Nasal Brushing Molecular Endotyping Distinguishes CRSwNP Patients with Better Response to Dupilumab.

BACKGROUND: There is evidence of pathophysiologic diversity in chronic rhinosinusitis with nasal polyps (CRSwNP), but data characterizing the molecular endotypes of CRSwNP and their association with treatment is lacking. OBJECTIVES: To identify gene signatures associated with CRSwNP endotypes, clinical features, and dupilumab treatment response. METHODS: Nasal brushing samples were collected from 89 patients randomized to dupilumab 300 mg every 2 weeks or placebo in the SINUS-52 trial (NCT02898454). Microarrays were used to identify transcriptional clusters and assess the relationship between gene expression and baseline clinical features and clinical response to dupilumab. Endotype signatures were determined using differential expression analysis. RESULTS: Two distinct transcriptional clusters (C1 and C2) were identified, both with elevated type 2 biomarkers. At baseline, C2 patients had higher mean Nasal Polyp Score and higher type 2 biomarker levels than C1 patients. At Week 24, significant improvements in clinical outcomes (dupilumab vs placebo) were observed in both clusters, although the magnitude of improvements was significantly greater in C2 than C1, and more C2 patients demonstrated clinically meaningful responses. Gene sets enrichment analyses supported the existence of two molecular endotypes: C2 was enriched in genes associated with type 2 inflammation (including periostin, cadherin-26, and type 2 cysteine protease inhibitors), while C1 was enriched in genes associated with T cell activation and interleukin-12 production. CONCLUSION: Two distinct gene signatures associated with CRSwNP clinical features were identified; the endotype signatures were associated with clinical outcome measures and magnitude of dupilumab response.

Differential modulation of allergic rhinitis nasal transcriptome by dupilumab and allergy immunotherapy.

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.

A Computational Approach for Identifying Synergistic Drug Combinations.

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.

ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers.

BACKGROUND: With the explosion of genomic data over the last decade, there has been a tremendous amount of effort to understand the molecular basis of cancer using informatics approaches. However, this has proven to be extremely difficult primarily because of the varied etiology and vast genetic heterogeneity of different cancers and even within the same cancer. One particularly challenging problem is to predict prognostic outcome of the disease for different patients. RESULTS: Here, we present ENCAPP, an elastic-net-based approach that combines the reference human protein interactome network with gene expression data to accurately predict prognosis for different human cancers. Our method identifies functional modules that are differentially expressed between patients with good and bad prognosis and uses these to fit a regression model that can be used to predict prognosis for breast, colon, rectal, and ovarian cancers. Using this model, ENCAPP can also identify prognostic biomarkers with a high degree of confidence, which can be used to generate downstream mechanistic and therapeutic insights. CONCLUSION: ENCAPP is a robust method that can accurately predict prognostic outcome and identify biomarkers for different human cancers.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and 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.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

The Identification of CELSR3 and Other Potential Cell Surface Targets in Neuroendocrine Prostate Cancer.

Although recent efforts have led to the development of highly effective androgen receptor (AR)-directed therapies for the treatment of advanced prostate cancer, a significant subset of patients will progress with resistant disease including AR-negative tumors that display neuroendocrine features [neuroendocrine prostate cancer (NEPC)]. On the basis of RNA sequencing (RNA-seq) data from a clinical cohort of tissue from benign prostate, locally advanced prostate cancer, metastatic castration-resistant prostate cancer and NEPC, we developed a multi-step bioinformatics pipeline to identify NEPC-specific, overexpressed gene transcripts that encode cell surface proteins. This included the identification of known NEPC surface protein CEACAM5 as well as other potentially targetable proteins (e.g., HMMR and CESLR3). We further showed that cadherin EGF LAG seven-pass G-type receptor 3 (CELSR3) knockdown results in reduced NEPC tumor cell proliferation and migration in vitro. We provide in vivo data including laser capture microdissection followed by RNA-seq data supporting a causal role of CELSR3 in the development and/or maintenance of the phenotype associated with NEPC. Finally, we provide initial data that suggests CELSR3 is a target for T-cell redirection therapeutics. Further work is now needed to fully evaluate the utility of targeting CELSR3 with T-cell redirection or other similar therapeutics as a potential new strategy for patients with NEPC. Significance: The development of effective treatment for patients with NEPC remains an unmet clinical need. We have identified specific surface proteins, including CELSR3, that may serve as novel biomarkers or therapeutic targets for NEPC.

Blocking common γ chain cytokine signaling ameliorates T cell-mediated pathogenesis in disease models.

The common γ chain (γc; IL-2RG) is a subunit of the interleukin (IL) receptors for the γc cytokines IL-2, IL-4, IL-7, IL-9, IL-15, and IL-21. The lack of appropriate neutralizing antibodies recognizing IL-2RG has made it difficult to thoroughly interrogate the role of γc cytokines in inflammatory and autoimmune disease settings. Here, we generated a γc cytokine receptor antibody, REGN7257, to determine whether γc cytokines might be targeted for T cell-mediated disease prevention and treatment. Biochemical, structural, and in vitro analysis showed that REGN7257 binds with high affinity to IL-2RG and potently blocks signaling of all γc cytokines. In nonhuman primates, REGN7257 efficiently suppressed T cells without affecting granulocytes, platelets, or red blood cells. Using REGN7257, we showed that γc cytokines drive T cell-mediated disease in mouse models of graft-versus-host disease (GVHD) and multiple sclerosis by affecting multiple aspects of the pathogenic response. We found that our xenogeneic GVHD mouse model recapitulates hallmarks of acute and chronic GVHD, with T cell expansion/infiltration into tissues and liver fibrosis, as well as hallmarks of immune aplastic anemia, with bone marrow aplasia and peripheral cytopenia. Our findings indicate that γc cytokines contribute to GVHD and aplastic anemia pathology by promoting these characteristic features. By demonstrating that broad inhibition of γc cytokine signaling with REGN7257 protects from immune-mediated disorders, our data provide evidence of γc cytokines as key drivers of pathogenic T cell responses, offering a potential strategy for the management of T cell-mediated diseases.

Drug-Induced Expression-Based Computational Repurposing of Small Molecules Affecting Transcription Factor Activity.

Inhibition of oncogenes and reactivation of tumor suppressors are well-established goals in anticancer drug development. Unfortunately many oncogenes and tumor suppressors are not classically druggable, in that they lack a targetable enzymatic activity and associated binding pockets that small molecule drugs can be directed to. This is especially relevant for transcription factors, which have long been thought to be undruggable. To address this gap, we have developed and described CRAFTT, a broadly applicable computational drug-repositioning approach for targeting transcription factors. CRAFTT combines transcription factor target gene sets with drug-induced expression profiling to identify small molecules that can perturb transcription factor activity. Network analysis is then used to derive a modulation index (MI) and prioritize predictions.

A Bayesian machine learning approach for drug target identification using diverse data types.

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.

Broad Targeting Specificity during Bacterial Type III CRISPR-Cas Immunity Constrains Viral Escape.

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.

A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials.

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.

A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors.

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.

N-Myc Induces an EZH2-Mediated Transcriptional Program Driving Neuroendocrine Prostate Cancer.

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.

Predicting cancer prognosis using functional genomics data sets.

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.

A comparison of computational efficiencies of stochastic algorithms in terms of two infection models.

In this paper, we investigate three particular algorithms: a stochastic simulation algorithm (SSA), and explicit and implicit tau-leaping algorithms. To compare these methods, we used them to analyze two infection models: a Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunodeficiency Virus (HIV) within host infection model. While the first has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational efficiency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.