Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties ...before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar “moneyball” approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.
•Computational approach predicts the likelihood of clinical trial toxicity•Identification of molecule and target properties associated with clinical toxicity•Development of a tool to facilitate interaction and interpretation of the model
Gayvert et al. present a data-driven approach that accurately predicts the likelihood of clinical trial toxicity by integrating the structural and target-based properties of a drug.
A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, ...a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.
CRISPR loci are a cluster of repeats separated by short “spacer” sequences derived from prokaryotic viruses and plasmids that determine the targets of the host’s CRISPR-Cas immune response against ...its invaders. For type I and II CRISPR-Cas systems, single-nucleotide mutations in the seed or protospacer adjacent motif (PAM) of the target sequence cause immune failure and allow viral escape. This is overcome by the acquisition of multiple spacers that target the same invader. Here we show that targeting by the Staphylococcus epidermidis type III-A CRISPR-Cas system does not require PAM or seed sequences, and thus prevents viral escape via single-nucleotide substitutions. Instead, viral escapers can only arise through complete target deletion. Our work shows that, as opposed to type I and II systems, the relaxed specificity of type III CRISPR-Cas targeting provides robust immune responses that can lead to viral extinction with a single spacer targeting an essential phage sequence.
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•Type III CRISPR-Cas immunity does not require PAM or seed sequence motifs•Escape from type III immunity requires complete deletion of the target sequence•Type III targeting of an essential phage gene leads to phage extinction•The targeting flexibility of type III systems provides a robust immune response
Exploring the target specificity of type III-A CRISPR-Cas systems, Pyenson et al. find that most point mutations in the target region still allow robust immunity. As a consequence, viral escape from the type III-A CRISPR-Cas immune response requires the full deletion of the target, which is a very rare event.
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data ...types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201-an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201's target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.
The transition from castration-resistant prostate adenocarcinoma (CRPC) to neuroendocrine prostate cancer (NEPC) has emerged as an important mechanism of treatment resistance. NEPC is associated with ...overexpression and gene amplification of MYCN (encoding N-Myc). N-Myc is an established oncogene in several rare pediatric tumors, but its role in prostate cancer progression is not well established. Integrating a genetically engineered mouse model and human prostate cancer transcriptome data, we show that N-Myc overexpression leads to the development of poorly differentiated, invasive prostate cancer that is molecularly similar to human NEPC. This includes an abrogation of androgen receptor signaling and induction of Polycomb Repressive Complex 2 signaling. Altogether, our data establishes N-Myc as an oncogenic driver of NEPC.
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•N-Myc drives the NEPC phenotype and associated molecular program•N-Myc abrogates AR signaling, which results in enhanced AKT activity•N-Myc redirects EZH2 activity and sensitizes cells to EZH2 inhibitors•N-Myc interacts with Aurora-A, which facilitates N-Myc target gene expression
Dardenne et al. demonstrate that N-Myc overexpression in pre-clinical models drives aggressive prostate cancer that mimics human neuroendocrine prostate cancer, including reduced AR signaling and enhanced PRC2 target gene repression, and sensitizes cells to an Aurora-A inhibitor and EZH2 SET domain inhibitors.
Mutations in transcription factor (TF) genes are frequently observed in tumors, often leading to aberrant transcriptional activity. Unfortunately, TFs are often considered undruggable due to the ...absence of targetable enzymatic activity. To address this problem, we developed CRAFTT, a computational drug-repositioning approach for targeting TF activity. CRAFTT combines ChIP-seq with drug-induced expression profiling to identify small molecules that can specifically perturb TF activity. Application to ENCODE ChIP-seq datasets revealed known drug-TF interactions, and a global drug-protein network analysis supported these predictions. Application of CRAFTT to ERG, a pro-invasive, frequently overexpressed oncogenic TF, predicted that dexamethasone would inhibit ERG activity. Dexamethasone significantly decreased cell invasion and migration in an ERG-dependent manner. Furthermore, analysis of electronic medical record data indicates a protective role for dexamethasone against prostate cancer. Altogether, our method provides a broadly applicable strategy for identifying drugs that specifically modulate TF activity.
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•A computational approach predicts drugs that modulate transcription factor activity•Known drug-transcription factor interactions are recovered•Dexamethasone is identified as a modulator of ERG activity•Experimental data functionally validate dexamethasone-ERG interaction
Gayvert et al. present a broadly applicable systems biology method for identifying small molecules and drugs that modulate transcription factor activity. They identified dexamethasone as a candidate for the inhibition of the oncogenic transcription factor ERG and validate this prediction experimentally in several systems.
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. ...However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve auROC = 0.86; area under the precision-recall curve auPR = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.
Background
Nasal epithelial cells are important regulators of barrier function and immune signaling; however, in allergic rhinitis (AR) these functions can be disrupted by inflammatory mediators. We ...aimed to better discern AR disease mechanisms using transcriptome data from nasal brushing samples from individuals with and without AR.
Methods
Data were drawn from a feasibility study of individuals with and without AR to Timothy grass and from a clinical trial evaluating 16 weeks of treatment with the following: dupilumab, a monoclonal antibody that binds interleukin (IL)‐4Rα and inhibits type 2 inflammation by blocking signaling of both IL‐4/IL‐13; subcutaneous immunotherapy with Timothy grass (SCIT), which inhibits allergic responses through pleiotropic effects; SCIT + dupilumab; or placebo. Using nasal brushing samples from these studies, we defined distinct gene signatures in nasal tissue of AR disease and after nasal allergen challenge (NAC) and assessed how these signatures were modulated by study drug(s).
Results
Treatment with dupilumab (normalized enrichment score NES = −1.73, p = .002) or SCIT + dupilumab (NES = −2.55, p < .001), but not SCIT alone (NES = +1.16, p = .107), significantly repressed the AR disease signature. Dupilumab (NES = −2.55, p < .001), SCIT (NES = −2.99, p < .001), and SCIT + dupilumab (NES = −3.15, p < .001) all repressed the NAC gene signature.
Conclusion
These results demonstrate type 2 inflammation is an important contributor to the pathophysiology of AR disease and that inhibition of the type 2 pathway with dupilumab may normalize nasal tissue gene expression.
The study aimed to understand the pathobiology of allergic rhinitis (AR) by analysing transcriptome data from nasal brushing samples of individuals with and without AR, and those undergoing different treatments. The study evaluated how treatments, including dupilumab, SCIT, and a combination of both, versus placebo, influenced AR and allergen challenge specific gene signatures in nasal tissue. Results showed that both dupilumab and SCIT+dupilumab significantly repressed the AR disease signature, and all three treatments repressed the nasal allergen challenge (NAC) gene signature. AR disease signature (select genes): TFF1, Trefoil Factor 1; TFF3, Trefoil Factor 3; GBP5, Guanylate Binding Protein 5; MUC13, Mucin 13; CCL5, C‐C Motif Chemokine Ligand 5; CCL26, C‐C Motif Chemokine Ligand 26; PTHLH, Parathyroid Hormone‐Like Hormone; CD44, CD44 Molecule (Indian Blood Group); CSTA, Cystatin A; ALOX15, Arachidonate 15‐Lipoxygenase; PHLDB2, Pleckstrin Homology Like Domain Family B Member 2; POSTN, Periostin; NTRK2, Neurotrophic Receptor Tyrosine Kinase 2; SERPINB2, Serpin Family B Member 2; ANO1, Anoctamin 1; CST1, Cystatin 1. NAC Signature (select genes): FBXO15, F‐Box Protein 15; FABP6, Fatty Acid Binding Protein 6; SRD5A2, Steroid 5 Alpha‐Reductase 2; FOSL1, FOS Like Antigen 1; EMP1, Epithelial Membrane Protein 1; HBEGF, Heparin‐Binding EGF‐Like Growth Factor; AREG, Amphiregulin; KRT16, Keratin 16; CCL2, C‐C Motif Chemokine Ligand 2; IL1B, Interleukin 1 Beta; MMP19, Matrix Metallopeptidase 19; IL1A, Interleukin 1 Alpha; CCR3, C‐C Motif Chemokine Receptor 3; IL1RL1, Interleukin 1 Receptor Like 1; CD69, CD69 Molecule; AK124805, Non‐coding RNA.Abbreviations: AR, allergic rhinitis; NES, normalized enrichment score; SCIT, subcutaneous immunotherapy with Timothy grass; NAC, nasal allergen challenge.
Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different ...aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.
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