Researchers from the University of California, San Diego, Yale University, and elsewhere describe a new epigenetic alteration that appears to contribute to glioblastoma (GBM) primary brain tumors. The team used DIP-sequencing, chromatin immunoprecipitation sequencing, bisulfite sequencing, RNA sequencing, and other approaches to identify an uptick in the presence of the N6-methyladenine modification in patient-derived GBM stem cells and primary tumor samples, and to explore the roots and consequences of this modification. In addition, the study's authors note that they could curb tumor proliferation in patient-derived and mouse models of GBM by targeting ALKBH1, a DNA demethylase enzyme that regulates N6-methyladenine.
A team from China and Korea considers the mutational landscape in secondary GBM tumors that arose from low-grade gliomas (LGG). For that analysis, the researchers did whole-genome sequencing, exome sequencing, and/or RNA sequencing on 188 secondary GBMs, comparing the mutational patterns present in these secondary GBMs with those in primary GBM or LGG tumors. Along with frequent mutations in the TP53 gene, they found that secondary GBM cases were prone to hypermutation and MET deletions or fusions that may respond to targeted MET kinase inhibitor treatment. GenomeWeb has more on the study, here.
Finally, members of the International Multiple Sclerosis Consortium search for rare and low-frequency variants contributing to MS risk using exome array- or custom array-based genotyping data for 32,367 individuals with MS and 36,012 without the condition. Using this approach, the team uncovered seven suspicious low-frequency coding variants falling near half a dozen genes outside of the major histocompatibility complex. The set spanned variants near four genes not associated with MS through prior searches for common risk variants, including PRKRA and NLRP8 genes involved in innate immune functions. "Our heritability modeling demonstrates that more low-frequency and rare variant associations remain to be discovered, though larger sample sizes will be required to increase statistical power," the authors write. GenomeWeb also has more on this, here.