prognostic markers. Here, we used a quantitative label-free proteomic technique to ascertain differences in the B-cell proteome from healthy donors and CLL patients with either mutated (M-CLL) or unmutated (UM-CLL) IGHV to identify new prognostic markers. In peripheral B-CLL cells, 349 (22%) proteins were differentially expressed between normal B cells and B-CLL cells and 189 (12%) were differentially expressed between M-CLL and UM-CLL. We also examined the proteome of proliferating CLL cells in the lymph nodes, and identified 76 (~8%) differentially expressed proteins between healthy and CLL lymph nodes. B-CLL cells show over-expression of proteins involved in lipid and cholesterol metabolism. A comprehensive lipidomic analysis highlighted large differences in glycolipids and sphingolipids. A shift was observed from the pro-apoptotic lipid ceramide towards the anti-apoptotic/chemoresistant lipid, glucosylceramide, which was more evident in patients with aggressive disease (UM-CLL). This study details a novel quantitative proteomic technique applied for the first time to primary patient samples in CLL and highlights that primary CLL lymphocytes display markers of a metabolic shift towards lipid synthesis and breakdown.

Patient similarity for precision medicine: A systematic review

Evidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research.

Unveiling differentially expressed genes upon regulation of transcription factors in sepsis

In this study, we integrated the gene expression data of sepsis to reveal more precise genome-wide expression signature to shed light on the pathological mechanism of sepsis. Differentially expressed genes via integrating five microarray datasets from the Gene Expression Omnibus database were obtained. The gene function and involved pathways of differentially expressed genes (DEGs) were detected by GeneCodis3. Transcription factors (TFs) targeting top 20 dysregulated DEGs (including up- and downregulated genes) were found based on the TRANSFAC. A total of 1339 DEGs were detected including 788 upregulated and 551 downregulated genes. These genes were mostly involved in DNA-dependent transcription regulation, blood coagulation, and innate immune response, pathogenic escherichia coli infection, epithelial cell signaling in helicobacter pylori infection, and chemokine signaling pathway. TFs bioinformatic analysis of 20 DEGs generated 374 pairs of TF-target gene involving 47 TFs. At last, we found that five top ten upregulated DEGs (S100A8, S100A9, S100A12, PGLYRP1 and MMP9) and three downregulated DEGs (ZNF84, CYB561A3 and BST1) were under the regulation of three hub TFs of Pax-4, POU2F1, and Nkx2-5. The identified eight DEGs may be regarded as the diagnosis marker and drug target for sepsis.