Target Prioritization Research Articles

Social Media as Pharmacovigilance: The Potential for Patient Reports to Inform Clinical Research on Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists for Substance Use Disorders.

The surge in popularity of semaglutide (Ozempic, Wegovy, Rybelsus) and other glucagon-like-peptide 1 (GLP-1) receptor agonists has been accompanied by widespread reports of unintended reductions in alcohol use (and other addictive behaviors) during treatment. With clinical trials of GLP-1 receptor agonists for substance use only recently under way, anecdotal reports (including via social media) are now a primary reason for interest in potential effects of GLP-1 receptor agonists on alcohol use in patient populations. The nature and volume of these reports raises the prospect that social media data can potentially be leveraged to inform the study of novel addiction treatments and the prioritization of behavioral or neurobiological targets for mechanistic research. This approach, which aligns with recent efforts to apply social media data to pharmacovigilance, may be particularly relevant for drug repurposing efforts. This possibility is illustrated by a thematic analysis of anonymous online reports concerning changes in alcohol use or alcohol-related effects during treatment with GLP-1 receptor agonists. These reports not only support the rationale for clinical trials but also point to potential neurobehavioral mechanisms (e.g., satiety, craving/preoccupation, aversion, altered subjective response) that might inform hypotheses for human laboratory and neuroscience studies. Refined methods for capturing patient reports of incidental medication effects on addictive behaviors at large scale could potentially lead to novel, pharmacovigilance-based approaches to identify candidate therapies for drug repurposing efforts.

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets.

Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

In-silico identification and prioritization of therapeutic targets of asthma

Asthma is a “common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asthma and the response to treatment" [cited from: National Heart, Lung, and Blood Institute. Expert Panel 3 Report. Guidelines for the Diagnosis and Management of Asthma 2007 (EPR-3). Available at: https://www.ncbi.nlm.nih.gov/books/NBK7232/ (accessed on January 3, 2023)]. As per the WHO, 262 million people were affected by asthma in 2019 that leads to 455,000 deaths (https://www.who.int/news-room/fact-sheets/detail/asthma). In this current study, our aim was to evaluate thousands of scientific documents and asthma associated omics datasets to identify the most crucial therapeutic target for experimental validation. We leveraged the proprietary tool Ontosight® Discover to annotate asthma associated genes and proteins. Additionally, we also collected and evaluated asthma related patient datasets through bioinformatics and machine learning based approaches to identify most suitable targets. Identified targets were further evaluated based on the various biological parameters to scrutinize their candidature for the ideal therapeutic target. We identified 7237 molecular targets from published scientific documents, 2932 targets from genomic structured databases and 7690 dysregulated genes from the transcriptomics and 560 targets from genomics mutational analysis. In total, 18,419 targets from all the desperate sources were analyzed and evaluated though our approach to identify most promising targets in asthma. Our study revealed IL-13 as one of the most important targets for asthma with approved drugs on the market currently. TNF, VEGFA and IL-18 were the other top targets identified to be explored for therapeutic benefit in asthma but need further clinical testing. HMOX1, ITGAM, DDX58, SFTPD and ADAM17 were the top novel targets identified for asthma which needs to be validated experimentally.

State-of-the-art strategies to prioritize Mycobacterium tuberculosis drug targets for drug discovery using a subtractive genomics approach

Tuberculosis remains one of the causes of death from a single infectious bacterium. The inappropriate use of antibiotics and patients’ non-compliance among other factors drive the emergence of drug-resistant tuberculosis. Multidrug-resistant and extensively drug-resistant strains of tuberculosis pose significant challenges to current treatment regimens, as their reduced efficacy against these strains limits successful patient outcomes. Furthermore, the limited effectiveness and associated toxicity of second-line drugs further compound the issue. Moreover, the scarcity of novel pharmacological targets and the subsequent decline in the number of anti-TB compounds in the drug development pipeline has further hindered the emergence of new therapies. As a result, researchers need to develop innovative approaches to identify potential new anti-TB drugs. The evolution of technology and the breakthrough in omics data allow the use of computational biology approaches, for example, metabolomic analysis to uncover pharmacological targets for structured-based drug design. The role of metabolism in pathogen development, growth, survival, and infection has been established. Therefore, this review focuses on the M. tb metabolic network as a hub for novel target identification and highlights a step-by-step subtractive genomics approach for target prioritization.

Evaluation of an Optional English Preparatory Program with CIPP Scale

The quality of English language education in universities is vital in preparing individuals for the increasingly globalized world. Assessing the efficacy of English teaching programs is therefore crucial to ensuring such quality. Consequently, this study aims to evaluate the optional English preparatory program of a state university using the CIPP model dimensions: context, input, process, and product. The current study employs a mixed-methods research approach, combining quantitative and qualitative data to address the research questions. The study sample comprised 247 preparatory graduate and 77 preparatory students, as well as 6 lecturers. In the quantitative part, The CIPP scale (Context, Input, Process, and Product), developed by the researchers based on item analysis, second-order exploratory factor analysis, reliability analysis, criterion validity, and content validity, was used to determine the strengths and weaknesses of the English preparatory program based on responses of participants to the items in four dimensions of the scale. The qualitative aspect of the study involved obtaining responses from participant students through open-ended questions and conducting semi-structured interviews with lecturers. These responses were analyzed, and the resulting excerpts were used to support and complement the findings obtained from the quantitative findings. The results indicate that the participants were highly satisfied with the optional nature of the program and held positive perceptions of it. However, some improvements, particularly in the physical conditions of the program's context dimension, such as the library, laboratory, and school building, were required. The findings from the input and process dimensions reveal the importance of increasing target language use and prioritization of speaking skills in the product dimension. The developed CIPP scale, which is statistically reliable and valid, can be utilized by researchers to evaluate English preparatory programs using the CIPP model. The study concludes by offering several key recommendations for creating a more effective English preparatory program based on the findings.

Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis

Drug targets with genetic support are several-fold more likely to succeed in clinical trials. We introduce a genetic-driven approach based on causal inferences that can inform drug target prioritization, repurposing, and adverse effects of using lipid-lowering agents. Given that a multi-trait approach increases the power to detect meaningful variants/genes, we conduct multi-omics and multi-trait analyses, followed by network connectivity investigations, and prioritize 30 potential therapeutic targets for dyslipidemia, including SORT1, PSRC1, CELSR2, PCSK9, HMGCR, APOB, GRN, HFE2, FJX1, C1QTNF1, and SLC5A8. 20% (6/30) of prioritized targets from our hypothesis-free drug target search are either approved or under investigation for dyslipidemia. The prioritized targets are 22-fold higher in likelihood of being approved or under investigation in clinical trials than genome-wide association study (GWAS)-curated targets. Our results demonstrate that the genetic-driven approach used in this study is a promising strategy for prioritizing targets while informing about the potential adverse effects and repurposing opportunities.

Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

Profiling cell envelope-antibiotic interactions reveals vulnerabilities to β-lactams in a multidrug-resistant bacterium

The cell envelope of Gram-negative bacteria belonging to the Burkholderia cepacia complex (Bcc) presents unique restrictions to antibiotic penetration. As a consequence, Bcc species are notorious for causing recalcitrant multidrug-resistant infections in immunocompromised individuals. Here, we present the results of a genome-wide screen for cell envelope-associated resistance and susceptibility determinants in a Burkholderia cenocepacia clinical isolate. For this purpose, we construct a high-density, randomly-barcoded transposon mutant library and expose it to 19 cell envelope-targeting antibiotics. By quantifying relative mutant fitness with BarSeq, followed by validation with CRISPR-interference, we profile over a hundred functional associations and identify mediators of antibiotic susceptibility in the Bcc cell envelope. We reveal connections between β-lactam susceptibility, peptidoglycan synthesis, and blockages in undecaprenyl phosphate metabolism. The synergy of the β-lactam/β-lactamase inhibitor combination ceftazidime/avibactam is primarily mediated by inhibition of the PenB carbapenemase. In comparison with ceftazidime, avibactam more strongly potentiates the activity of aztreonam and meropenem in a panel of Bcc clinical isolates. Finally, we characterize in Bcc the iron and receptor-dependent activity of the siderophore-cephalosporin antibiotic, cefiderocol. Our work has implications for antibiotic target prioritization, and for using additional combinations of β-lactam/β-lactamase inhibitors that can extend the utility of current antibacterial therapies.

Graded prioritisation of targets in search: reward diminishes the low prevalence effect

In many real-life contexts, observers are required to search for targets that are rarely present (e.g. tumours in X-rays; dangerous items in airport security screenings). Despite the rarity of these items, they are of enormous importance for the health and safety of the public, yet they are easily missed during visual search. This is referred to as the prevalence effect. In the current series of experiments, we investigate whether unequal reward can modulate the prevalence effect, in a multiple target search task. Having first established the impact of prevalence (Experiment 1) and reward (Experiment 2) on how efficiently participants can find one of several targets in the current paradigm, we then combined the two forms of priority to investigate their interaction. An unequal reward distribution (where lower prevalence items are more rewarded; Experiment 3) was found to diminish the effect of prevalence, compared to an equal reward distribution (Experiment 4) as indicated by faster response times and fewer misses. These findings suggest that when combined with an unequal reward distribution, the low prevalence effect can be diminished.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody–drug conjugates and immunotherapeutic agents.

Prioritization of alien plant targets for biological control in South Africa's offshore sub-Antarctic islands

AbstractInvasive alien plants (IAPs) are a significant threat to the biodiversity and ecological functioning of the sub-Antarctic Prince Edward Islands (PEIs); however, weed biological control has not yet been implemented as a management tool. The PEIs have had 23 alien plants recorded, of which several are considered beyond eradication and therefore could benefit from the release of safe and effective biocontrol agents. We used the South African Biological Control Target Selection (BCTS) system to provide a ranked list of the target species in order of priority for further biocontrol research. The highest-scoring species, and therefore those that should be considered first to be targeted, were Sagina procumbens and Cerastium fontanum. No biocontrol agents for IAPs have been released in any climate with as consistent low temperatures as in the PEIs, so the climatic compatibility of agents will be essential prior to any decision to release. However, with very few native species present on the PEIs, the programmes would involve limited host specificity testing, which would improve the feasibility and speed with which new biocontrol agents could be developed. Biocontrol may offer an environmentally safe and sustainable method of reducing the negative impacts of these priority species and thus warrants further investigation.

Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease.

We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.

Priority index for asthma (PIA): In silico discovery of shared and distinct drug targets for adult- and childhood-onset disease

Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.

A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein–protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Prioritization of Drug Targets for Neurodegenerative Diseases by Integrating Genetic and Proteomic Data From Brain and Blood

BackgroundNeurodegenerative diseases are among the most prevalent and devastating neurological disorders, with few effective prevention and treatment strategies. We aimed to integrate genetic and proteomic data to prioritize drug targets for neurodegenerative diseases. MethodsWe screened human proteomes through Mendelian randomization to identify causal mediators of Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, multiple sclerosis, frontotemporal dementia, and Lewy body dementia. For instruments, we used brain and blood protein quantitative trait loci identified from one genome-wide association study with 376 participants and another with 3301 participants, respectively. Causal associations were subsequently validated by sensitivity analyses and colocalization. The safety and druggability of identified targets were also evaluated. ResultsOur analyses showed targeting BIN1, GRN, and RET levels in blood as well as ACE, ICA1L, MAP1S, SLC20A2, and TOM1L2 levels in brain might reduce Alzheimer’s disease risk, while ICA1L, SLC20A2, and TOM1L2 were not recommended as prioritized drugs due to the identified potential side effects. Brain CD38, DGKQ, GPNMB, and SEC23IP were candidate targets for Parkinson’s disease. Among them, GPNMB was the most promising target for Parkinson’s disease with their causal relationship evidenced by studies on both brain and blood tissues. Interventions targeting FCRL3, LMAN2, and MAPK3 in blood and DHRS11, FAM120B, SHMT1, and TSFM in brain might affect multiple sclerosis risk. The risk of amyotrophic lateral sclerosis might be reduced by medications targeting DHRS11, PSMB3, SARM1, and SCFD1 in brain. ConclusionsOur study prioritized 22 proteins as targets for neurodegenerative diseases and provided preliminary evidence for drug development. Further studies are warranted to validate these targets.

Applications of single-cell RNA sequencing in drug discovery and development.

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Omics data integration facilitates target selection for new antiparasitic drugs against TriTryp infections.

Introduction: Trypanosoma cruzi, Trypanosoma brucei, and Leishmania spp., commonly referred to as TriTryps, are a group of protozoan parasites that cause important human diseases affecting millions of people belonging to the most vulnerable populations worldwide. Current treatments have limited efficiencies and can cause serious side effects, so there is an urgent need to develop new control strategies. Presently, the identification and prioritization of appropriate targets can be aided by integrative genomic and computational approaches. Methods: In this work, we conducted a genome-wide multidimensional data integration strategy to prioritize drug targets. We included genomic, transcriptomic, metabolic, and protein structural data sources, to delineate candidate proteins with relevant features for target selection in drug development. Results and Discussion: Our final ranked list includes proteins shared by TriTryps and covers a range of biological functions including essential proteins for parasite survival or growth, oxidative stress-related enzymes, virulence factors, and proteins that are exclusive to these parasites. Our strategy found previously described candidates, which validates our approach as well as new proteins that can be attractive targets to consider during the initial steps of drug discovery.

Abstract 2059: Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets

Abstract Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&amp;E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&amp;E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.

Abstract 4121: Heterogenous cellular responses to GITR and TIGIT immunotherapy in the human gastrointestinal tumor microenvironment

Abstract Many new immunotherapy agents fail in clinical trials. Major gaps remain in our understanding of their precise mechanism of action, with findings from preclinical studies that do not translate into the clinic. We identified the cellular and molecular responses to co-stimulatory GITR and co-inhibitory TIGIT immunotherapy by using an experimental approach that overcomes these challenges. We investigated these responses in a patient-derived ex vivo system that maintains the original tumor microenvironment (TME) in its near-native state. Using single-cell RNA sequencing (scRNA-seq) and paired single-cell TCR sequencing (scTCR-seq), we identified treatment induced transcriptional reprogramming in different cell lineages in the TME. We established ex vivo slice cultures from ten surgical resections of colorectal or gastric cancer. These cultures maintained all epithelial, stromal, and immune cell types and transcriptional cell states found in the original tissue sample. All tumors responded with activation of CD8, CD4 and T regulatory (Treg) cells following PMA/Ionomycin treatment, confirming the viable and functional status of the model system. GITR agonist led to a modest increase in cytotoxic gene expression in CD8 T cells in four out of nine patients. Transcriptional non-responders had significantly increased dysfunctional phenotype at baseline compared to responders. Ex vivo analysis confirmed that only cells with an effector non-exhausted cell state responded to GITR agonist, while exhausted cells did not. GITR agonist did not induce significant changes in expression in regulatory T (Treg) or T follicular helper-like (TFh-like) cells. TIGIT antagonist increased TCR signaling and activation in all five patients. These responses were observed in both effector and exhausted cells. A reduced transcriptional response to TIGIT in CD8 T cells correlated with the increased expression of GZMK, TXNIP and RGS1 together with reduced expression of metallothionine family genes. TIGIT antagonist also increased activation specifically in expanded TCR clonotypes, which are indicative of tumor antigen reactive cells. TIGIT antagonist further increased activation of TFh-like cells together with increased expression of B cell attracting chemokine CXCL13. Tregs responded with a reduced expression of CTLA4 and TNFRSF4 (OX40), indicative of modulation of immunosuppressive cell state.Our approach successfully identified heterogenous cellular and patient responses to GITR stimulation and TIGIT inhibition in gastrointestinal cancers. TIGIT antagonist led to a wider reprogramming of the TME compared to the limited effects of GITR agonist. Our strategy identified mechanisms of action of immunotherapy and factors associated with response or resistance, which can aid in prioritization of targets and their clinical translation. Citation Format: Anuja Sathe, Carlos Ayala, Xiangqi Bai, Sue M. Grimes, Andrew Shelton, Byrne Lee, Cindy Kin, George Poultsides, Hanlee P. Ji. Heterogenous cellular responses to GITR and TIGIT immunotherapy in the human gastrointestinal tumor microenvironment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4121.

Abstract 4515: PIONEER: harnessing multi-omics data to enhance immunotherapeutic target discovery and development

Abstract Introduction: Immunotherapeutic strategies have produced remarkable results in some malignancies. However, optimal cell surface targets in many childhood cancers remain elusive and tools for novel target discovery are limited. We developed a proteogenomic approach to identify high confidence cell surface proteins for immunotherapy development and applied it to neuroblastoma, an often fatal childhood cancer. Through the Pediatric Immunotherapy Discovery and Development Network (PI-DDN), we have extended this approach to 14 high-risk childhood cancers. This effort includes MS-based surfaceome data generation for 175 patient-derived xenograft (PDX) models, 30 primary patient tumors, and 10 human derived cell line models. Methods/Results: To optimize the utility of our approach and data, we are developing a web-based application called PIONEER (Pediatric Integrative Omics Network Enhancing Early Research). The goal of PIONEER is to disseminate data to the broader scientific community and to provide the necessary analysis, query and visualization tools to make these data accessible to everyone, regardless of computational expertise. A pilot version of PIONEER was developed using R Shiny Dashboard and is derived from our neuroblastoma efforts. The application is comprised of two main categories: (1) target discovery data and prioritization (2) target validation and preclinical development. Modules include proteomics, transcriptomics, epigenomics, multi-omics, validation and pre-clinical drug development. Cancer ‘omics data currently housed in PIONEER include tumor and cancer cell line mass-spectrometry based proteomics, RNA-sequencing, and chromatin immunoprecipitation (ChIP) sequencing. Extensive normal tissue expression data from GTEx and mass spectrometry will be integrated. Surface proteins are prioritized through an integrative multi-omic analysis of tumor and normal tissue data. Users can perform queries and cancer subtype and cross-histotype studies, apply custom cutoffs, and generate plots for visualization. We are currently adding functionality to support automatic data analysis and integration for surface proteins (SPACE: Surface Protein Analysis for Collaborative Efforts). Through the target validation and preclinical development modules, users can view an antibody repository, immunofluorescence, immunohistochemistry, drugs in development for each protein, and efficacy in patient derived xenograft models. PIONEER will be deployed using R Connect; data for additional histotypes will be incorporated as available. Conclusion: PIONEER will provide a comprehensive characterization of the surfaceome of high-risk pediatric cancers and a web-based application for data integration, visualization and sharing. This interface facilitates the discovery of optimal immunotherapeutic drug targets in high-risk childhood cancers. Citation Format: Amber K. Weiner, Hemma Murali, Rawan Shraim, Karina L. Conkrite, Alexander B. Radaoui, Daniel Martinez, Brian Mooney, Sandra E. Spencer Miko, Gian Negri, Alberto Delaidelli, Caitlyn de Jong, Yuankun Zhu, Allison P. Heath, Jennifer Pogoriler, Yael P. Mosse, Deanne M. Taylor, Poul H. Sorensen, Gregg B. Morin, Benjamin A. Garcia, John M. Maris, Sharon J. Diskin. PIONEER: harnessing multi-omics data to enhance immunotherapeutic target discovery and development. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4515.