Researchers from the US and Korea consider within-tumor heterogeneity uncovered with transcriptional and genetic clues. The team used a combination of single-cell RNA sequencing and a computational method known as HoneyBADGER to quantify copy number variants and loss-of-heterozygosity in individual tumor cells from progressing cancers. "By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct underlying subclonal architecture," the authors note. For their proof-of-principle experiments, they applied this strategy to 44 single cells from serial samples collected from one individual with progressive multiple myeloma, identifying both genetic and transcriptional subpopulations in the tumor.
A team from the University of Illinois at Urbana-Champaign and the Mayo Clinic explore gene regulatory mechanisms mediating phenotypic differences that occur between individuals when it comes to features such as drug sensitivity or cancer subtype susceptibility. With the help of pGENMi — a computational model for teasing out the regulatory features behind a given phenotype based on genotyping, methylation, expression, and transcription factor binding profiles — the researchers narrowed in on interactions between transcription factors and two dozen drug compounds in lymphoblastoid cell lines, for example. "The new method is also applicable to other studies where one seeks mechanistic factors underlying individual variation in a quantitative phenotype in the presence of genotype and epigenotype information," they write.
Members of the Brazilian EPIGEN Consortium describe imputation and workflow resources developed to support EPIGEN-Brazil, a Latin American project focused on genomics, computational biology, and precision medicine. On the imputation side, the researchers introduced the EPIGEN-5M+1KGP panel for bringing together EPIGEN-5M haplotype insights gleaned from SNP profiling on hundreds of admixed individuals with imputation information from phase 3 of the 1000 Genomes Project — a resource they used to impute variants in nearly 6,500 more admixed EPIGEN-Brazil participants. With a new scientific workflow, meanwhile, the team is bringing together related publications, flowcharts, bioinformatics tools, and the like.