70 Beyond PD-L1: novel PD-1 biomarkers identified by driving T cell dysfunction in vitro
Simarjot PablaTenzing KhenduDhan ChandBülent Arman AksoyBenjamin DucklessAndrew J. BasinskiCailin E. JoyceThomas HornLukasz SwiechJeremy D. WaightDavid A. SavitskyJennifer S. Buell
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Abstract:
Background
Anti-PD-1 therapies have achieved durable clinical responses in a wide range of malignancies, but responses are limited to a small subset of patients. Expression of PD-L1 on tumor cells by immunohistochemistry (IHC) has been applied as a companion diagnostic for anti-PD-1 therapy. However, recent studies have called in to question the reliability of this method to predict response.Methods
Here we developed a novel platform that integrates in vitro pharmacogenomic and functional data with clinical pharmacodynamic responses to immunotherapy using proprietary in silico approaches. The data originate from a long-term co-culture of primary antigen-specific T cells and cancer cells which drives T cells to a terminally dysfunctional, PD-1 refractory state. T cell effector functions and gene expression changes were monitored in the presence or absence of anti-PD-1 antibody or genetic knockouts. RNA expression signatures were refined with a randomized sliding window approach to generate a deep learning neural network for PD-1 response prediction.Results
We defined five T cell states associated with distinct phenotypic and molecular features - naïve, active, effector, transition and dysfunction. Among the genes that were selectively expressed in the dysfunction state, we identified a 96-gene signature that is closely associated with clinical outcomes to anti-PD-1 therapy. In PD-1 treated patients across multiple solid tumor indications, this signature correlates with objective response rate and outperforms traditional metrics such as tumor mutation burden or PD-L1 IHC signal. Moreover, this signature combines with tumor sequencing data to generate a powerful machine-learning model that predicts anti-PD-1 responses in metastatic melanoma patients with significantly higher accuracy than PD-L1 IHC. Having established that the T cell states in our co-culture relate to clinical outcomes, we leveraged the system to investigate the molecular basis for PD-1 responses. Single cell mapping of transition state T cells in the presence of anti-PD-1 revealed an expanded population of T cells that co-expresses PD-1, TIGIT and activation markers. Likewise, PD-L1 knockout on cancer cells identified the TIGIT ligand, CD155, as a potential tumor escape mechanism to anti-PD-1 therapy. Consistent with this, the combination of PD-1 and TIGIT blockade enhanced T cell cytotoxicity of tumor cells relative to monotherapies.Conclusions
Agenus' T cell dysfunction platform combines deep in vitro profiling and AI-based approaches to predict clinical outcomes. Here, we defined a predictive biomarker signature that outperforms standard PD-L1 IHC. Further, we identified known (TIGIT) and potentially novel combination partners predicted to enhance the durability of anti-PD-1 responses.Ethics Approval
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Saponins is one of medical compound which synthesized by several key genes in plants, including β-AS. Fisrt step to enhance of plant secondary metabolites was by determining of TFs which controlled target genes. in vitro and in vivo approach cannot be used directly to determine the kinds of the TFs which controls the target gene because both of approach above were inefficient and ineffective. Therefore, the initial determination of the type TFs-based in silico studies need to be done. This paper was aims to describe the steps that can be done to determine the type of the TFs gene β-AS in A. thaliana as a model plant using in silico approach. This study reveals that there were four main stages i.e. determining β-AS gene sequence and direction of transcription, determine the sequence of the promoter from start CDS, determine of TFs by using tfbind and look for the FTs which have highest interaction score, mapping of selected TFs by using PlantFTDB . The kinds of acquired TFs can be used as a information base to determine of the next method and laboratories technique.
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The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 “virtual hit” compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12–21% (full dose–response curves, K i < 5 μM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.
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High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants.Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant.Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant.Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives.Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm.Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection.For the individual genomes the false positive contaminants reported depended on the genus with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives.For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells.Though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of 1 in 10 cells.Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in effort to ensure that contaminants in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.
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Elucidation of gene regulatory complexity holds much promise towards aiding therapeutic interventions in medical research. It has become progressively more evident that the characterization of highly conserved regulatory modules within promoters may assist in the elucidation of distinct cis-motif and trans-element regulatory interactions, shared in response to stimulus-evoked pathological changes. With special emphasis on the promoter, accurate analyses of cis-motif architecture combined with integrative in silico modelling might serve as a more refined approach for prediction and study of regulatory targets and major regulators governing transcriptional control. In this review, we have highlighted key examples and recent advances implementing in silico promoter models that could serve as essential contributions for future research in molecular medicine.
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A major goal of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. Here we demonstrate that in the yeast Saccharomyces cerevisiae the phenotypic effect of single gene deletions simultaneously correlates with fluctuations in mRNA expression profiles, the functional categorization of the gene products, and their connectivity in the yeasts protein-protein interaction network. Building on these quantitative correlations, we developed a computational method for predicting the phenotypic effect of a given genes functional disabling or removal. Our subsequent analyses were in good agreement with the results of systematic gene deletion experiments, allowing us to predict the deletion phenotype of a number of untested yeast genes. The results underscore the utility of large genomic databases for in silico systematic drug target identification in the postgenomic era.
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Identification of promiscuous peptides, which bind to human leukocyte antigen, is indispensable for global vaccination. However, the development of such vaccines is impaired due to the exhaustive polymorphism in human leukocyte antigens. The use of in silico tools for mining such peptides circumvents the expensive and laborious experimental screening methods. Nevertheless, the intrepid use of such tools warrants a rational assessment with respect to experimental findings. Here, we have adopted a 'bottom up' approach, where we have used experimental data to assess the reliability of existing in silico methods. We have used a data set of 179 peptides from diverse antigens and have validated six commonly used in silico methods; ProPred, MHC2PRED, RANKPEP, SVMHC, MHCPred, and MHC-BPS. We observe that the prediction efficiency of the programs is not balanced for all the HLA-DR alleles and there is extremely high level of discrepancy in the prediction efficiency apropos of the nature of the antigen. It has not escaped our notice that the in silico methods studied here are not very proficient in identifying promiscuous peptides. This puts a much constraint on the intrepid use of such programs for human leukocyte antigen class II binding peptides. We conclude from this study that the in silico methods cannot be wholly relied for selecting crucial peptides for development of vaccines.
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Computational approaches are becoming increasingly popular for the discovery of drug candidates against a target of interest. Proteins have historically been the primary targets of many virtual screening efforts. While in silico screens targeting proteins has proven successful, other classes of targets, in particular DNA, remain largely unexplored using virtual screening methods. With the realization of the functional importance of many non-cannonical DNA structures such as G-quadruplexes, increased efforts are underway to discover new small molecules that can bind selectively to DNA structures. Here, we describe efforts to build an integrated in silico and in vitro platform for discovering compounds that may bind to a chosen DNA target. Millions of compounds are initially screened in silico for selective binding to a particular structure and ranked to identify several hundred best hits. An important element of our strategy is the inclusion of an array of possible competing structures in the in silico screen. The best hundred or so hits are validated experimentally for binding to the actual target structure by a high-throughput 96-well thermal denaturation assay to yield the top ten candidates. Finally, these most promising candidates are thoroughly characterized for binding to their DNA target by rigorous biophysical methods, including isothermal titration calorimetry, differential scanning calorimetry, spectroscopy and competition dialysis.This platform was validated using quadruplex DNA as a target and a newly discovered quadruplex binding compound with possible anti-cancer activity was discovered. Some considerations when embarking on virtual screening and in silico experiments are also discussed.
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