Focal structural variants revealed by whole genome sequencing disrupt the histone demethylase KDM4C in B-cell lymphomas

Targeting N-Methyl-lysine Histone Demethylase KDM4 in Cancer: Natural Products Inhibitors as a Driving Force for Epigenetic Drug Discovery

KDM4A-F enzymes are a subfamily of histone demethylases containing the Jumonji C domain (JmjC) using Fe(II) and 2-oxoglutarate for their catalytic function. Overexpression or deregulation of KDM4 enzymes is associated with various cancers, altering chromatin structure and causing transcriptional dysfunction. As KDM4 enzymes have been associated with malignancy, they may represent novel targets for developing innovative therapeutic tools to treat different solid and blood tumors. KDM4A is the isozyme most frequently associated with aggressive phenotypes of these tumors. To this aim, industrial and academic medicinal chemistry efforts have identified different KDM4 inhibitors. Industrial and academic efforts in medicinal chemistry have identified numerous KDM4 inhibitors, primarily pan-KDM4 inhibitors, though they often lack selectivity against other Jumonji family members. The pharmacophoric features of the inhibitors frequently include a chelating group capable of coordinating the catalytic iron within the active site of the KDM4 enzyme. Nonetheless, non-chelating compounds have also demonstrated promising inhibitory activity, suggesting potential flexibility in the drug design. Several natural products, containing monovalent or bivalent chelators, have been identified as KDM4 inhibitors, albeit with a micromolar inhibition potency. This highlights the potential for leveraging them as templates for the design and synthesis of new derivatives, exploiting nature's chemical diversity to pursue more potent and selective KDM4 inhibitors.

Navigating Illumina DNA methylation data: biology versus technical artefacts

Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains challenging within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score) and consistently methylated CpGs (CM-Score) that rely on biological features rather than internal array controls. These scores, designed to be adjustable for different analysis tools and sample cohort characteristics, were explored and benchmarked across independent cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost reproducibility of the Illumina-like normalization algorithm. Overall, our study delivers valuable insights, practical recommendations and R functions designed to enhance reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.