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Genome Biology Studies Consider Lung Cancer-Associated B Cells, Gut Species Population Patterns, More

Researchers from Shanghai Pulmonary Hospital and elsewhere take a look at the main B cell subtypes that interact with non-small cell lung cancer (NSCLC) tumors. Using single-cell transcriptome sequencing, flow cytometry-based cell sorting, immunohistochemistry, and other approaches, the team assessed samples from more than 150 NSCLC cases. In the process, it identified naïve-like B cells in the tumor microenvironment that appeared to be dialed down in advanced NSCLC cases, particularly those with poorer outcomes, along with antibody-producing plasma-like B cells that appeared to curb early-stage lung cancers, while potentially providing a boost to growth in more advanced NSCLC tumors. "The newly established versatile function of B cells … highlighted possible clinical implications of targeting B cell subtypes in the microenvironment of NSCLC," the authors conclude, noting that the single-cell transcriptome and proteomic clues to related antibody and antigen features "provide important resources for future investigation of B cells and other immune/stromal cells in NSCLC."

An international team led by investigators at the University of Trento report on findings from a population genomic analysis of a relatively common human gut microbial species called Eubacterium rectale. By searching for E. rectale sequences in some 6,775 new and publicly available gut metagenomic datasets representing individuals from 30 countries, the researchers put together 1,321 high-quality E. rectale genomes, using these sequences to analyze the sub-species found in individuals from different parts of the world and the relationships between them. For example, their E. rectale population structure analysis "revealed an extreme degree of biogeographic stratification and specificity to the human host," the authors write, adding that the available data "largely supports the hypothesis that the observed stratification is at least in part the consequence of isolation by distance brought about by host-microbe co-dispersal, possibly due to migration movements of early humans."

Finally, a St. Jude Children's Research Hospital-led team describes an algorithm called CICERO for finding fusion-based driver mutations from tumor RNA sequence data. The local assembly-centered approach is designed to pick up the range of complicated fusions that can drive cancer, the researchers note. Based on their proof-of-principle experiments using RNA-seq data from 119 pediatric leukemia cases, dozens of pediatric solid tumor samples, and adult glioblastomas assessed for the Cancer Genome Atlas project suggest the method compares favorably to other driver fusion detection approaches. "CICERO enables detection of diverse types of gene fusions in RNA-seq," the authors write, "greatly improving our ability to discover non-canonical fusions and [internal tandem duplications] which are overlooked by existing fusion detection methods."