Multiparametric profiling of non-small-cell lung cancers reveals distinct immunophenotypes.

Immune checkpoint blockade improves survival in a subset of patients with non-small-cell lung cancer (NSCLC), but robust biomarkers that predict response to PD-1 pathway inhibitors are lacking. Furthermore, our understanding of the diversity of the NSCLC tumor immune microenvironment remains limited. We performed comprehensive flow cytometric immunoprofiling on both tumor and immune cells from 51 NSCLCs and integrated this analysis with clinical and histopathologic characteristics, next-generation sequencing, mRNA expression, and PD-L1 immunohistochemistry (IHC).

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling.

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins.

Adaptive resistance to therapeutic PD-1 blockade is associated with upregulation of alternative immune checkpoints.

Despite compelling antitumour activity of antibodies targeting the programmed death 1 (PD-1): programmed death ligand 1 (PD-L1) immune checkpoint in lung cancer, resistance to these therapies has increasingly been observed. In this study, to elucidate mechanisms of adaptive resistance, we analyse the tumour immune microenvironment in the context of anti-PD-1 therapy in two fully immunocompetent mouse models of lung adenocarcinoma. In tumours progressing following response to anti-PD-1 therapy, we observe upregulation of alternative immune checkpoints, notably T-cell immunoglobulin mucin-3 (TIM-3), in PD-1 antibody bound T cells and demonstrate a survival advantage with addition of a TIM-3 blocking antibody following failure of PD-1 blockade.

Digital multiplexed gene expression analysis using the NanoString nCounter system.

This unit presents the protocol for the NanoString nCounter Gene Expression Assay, a robust and highly reproducible method for detecting the expression of up to 800 genes in a single reaction with high sensitivity and linearity across a broad range of expression levels. The methodology serves to bridge the gap between genome-wide (microarrays) and targeted (real-time quantitative PCR) expression profiling. The nCounter assay is based on direct digital detection of mRNA molecules of interest using target-specific, color-coded probe pairs.

Evidence of off-target effects associated with long dsRNAs in Drosophila melanogaster cell-based assays.

To evaluate the specificity of long dsRNAs used in high-throughput RNA interference (RNAi) screens performed at the Drosophila RNAi Screening Center (DRSC), we performed a global analysis of their activity in 30 genome-wide screens completed at our facility. Notably, our analysis predicts that dsRNAs containing > or = 19-nucleotide perfect matches identified in silico to unintended targets may contribute to a significant false positive error rate arising from off-target effects. We confirmed experimentally that such sequences in dsRNAs lead to false positives and to efficient knockdown of a cross-hybridizing transcript, raising a cautionary note about interpreting results based on the use of a single dsRNA per gene.

Super-size flies.

The increasing prevalence of obesity and other nutrition-related chronic diseases has prompted considerable efforts to understand their pathogenesis and treatment. One experimental approach is to overexpress, inactivate, or manipulate specific genes that regulate energy metabolism and fat storage. Many such techniques are fully established, routine tools in Drosophila and C.

cis-regulatory logic of short-range transcriptional repression in Drosophila melanogaster.

Bioinformatics analysis of transcriptional control is guided by knowledge of the characteristics of cis-regulatory regions or enhancers. Features such as clustering of binding sites and co-occurrence of binding sites have aided enhancer identification, but quantitative predictions of enhancer function are not yet generally feasible. To facilitate the analysis of regulatory sequences in Drosophila melanogaster, we identified quantitative parameters that affect the activity of short-range transcriptional repressors, proteins that play key roles in development.

Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards?

In higher eukaryotes, transcriptional enhancers play critical roles in the integration of cellular signaling information, but apart from a few well-studied model enhancers, we lack a general picture of transcriptional information processing by most enhancers. Here we discuss recent studies that have provided fresh insights on information processing that occurs on enhancers, and propose that in addition to the highly cooperative and coordinate action of "enhanceosomes", a less integrative, but more flexible form of information processing is mediated by information display or "billboard" enhancers. Application of these models has important ramifications not only for the biochemical analysis of transcription, but also for the wider fields of bioinformatics and evolutionary biology.

Information display by transcriptional enhancers.

Transcriptional enhancers integrate positional and temporal information to regulate the complex expression of developmentally controlled genes. Current models suggest that enhancers act as computational devices, receiving multiple inputs from activators and repressors and resolving them into a single positive or a negative signal that is transmitted to the basal transcriptional machinery. We show that a simple, compact enhancer is capable of representing both repressed and activated states at the same time and in the same nucleus.