Systematic, network-based characterization of therapeutic target inhibitors

Molecular Signatures for Microbe-Associated Colorectal Cancers

Background

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics.

Methods

A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS).

Results

The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs.

Conclusions

Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

NaRnEA: An Information Theoretic Framework for Gene Set Analysis

Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.

Validation of a non-oncogene encoded vulnerability to exportin 1 inhibition in pediatric renal tumors

BACKGROUND

Malignant rhabdoid tumors (MRTs) and Wilms' tumors (WTs) are rare and aggressive renal tumors of infants and young children comprising ∼5% of all pediatric cancers. MRTs are among the most genomically stable cancers, and although WTs are genomically heterogeneous, both generally lack therapeutically targetable genetic mutations.

METHODS

Comparative protein activity analysis of MRTs (n = 68) and WTs (n = 132) across TCGA and TARGET cohorts, using metaVIPER, revealed elevated exportin 1 (XPO1) inferred activity. In vitro studies were performed on a panel of MRT and WT cell lines to evaluate effects on proliferation and cell-cycle progression following treatment with the selective XPO1 inhibitor selinexor. In vivo anti-tumor activity was assessed in patient-derived xenograft (PDX) models of MRTs and WTs.

FINDINGS

metaVIPER analysis identified markedly aberrant activation of XPO1 in MRTs and WTs compared with other tumor types. All MRT and most WT cell lines demonstrated baseline, aberrant XPO1 activity with in vitro sensitivity to selinexor via cell-cycle arrest and induction of apoptosis. In vivo, XPO1 inhibitors significantly abrogated tumor growth in PDX models, inducing effective disease control with sustained treatment. Corroborating human relevance, we present a case report of a child with multiply relapsed WTs with prolonged disease control on selinexor.

CONCLUSIONS

We report on a novel systems-biology-based comparative framework to identify non-genetically encoded vulnerabilities in genomically quiescent pediatric cancers. These results have provided preclinical rationale for investigation of XPO1 inhibitors in an upcoming investigator-initiated clinical trial of selinexor in children with MRTs and WTs and offer opportunities for exploration of inferred XPO1 activity as a potential predictive biomarker for response.

FUNDING

This work was funded by CureSearch for Children's Cancer, Alan B. Slifka Foundation, NIH (U01 CA217858, S10 OD012351, and S10 OD021764), Michael's Miracle Cure, Hyundai Hope on Wheels, Cannonball Kids Cancer, Conquer Cancer the ASCO Foundation, Cycle for Survival, Paulie Strong Foundation, and the Grayson Fund.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection

SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.

Single-cell transcriptional pharmacodynamics of trifluridine in a tumor-immune model

Understanding the immunological effects of chemotherapy is of great importance, especially now that we have entered an era where ever-increasing pre-clinical and clinical efforts are put into combining chemotherapy and immunotherapy to combat cancer. Single-cell RNA sequencing (scRNA-seq) has proved to be a powerful technique with a broad range of applications, studies evaluating drug effects in co-cultures of tumor and immune cells are however scarce. We treated a co-culture comprised of human colorectal cancer (CRC) cells and peripheral blood mononuclear cells (PBMCs) with the nucleoside analogue trifluridine (FTD) and used scRNA-seq to analyze posttreatment gene expression profiles in thousands of individual cancer and immune cells concurrently. ScRNA-seq recapitulated major mechanisms of action previously described for FTD and provided new insight into possible treatment-induced effects on T-cell mediated antitumor responses.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

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Drug-perturbed RNA sequencing data can be used to identify drug targets

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Technology-based drug-target definitions often subsume literature definitions

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Literature and screening datasets provide complementary information on drug mechanisms

Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease

Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here we outline an unbiased AI-assisted approach for target identification and validation. A network was built in which clusters of genes are connected by directed edges that highlight asymmetric Boolean relationships. Using machine-learning, a path of continuum states was pinpointed, which most effectively predicted disease outcome. This path was enriched in gene-clusters that maintain the integrity of the gut epithelial barrier. We exploit this insight to prioritize one target, choose appropriate pre-clinical murine models for target validation and design patient-derived organoid models. Potential for treatment efficacy is confirmed in patient-derived organoids using multivariate analyses. This AI-assisted approach identifies a first-in-class gut barrier-protective agent in IBD and predicted Phase-III success of candidate agents.

Traditional drug discovery process use differential, Bayesian and other network based approaches. We developed a Boolean approach for building disease maps and prioritizing pre-clinical models to discover a first-in-class therapy to restore and protect the leaky gut barrier in inflammatory bowel disease.

Network-guided search for genetic heterogeneity between gene pairs

MOTIVATION

Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that genetic variants at different loci lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions.

RESULTS

We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple Arabidopsis thaliana phenotypes, and, with an adapted variant of SiNIMin, for a study of rare variants in migraine patients.

AVAILABILITY AND IMPLEMENTATION

Code available at https://github.com/BorgwardtLab/SiNIMin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method

Genes are organized in functional modules (or pathways), thus their action and their dysregulation in diseases may be better understood by the identification of the modules most affected by the disease (aka disease modules, or active subnetworks). We describe how an algorithm based on the Core&Peel method is used to detect disease modules in co-expression networks of genes. We first validate Core&Peel for the general task of functional module detection by comparison with 42 methods participating in the Disease Module Identification DREAM challenge. Next, we use four specific disease test cases (colorectal cancer, prostate cancer, asthma, and rheumatoid arthritis), four state-of-the-art algorithms (ModuleDiscoverer, Degas, KeyPathwayMiner, and ClustEx), and several pathway databases to validate the proposed algorithm. Core&Peel is the only method able to find significant associations of the predicted disease module with known validated relevant pathways for all four diseases. Moreover, for the two cancer datasets, Core&Peel detects further eight relevant pathways not discovered by the other methods used in the comparative analysis. Finally, we apply Core&Peel and other methods to explore the transcriptional response of human cells to SARS-CoV-2 infection, finding supporting evidence for drug repositioning efforts at a pre-clinical level.

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks.

Precision Targeting with EZH2 and HDAC Inhibitors in Epigenetically Dysregulated Lymphomas

PURPOSE

Both gain-of-function enhancer of zeste homolog 2 (EZH2) mutations and inactivating histone acetyltransferases mutations, such as CREBBP and EP300, have been implicated in the pathogenesis of germinal center (GC)-derived lymphomas. We hypothesized that direct inhibition of EZH2 and histone deacetyltransferase (HDAC) would be synergistic in GC-derived lymphomas.

EXPERIMENTAL DESIGN

Lymphoma cell lines (n = 21) were exposed to GSK126, an EZH2 inhibitor, and romidepsin, a pan-HDAC inhibitor. Synergy was assessed by excess over bliss. Western blot, mass spectrometry, and coimmunoprecipitation were performed. A SU-DHL-10 xenograft model was utilized to validate in vitro findings. Pretreatment RNA-sequencing of cell lines was performed. MetaVIPER analysis was used to infer protein activity.

RESULTS

Exposure to GSK126 and romidepsin demonstrated potent synergy in lymphoma cell lines with EZH2 dysregulation. Combination of romidepsin with other EZH2 inhibitors also demonstrated synergy suggesting a class effect of EZH2 inhibition with romidepsin. Dual inhibition of EZH2 and HDAC led to modulation of acetylation and methylation of H3K27. The synergistic effects of the combination were due to disruption of the PRC2 complex secondary to acetylation of RbAP 46/48. A common basal gene signature was shared among synergistic lymphoma cell lines and was characterized by upregulation in chromatin remodeling genes and transcriptional regulators. This finding was supported by metaVIPER analysis which also revealed that HDAC 1/2 and DNA methyltransferase were associated with EZH2 activation.

CONCLUSIONS

Inhibition of EZH2 and HDAC is synergistic and leads to the dissociation of PRC2 complex. Our findings support the clinical translation of the combination of EZH2 and HDAC inhibition in EZH2 dysregulated lymphomas.

Insights into respiratory disease through bioinformatics

Respiratory diseases such as asthma, chronic obstructive pulmonary disease and lung cancer represent a critical area for medical research as millions of people are affected globally. The development of new strategies for treatment and/or prevention, and the identification of biomarkers for patient stratification and early detection of disease inception are essential to reducing the impact of lung diseases. The successful translation of research into clinical practice requires a detailed understanding of the underlying biology. In this regard, the advent of next-generation sequencing and mass spectrometry has led to the generation of an unprecedented amount of data spanning multiple layers of biological regulation (genome, epigenome, transcriptome, proteome, metabolome and microbiome). Dealing with this wealth of data requires sophisticated bioinformatics and statistical tools. Here, we review the basic concepts in bioinformatics and genomic data analysis and illustrate the application of these tools to further our understanding of lung diseases. We also highlight the potential for data integration of multi-omic profiles and computational drug repurposing to define disease subphenotypes and match them to targeted therapies, paving the way for personalized medicine.