Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies.

Genetic screen in myeloid cells identifies TNF-α autocrine secretion as a factor increasing MDSC suppressive activity via Nos2 up-regulation.

The suppressive microenvironment of tumors remains one of the limiting factors for immunotherapies. In tumors, the function of effector T cells can be inhibited by cancer cells as well as myeloid cells including tumor associated macrophages and myeloid-derived suppressor cells (MDSC). A better understanding of how myeloid cells inhibit T cell function will guide the design of therapeutic strategies to increase anti-tumor responses.

GSE: a comprehensive database system for the representation, retrieval, and analysis of microarray data.

We present GSE, the Genomic Spatial Event database, a system to store, retrieve, and analyze all types of high-throughput microarray data. GSE handles expression datasets, ChIP-chip data, genomic annotations, functional annotations, the results of our previously published Joint Binding Deconvolution algorithm for ChIP-chip, and precomputed scans for binding events. GSE can manage data associated with multiple species; it can also simultaneously handle data associated with multiple 'builds' of the genome from a single species.

High-resolution computational models of genome binding events.

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information.

Genome-wide map of nucleosome acetylation and methylation in yeast.

Eukaryotic genomes are packaged into nucleosomes whose position and chemical modification state can profoundly influence regulation of gene expression. We profiled nucleosome modifications across the yeast genome using chromatin immunoprecipitation coupled with DNA microarrays to produce high-resolution genome-wide maps of histone acetylation and methylation. These maps take into account changes in nucleosome occupancy at actively transcribed genes and, in doing so, revise previous assessments of the modifications associated with gene expression.

Global position and recruitment of HATs and HDACs in the yeast genome.

Chromatin regulators play fundamental roles in the regulation of gene expression and chromosome maintenance, but the regions of the genome where most of these regulators function has not been established. We explored the genome-wide occupancy of four different chromatin regulators encoded in Saccharomyces cerevisiae. The results reveal that the histone acetyltransferases Gcn5 and Esa1 are both generally recruited to the promoters of active protein-coding genes.

Transcriptional regulatory code of a eukaryotic genome.

DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species.