A First-in-Human Phase I Study to Evaluate the ERK1/2 Inhibitor GDC-0994 in Patients with Advanced Solid Tumors.

PURPOSE: ERK1/2 signaling can be dysregulated in cancer. GDC-0994 is an oral inhibitor of ERK1/2. A first-in-human, phase I dose escalation study of GDC-0994 was conducted in patients with locally advanced or metastatic solid tumors. PATIENTS AND METHODS: GDC-0994 was administered once daily on a 21-day on/7-day off schedule to evaluate safety, pharmacokinetics, and preliminary signs of efficacy. Patients with pancreatic adenocarcinoma and BRAF-mutant colorectal cancer were enrolled in the expansion stage. RESULTS: Forty-seven patients were enrolled in six successive cohorts (50-800 mg). A single DLT of grade 3 rash occurred at 600 mg. The most common drug-related adverse events (AE) were diarrhea, rash, nausea, fatigue, and vomiting. Pharmacokinetic data showed dose-proportional increases in exposure, with a mean half-life of 23 hours, supportive of once daily dosing. In evaluable paired biopsies, MAPK pathway inhibition ranged from 19% to 51%. Partial metabolic responses by FDG-PET were observed in 11 of 20 patients across dose levels in multiple tumor types. Overall, 15 of 45 (33%) patients had a best overall response of stable disease and 2 patients with BRAF-mutant colorectal cancer had a confirmed partial response. CONCLUSIONS: GDC-0994 had an acceptable safety profile and pharmacodynamic effects were observed by FDG-PET and in serial tumor biopsies. Single-agent activity was observed in 2 patients with BRAF-mutant colorectal cancer.

Correction: First-in-human phase 1 study of the BTK inhibitor GDC-0853 in relapsed or refractory B-cell NHL and CLL.

[This corrects the article DOI: 10.18632/oncotarget.24310.].

Analysis and correction of compositional bias in sparse sequencing count data.

BACKGROUND: Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size. RESULTS: We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it. CONCLUSIONS: Compositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.

The Splicing Factor RNA-Binding Fox Protein 1 Mediates the Cellular Immune Response in Drosophila melanogaster.

The uptake and destruction of bacteria by phagocytic cells is an essential defense mechanism in metazoans. To identify novel genes involved in the phagocytosis of Staphylococcus aureus, a major human pathogen, we assessed the phagocytic capacity of adult blood cells (hemocytes) of the fruit fly, Drosophila melanogaster, by testing several lines of the Drosophila Genetic Reference Panel. Natural genetic variation in the gene RNA-binding Fox protein 1 (Rbfox1) correlated with low phagocytic capacity in hemocytes, pointing to Rbfox1 as a candidate regulator of phagocytosis. Loss of Rbfox1 resulted in increased expression of the Ig superfamily member Down syndrome adhesion molecule 4 (Dscam4). Silencing of Dscam4 in Rbfox1-depleted blood cells rescued the fly's cellular immune response to S. aureus, indicating that downregulation of Dscam4 by Rbfox1 is critical for S. aureus phagocytosis in Drosophila To our knowledge, this study is the first to demonstrate a link between Rbfox1, Dscam4, and host defense against S. aureus.

A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types.

KRAS- and BRAF-mutant tumors are often dependent on MAPK signaling for proliferation and survival and thus sensitive to MAPK pathway inhibitors. However, clinical studies have shown that MEK inhibitors are not uniformly effective in these cancers indicating that mutational status of these oncogenes does not accurately capture MAPK pathway activity. A number of transcripts are regulated by this pathway and are recurrently identified in genome-based MAPK transcriptional signatures. To test whether the transcriptional output of only 10 of these targets could quantify MAPK pathway activity with potential predictive or prognostic clinical utility, we created a MAPK Pathway Activity Score (MPAS) derived from aggregated gene expression. In vitro, MPAS predicted sensitivity to MAPK inhibitors in multiple cell lines, comparable to or better than larger genome-based statistical models. Bridging in vitro studies and clinical samples, median MPAS from a given tumor type correlated with cobimetinib (MEK inhibitor) sensitivity of cancer cell lines originating from the same tissue type. Retrospective analyses of clinical datasets showed that MPAS was associated with the sensitivity of melanomas to vemurafenib (HR: 0.596) and negatively prognostic of overall or progression-free survival in both adjuvant and metastatic CRC (HR: 1.5 and 1.4), adrenal cancer (HR: 1.7), and HER2+ breast cancer (HR: 1.6). MPAS thus demonstrates potential clinical utility that warrants further exploration.

First-in-human phase 1 study of the BTK inhibitor GDC-0853 in relapsed or refractory B-cell NHL and CLL.

GDC-0853 is a selective, reversible, and non-covalent inhibitor of Bruton's tyrosine kinase (BTK) that does not require interaction with the Cys481 residue for activity. In this first-in-human phase 1 study we evaluated safety, tolerability, pharmacokinetics, and activity of GDC-0853 in patients with relapsed or refractory non-Hodgkin lymphoma (NHL) or chronic lymphocytic leukemia (CLL). Twenty-four patients, enrolled into 3 cohorts, including 6 patients who were positive for the C481S mutation, received GDC-0853 at 100, 200, or 400 mg once daily, orally. There were no dose limiting toxicities. GDC-0853 was well tolerated and the maximum tolerated dose (MTD) was not reached due to premature study closure. Common adverse events (AEs) in ≥ 15% of patients regardless of causality included fatigue (37%), nausea (33%), diarrhea (29%), thrombocytopenia (25%), headache (20%), and abdominal pain, cough, and dizziness (16%, each). Nine serious AEs were reported in 5 patients of whom 2 had fatal outcomes (confirmed H1N1 influenza and influenza pneumonia). A third death was due to progressive disease. Eight of 24 patients responded to GDC-0853: 1 complete response, 4 partial responses, and 3 partial responses with lymphocytosis, including 1 patient with the C481S mutation. Two additional C481S mutation patients had a decrease in size of target tumors (-23% and -44%). These data demonstrate GDC-0853 was generally well-tolerated with antitumor activity.

Smooth quantile normalization.

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

DNA Methylation Patterns in Cord Blood of Neonates Across Gestational Age: Association With Cell-Type Proportions.

BACKGROUND: A statistical methodology is available to estimate the proportion of cell types (cellular heterogeneity) in adult whole blood specimens used in epigenome-wide association studies (EWAS). However, there is no methodology to estimate the proportion of cell types in umbilical cord blood (also a heterogeneous tissue) used in EWAS. OBJECTIVES: The objectives of this study were to determine whether differences in DNA methylation (DNAm) patterns in umbilical cord blood are the result of blood cell type proportion changes that typically occur across gestational age and to demonstrate the effect of cell type proportion confounding by comparing preterm infants exposed and not exposed to antenatal steroids. METHODS: We obtained DNAm profiles of cord blood using the Illumina HumanMethylation27k BeadChip array for 385 neonates from the Boston Birth Cohort. We estimated cell type proportions for six cell types using the deconvolution method developed by . RESULTS: The cell type proportion estimates segregated into two groups that were significantly different by gestational age, indicating that gestational age was associated with cell type proportion. Among infants exposed to antenatal steroids, the number of differentially methylated CpGs dropped from 127 to 1 after controlling for cell type proportion. DISCUSSION: EWAS utilizing cord blood are confounded by cell type proportion. Careful study design including correction for cell type proportion and interpretation of results of EWAS using cord blood are critical.

BatchQC: interactive software for evaluating sample and batch effects in genomic data.

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. There are several existing batch adjustment tools for '-omics' data, but they do not indicate a priori whether adjustment needs to be conducted or how correction should be applied. We present a software pipeline, BatchQC, which addresses these issues using interactive visualizations and statistics that evaluate the impact of batch effects in a genomic dataset. BatchQC can also apply existing adjustment tools and allow users to evaluate their benefits interactively. We used the BatchQC pipeline on both simulated and real data to demonstrate the effectiveness of this software toolkit. AVAILABILITY AND IMPLEMENTATION: BatchQC is available through Bioconductor: http://bioconductor.org/packages/BatchQC and GitHub: https://github.com/mani2012/BatchQC CONTACT: wej@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Transcriptome Remodeling in Trypanosoma cruzi and Human Cells during Intracellular Infection.

Intracellular colonization and persistent infection by the kinetoplastid protozoan parasite, Trypanosoma cruzi, underlie the pathogenesis of human Chagas disease. To obtain global insights into the T. cruzi infective process, transcriptome dynamics were simultaneously captured in the parasite and host cells in an infection time course of human fibroblasts. Extensive remodeling of the T. cruzi transcriptome was observed during the early establishment of intracellular infection, coincident with a major developmental transition in the parasite. Contrasting this early response, few additional changes in steady state mRNA levels were detected once mature T. cruzi amastigotes were formed. Our findings suggest that transcriptome remodeling is required to establish a modified template to guide developmental transitions in the parasite, whereas homeostatic functions are regulated independently of transcriptomic changes, similar to that reported in related trypanosomatids. Despite complex mechanisms for regulation of phenotypic expression in T. cruzi, transcriptomic signatures derived from distinct developmental stages mirror known or projected characteristics of T. cruzi biology. Focusing on energy metabolism, we were able to validate predictions forecast in the mRNA expression profiles. We demonstrate measurable differences in the bioenergetic properties of the different mammalian-infective stages of T. cruzi and present additional findings that underscore the importance of mitochondrial electron transport in T. cruzi amastigote growth and survival. Consequences of T. cruzi colonization for the host include dynamic expression of immune response genes and cell cycle regulators with upregulation of host cholesterol and lipid synthesis pathways, which may serve to fuel intracellular T. cruzi growth. Thus, in addition to the biological inferences gained from gene ontology and functional enrichment analysis of differentially expressed genes in parasite and host, our comprehensive, high resolution transcriptomic dataset provides a substantially more detailed interpretation of T. cruzi infection biology and offers a basis for future drug and vaccine discovery efforts.

Simultaneous transcriptional profiling of Leishmania major and its murine macrophage host cell reveals insights into host-pathogen interactions.

BACKGROUND: Parasites of the genus Leishmania are the causative agents of leishmaniasis, a group of diseases that range in manifestations from skin lesions to fatal visceral disease. The life cycle of Leishmania parasites is split between its insect vector and its mammalian host, where it resides primarily inside of macrophages. Once intracellular, Leishmania parasites must evade or deactivate the host's innate and adaptive immune responses in order to survive and replicate. RESULTS: We performed transcriptome profiling using RNA-seq to simultaneously identify global changes in murine macrophage and L. major gene expression as the parasite entered and persisted within murine macrophages during the first 72 h of an infection. Differential gene expression, pathway, and gene ontology analyses enabled us to identify modulations in host and parasite responses during an infection. The most substantial and dynamic gene expression responses by both macrophage and parasite were observed during early infection. Murine genes related to both pro- and anti-inflammatory immune responses and glycolysis were substantially upregulated and genes related to lipid metabolism, biogenesis, and Fc gamma receptor-mediated phagocytosis were downregulated. Upregulated parasite genes included those aimed at mitigating the effects of an oxidative response by the host immune system while downregulated genes were related to translation, cell signaling, fatty acid biosynthesis, and flagellum structure. CONCLUSIONS: The gene expression patterns identified in this work yield signatures that characterize multiple developmental stages of L. major parasites and the coordinated response of Leishmania-infected macrophages in the real-time setting of a dual biological system. This comprehensive dataset offers a clearer and more sensitive picture of the interplay between host and parasite during intracellular infection, providing additional insights into how pathogens are able to evade host defenses and modulate the biological functions of the cell in order to survive in the mammalian environment.

Transcriptomic profiling of gene expression and RNA processing during Leishmania major differentiation.

Protozoan parasites of the genus Leishmania are the etiological agents of leishmaniasis, a group of diseases with a worldwide incidence of 0.9-1.6 million cases per year. We used RNA-seq to conduct a high-resolution transcriptomic analysis of the global changes in gene expression and RNA processing events that occur as L. major transforms from non-infective procyclic promastigotes to infective metacyclic promastigotes. Careful statistical analysis across multiple biological replicates and the removal of batch effects provided a high quality framework for comprehensively analyzing differential gene expression and transcriptome remodeling in this pathogen as it acquires its infectivity. We also identified precise 5' and 3' UTR boundaries for a majority of Leishmania genes and detected widespread alternative trans-splicing and polyadenylation. An investigation of possible correlations between stage-specific preferential trans-splicing or polyadenylation sites and differentially expressed genes revealed a lack of systematic association, establishing that differences in expression levels cannot be attributed to stage-regulated alternative RNA processing. Our findings build on and improve existing expression datasets and provide a substantially more detailed view of L. major biology that will inform the field and potentially provide a stronger basis for drug discovery and vaccine development efforts.

Shape analysis of high-throughput transcriptomics experiment data.

The recent growth of high-throughput transcriptome technology has been paralleled by the development of statistical methodologies to analyze the data they produce. Some of these newly developed methods are based on the assumption that the data observed or a transformation of the data are relatively symmetric with light tails, usually summarized by assuming a Gaussian random component. It is indeed very difficult to assess this assumption for small sample sizes. In this article, we utilize L-moments statistics as the basis of exploratory data analysis, the assessment of distributional assumptions, and the hypothesis testing of high-throughput transcriptomic data. In particular, we use L-moments ratios for assessing the shape (skewness and kurtosis) of high-throughput transcriptome data. Based on these statistics, we propose an algorithm for identifying genes with distributions that are markedly different from the majority in the data. In addition, we also illustrate the utility of this framework to characterize the robustness of distributional assumptions. We apply it to RNA-seq data and find that methods based on the simple [Formula: see text]-test for differential expression analysis using L-moments as weights are robust.