Differential substrate use in EGF- and oncogenic KRAS-stimulated human mammary epithelial cells

Many metabolic phenotypes in cancer cells are also characteristic of proliferating nontransformed mammalian cells, and attempts to distinguish between phenotypes resulting from oncogenic perturbation from those associated with increased proliferation are limited. Here, we examined the extent to which metabolic changes corresponding to oncogenic KRAS expression differed from those corresponding to epidermal growth factor (EGF)-driven proliferation in human mammary epithelial cells (HMECs). Removal of EGF from culture medium reduced growth rates and glucose/glutamine consumption in control HMECs despite limited changes in respiration and fatty acid synthesis, while the relative contribution of branched-chain amino acids to the TCA cycle and lipogenesis increased in the near-quiescent conditions. Most metabolic phenotypes measured in HMECs expressing mutant KRAS were similar to those observed in EGF-stimulated control HMECs that were growing at comparable rates. However, glucose and glutamine consumption as well as lactate and glutamate production were lower in KRAS-expressing cells cultured in media without added EGF, and these changes correlated with reduced sensitivity to GLUT1 inhibitor and phenformin treatment. Our results demonstrate the strong dependence of metabolic behavior on growth rate and provide a model to distinguish the metabolic influences of oncogenic mutations and nononcogenic growth.

CDK4/6 inhibition reprograms the breast cancer enhancer landscape by stimulating AP-1 transcriptional activity

Pharmacologic inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6) were designed to induce cancer cell cycle arrest. Recent studies have suggested that these agents also exert other effects, influencing cancer cell immunogenicity, apoptotic responses, and differentiation. Using cell-based and mouse models of breast cancer together with clinical specimens, we show that CDK4/6 inhibitors induce remodeling of cancer cell chromatin characterized by widespread enhancer activation, and that this explains many of these effects. The newly activated enhancers include classical super-enhancers that drive luminal differentiation and apoptotic evasion, as well as a set of enhancers overlying endogenous retroviral elements that is enriched for proximity to interferon-driven genes. Mechanistically, CDK4/6 inhibition increases the level of several Activator Protein-1 (AP-1) transcription factor proteins, which are in turn implicated in the activity of many of the new enhancers. Our findings offer insights into CDK4/6 pathway biology and should inform the future development of CDK4/6 inhibitors.

High-throughput identification of RNA nuclear enrichment sequences

In the post‐genomic era, thousands of putative noncoding regulatory regions have been identified, such as enhancers, promoters, long noncoding RNAs (lncRNAs), and a cadre of small peptides. These ever‐growing catalogs require high‐throughput assays to test their functionality at scale. Massively parallel reporter assays have greatly enhanced the understanding of noncoding DNA elements en masse. Here, we present a massively parallel RNA assay (MPRNA) that can assay 10,000 or more RNA segments for RNA‐based functionality. We applied MPRNA to identify RNA‐based nuclear localization domains harbored in lncRNAs. We examined a pool of 11,969 oligos densely tiling 38 human lncRNAs that were fused to a cytosolic transcript. After cell fractionation and barcode sequencing, we identified 109 unique RNA regions that significantly enriched this cytosolic transcript in the nucleus including a cytosine‐rich motif. These nuclear enrichment sequences are highly conserved and over‐represented in global nuclear fractionation sequencing. Importantly, many of these regions were independently validated by single‐molecule RNA fluorescence in situ hybridization. Overall, we demonstrate the utility of MPRNA for future investigation of RNA‐based functionalities.

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.

MADGiC: a model-based approach for identifying driver genes in cancer

Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of cancer research that can provide new insights into gene function as well as new targets for drug development. Most methods for prioritizing mutations rely primarily on frequency-based criteria, where a gene is identified as having a driver mutation if it is altered in significantly more samples than expected according to a background model. Although useful, frequency-based methods are limited in that all mutations are treated equally. It is well known, however, that some mutations have no functional consequence, while others may have a major deleterious impact. The spatial pattern of mutations within a gene provides further insight into their functional consequence. Properly accounting for these factors improves both the power and accuracy of inference. Also important is an accurate background model.

Results: Here, we develop a Model-based Approach for identifying Driver Genes in Cancer (termed MADGiC) that incorporates both frequency and functional impact criteria and accommodates a number of factors to improve the background model. Simulation studies demonstrate advantages of the approach, including a substantial increase in power over competing methods. Further advantages are illustrated in an analysis of ovarian and lung cancer data from The Cancer Genome Atlas (TCGA) project.

Availability and implementation: R code to implement this method is available at http://www.biostat.wisc.edu/ kendzior/MADGiC/.

Contact: kendzior@biostat.wisc.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Methods for collapsing multiple rare variants in whole-genome sequence data

Genetic Analysis Workshop 18 provided whole-genome sequence data in a pedigree-based sample and longitudinal phenotype data for hypertension and related traits, presenting an excellent opportunity for evaluating analysis choices. We summarize the nine contributions to the working group on collapsing methods, which evaluated various approaches for the analysis of multiple rare variants. One contributor defined a variant prioritization scheme, whereas the remaining eight contributors evaluated statistical methods for association analysis. Six contributors chose the gene as the genomic region for collapsing variants, whereas three contributors chose nonoverlapping sliding windows across the entire genome. Statistical methods spanned most of the published methods, including well-established burden tests, variance-components-type tests, and recently developed hybrid approaches. Lesser known methods, such as functional principal components analysis, higher criticism, and homozygosity association, and some newly introduced methods were also used. We found that performance of these methods depended on the characteristics of the genomic region, such as effect size and direction of variants under consideration. Except for MAP4 and FLT3, the performance of all statistical methods to identify rare casual variants was disappointingly poor, providing overall power almost identical to the type I error. This poor performance may have arisen from a combination of (1) small sample size, (2) small effects of most of the causal variants, explaining a small fraction of variance, (3) use of incomplete annotation information, and (4) linkage disequilibrium between causal variants in a gene and noncausal variants in nearby genes. Our findings demonstrate challenges in analyzing rare variants identified from sequence data.