Gene-drug Associations Research Articles

Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance.

Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNswere used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment.

Pharmacogenomics in Psychiatry: An Asian Perspective

IntroductionPharmacogenomic testing in psychiatry is an emerging area with the potential clinical application of guiding medication choice and dosing. However, this has not been adopted widely due to a combination of barriers that include a varying evidence base, clinician and patient familiarity and acceptance, uncertainty about cost-effectiveness, and regulatory requirements.ObjectivesThis review aims to examine recent updates in this field and provide a contextualised summary and recommendations for Asian populations. The recommendations serve to guide healthcare professionals in the utility of pharmacogenomic testing in psychiatric practice.MethodsA review of recent literature about current evidence and guidelines surrounding pharmacogenomics in psychiatric practice was carried out with particular attention paid to literature evaluating Asian populations. Literature was reviewed for the different classes of psychotropics with supplementary information about Asian populations included where available. Existing evidence about combinatorial pharmacogenomic panels was also reviewed.ResultsIn line with the available body of evidence, we recommend that pharmacogenomic testing should be employed as an augmenting tool to guide medication selection and dosing in certain clinical situations, and not as part of standard or routine clinical practice. Pharmacogenomic testing should also be mainly limited to the known drug-gene pairs such as the anti-depressants and CYP2C19 or CYP2D6. Clinicians should also be aware that many of the gene-drug associations have not been evaluated for clinical outcomes. Combinatorial pharmacogenomic panels are not presently recommended as there is limited and inconclusive available evidence on clinical outcomes.ConclusionsPharmacogenomic testing in psychiatry is not recommended as standard or routine clinical practice. Exceptions may include concerns about drug concentrations (due to metaboliser status) or potential severe adverse drug reactions/ Pharmacogenomic testing should be mainly limited to the known drug-gene pairs such as the anti-depressants and CYP2C19 or CYP2D6.Disclosure of InterestNone Declared

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

Transcriptomics analysis reveals distinct mechanism of breast cancer stem cells regulation in mammospheres from MCF-7 and T47D cells

Luminal A breast cancer, constituting 70 % of breast cancer cases, presents a challenge due to the development of resistance and recurrence caused by breast cancer stem cells (BCSC). Luminal breast tumors are characterized by TP53 expression, a tumor suppressor gene involved in maintaining stem cell attributes in cancer. Although a previous study successfully developed mammospheres (MS) from MCF-7 (with wild-type TP53) and T47D (with mutant TP53) luminal breast cancer cells for BCSC enrichment, their transcriptomic profiles remain unclear. We aimed to elucidate the transcriptomic disparities between MS of MCF-7 and T47D cells using bioinformatics analyses of differentially expressed genes (DEGs), including the KEGG pathway, Gene Ontology (GO), drug-gene association, disease-gene association, Gene Set Enrichment Analysis (GSEA), DNA methylation analysis, correlation analysis of DEGs with immune cell infiltration, and association analysis of genes and small-molecule compounds via the Connectivity Map (CMap). Upregulated DEGs were enriched in metabolism-related KEGG pathways, whereas downregulated DEGs were enriched in the MAPK signaling pathway. Drug-gene association analysis revealed that both upregulated and downregulated DEGs were associated with fostamatinib. The KEGG pathway GSEA results indicated that the DEGs were enriched for oxidative phosphorylation, whereas the downregulated DEGs were negatively enriched for the p53 signaling pathway. Examination of DNA methylation revealed a noticeable disparity in the expression patterns of the PKM2, ERO1L, SLC6A6, EPAS1, APLP2, RPL10L, and NEDD4 genes when comparing cohorts with low- and high-risk breast cancer. Furthermore, a significant positive correlation was identified between SLC6A6 expression and macrophage presence, as well as MSN, and AKR1B1 expression and neutrophil and dentritic cell infiltration. CMap analysis unveiled SA-83851 as a potential candidate to counteract the effects of DEGs, specifically in cells harbouring mutant TP53. Further research, including in vitro and in vivo validations, is warranted to develop drugs targeting BCSCs.

Caffeine, Lorazepam, and Morphine Use in Neonatal Intensive Care Unit Population and Potential Relevance of Pharmacogenomics

Abstract Medications are critical for managing acutely ill infants in neonatal intensive care unit (NICU). Tailoring therapies to individual differences in response and dose requirements might help improve outcomes. However, tools for optimizing pharmacotherapy remain limited. In this retrospective study, we explored the potential utility of pharmacogenetics for commonly prescribed NICU medications. All patients admitted to our local NICU March-December, 2022 were eligible for inclusion in the study. In infants who received at least three doses of one or more medications, we extracted DNA from residual EDTA blood collected for routine clinical care according to IRB-approved study protocols. Demographic, pharmacy, and clinical information was recorded retrospectively. Targeted pharmacogenetics testing was performed with a custom TaqMan OpenArray and a QuantStudio 12K Flex (ThermoFisher Scientific) with DNA normalized to 50 ng/mL. Results were analyzed using Genotyper Software version 1.3 and allele calls were made based on the combination of variants detected and consensus nomenclature. For drugs administered to at least 30 NICU patients, pharmacogenes were evaluated for variant allele frequency and potential clinical relevance. Of the 132 patients enrolled, 81 were male. Average length of stay was 47 days. Of the 64 different medications prescribed to at least one infant, for 15 of them are described clinical annotations with specific pharmacogenes by the PharmGKB and/or Clinical Pharmacogenetics Implementation Consortium. Of those 15 medications, three were administered at least three times to at least 30 infants: caffeine (n=102), morphine (n=56), and lorazepam (n=31). The associated pharmacogenes were CYP1A2 (caffeine); CYP3A4, OPRM1, COMT, ABCB1 (morphine); and UGT2B15 (lorazepam). Relevant to the rate of clearance of caffeine, rs2069514 (CYP1A2) was heterozygous in 18% of patients, and homozygous in 5%. Relevant to the rate of clearance of morphine, CYP3A4*22 was heterozygous in 5%. The common variants associated with response to morphine, c.472G>A (COMT) and c.118A>G (OPRM1) were heterozygous in 43% and 26%, and were homozygous in 22% and 8%, respectively. The transporter variant ABCB1*2 was heterozygous in 6% and homozygous in 11%. Relevant to the rate of clearance of lorazepam, UGT2B15*2 (rs1902023) was heterozygous in 45%, and homozygous in 31%. Correlation of pharmacogenetic results to specific clinical phenotypes is currently in progress. Pharmacogenetic variants relevant to commonly prescribed medications in the NICU are prevalent. Further clinical correlation of pharmacogenetic results may help inform selection and dosing of medications for critically ill NICU infants. This research may also facilitate detection and definition of new drug-gene associations unique to this patient population.

Transcriptomics analyses reveal the effects of Pentagamaboronon-0-ol on PI3K/Akt and cell cycle of HER2+ breast cancer cells

IntroductionMonoclonal antibodies and targeted therapies against HER2+ breast cancer has improved overall and disease-free survival in patients; however, encountering drug resistance causes recurrence, necessitating the development of newer HER2-targeted medications. A curcumin analog PGB-0-ol showed most cytotoxicity against HCC1954 HER2+ breast cancer cells than against other subtypes of breast cancer cells. ObjectiveHere, we employed next-generation sequencing technology to elucidate the molecular mechanism underlying the effect of PGB-0-ol on HCC1954 HER2+ breast cancer cells. MethodsThe molecular mechanism underlying the action of PGB-0-ol on HCC1954 HER2+ breast cancer cells was determined using next-generation sequencing technologies. Additional bioinformatics studies were performed, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, disease-gene, and drug-gene associations, network topology analysis (NTA), and gene set enrichment analysis (GSEA). ResultsWe detected 2,263 differentially expressed genes (DEGs) (1,459 upregulated and 804 downregulated) in the PGB-0-ol- and DMSO-treated HCC1954 cells. KEGG enrichment data revealed the control of phosphatidylinositol signaling system, and ErbB signaling following PGB-0-ol treatment. Gene ontology (GO) enrichment analysis demonstrated that these DEGs governed cell cycle, participated in the mitotic spindle and nuclear membrane, and controlled kinase activity at the molecular level. According to the NTA data for GO enrichment, GSEA data for KEGG, drug-gene and disease-gene, PGB-0-ol regulated PI3K/Akt signaling and cell cycle in breast cancer. Overall, our investigation revealed the transcriptomic profile of PGB-0-ol-treated HCC1954 breast cancer cells following PGB-0-ol therapy. Bioinformatics analyses showed that PI3K/Akt signaling and cell cycle was modulated. However, further studies are required to validate the findings of this study.

Lessons from clinical implementation of a preemptive pharmacogenetic panel as part of a testing pilot program with an employer-sponsored medical plan.

Introduction: This manuscript reports on a pilot program focused on implementing pharmacogenetic testing within the framework of an employer-sponsored medical plan at University of Florida (UF) Health. The aim was to understand the challenges associated with program implementation and to gather insights into patient attitudes towards PGx testing. Methods: The pilot program adopted a partially preemptive approach, targeting patients on current prescriptions for medications with relevant gene-drug associations. Patients were contacted via phone or through the MyChart system and offered pharmacogenetic testing with no additional direct costs. Results: Of 244 eligible patients, 110 agreed to participate. However, only 61 returned the mailed DNA collection kits. Among these, 89% had at least one potentially actionable genotype-based phenotype. Post-test follow-up revealed that while the majority viewed the process positively, 71% preferred a consultation with a pharmacogenetic specialist for better understanding of their results. Barriers to implementation ranged from fatigue with the healthcare system to a lack of understanding of the pharmacogenetic testing and concerns about privacy and potential misuse of genetic data. Conclusion: The findings underscore the need for clearer patient education on pharmacogenetic results and suggest the importance of the role of pharmacogenetic-trained pharmacists in delivering this education. They also highlight issues with relying on incomplete or inaccurate medication lists in patients' electronic health record. The implementation revealed less obvious challenges, the understanding of which could be beneficial for the success of future preemptive pharmacogenetic implementation programs. The insights from the pilot program served to bridge the information gap between patients, providers, and pharmacogenetic -specialists, with the ultimate goal of improving patient care.

Predicting Drug-Gene-Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning.

Drug repositioning offers the significant advantage of greatly reducing the cost and time of drug discovery by identifying new therapeutic indications for existing drugs. In particular, computational approaches using networks in drug repositioning have attracted attention for inferring potential associations between drugs and diseases efficiently based on the network connectivity. In this article, we proposed a network-based drug repositioning method to construct a drug-gene-disease tensor by integrating drug-disease, drug-gene, and disease-gene associations and predict drug-gene-disease triple associations through tensor decomposition. The proposed method, which ensembles generalized tensor decomposition (GTD) and multi-layer perceptron (MLP), models drug-gene-disease associations through GTD and learns the features of drugs, genes, and diseases through MLP, providing more flexibility and non-linearity than conventional tensor decomposition. We experimented with drug-gene-disease association prediction using two distinct networks created by chemical structures and ATC codes as drug features. Moreover, we leveraged drug, gene, and disease latent vectors obtained from the predicted triple associations to predict drug-disease, drug-gene, and disease-gene pairwise associations. Our experimental results revealed that the proposed ensemble method was superior for triple association prediction. The ensemble model achieved an AUC of 0.96 in predicting triple associations for new drugs, resulting in an approximately 7% improvement over the performance of existing models. It also showed competitive accuracy for pairwise association prediction compared with previous methods. This study demonstrated that incorporating genetic information leads to notable advancements in drug repositioning.

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.

Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Comparing the value of mono- vs coculture for high-throughput compound screening in hematological malignancies

AbstractLarge-scale compound screens are a powerful model system for understanding variability of treatment response and discovering druggable tumor vulnerabilities of hematological malignancies. However, as mostly performed in a monoculture of tumor cells, these assays disregard modulatory effects of the in vivo microenvironment. It is an open question whether and to what extent coculture with bone marrow stromal cells could improve the biological relevance of drug testing assays over monoculture. Here, we established a high-throughput platform to measure ex vivo sensitivity of 108 primary blood cancer samples to 50 drugs in monoculture and coculture with bone marrow stromal cells. Stromal coculture conferred resistance to 52% of compounds in chronic lymphocytic leukemia (CLL) and 36% of compounds in acute myeloid leukemia (AML), including chemotherapeutics, B–cell receptor inhibitors, proteasome inhibitors, and Bromodomain and extraterminal domain inhibitors. Only the JAK inhibitors ruxolitinib and tofacitinib exhibited increased efficacy in AML and CLL stromal coculture. We further confirmed the importance of JAK-STAT signaling for stroma-mediated resistance by showing that stromal cells induce phosphorylation of STAT3 in CLL cells. We genetically characterized the 108 cancer samples and found that drug-gene associations strongly correlated between monoculture and coculture. However, effect sizes were lower in coculture, with more drug-gene associations detected in monoculture than in coculture. Our results justify a 2-step strategy for drug perturbation testing, with large-scale screening performed in monoculture, followed by focused evaluation of potential stroma-mediated resistances in coculture.

A novel risk assessment metric for antimyeloma therapies and drug interactions.

e24082 Background: New treatments for multiple myeloma (MM) improved both the clinical course and survival. These differ in their mode of action, safety profile, and adverse events (AEs). The goal of this study is to establish the benchmark for an integrated systematic investigation to assess the toxicity of different anti-MM drug regimens (AMDR). Methods: We curated 342,213 MM patient information from 2016–2022 from the FDA Adverse Event Reporting System. A total of 71 AMDR were evaluated in 7 different system organ classes when mapped to MedDRA. Drug-associated genes and AEs were also quantified as genetic evidence of a drug-AE combination. Gene-gene associations were then explored using the STRING database. We then developed a novel risk assessment metric that combined historical data from MM patients with drug-AE scores regarding the AE occurrence, the number of reported AE, and the genetic evidence of drug-gene and gene-AE associations (GAA). This likelihood-based rating metric includes (vH): (very) high MDR risk–(very) frequent AEs and mortality with (very) significant GAA evidence; and similarly computed for M: medium risk; and L: low risk. Results: Our risk assessment findings revealed single agents (carfilzomib (K), bortezomib (V), daratumumab (D), lenalidomide (R), melphalan (Mel), thalidomide (T), ixazomib (Ix), elotuzumab (El), isatuximab (Is) or dexamethasone (d)) illustrated very low profile of toxicity compared to combination regimens. Overall, we found that AMDR profiles showed higher toxicities in 4 different SyOCs (hepatobiliary, cardiac, nervous, renal) with nominal issues in 3 SyOCs (vascular, neoplasm, GI). AMDR toxicity significantly increased when d was added to the treatment compared to AMDR without d. Cardiac and vascular AEs were higher with K-based combinations. Hepatobiliary toxicities were higher with V, K, or T-based combinations. Secondary neoplasms were seen more frequently in patients who received high dose Mel followed by R. Conclusions: Our analysis of large data shows the association of SyOCs and graded toxicity profiles with unique AMDR. The single drug or doublets without dexamethasone seemed to show an improved AE profile compared to those with dexamethasone used as doublets, triplets, or quadruplets. Secondary cancer risk was much higher with high dose Mel followed by R compared with other AMDR. Our assessment system provides a framework for AMDR comparison toward future precision MM therapeutics based on unique drug doses, combination regimens, patient phenotypic risk profiles, secondary cancer surveillance and interplay of genetics and comorbidities. [Table: see text]

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support>74K drug-gene associations obtained from 4642 genes and 14659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

Abstract 4486: Proof of principle for pharmacogenomic-guided precision oncology for pediatric malignancy

Abstract Childhood cancers are rare and clinically diverse. They are typically associated with few driver mutational events than adult cancers, constituting potential targets for patient-specific precision therapy approaches. Here, we evaluated the feasibility of using a patient-derived tumor cell (PDC) based drug screening system and integrated multi-omics data to achieve precision oncology for pediatric patients. We established a PDC library derived from a few passage-cultured tumor cells from surgically resected tumor specimens and conducted chemical screening, which composed of various target agents of major oncogenic pathways (e.g. receptor tyrosine kinase inhibitor, proteasome inhibitor and histone deacetylase etc.). Next Generation Sequencing was performed to characterize the genomic and transcriptomic traits of tumors. Overall, success of establishing PDCs of pediatric cancers was high (80.4%) and comparable to adult cancers. The amount of obtained tissue was an important factor for the success of PDC establishment; the estimated optimal weight for PDC establishment was small (1.14g) and it is noteworthy that a small amount of tumor sample is sufficient to identify the potential hit(s) of parental tumors. The platform provided therapeutic options to pediatric tumors regardless of actionable targets. Our PDC approach identified several potential gene-drug associations, including anti-PI3K/Akt agents for neuroblastomas and anti-SHH agents for sarcomas with EWSR1 fusion. Given that a considerable proportion of pediatric tumors still lack actionable targets with matched treatment options, PDC-based drug screening profiles can be an optimal therapeutic strategy for these cases. Collectively, our analysis confirmed the feasibility of PDC-based in vitro drug screening systems to guide the therapeutic strategy for pediatric patients. This approach will accelerate the preclinical research for pediatric tumors to understand their pathophysiology and investigate the potential therapeutic strategies to fulfill the future precision oncology. Citation Format: Gi Ju Lee, Seung-Won Choi, Seung Ah Choi, Hee-Jin Cho, Robyn Gartrell, Ji Won Lee, Joo Whan Kim, Nam-Gu Her, Raul Rabadan, Ki Woong Sung, Do-Hyun Nam, Seung-Ki Kim. Proof of principle for pharmacogenomic-guided precision oncology for pediatric malignancy. [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 4486.

Systems Pharmacology and Network Analysis to Advance Pharmacogenomics and Precision Medicine Decisions in Type-2 Diabetes Therapy

Diabetes mellitus type-2 (DMT2) molecular pathophysiology is still challenging since the disease represents a complex, multifactorial metabolic disease caused by polygenic defects and environmental factors. In addition, the resulting secondary organ complications can be affected by various environmental and life-style factors over the years. The metabolic imbalance in DMT2 is manifested by the dysfunction of pancreatic β-cells in secreting insulin and the inability of other tissue cells to respond to insulin and utilize blood glucose. However, over recent years, through the advances in genomics and molecular analysis, several genes and microRNAs have been shown to be correlated as potential biomarkers with DMT2 prognosis, diagnosis, and therapy. Furthermore, drug therapy and clinical pharmacology have benefited from pharmacogenomics in a manner where the molecular knowledge can be translated into clinical information aiming to improve precision and personalized medicine therapeutic methodologies in healthcare. In this work, using systems pharmacology and network analysis approaches, we comprehensively assessed the molecular and genomics data associated with DMT2 to: (a) Better understand miRNA, gene, and drug associations; (b) Create connectivity and interaction maps of practical clinical utility; and (c) Facilitate the application of precision medicine therapeutic decisions in group and individual patients. Moreover, in order for the clinical pharmacology guidelines to be implemented in parallel with the generated molecular data, we also carried out an assessment of drug interactions in specific pharmacological classes that affect DMT2 pharmacotherapy outcomes. Overall, the proposed methodology and the results obtained: (a) Enrich our understanding of DMT2 molecular pathophysiology; (b) Unveil important biomarker and drug-gene pharmacogenomics associations; (c) Help the use of personalized therapy options; and (d) Allow precision medicine concepts to be broadly exploited in new therapeutic developments and within the clinical setting.

Drug Repositioning Based on a Multiplex Network by Integrating Disease, Gene, and Drug Information

Background: The research of new drugs is very expensive and the cycle is relatively long, so it has broad development prospects and good economic benefits to use validated drugs in the treatment of other diseases. Objective: The purpose of drug repositioning is to identify other indications for existing drugs. In addition to using disease and drug information for drug repositioning, other biomolecular information can also be integrated for drug repositioning. Integrating multiple biomolecular data of different types can improve the predictive performance of drug repositioning models. Methods: This paper proposes a drug repositioning algorithm based on a multiplex network (DRMN algorithm) by integrating disease, gene, and drug information. DRMN algorithm utilizes known disease-gene and gene-drug associations to connect disease phenotype similarity network, gene expression similarity network, and drug response similarity network. Then they are constructed into a multiplex network, and the importance score of each node is calculated by PageRank (PR) algorithm. Finally, disease-drug association scores are sorted to achieve drug repositioning. Results: DRMN algorithm is applied to two sets of sample data. Disease-drug association scores are calculated separately from disease PR values and drug PR values in both datasets. In top 50% of association scores, lots of disease-drug association prediction results have been verified by existing results. Compared with other algorithms, DRMN algorithm also shows better performance. Conclusion: DRMN algorithm can effectively integrate multi-omics data for drug repositioning and obtain better prediction results.

Abstract A064: High-throughput organoid drug screening to identify molecular vulnerabilities in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that typically presents at the advanced or metastatic stage. Although some patients achieve partial responses with chemotherapy, most tumors progress rapidly and become resistant to therapy. Finding targeted therapy for PDAC remains a major challenge due to the therapeutic intractability of the four major drivers (KRAS, TP53, CDKN2A, SMAD4), and the diversity in secondary alterations driven by genomic instability. We hypothesize that this genomic complexity exposes unique molecular vulnerabilities in each patient which may be uncovered by phenotypic screening. Patient derived organoids are a novel 3-D cell culture model which can reliably expand tumors ex vivo and recapitulate genomic, transcriptomic, and drug response characteristics of the patient. Most organoid work has focused on predicting patient response to standard chemotherapy or a small number of targeted therapies, but to date there has been no effort to systematically profile a large number of agents with integrated genomic and transcriptomic data. This will allow us to define the molecular basis of patient-specific drug vulnerabilities and identify biomarkers for drug repurposing and discovery specific to PDAC. To define the functional genomic landscape of drug response in PDAC, we optimized a high-throughput drug screening platform for rapid drug profiling in organoid models with matched WGS and RNAseq. Retrospective screening of 2935 FDA-approved and investigational drugs in a cohort of 58 primary and metastatic organoids identified 799 drugs with cytotoxic activity in at least one organoid. 600 drugs were cherry-picked to enrich for diverse mechanisms of action and dose-response curve validation was performed. Drug sensitivity profiles were highly organoid-specific, and all organoids had unique sensitivities when responses were compared across the cohort. Integration of functional and molecular profiling revealed known gene-drug associations such as MDM2 inhibitor activity in TP53 wildtype organoids, but also identified novel associations such as anagrelide sensitivity in an organoid with high PDE3A expression, which has recently been linked to interactions between PDE3A and the poorly characterized Schlafen 12 protein. Anti-tumor activity of anagrelide validated in a xenograft model. Our work highlights the utility of patient-derived organoids for phenotypic drug screening and biomarker discovery, which we plan to investigate further in an upcoming prospective trial. Citation Format: Irene Y Xie, Zhen-Mei Liu, Karen Ng, Eugenia Flores-Figueroa, Gun Ho Jang, Amy X. Zhang, Stephanie Ramotar, Anna Dodd, Julie Wilson, Grainne M. O'Kane, Jennifer J. Knox, Steven Gallinger, Faiyaz Notta. High-throughput organoid drug screening to identify molecular vulnerabilities in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A064.

Analysis of clinically relevant variants from ancestrally diverse Asian genomes

Asian populations are under-represented in human genomics research. Here, we characterize clinically significant genetic variation in 9051 genomes representing East Asian, South Asian, and severely under-represented Austronesian-speaking Southeast Asian ancestries. We observe disparate genetic risk burden attributable to ancestry-specific recurrent variants and identify individuals with variants specific to ancestries discordant to their self-reported ethnicity, mostly due to cryptic admixture. About 27% of severe recessive disorder genes with appreciable carrier frequencies in Asians are missed by carrier screening panels, and we estimate 0.5% Asian couples at-risk of having an affected child. Prevalence of medically-actionable variant carriers is 3.4% and a further 1.6% harbour variants with potential for pathogenic classification upon additional clinical/experimental evidence. We profile 23 pharmacogenes with high-confidence gene-drug associations and find 22.4% of Asians at-risk of Centers for Disease Control and Prevention Tier 1 genetic conditions concurrently harbour pharmacogenetic variants with actionable phenotypes, highlighting the benefits of pre-emptive pharmacogenomics. Our findings illuminate the diversity in genetic disease epidemiology and opportunities for precision medicine for a large, diverse Asian population.

An ErbB Lineage Co-Regulon Harbors Potentially Co-Druggable Targets for Multimodal Precision Therapy in Head and Neck Squamous Cell Carcinoma.

The ErbB lineage of oncogenic receptor tyrosine kinases is frequently overexpressed in head and neck squamous cell carcinomas. A common co-regulon triggered by the ErbB proteins; involving shared signaling circuitries; may harbor co-druggable targets or response biomarkers for potential future multimodal precision therapy in ErbB-driven head and neck squamous cell carcinoma. We here present a cohort-based; genome-wide analysis of 488 head and neck squamous cell carcinomas curated as part of The Cancer Genome Atlas Project to characterize genes that are significantly positively co-regulated with the four ErbB proteins and those that are shared among all ErbBs denoting a common ErbB co-regulon. Significant positive gene correlations involved hundreds of genes that were co-expressed with the four ErbB family members (q < 0.05). A common; overlapping co-regulon consisted of a core set of 268 genes that were uniformly co-regulated with all four ErbB genes and highly enriched for functions in chromatin organization and histone modifications. This high-priority set of genes contained ten putative antineoplastic drug-gene interactions. The nature and directionality of these ten drug-gene associations was an inhibiting interaction for seven (PIK3CB; PIK3C2B; HDAC4; FRK; PRKCE; EPHA4; and DYRK1A) of them in which the drug decreases the biological activity or expression of the gene target. For three (CHD4; ARID1A; and PBRM1) of the associations; the directionality of the interaction was such that the gene predicted sensitivit y to the drug suggesting utility as potential response biomarkers. Drug-gene interactions that predicted the gene product to be reduced by the drug included a variety of potential targeted molecular agent classes. This unbiased genome-wide analysis identified a target-rich environment for multimodal therapeutic approaches in tumors that are putatively ErbB-driven. The results of this study require preclinical validation before ultimately devising lines of combinatorial treatment strategies for ErbB-dependent head and neck squamous cell carcinomas that incorporate these findings.

Pharmacogenetic profiling via genome sequencing in children with medical complexity

BackgroundChildren with medical complexity (CMC) are a priority pediatric population, with high resource use and associated costs. Genome-wide sequencing is increasingly organized for CMC early in life as a diagnostic test. Polypharmacy becomes common as CMC age. Clinically relevant pharmacogenetic (PGx) information can be extracted from existing genome sequencing (GS) data via GS-PGx profiling. The role of GS-PGx profiling in the CMC population is unclear.MethodsPrescribed medications were extracted from care plans of 802 eligible CMC enrolled in a structured Complex Care Program over a 10-year period. Drug-gene associations were annotated using curated Clinical Pharmacogenetics Implementation Consortium data. GS-PGx profiling was then performed for a subset of 50 CMC.ResultsOverall, 546 CMC (68%) were prescribed at least one medication with an established PGx association. In the GS-PGx subgroup, 24 (48%) carried variants in pharmacogenes with drug-gene guidelines for one or more of their current medications. All had findings of potential relevance to some medications, including 32 (64%) with variants in CYP2C19 that could affect their metabolism of proton-pump inhibitors.ConclusionGS-PGx profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of CMC.ImpactPolypharmacy and genetic test utilization are both common in children with medical complexity.The role of repurposing genome sequencing data for pharmacogenetic profiling in children with medical complexity was previously unclear.We identified a high rate of medication use with clinically relevant drug-gene associations in this priority pediatric population and demonstrated that relevant pharmacogenetic information can be extracted from their existing genome sequencing data.Pharmacogenetic profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of children with medical complexity.

Computational analysis of G-protein-coupled receptor kinase family members as potential targets for colorectal cancer therapy

BackgroundG-protein-coupled receptor (GPCR) kinases (GRKs) interact with ligand-activated GPCR, causing intracellular phosphorylation and interfering with the intracellular signal transduction associated with the development of cancer. Colorectal cancer (CRC) is a fast-growing disease, and its molecular mechanism involves various regulatory proteins, including kinases. However, the GRK mechanism in CRC has not been explored.MethodsWe used an integrated computational approach to investigate the potential of GRK family members as targeted proteins in CRC. The GRK expression levels in tumor and normal tissues, colon adenocarcinoma samples, and metastatic colon adenocarcinoma were analyzed using ONCOMINE, GEPIA, and UALCAN, as well as TNM plots. Genetic changes in the GRK family genes were investigated using cBioportal. The prognostic value related to the gene expression of the GRK family was examined using GEPIA and UALCAN. Co-expression analysis of the GRK family was conducted using COXPRESdb. Association analysis of the Gene Ontology, KEGG pathway enrichment, and drug-gene analyses were performed using the over-representation analysis (ORA) in WebGestalt.ResultsGRK2, GRK3, and GRK5 mRNA levels increased significantly in patients with CRC and metastatic CRC. Genetic changes were detected in patients with CRC, including GRK7 (1.1%), GRK2 (1.7%), GRK4 (2.3%), GRK5 (2.5%), GRK6 (2.5%), GRK3 (2.9%), and GRK1 (4%). CRC patients with low mRNA of GRK7 levels had better disease-free and overall survival than those with high GRK7 levels. Hierarchical clustering analysis revealed significant positive correlations between GRK5 and GRK2 and between GRK2 and GRK6. KEGG pathway enrichment analysis showed that the gene network (GN) regulated several cellular pathways, such as the morphine addiction signaling and chemokine signaling pathways in cancer. The drug-gene association analysis indicated that the GN was associated with several drugs, including reboxetine, pindolol, beta-blocking agents, and protein kinase inhibitors.ConclusionNo research has been conducted on the relation of GRK1 and GRK7 to cancer, particularly CRC. In this work, genes GRK2, GRK3, GRK5, and GRK6 were found to be oncogenes in CRC. Although inhibitors against GRK2, GRK5, and GRK6 have previously been developed, further research, particularly preclinical and clinical studies, is needed before these agents may be used to treat CRC.