Investigators at the University of Pennsylvania and the Children's Hospital of Philadelphia search for cross-cancer transcriptome signatures using machine learning methods. By training deep learning models with RNA sequencing data from nearly 13,500 samples spanning 18 solid tumor types and 19 normal tissues, the team uncovered gene expression, transcript splicing, long noncoding RNA, and other transcriptome features that differed between cancer and normal samples. "Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints," the authors report, adding that "we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer."
An Indiana University School of Medicine-led team describes molecular subtype and tumor microenvironment patterns detected with single-cell transcriptome analyses of colorectal cancer (CRC). Using droplet-based single-cell RNA sequencing, the team profiled more than 487,800 individual cells from 16 yet-to-be-treated CRC cases in individuals from a range of ancestry groups and compared the patterns to those present in seven normal colon tissue. Along with heterogeneity within tumors from known molecular subtypes and potentially prognostic tumor microenvironment features, the authors found that the CRC expression patterns "exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics."
Researchers from the Baylor College of Medicine, University of California, Riverside, and Columbia University present a methylome profiling approach called NT-seq, designed for detecting three distinct DNA methylation types that can occur in prokaryotic organisms — an approach they used to assess multiple methylation types in a commercial microbial community and in the genomes of Escherichia coli and Helicobacter pylori bacteria. "We developed a method (NT-seq) to simultaneously map all three major types of DNA methylation in prokaryotic genomes," the researchers write, noting that the method "allows accurate detection of methylation motifs in both single species and microbial community samples."