Nasal brushing molecular endotyping distinguishes patients with chronic rhinosinusitis with nasal polyps 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 are lacking. OBJECTIVE: This study aimed 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 in C1, and more C2 patients demonstrated clinically meaningful responses. Gene set enrichment analysis supported the existence of 2 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 IL-12 production. CONCLUSIONS: 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.

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.

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.

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 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.

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.

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.

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.

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.