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

UNLABELLED 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

Precision Medicine In The Age Of Big Data: Leveraging Machine Learning And Genomics For Drug Discovery

Targeted therapies designed to specifically target molecules involved in carcinogenesis have achieved remarkable antitumor efficacy. However resistance inevitably develops and many cancer patients are not candidates for these targeted therapies. Furthermore the clinical attrition rate continues to rise, which remains a barrier in the development of novel targeted therapies. Integration of extensive genomics datasets with large drug databases allows us to begin to tackle questions about target discovery and drug toxicity with the ultimate goal of accelerating personalized anticancer drug discovery. The purpose of this dissertation was to address these problems through the development of drug repurposing, toxicity prediction, and drug synergy prediction models. First to target the role of transcription factors as drivers of oncogenic activity, we developed a computational drug repositioning approach (CRAFTT) that makes predictions about drugs that specifically disrupt transcription factor activity. To do this, CRAFTT integrates transcription factor binding site information with drug-induced expression profiling. We found that CRAFTT was able to recover a significant number of known drug-transcription factor interactions and identified a novel interaction that we subsequently validated. Our work in drug discovery led us to ask questions about what makes a drug safe. We developed a data-driven approach (PrOCTOR) that integrates the properties of a compound’s targets and its structure to directly predict the likelihood of toxicity in clinical trials and was able to accurately classify known safe and toxic drugs. Finally to address the problem of drug resistance, we developed a machine learning approach to identify synergistic and effective drug combinations based on single drug efficacy information and limited drug combination testing. When applied to mutant BRAF melanoma, this approach exhibited significant predictive power upon evaluation with cross-validation and further experimental testing of previously untested drug combinations in cell lines independent of the training set. Altogether this work demonstrates how the integration of orthogonal datasets gives us power to address difficult questions that are critical for precision medicine and drug discovery. Approaches such as these have the potential to make a direct impact on how patients are treated, as well as to help prioritize and guide additional focused studies

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 sto- chastic simulation algorithm (SSA), and explicit and implicit tau-leaping al- gorithms. 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 Immunode ciency Virus (HIV) within host in- fection model. While the rst has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative effciency 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 effciency 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 modication of tau-Leaping methods are preferred

Simulation Algorithms for Continuous Time Markov Chain Models

Continuous time Markov chains are often used in the literature to model the dynamics of a system with low species count and uncertainty in transitions. In this paper, we investigate three particular algorithms that can be used to numerically simulate continuous time Markov chain models (a stochastic simulation algorithm, explicit and implicit tau-leaping algorithms). To compare these methods, we used them to analyze two stochastic infection models with different level of complexity. One of these models describes the dynamics of Vancomycin-Resistant Enterococcus (VRE) infection in a hospital, and the other is for the early infection of Human Immunodeficiency Virus (HIV) within a host. 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 similar computational efficiency for the VRE model due to the low number of species and small number of transitions. However, we found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred

A Computational Approach for Identifying Synergistic Drug Combinations

<div><p>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.</p></div

Identification of Synergistic Combinations involving the BRAF Inhibitor PLX4720.

<p>A The observed growth inhibition levels for PLX4720 alone (blue) 17AAG alone (red), PLX4720 varying while 17AAG held constant at 1uM (navy blue), and 17AAG varying while PLX4720 held constant at 1 uM (dark red). B The observed growth inhibition levels for PLX4720 alone (blue) FAK Inhibitor 14 alone (red), PLX4720 varying while FAK Inhibitor 14 held constant at 0.1uM (navy blue), and FAK Inhibitor 14 varying while PLX4720 held constant at 0.1 uM (dark red).</p

Experimental Validation of Predicted BRAF Effective and Synergistic Combinations.

<p>A We selected a set of 11 combinations involving 7 drugs with a diverse set of predictions for experimental validation. These included traditional chemotherapeutic agents (doxorubicin, paclitaxel), targeted agents (PLX4720, gefitinib, FAK Inhibitor 14), a statin (simvastatin), and an antitumor antibiotic (17AAG). B Each drug was tested in combination at medium, and high concentrations, estimated from their GI₁₀, GI₂₅, and GI₅₀ values respectively. The observed growth inhibition levels for all dosage level combinations involving each tested drug combination are shown in violin plots. Violin plots that are colored navy blue are those whose third quantile values where 70% or greater. The predictions for each combination are shown below the plot, with dark blue representing an effective prediction and grey representing an ineffective prediction. C For each tested drug combination, the Chou-Talalay synergy scores were calculated. The observed synergy scores for all dosage level combinations involving each tested drug combination are shown in violin plots. The predictions for each combination are shown below the plot, with green representing a synergy prediction and red representing a non-synergy prediction.</p

Method schematic and Evaluation of individual model performance.

<p>A Our approach integrates a large single drug screen of 150 drugs with a combinatorial drug screen, which tested 780 combinations of 40 unique drugs. This was used to train a random forest model that predicts synergy and genotype-selective efficacy for untested drug combinations. B Receiver operating characteristic (ROC) curves for 10-fold cross-validation of the BRAF-specific effectiveness (top) and synergy (bottom) models. C The effect of randomly removing samples on model accuracy, sensitivity and specificity. At 25% of the original number of combinations was used to train the model, the approach maintained 77.56% accuracy, 89.27% specificity, and 54.91% sensitivity.</p