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. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel 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. We have previously reported the in vitro differentiation of MDSC from immortalized mouse hematopoietic progenitors and characterized the impact of retinoic acid and 3-deazaneplanocin A on MDSC development and function. We describe here the effect of these compounds on MDSC transcriptome and identify genes and pathway affected by the treatment. In order to accelerate the investigation of gene function in MDSC suppressive activity, we developed protocols for CRISPR/Cas9-mediated gene editing in MDSC. Through screening of 217 genes, we found that autocrine secretion of TNF-α contributes to MDSC immunosuppressive activity through up-regulation of Nos2. The approach described here affords the investigation of gene function in myeloid cells such as MDSC with unprecedented ease and throughput.

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. The GSE system is built upon a middle software layer for representing streams of biological data; we outline this layer, called GSEBricks, and show how it is used to build an interactive visualization application for ChIP-chip data. The visualizer software is written in Java and communicates with the GSE database system over the network. We also present a system to formulate and record binding hypotheses--simple descriptions of the relationships that may hold between different ChIP-chip experiments. We provide a reference software implementation for the GSE system.

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. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

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. In contrast, the histone deacetylases Hst1 and Rpd3 are recruited to specific sets of genes associated with distinct cellular functions. Our results provide new insights into the association of histone acetyltransferases and histone deacetylases with the yeast genome, and together with previous studies, suggest how these chromatin regulators are recruited to specific regions of the genome.