Researchers from Rutgers Cancer Institute of New Jersey present findings from a pan-cancer analysis focused on finding the sources of gene expression changes across thousands of tumor samples. Using data from nearly 3,900 tumor samples representing 10 cancer types, the team searched for ties between gene expression, copy number, transcription factor activity, and microRNA patterns. The analyses indicated that such factors explain just a fraction of the expression variation detected in cancer, prompting a more detailed search for new somatic, regulatory mutations using whole-genome sequence data for a subset of the tumor samples. The integrative approach led to "candidates with large unexplained variation in gene expression after accounting for the effects of different modes of regulation," the authors write, arguing that their approach "presents a rational strategy to prioritize targets for an investigation into potential regulatory alterations."
A University of Southern California and University of California at Riverside team describes a single-cell RNA sequencing analysis method designed to dial down expression quantification biases and boost accuracy. The "data-adaptive" approach, known as "bias-corrected sequencing analysis" (BCseq), brings together an existing statistical model for teasing out technical bias with a modeling method for sharing data from one individual cell to the next. "Cells with higher sequencing depths contribute more to the quantification non-linearly," the authors explain, noting that BCseq compared favorably to other methods available for analyzing single-cell RNA sequencing data.
Australian researchers report on findings from a DNA methylation and mitochondrial DNA copy number analysis of glioblastoma multiforme. Following from past studies pointing to incomplete mtDNA copy number expansion in GBM and some other cancer types, the team used methylated DNA immunoprecipitation sequencing, RNA sequencing, and real-time PCR arrays to profile a GBM tumor cell line grown in the presence or absence of DNA demethylating compounds, tracking the epigenetic and mtDNA consequences of such treatment. In the process, the authors report, they "identified key methylated regions modulated by the DNA methylation agents that also induced synchronous changes to mtDNA copy number and nuclear gene expression."