Papers by Kaitlyn Gayvert
Targeted therapies designed to specifically target molecules involved in carcinogenesis have achi... more 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 fa...
Simulation algorithms for continuous time Markov chain models
North Carolina State University. Center for Research in Scientific Computation, 2011
Continuous time Markov chains are often used in the literature to model the dynamics of a system ... more 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 Single Dose Of Fel d 1 Monoclonal Antibodies Regulates Molecular Signatures of Asthma In Nasal Mucosa Upon Cat Allergen Challenge In A Phase 2 Study
Journal of Allergy and Clinical Immunology
Blocking common γ chain cytokine signaling ameliorates T cell–mediated pathogenesis in disease models
Science Translational Medicine
The common γ chain (γc; IL-2RG) is a subunit of the interleukin (IL) receptors for the γc cytokin... more 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 tha...
Evaluation of Common Gamma Chain Cytokine Signaling Blockade with REGN7257, an Interleukin 2 Receptor Gamma (IL2RG) Monoclonal Antibody, on Immune Cell Populations in Monkey and Human
Blood
Biomechanical gait analysis informs clinical practice and research by linking characteristics of ... more Biomechanical gait analysis informs clinical practice and research by linking characteristics of gait with neurological or musculoskeletal injury or disease. However, there are limitations to analyses conducted at gait labs as they require onerous construction of force plates into laboratories mimicking the lived environment, on-site patient assessments, as well as requiring specialist technicians to operate. Digital insoles may offer patient-centric solutions 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 healthy 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....
ABSTRACTPublic health surveillance, drug treatment development, and optimization of immunological... more ABSTRACTPublic health surveillance, drug treatment development, and optimization of immunological interventions all depend on understanding pathogen adaptation, which differ for specific pathogens. SARS-CoV-2 is an exceptionally successful human pathogen, yet complete understanding of the forces driving its evolution is lacking. Here, we leveraged almost four million SARS-CoV-2 sequences originating mostly from non-vaccinated naïve patients to investigate the impact of functional constraints and natural immune pressures on the sequence diversity of the SARS-CoV-2 genome. Overall, we showed that the SARS-CoV-2 genome is under strong and intensifying levels of purifying selection with a minority of sites under diversifying pressure. With a particular focus on the spike protein, we showed that sites under selection were critical for protein stability and virus fitness related to increased infectivity and/or reduced neutralization by convalescent sera. We investigated the genetic divers...
Drug-Induced Expression-Based Computational Repurposing of Small Molecules Affecting Transcription Factor Activity
Methods in molecular biology, 2019
Inhibition of oncogenes and reactivation of tumor suppressors are well-established goals in antic... more 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.
Nature Communications, 2019
Drug target identification is a crucial step in development, yet is also among the most complex. ... more 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 r...
ABSTRACTLoss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vuln... more ABSTRACTLoss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated dr...
One of the main causes for failure in the drug development pipeline or withdrawal post approval i... more One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the "moneyball" approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compound's structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 diff...
Abstract 3916: A “moneyball” approach to predicting clinical trial toxicity events
Cancer Research, 2016
Over the past decade, significant strides have been made in the management and treatment of vario... more Over the past decade, significant strides have been made in the management and treatment of various diseases. Despite this progress, clinical attrition rates have continued to substantially rise. Clinical trials can fail for a variety of reasons, ranging from design issues to drug efficacy and safety problems. Drug-likeness approaches, as first proposed by Lipinski almost two decades ago, have become a key tool for the pre-selection of compounds that are likely to have manageable toxicity in clinical studies. However all these methods consider molecular properties of the drug itself alone. In general, these approaches struggle to simultaneously well-characterize the properties of both FDA approved drugs (which we term the sensitivity) and drugs that fail clinical trials (specificity). We introduce an approach that integrates chemical properties of a compound, along with that of its targets, to provide a new quantitative measure that helps predict whether drugs in clinical trials wil...
Cancer Research, 2018
Background: Delta-like protein 3 (DLL3) is expressed on the surface of small cell lung cancer (SC... more Background: Delta-like protein 3 (DLL3) is expressed on the surface of small cell lung cancer (SCLC) tumor-initiating cells, and the DLL3 targeted antibody-drug conjugate, Rovalpituzumab tesirine (Rova-T™; SC16LD6.5), has shown promise for patients with SCLC. Neuroendocrine prostate cancer (NEPC) is an emerging late stage subtype of castration resistant prostate cancer with limited therapeutic options. Based on clinical and molecular similarities with SCLC, we investigated expression of DLL3 and the use of Rovalpituzumab tesirine in NEPC xenografts. Methods: We evaluated mRNA and/or protein expression of DLL3 in a cohort of 361 patients (552 samples) ranging from benign prostate (BEN), localized prostate adenocarcinoma (PCA), castration resistant adenocarcinoma (CRPC), and castration resistant NEPC and correlated with pathologic and genomic features. mRNA was assessed by RNAseq. Protein was assessed by immunohistochemistry (DLL3 SP347 antibody). Prostate cancer cell lines were engra...
Science Translational Medicine, 2019
DLL3 is overexpressed in neuroendocrine prostate cancer and is a potential therapeutic target.
Journal of Clinical Oncology, 2017
5029 Background: TheNotch ligand Delta like ligand 3 (DLL3) is aberrantly expressed on the cell s... more 5029 Background: TheNotch ligand Delta like ligand 3 (DLL3) is aberrantly expressed on the cell surface of small cell lung cancer (SCLC), and the DLL3-antibody drug conjugate, Rova-T, has shown promise for patients with SCLC (Rudin et al, Lancet Onc 2017). NEPC is a late stage subtype of castration resistant prostate cancer with limited therapeutic options. Based on clinical and molecular similarities with SCLC, we investigated expression of DLL3 and the use of Rova-T in NEPC. Methods: We evaluated mRNA and/or protein expression of DLL3 in a cohort of 395 patients (535 samples) ranging from benign prostate (BEN), localized prostate adenocarcinoma (PCA), castration resistant adenocarcinoma (CRPC), and NEPC and correlated with pathologic and genomic features. Prostate cancer cell lines and patient-derived organoids were treated with Rova-T (SC16LD6.5) in vitro and in vivo. Results: DLL3 was expressed at the mRNA and/or protein level in 0/143 BEN (0%), 4/266 PCA (1%), 8/76 CRPC (10%), ...
Abstract B142: A “moneyball” approach to predicting clinical trial failures and successes
Molecular Cancer Therapeutics, 2015
Over the past decade, significant strides have been made in the management and treatment of vario... more Over the past decade, significant strides have been made in the management and treatment of various diseases. Despite this progress, drug attrition rates due to clinical trial failure have continued to substantially rise. Clinical trials can fail for various reasons, ranging from design issues to drug efficacy and safety problems. Drug-likeness approaches, as first proposed by Lipinski almost two decades ago, have become a key tool for the pre-selection of compounds that are likely to have manageable toxicity in clinical studies. However all these methods consider molecular properties of the drug itself alone. In general, these approaches struggle to simultaneously well-characterize the properties of both FDA approved drugs (sensitivity) (and drugs that fail clinical trials (specificity). We introduce a novel data-driven approach (PrOCTOR) that integrates chemical properties of a compound, along with that of its targets, to provide a new measure, the PrOCTOR score, that helps predic...
Abstract 5039: A data driven approach to predicting tissue-specific adverse events
Cancer Research, 2017
Adverse events are currently one of the main causes of failure in drug development and withdrawal... more Adverse events are currently one of the main causes of failure in drug development and withdrawal after approval. As a result, predicting drug side effects is an incredibly important part of drug discovery and development. With the emergence of precision medicine there has been a surge in interest on creating drugs for specific protein targets, however we lack accurate ways to connect drug targets and mechanisms to specific side effects. Here we take a target-centric approach to in-silico drug side effect prediction. We have mined drug side effect databases and grouped sets of side effects to the originating human tissue. For each of 30+ tissues, we defined a set of “toxic targets”- proteins that are only targeted by drugs with toxicity in that tissue - and “safe targets” - proteins only targeted by drugs with no related tissue toxicities. We found that toxic targets are consistently more highly expressed than safe targets, indicating that their mechanisms may be more crucial in the...
Abstract 1563: A machine learning approach to predict platform specific gene essentiality in cancer
Cancer Research, 2017
Loss-of-function (LOF) screenings across a set of diverse cancer cell lines has the potential to ... more Loss-of-function (LOF) screenings across a set of diverse cancer cell lines has the potential to reveal novel synthetic lethal interactions, cancer-specific vulnerabilities, and guide treatment options. These were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. The Achilles project is to date the largest cancer LOF screening effort undertaken, however we found a large amount of inconsistency between their shRNA and CRISPR-Cas9 essentiality results for the same set of cell lines. Here we characterize the differences between genes found to be essential in either CRISPR or shRNA screens. We found that certain features such as gene expression, network connectivity and conservation could accurately separate out essential genes that were found exclusively in either one of these screens. This information could be tremendously useful in understanding the differences in the CRISPR and shRNA screening results. Furtherm...
Drug target identification is one of the most important aspects of pre-clinical development yet i... more Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, includin...
European Journal of Cancer, 2016
Background: Human Epidermal Growth Factor Receptor 3 (HER3) is a member of the HER family of rece... more Background: Human Epidermal Growth Factor Receptor 3 (HER3) is a member of the HER family of receptor tyrosine kinases implicated in the development and progression of several human cancers. HER3 expression is frequently observed in malignant tumors and associated with resistance to therapy and poor prognosis. In spite of its important role, no standard methods to determine the expression of HER3 in clinical samples have been devised for patient stratification or diagnostic purposes. Here, we quantitatively assessed HER3 mRNA and protein expression in human cancer tissue samples applying automated in situ hybridization (ISH) and immunohistochemistry (IHC) in conjunction with digital image analysis. Material and Methods: We successfully established RNAscope HER3 ISH and anti-HER3 IHC on the Ventana DISCOVERY XT automated staining platform and analyzed a tissue microarray (TMA) of 39 formalin-fixed paraffin-embedded tumor cores representing seven human cancer types (non-small cell lung cancer, breast cancer, colorectal cancer, gastric cancer, prostate cancer, hepatocellular carcinoma and ovarian cancer). To quantify HER3 mRNA and protein expression levels, image analysis algorithms were developed using Indica Labs HALO software. A pan-cytokeratin (pan-CK) IHC stained serial TMA section was used to define tumor regions of interest (ROI) within each core. Pan-CK-stained cores were then coregistered with their associated HER3 ISH and anti-HER3 IHC cores to quantify numbers of HER3 mRNA and protein positive cells per mm 2 tumor ROI. Results: Confirming published results on the expression of HER3 in different cancer indications, HER3 mRNA and protein were detected in the majority of the analyzed tumor samples. Data generated suggest a strong correlation between HER3 mRNA and protein expression within tumor tissue of lung adenocarcinoma, breast cancer and hepatocellular carcinoma cores. A lower correlation was observed for the other cancer tissue cores analyzed. Conclusions: We showed the feasibility of automated ISH and IHC followed by digital image analysis to quantify the expression of HER3 mRNA and protein in clinical samples. Further studies are required to confirm the robustness and reproducibility of observed correlations between HER3 mRNA and protein per cancer. The standardized quantification offered by our approach could support molecular diagnostic and patient stratification applications in clinical oncology.
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Papers by Kaitlyn Gayvert