Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.

A cell state-specific metabolic vulnerability to GPX4-dependent ferroptosis in glioblastoma.

Glioma cells hijack developmental programs to control cell state. Here, we uncover a glioma cell state-specific metabolic liability that can be therapeutically targeted. To model cell conditions at brain tumor inception, we generated genetically engineered murine gliomas, with deletion of p53 alone (p53) or with constitutively active Notch signaling (N1IC), a pathway critical in controlling astrocyte differentiation during brain development. N1IC tumors harbored quiescent astrocyte-like transformed cell populations while p53 tumors were predominantly comprised of proliferating progenitor-like cell states. Further, N1IC transformed cells exhibited increased mitochondrial lipid peroxidation, high ROS production and depletion of reduced glutathione. This altered mitochondrial phenotype rendered the astrocyte-like, quiescent populations more sensitive to pharmacologic or genetic inhibition of the lipid hydroperoxidase GPX4 and induction of ferroptosis. Treatment of patient-derived early-passage cell lines and glioma slice cultures generated from surgical samples with a GPX4 inhibitor induced selective depletion of quiescent astrocyte-like glioma cell populations with similar metabolic profiles. Collectively, these findings reveal a specific therapeutic vulnerability to ferroptosis linked to mitochondrial redox imbalance in a subpopulation of quiescent astrocyte-like glioma cells resistant to standard forms of treatment.

Silencing c-Myc translation as a therapeutic strategy through targeting PI3Kδ and CK1ε in hematological malignancies.

Phosphoinositide 3-kinase (PI3K) and the proteasome pathway are both involved in activating the mechanistic target of rapamycin (mTOR). Because mTOR signaling is required for initiation of messenger RNA translation, we hypothesized that cotargeting the PI3K and proteasome pathways might synergistically inhibit translation of c-Myc. We found that a novel PI3K δ isoform inhibitor TGR-1202, but not the approved PI3Kδ inhibitor idelalisib, was highly synergistic with the proteasome inhibitor carfilzomib in lymphoma, leukemia, and myeloma cell lines and primary lymphoma and leukemia cells. TGR-1202 and carfilzomib (TC) synergistically inhibited phosphorylation of the eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4E-BP1), leading to suppression of c-Myc translation and silencing of c-Myc-dependent transcription. The synergistic cytotoxicity of TC was rescued by overexpression of eIF4E or c-Myc. TGR-1202, but not other PI3Kδ inhibitors, inhibited casein kinase-1 ε (CK1ε). Targeting CK1ε using a selective chemical inhibitor or short hairpin RNA complements the effects of idelalisib, as a single agent or in combination with carfilzomib, in repressing phosphorylation of 4E-BP1 and the protein level of c-Myc. These results suggest that TGR-1202 is a dual PI3Kδ/CK1ε inhibitor, which may in part explain the clinical activity of TGR-1202 in aggressive lymphoma not found with idelalisib. Targeting CK1ε should become an integral part of therapeutic strategies targeting translation of oncogenes such as c-Myc.

Systematic, network-based characterization of therapeutic target inhibitors.

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

Multiwell-based G0-PCC assay for radiation biodosimetry.

In major radiological events, rapid assays to detect ionizing radiation exposure are crucial for effective medical interventions. The purpose of these assays is twofold: to categorize affected individuals into groups for initial treatments, and to provide definitive dose estimates for continued care and epidemiology. However, existing high-throughput cytogenetic biodosimetry assays take about 3 days to yield results, which delays critical interventions. We have developed a multiwell-based variant of the chemical-induced G0-phase Premature Chromosome Condensation Assay that delivers same-day results. Our findings revealed that using a concentration of phosphatase inhibitor lower than recommended significantly increases the yield of cells with highly condensed chromosomes. These chromosomes exhibited increased fragmentation in a dose-dependent manner, enabling to quantify radiation damage using a custom Deep Learning algorithm. This algorithm demonstrated reasonable performance in categorizing doses into distinct treatment groups (84% and 80% accuracy for three and four iso-treatment dose bins, respectively) and showed reliability in determining the actual doses received (correlation coefficient of 0.879). This method is amendable to full automation and has the potential to address the need for same-day, high-throughput cytogenetic test for both dose categorization and dose reconstruction in large-scale radiation emergencies.

Multiwell-based G0-PCC assay for radiation biodosimetry.

In cytogenetic biodosimetry, assessing radiation exposure typically requires over 48 hours for cells to reach mitosis, significantly delaying the administration of crucial radiation countermeasures needed within the first 24 hours post-exposure. To improve medical response times, we incorporated the G0-Premature Chromosome Condensation (G0-PCC) technique with the Rapid Automated Biodosimetry Tool-II (RABiT-II), creating a faster alternative for large-scale radiation emergencies. Our findings revealed that using a lower concentration of Calyculin A (Cal A) than recommended effectively increased the yield of highly-condensed G0-PCC cells (hPCC). However, integrating recombinant CDK1/Cyclin B kinase, vital for chromosome condensation, proved challenging due to the properties of these proteins affecting interactions with cellular membranes. Interestingly, Cal A alone was capable of inducing chromosome compaction in some G0 cells even in the absence of mitotic kinases, although these chromosomes displayed atypical morphologies. This suggests that Cal A mechanism for compacting G0 chromatin may differ from condensation driven by mitotic kinases. Additionally, we observed a correlation between radiation dose and extent of hPCC chromosome fragmentation, which allowed us to automate radiation damage quantification using a Convolutional Neural Network (CNN). Our method can address the need for a same-day cytogenetic biodosimetry test in radiation emergency situations.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Molsidomine provides neuroprotection against vincristine-induced peripheral neurotoxicity through soluble guanylyl cyclase activation.

Peripheral neurotoxicity is a dose-limiting adverse reaction of primary frontline chemotherapeutic agents, including vincristine. Neuropathy can be so disabling that patients drop out of potentially curative therapy, negatively impacting cancer prognosis. The hallmark of vincristine neurotoxicity is axonopathy, yet its underpinning mechanisms remain uncertain. We developed a comprehensive drug discovery platform to identify neuroprotective agents against vincristine-induced neurotoxicity. Among the hits identified, SIN-1-an active metabolite of molsidomine-prevents vincristine-induced axonopathy in both motor and sensory neurons without compromising vincristine anticancer efficacy. Mechanistically, we found that SIN-1's neuroprotective effect is mediated by activating soluble guanylyl cyclase. We modeled vincristine-induced peripheral neurotoxicity in rats to determine molsidomine therapeutic potential in vivo. Vincristine administration induced severe nerve damage and mechanical hypersensitivity that were attenuated by concomitant treatment with molsidomine. This study provides evidence of the neuroprotective properties of molsidomine and warrants further investigations of this drug as a therapy for vincristine-induced peripheral neurotoxicity.

Discovering Venom-Derived Drug Candidates Using Differential Gene Expression.

Venoms are a diverse and complex group of natural toxins that have been adapted to treat many types of human disease, but rigorous computational approaches for discovering new therapeutic activities are scarce. We have designed and validated a new platform-named VenomSeq-to systematically identify putative associations between venoms and drugs/diseases via high-throughput transcriptomics and perturbational differential gene expression analysis. In this study, we describe the architecture of VenomSeq and its evaluation using the crude venoms from 25 diverse animal species and 9 purified teretoxin peptides. By integrating comparisons to public repositories of differential expression, associations between regulatory networks and disease, and existing knowledge of venom activity, we provide a number of new therapeutic hypotheses linking venoms to human diseases supported by multiple layers of preliminary evidence.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Validation of a High-Throughput Dicentric Chromosome Assay Using Complex Radiation Exposures.

Validation of biodosimetry assays is routinely performed using primarily orthovoltage irradiators at a conventional dose rate of approximately 1 Gy/min. However, incidental/ accidental exposures caused by nuclear weapons can be more complex. The aim of this work was to simulate the DNA damage effects mimicking those caused by the detonation of a several kilotons improvised nuclear device (IND). For this, we modeled complex exposures to: 1. a mixed (photons + IND-neutrons) field and 2. different dose rates that may come from the blast, nuclear fallout, or ground deposition of radionuclides (ground shine). Additionally, we assessed whether myeloid cytokines affect the precision of radiation dose estimation by modulating the frequency of dicentric chromosomes. To mimic different exposure scenarios, several irradiation systems were used. In a mixed field study, human blood samples were exposed to a photon field enriched with neutrons (ranging from 10% to 37%) from a source that mimics Hiroshima's A-bomb's energy spectrum (0.2-9 MeV). Using statistical analysis, we assessed whether photons and neutrons act in an additive or synergistic way to form dicentrics. For the dose rates study, human blood was exposed to photons or electrons at dose rates ranging from low (where the dose was spread over 32 h) to extremely high (where the dose was delivered in a fraction of a microsecond). Potential effects of cytokine treatment on biodosimetry dose predictions were analyzed in irradiated blood subjected to Neupogen or Neulasta for 24 or 48 h at the concentration recommended to forestall manifestation of an acute radiation syndrome in bomb survivors. All measurements were performed using a robotic station, the Rapid Automated Biodosimetry Tool II, programmed to culture lymphocytes and score dicentrics in multiwell plates (the RABiT-II DCA). In agreement with classical concepts of radiation biology, the RABiT-II DCA calibration curves suggested that the frequency of dicentrics depends on the type of radiation and is modulated by changes in the dose rate. The resulting dose-response curves suggested an intermediate dicentric yields and additive effects of photons and IND-neutrons in the mixed field. At ultra-high dose rate (600 Gy/s), affected lymphocytes exhibited significantly fewer dicentrics (P < 0.004, t test). In contrast, we did not find the dose-response modification effects of radiomitigators on the yields of dicentrics (Bonferroni corrected P > 0.006, ANOVA test). This result suggests no bias in the dose predictions should be expected after emergency cytokine treatment initiated up to 48 h prior to blood collection for dicentric analysis.

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium.

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have implemented an efficient integrative experimental and computational framework leveraging the systematic generation and analysis of drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Protein activity-based analyses revealed highly reproducible, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide polypharmacology map, characterization of MoA-related drug clusters and off-target effects, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins. The proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, could help support more systematic and quantitative approaches to precision oncology.

Systematic elucidation and pharmacological targeting of tumor-infiltrating regulatory T cell master regulators.

Due to their immunosuppressive role, tumor-infiltrating regulatory T cells (TI-Tregs) represent attractive immuno-oncology targets. Analysis of TI vs. peripheral Tregs (P-Tregs) from 36 patients, across four malignancies, identified 17 candidate master regulators (MRs) as mechanistic determinants of TI-Treg transcriptional state. Pooled CRISPR-Cas9 screening in vivo, using a chimeric hematopoietic stem cell transplant model, confirmed the essentiality of eight MRs in TI-Treg recruitment and/or retention without affecting other T cell subtypes, and targeting one of the most significant MRs (Trps1) by CRISPR KO significantly reduced ectopic tumor growth. Analysis of drugs capable of inverting TI-Treg MR activity identified low-dose gemcitabine as the top prediction. Indeed, gemcitabine treatment inhibited tumor growth in immunocompetent but not immunocompromised allografts, increased anti-PD-1 efficacy, and depleted MR-expressing TI-Tregs in vivo. This study provides key insight into Treg signaling, specifically in the context of cancer, and a generalizable strategy to systematically elucidate and target MR proteins in immunosuppressive subpopulations.

OncoLoop: A Network-Based Precision Cancer Medicine Framework.

Prioritizing treatments for individual patients with cancer remains challenging, and performing coclinical studies using patient-derived models in real time is often unfeasible. To circumvent these challenges, we introduce OncoLoop, a precision medicine framework that predicts drug sensitivity in human tumors and their preexisting high-fidelity (cognate) model(s) by leveraging drug perturbation profiles. As a proof of concept, we applied OncoLoop to prostate cancer using genetically engineered mouse models (GEMM) that recapitulate a broad spectrum of disease states, including castration-resistant, metastatic, and neuroendocrine prostate cancer. Interrogation of human prostate cancer cohorts by Master Regulator (MR) conservation analysis revealed that most patients with advanced prostate cancer were represented by at least one cognate GEMM-derived tumor (GEMM-DT). Drugs predicted to invert MR activity in patients and their cognate GEMM-DTs were successfully validated in allograft, syngeneic, and patient-derived xenograft (PDX) models of tumors and metastasis. Furthermore, OncoLoop-predicted drugs enhanced the efficacy of clinically relevant drugs, namely, the PD-1 inhibitor nivolumab and the AR inhibitor enzalutamide. SIGNIFICANCE: OncoLoop is a transcriptomic-based experimental and computational framework that can support rapid-turnaround coclinical studies to identify and validate drugs for individual patients, which can then be readily adapted to clinical practice. This framework should be applicable in many cancer contexts for which appropriate models and drug perturbation data are available. This article is highlighted in the In This Issue feature, p. 247.

A cell state specific metabolic vulnerability to GPX4-dependent ferroptosis in glioblastoma.

Glioma cells hijack developmental transcriptional programs to control cell state. During neural development, lineage trajectories rely on specialized metabolic pathways. However, the link between tumor cell state and metabolic programs is poorly understood in glioma. Here we uncover a glioma cell state-specific metabolic liability that can be leveraged therapeutically. To model cell state diversity, we generated genetically engineered murine gliomas, induced by deletion of p53 alone (p53) or with constitutively active Notch signaling (N1IC), a pathway critical in controlling cellular fate. N1IC tumors harbored quiescent astrocyte-like transformed cell states while p53 tumors were predominantly comprised of proliferating progenitor-like cell states. N1IC cells exhibit distinct metabolic alterations, with mitochondrial uncoupling and increased ROS production rendering them more sensitive to inhibition of the lipid hydroperoxidase GPX4 and induction of ferroptosis. Importantly, treating patient-derived organotypic slices with a GPX4 inhibitor induced selective depletion of quiescent astrocyte-like glioma cell populations with similar metabolic profiles.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies.

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study. SIGNIFICANCE: Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

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.

A Model for Network-Based Identification and Pharmacological Targeting of Aberrant, Replication-Permissive Transcriptional Programs Induced by Viral Infection.

Precise characterization and targeting of host cell transcriptional machinery hijacked by viral infection remains challenging. Here, we show that SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Specifically, analysis of Master Regulator (MR) proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of MRs 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 MRs, based on their experimentally elucidated, context-specific mechanism of action. Overall, >80% of 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 MR signatures induced by virtually any pathogen.

Development of optimized drug-like small molecule inhibitors of the SARS-CoV-2 3CL protease for treatment of COVID-19.

The SARS-CoV-2 3CL protease is a critical drug target for small molecule COVID-19 therapy, given its likely druggability and essentiality in the viral maturation and replication cycle. Based on the conservation of 3CL protease substrate binding pockets across coronaviruses and using screening, we identified four structurally distinct lead compounds that inhibit SARS-CoV-2 3CL protease. After evaluation of their binding specificity, cellular antiviral potency, metabolic stability, and water solubility, we prioritized the GC376 scaffold as being optimal for optimization. We identified multiple drug-like compounds with <10 nM potency for inhibiting SARS-CoV-2 3CL and the ability to block SARS-CoV-2 replication in human cells, obtained co-crystal structures of the 3CL protease in complex with these compounds, and determined that they have pan-coronavirus activity. We selected one compound, termed coronastat, as an optimized lead and characterized it in pharmacokinetic and safety studies in vivo. Coronastat represents a new candidate for a small molecule protease inhibitor for the treatment of SARS-CoV-2 infection for eliminating pandemics involving coronaviruses.

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.