Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

Autocrine signaling can explain the emergence of Allee effects in cancer cell populations

In many human cancers, the rate of cell growth depends crucially on the size of the tumour cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy

Tissue-specific regulatory elements in mammalian promoters

Transcription factor-binding sites and the cis-regulatory modules they compose are central determinants of gene expression. We previously showed that binding site motifs and modules in proximal promoters can be used to predict a significant portion of mammalian tissue-specific transcription. Here, we report on a systematic analysis of promoters controlling tissue-specific expression in heart, kidney, liver, pancreas, skeletal muscle, testis and CD4 T cells, for both human and mouse. We integrated multiple sources of expression data to compile sets of transcripts with strong evidence for tissue-specific regulation. The analysis of the promoters corresponding to these sets produced a catalog of predicted tissue-specific motifs and modules, and cis-regulatory elements. Predicted regulatory interactions are supported by statistical evidence, and provide a foundation for targeted experiments that will improve our understanding of tissue-specific regulatory networks. In a broader context, methods used to construct the catalog provide a model for the analysis of genomic regions that regulate differentially expressed genes

Modeling glioblastoma heterogeneity as a dynamic network of cell states

Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3\ua0weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition

Off-target effects dominate a large-scale RNAi screen for modulators of the TGF-β pathway and reveal microRNA regulation of TGFBR2

10.1186/1758-907X-2-3Silence21

Optimal drug combinations and minimal hitting sets

<p>Abstract</p> <p>Background</p> <p>Identifying effective drug combinations that significantly improve over single agents is a challenging problem. Pairwise combinations already represent a huge screening effort. Beyond two drug combinations the task seems unfeasible.</p> <p>Results</p> <p>In this work we introduce a method to uncover drug combinations with a putative effective response when presented to a heterogeneous population of malignant agents (strains), such as cancer cell lines or viruses. Using data quantifying the effect of single drugs over several individual strains, we search for minimal drug combinations that successfully target all strains. We show that the latter problem can be mapped to a minimal hitting set problem in mathematics. We illustrate this approach using data for the NCI60 panel of tumor derived cell lines, uncovering 14 anticancer drug combinations.</p> <p>Conclusion</p> <p>The drug-response graph and the associated minimal hitting set method can be used to uncover effective drug combinations in anticancer drug screens and drug development programs targeting heterogeneous populations of infectious agents such as HIV.</p

NF1 regulates mesenchymal glioblastoma plasticity and aggressiveness through the AP-1 transcription factor FOSL1

Summary: The molecular basis underlying Glioblastoma (GBM) heterogeneity and plasticity are not fully understood. Using transcriptomic data of patient-derived brain tumor stem cell lines (BTSCs), classified based on GBM-intrinsic signatures, we identify the AP-1 transcription factor FOSL1 as a master regulator of the mesenchymal (MES) subtype. We provide a mechanistic basis to the role of the Neurofibromatosis type 1 gene (NF1), a negative regulator of the RAS/MAPK pathway, in GBM mesenchymal transformation through the modulation of FOSL1 expression. Depletion of FOSL1 in NF1-mutant human BTSCs and Kras-mutant mouse neural stem cells results in loss of the mesenchymal gene signature, reduction in stem cell properties and in vivo tumorigenic potential. Our data demonstrate that FOSL1 controls GBM plasticity and aggressiveness in response to NF1 alterations

A Patient-Derived Cell Atlas Informs Precision Targeting of Glioblastoma

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents with a complex spectrum of genomic aberrations, but the pharmacological consequences of these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis of 100 patient-derived GBM cell cultures from the human glioma cell culture (HGCC) cohort. Exploring 1,544 drugs, we find that GBM has two main pharmacological subgroups, marked by differential response to proteasome inhibitors and mutually exclusive aberrations in TP53 and CDKN2A/B. We confirm this trend in cell and in xenotransplantation models, and identify both Bcl-2 family inhibitors and p53 activators as potentiators of proteasome inhibitors in GBM cells, We can further predict the responses of individual cell cultures to several existing drug classes, presenting opportunities for drug repurposing and design of stratified trials. Our functionally profiled biobank provides a valuable resource for the discovery of new treatments for GBM.Patrik Johansson, Cecilia Krona and Soumi Kundu share first authorship</p

Comparative epigenetic analysis of tumour initiating cells and syngeneic EPSC-derived neural stem cells in glioblastoma

Epigenetic mechanisms which play an essential role in normal developmental processes, such as self-renewal and fate specification of neural stem cells (NSC) are also responsible for some of the changes in the glioblastoma (GBM) genome. Here we develop a strategy to compare the epigenetic and transcriptional make-up of primary GBM cells (GIC) with patient-matched expanded potential stem cell (EPSC)-derived NSC (iNSC). Using a comparative analysis of the transcriptome of syngeneic GIC/iNSC pairs, we identify a glycosaminoglycan (GAG)-mediated mechanism of recruitment of regulatory T cells (Tregs) in GBM. Integrated analysis of the transcriptome and DNA methylome of GBM cells identifies druggable target genes and patient-specific prediction of drug response in primary GIC cultures, which is validated in 3D and in vivo models. Taken together, we provide a proof of principle that this experimental pipeline has the potential to identify patient-specific disease mechanisms and druggable targets in GBM

Designing stem-cell-based dopamine cell replacement trials for Parkinson's disease

Clinical studies of Parkinson’s disease (PD) using a dopamine cell replacment strategy have been tried for more than 30 years. The outcomes following transplantation of human fetal ventral mesencephalic tissue (hfVM) have been variable, with some patients coming off their anti-PD treatment for many years and others not responding and/or developing significant side effects, including graft-induced dyskinesia. This led to a re-appraisal of the best way to do such trials, which resulted in a new European-Union-funded allograft trial with fetal dopamine cells across several centers in Europe. This new trial, TRANSEURO (NCT01898390), is an open-label study in which some individuals in a large observational cohort of patients with mild PD who were undergoing identical assessments were randomly selected to receive transplants of hfVM. The TRANSEURO trial is currently ongoing as researchers have completed both recruitment into a large multicenter observational study of younger onset early-stage PD and transplantation of hfVM in 11 patients. While completion of TRANSEURO is not expected until 2021, we feel that sharing the rationale for the design of TRANSEURO, along with the lessons we have learned along the way, can help inform researchers and facilitate planning of transplants of dopamine-producing cells derived from human pluripotent stem cells for future clinical trials