Drug Target Genes Research Articles

Associations between lipid-lowering drugs and pregnancy and perinatal outcomes: a Mendelian randomization study.

Mounting evidence has indicated that maternal dyslipidemia is associated with adverse obstetric outcomes, and the actions of lipid-lowering drugs in pregnant women remain controversial. Hence, this study aimed to appraise the causal relationship of lipid-lowering drugs [hydroxymethylglutaryl-coenzyme reductase (HMGCR) inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors] with pregnancy and perinatal outcomes using drug-targeting Mendelian randomization analysis. As a proxy for lipid-lowering drug exposure, two genetic instruments were used: genetic variants within or near the gene linked to low-density lipoprotein cholesterol (LDL-C) and the expression of quantitative trait loci of the drug target gene. Effect estimates were calculated using the inverse variance weighting (IVW) method and summary data-based Mendelian randomization (SMR) method. Heterogeneity and pleiotropy were assessed by Mendelian randomization-Egger regression, the Cochran Q test, and MR-PRESSO analysis. HMGCR inhibitors were ascribed to a reduced risk of preeclampsia in both the IVW-MR method [odds ratio (OR) 0.583; 95% confidence interval (CI) 0.418-0.812; P = 0.001] and SMR analysis (OR 0.816; 95% CI 0.675-0.986; P = 0.036). The causal link between HMGCR inhibitors and offspring birthweight was statistically significant only in the analysis using the IVW method (OR, 0.879; 95% CI, 0.788-0.980; P = 0.020), and the combined results of the OR values supported the potential inhibitory effect of HMGCR inhibitors on offspring birthweight. Causal associations between lipid-lowering drugs and gestational diabetes, preterm birth, and congenital anomalies were not detected in either analysis. No causal associations were observed between lipid-lowering drugs and gestational diabetes, preterm birth or congenital anomalies, whereas genetically predicted HMGCR inhibition dramatically reduced the risk of preeclampsia but attenuated offspring birthweight.

Systematic analysis of the transcriptional landscape of melanoma reveals drug-target expression plasticity.

Metastatic melanoma originates from melanocytes of the skin. Melanoma metastasis results in poor treatment prognosis for patients and is associated with epigenetic and transcriptional changes that reflect the developmental program of melanocyte differentiation from neural crest stem cells. Several studies have explored melanoma transcriptional heterogeneity using microarray, bulk and single-cell RNA-sequencing technologies to derive data-driven models of the transcriptional-state change which occurs during melanoma progression. No study has systematically examined how different models of melanoma progression derived from different data types, technologies and biological conditions compare. Here, we perform a cross-sectional study to identify averaging effects of bulk-based studies that mask and distort apparent melanoma transcriptional heterogeneity; we describe new transcriptionally distinct melanoma cell states, identify differential co-expression of genes between studies and examine the effects of predicted drug susceptibilities of different cell states between studies. Importantly, we observe considerable variability in drug-target gene expression between studies, indicating potential transcriptional plasticity of melanoma to down-regulate these drug targets and thereby circumvent treatment. Overall, observed differences in gene co-expression and predicted drug susceptibility between studies suggest bulk-based transcriptional measurements do not reliably gauge heterogeneity and that melanoma transcriptional plasticity is greater than described when studies are considered in isolation.

New perspectives on the potential of tetrandrine in the treatment of non-small cell lung cancer: bioinformatics, Mendelian randomization study and experimental investigation.

Although there are numerous treatment methods for NSCLC, long-term survival remains a challenge for patients. The objective of this study is to investigate the role and causal relationship between the target of tetrandrine and non-small cell lung cancer (NSCLC) through transcriptome and single-cell sequencing data, summary-data-based Mendelian Randomization (SMR) and basic experiments. The aim is to provide a new perspective for the treatment of NSCLC. We obtained the drug target gene of tetrandrine through the drug database, and then used the GSE19188 data set to obtain the NSCLC pathogenic gene, established a drug-disease gene interaction network, screened out the hub drug-disease gene, and performed bioinformatics and tumor cell immune infiltration analysis. Single-cell sequencing data (GSE148071) to determine gene location, SMR to clarify causality and drug experiment verification. 10 drug-disease genes were obtained from 213 drug targets and 529 disease genes. DO/GO/KEGG analysis showed that the above genes were all related to the progression and invasion of NSCLC. Four drug-disease genes were identified from a drug-disease PPI network. These four genes were highly expressed in tumors and positively correlated with plasma cells, T cells, and macrophages. Subsequent single-cell sequencing data confirmed that these four genes were distributed in epithelial cells, and SMR analysis revealed the causal relationship between CCNA2 and CCNB1 and the development of NSCLC. The final molecular docking and drug experiments showed that CCNA2 and CCNB1 are key targets for tetrandrine in the treatment of NSCLC.

Genetic Association of Lipids and Lipid-Lowering Drug Target Genes With Attention Deficit Hyperactivity Disorder

Background: Lipid metabolism plays an essential role in nervous system development. Cholesterol deficiency leads to a variety of neurodevelopmental disorders, such as autism spectrum disorder and fragile X syndrome. There have been a lot of efforts to search for biological markers associated with and causal to ADHD, among which lipid is one possible etiological factor that is quite widely studied. We aimed to evaluate the causal relationship between lipids traits, lipid-lowering drugs, and attention deficit hyperactivity disorder (ADHD) outcomes using Mendelian randomization (MR) studies. Methods: We used summary data from genome-wide association studies to explore the causal relationships between circulating lipid-related traits and ADHD. Then, quantitative trait loci for the expression of lipid-lowering drug target genes and genetic variants associated with lipid traits were extracted. Summary-data-based MR and inverse-variance-weighted MR (IVW-MR) were used to investigate the correlation between the expression of these drug-target genes and ADHD. Results: After rigorous screening, 939 instrumental variables were finally included for univariable mendelian randomization analysis. However, there is no correlation between lipid profile and ADHD risk. Drug target analysis by IVW-MR method observed that APOB-mediated low-density lipoprotein cholesterol was associated with lower ADHD risk (odds ratio [OR] = 0.90, 95% confidence interval [CI] [0.84, 0.97]; p = .007), whereas LPL-mediated triglycerides levels were associated with a higher risk of ADHD (OR = 1.13, 95% CI [1.06, 1.21]; p < .001). Conclusion: Our results suggest that APOB gene and LPL gene may be candidate drug target genes for the treatment of ADHD.

Genetic prediction of antihyperglycemic drug targets and risk of epilepsy: a mendelian randomisation study

A connection between diabetes and an increased risk of epilepsy has been suggested by observational studies. Animal studies have also shown that antihyperglycemic drugs can improve seizures. However, it is unclear whether antihyperglycemic drugs have a causal role in epilepsy in humans. To investigate this potential causal relationship, a Mendelian randomisation study was conducted using International League Against Epilepsy data as the discovery set and FinnGen data as the replication set. It was discovered that three antidiabetic drug target genes, ETFDH, CYP21A2 and CYP2D6, were involved in the occurrence of epilepsy. In particular, ETFDH was identified as a target gene in both the discovery set (inverse variance weighting [IVW], odds ratio [OR] = 1.018, 95% confidence interval [CI], 1.004–1.033, p = 0.009) and replication set (IVW, OR = 1.074, 95% CI, 1.034–1.114, p = 0.00016), and CYP21A2 was identified in the discovery set (IVW, OR = 1.029, 95% CI, 1.005–1.053, p = 0.016) and replication set (IVW, OR = 1.057, 95% CI, 1.001–1.116, p = 0.045) as having a causal association with an increased risk of epilepsy. Conversely, the CYP2D6 gene was found to be a protective factor for epilepsy in both the discovery set (IVW, OR = 0.0984, 95% CI, 0.969–0.998, p = 0.025) and replication set (IVW, OR = 0.977, 95% CI, 0.955–1.000, p = 0.046). A search of DrugBank revealed that metformin, an anti-glucose drug, is an inhibitor of the ETFDH gene and may have a potential therapeutic effect on epilepsy.

Genetic association of lipids and lipid-lowering drug target genes with Endometrial carcinoma: a drug target Mendelian randomization study.

Aberrant lipid metabolism is intricately linked to the development of endometrial cancer, and statin lipid-lowering medications are regarded as promising adjunctive therapies for future management of this malignancy. This study employed Mendelian randomization (MR) to explore the causal association between lipid traits and endometrial cancer while assessing the potential impact of drug targets on lower lipids on endometrial cancer. Two-sample Mendelian randomization was employed to probe the causal association between lipid traits and endometrial carcinoma. Drug-target Mendelian randomization was also utilized to identify potential drug-target genes for managing endometrial carcinoma. In instances where lipid-mediated effects through particular drug targets were notable, the impacts of these drug targets on endometrial carcinoma risk factors were investigated to bolster the findings. No causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC was found in two-sample Mendelian randomization. In drug target Mendelian randomization, genetic modeling of apolipoprotein B (APOB) (OR [95%CI]=0.31, [0.16-0.60]; p=4.73e-04) and cholesteryl ester transfer protein (CETP) (OR [95%CI]=1.83, [1.38-2.43]; p=2.91e-05) genetic mimicry was associated with non-endometrioid carcinoma. The results of our MR study revealed no causal association between genetically predicted lipid traits (LDL-C, TG, TC, and HDL-C) and EC. Among the six lipid-lowering drug targets, we observed a significant association between lower predicted APOB levels and higher CETP levels with an increased risk of endometrioid carcinoma. These findings provide novel insights into the importance of lipid regulation in individuals with endometrial carcinoma, warranting further clinical validation and mechanistic investigations.

Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data.

Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.

Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications.

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.

Causal effects of lipid-lowering therapies on aging-related outcomes and risk of cancers: a drug-target Mendelian randomization study.

Despite the widespread use of statins, newer lipid-lowering drugs have been emerging. It remains unclear how the long-term use of novel lipid-lowering drugs affects the occurrence of cancers and age-related diseases. A drug-target Mendelian randomization study was performed. Genetic variants of nine lipid-lowering drug-target genes (HMGCR, PCKS9, NPC1L1, LDLR, APOB, CETP, LPL, APOC3, and ANGPTL3) were extracted as exposures from the summary data of Global Lipids Genetics Consortium Genome-Wide Association Studies (GWAS). GWAS summary data of cancers and noncancerous diseases were used as outcomes. The inverse-variance weighted method was applied as the main statistical approach. Sensitivity tests were conducted to evaluate the robustness, pleiotropy, and heterogeneity of the results. In addition to marked effects on decreased risks of atherosclerotic cardiovascular diseases, genetically proxied lipid-lowering variants of PCKS9, CETP, LPL, LDLR, and APOC3 were associated with longer human lifespans (q<0.05). Lipid-lowering variants of ANGPTL3 and LDLR were associated with reduced risks of colorectal cancer, and ANGPTL3 was also associated with lower risks of gastric cancer (q<0.05). Lipid-lowering LPL variants were associated with decreased risks of hypertension, type 2 diabetes, nonalcoholic fatty liver disease, and bladder cancer (q<0.05). Lipid-lowering variants of PCKS9 and HMGCR were associated with decreased risks of osteoporosis (q<0.05). Lipid-lowering APOB variants were associated with a decreased risk of thyroid cancer (q<0.05). Our study provides genetic evidence that newer nonstatin lipid-lowering agents have causal effects on decreased risks of several common cancers and cardiometabolic diseases. These data provide genetic insights into the potential benefits of newer nonstatin therapies.

CRISPR Screen of Druggable Targets in Small Cell Lung Cancer Identified ATM Inhibitor (AZD1390) as a Radiosensitizer

Small cell lung cancer (SCLC) is an aggressive and lethal form of lung cancer and the overall 5-year survival (OS) for patients is a dismal 7%. Radiation therapy (RT) provides some benefit for selected patients with SCLC but could be improved with radiosensitizing agents. In this study, we identified novel radiosensitizers for SCLC by a CRISPR-Cas9 screen and evaluated the efficacy of ATM inhibitor AZD1390 as a radiosensitizer of SCLC. We transduced the SCLC cell line SBC5 with a custom CRISPR sgRNA library focused on druggable gene targets and treated cells with RT. Cells collected at multiple timepoints were subjected to next-generation sequencing. We determined radiosensitization both in vitro with cell lines assessed by short-term viability and clonogenic assays, and in vivo mouse models by tumor growth delay. Pharmacodynamic effects of AZD1390 were quantified by ATM-Ser1981 phosphorylation, and RT-induced DNA damage by comet assay. Using a CRISPR dropout screen, we identified multiple radiosensitizing genes for SCLC at various timepoints with ATM as a top determinant gene for radiosensitivity. Validation by ATM knockout (KO) demonstrated increased radiosensitivity by short-term viability assay (dose modification factor [DMF]50 = 3.25-3.73 in SBC5 ATM-KO) and clonogenic assays (DMF37 1.25-1.65 in SBC5 ATM-KO). ATM inhibition by AZD1390 effectively abrogated ATM Ser1981 phosphorylation in SCLC cell lines and increased RT-induced DNA damage. AZD1390 synergistically increased the radiosensitivity of SCLC cell lines (cell viability assay: SBC5 DMF37 = 2.19, SHP77 DMF37 = 1.56, H446 DMF37 = 3.27, KP1 DMF37 = 1.65 at 100nM; clonogenic assay: SBC5 DMF37 = 4.23, H1048 DMF37 = 1.91), and in vivo murine syngeneic, KP1, and patient-derived xenograft (PDX) models, JHU-LX108 and JHU-LX33. In this study, we demonstrated that genetically and pharmacologically (AZD1390) inhibiting ATM markedly enhanced RT against SCLC, providing a novel pharmacologically tractable radiosensitizing strategy for patients with SCLC.

Genetic insights into the association of statin and newer nonstatin drug target genes with human longevity: a Mendelian randomization analysis

BackgroundIt remains controversial whether the long-term use of statins or newer nonstatin drugs has a positive effect on human longevity. Therefore, this study aimed to investigate the genetic associations between different lipid-lowering therapeutic gene targets and human longevity.MethodsTwo-sample Mendelian randomization analyses were conducted. The exposures comprised genetic variants that proxy nine drug target genes mimicking lipid-lowering effects (LDLR, HMGCR, PCKS9, NPC1L1, APOB, CETP, LPL, APOC3, and ANGPTL3). Two large-scale genome-wide association study (GWAS) summary datasets of human lifespan, including up to 500,193 European individuals, were used as outcomes. The inverse-variance weighting method was applied as the main approach. Sensitivity tests were conducted to evaluate the robustness, heterogeneity, and pleiotropy of the results. Causal effects were further validated using expression quantitative trait locus (eQTL) data.ResultsGenetically proxied LDLR variants, which mimic the effects of lowering low-density lipoprotein cholesterol (LDL-C), were associated with extended lifespan. This association was replicated in the validation set and was further confirmed in the eQTL summary data of blood and liver tissues. Mediation analysis revealed that the genetic mimicry of LDLR enhancement extended lifespan by reducing the risk of major coronary heart disease, accounting for 22.8% of the mediation effect. The genetically proxied CETP and APOC3 inhibitions also showed causal effects on increased life expectancy in both outcome datasets. The lipid-lowering variants of HMGCR, PCKS9, LPL, and APOB were associated with longer lifespans but did not causally increase extreme longevity. No statistical evidence was detected to support an association between NPC1L1 and lifespan.ConclusionThis study suggests that LDLR is a promising genetic target for human longevity. Lipid-related gene targets, such as PCSK9, CETP, and APOC3, might potentially regulate human lifespan, thus offering promising prospects for developing newer nonstatin therapies.

Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

PurposeThis study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.MethodsMicroarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein–protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.ResultsA total of 686 DEGs were identified between the two groups—of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA–mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.ConclusionFA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia via its action potential function.

Genomic Variants Driven Drug Repurposing for SARS-CoV-2 Using Bioinformatics-based Approach

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) first appeared in China in December 2019 and since then, the outbreak expanded globally. Due to the limited supply of SARS-CoV-2 medicines on the market, an immediate strategy was required to accelerate the development of new drugs. Drug repurposing is one of the novel approaches for the therapeutics of several life-threatening diseases. This study identified 408 SARS-CoV-2-associated SNPs from the Genome-Wide Association Study (GWAS) catalog and 391 risk genes associated with SARS-CoV-2. The prioritization was performed based on six categories of functional annotations. SARS-CoV-2 risk genes were mapped into an expanded PPI network using the STRING database. We discovered the drugs that target deregulate SARS-CoV-2 risk genes. Here, we demonstrated the drug target genes overlapped with the drugs for other clinical indications. Hence, we suggest that the identified drugs may be repurposed for the treatment of SARS-CoV-2 and its variants. Overall, this study emphasizes the integration of functional genomic variants and bioinformatics-based approaches to drive drug repurposing efforts for the therapeutics options for COVID-19.

Association of antihypertensive drugs with COVID-19 outcomes: a drug-target Mendelian randomization study.

Background: Observational investigations have provided conflicting results regarding the effect of antihypertensive drugs on the risk of COVID-19 outcomes. We intended to assess the causal effect of antihypertensive drugs on COVID-19 outcomes using drug-target Mendelian randomization (MR), mainly including angiotensin-converting enzyme inhibitors (ACEIs), β-blockers (BBs) and calcium channel blockers (CCBs). Methods: We used the genetic variants (minor allele frequency >1%, r 2 < 0.30) located within 100k bases of each drug target gene and associated with lower systolic blood pressure (p < 5 × 10-8) as genetic proxies for antihypertensive drugs. COVID-19 outcomes included COVID-19 susceptibility (122,616 cases and 2,475,240 controls), hospitalization (32,519 cases and 206,2805 controls), and severe illness (13,769 cases and 1,072,442 controls). All studies were conducted on populations of European ancestry. MR estimates were generated using an inverse variance weighted (IVW) model. Results: IVW-MR analysis observed a weak causality between CCBs and COVID-19 susceptibility (OR: 0.993, 95% CI: 0.988-0.999, p = 0.012). Sensitivity analysis suggested that this result was robust. No evidence was found for a link between other antihypertensive drugs and COVID-19 outcomes. Conclusion: The present study suggests that CCBs may reduce COVID-19 susceptibility in European populations.

Abstract IA047: A multiomic approach towards identifying actionable targets to combat prostate cancer health disparities

Abstract Prostate cancer (PC) is the most commonly diagnosed cancer among African American men (AA) and continues to be the largest difference in absolute incidence and cancer specific deaths between AA and Caucasian men (CA). AA have a 70% higher incidence of PC than CA and over a 2 times higher mortality rate. Among men undergoing prostate biopsy In an equal access setting (i.e. the VA), AA are ~50% more likely to have PC and ~80% more likely to have high-grade PC than CA. The frequency of PC associated genetic alterations and differences in oncogene expression in PCs is altered from CA and AA. While social economic status and environmental factors may play a role in PC health disparities, there are inherent genetic/molecular differences that account for the differences in PC outcomes between CA and AA. We sought to test our hypothesis that there is subset of actionable gene products that are essential for enhanced PC progression in AA. A focused and clinically informed CRISPR knockout screen was conducted to identify and functionally validate race specific actionable genes that contribute to the increased risks of fatal PC in AA. The CRISPR screen was designed leveraging a radical prostatectomy trancscriptomic dataset from the Durham VA Medical Center that was linked with long-term clinical outcome data. This unique dataset consists of ~600-profiled radical prostatectomy samples of which over 50% were from AA patients. A cox hazard analysis revealed differentially expressed genes (DEGs) which positively associated with increased risk of metastatic disease unique to AA PC patients. To identify drug targetable genes, these DEGs were filtered against the drug gene interaction database (DGIdb).This approach yielded 89 genes positively associated with increased risk of metastatic outcome in AA and 236 genes in CA only. The CRISPR KO screen resulted in 19 genes significantly depleted genes. Of these 19 hits, 11 genes were genes associated with metastasis in AA, while 8 hits were CA genes, with 5 of 11 AA hits have documented small molecule inhibitors. To further prioritize our hits, we have conducted spatial transcriptomic analysis of radical prostatectomy tissues from AA and CA PCs. By standard H and E staining these tumors looking quite similar. But spatial analysis of AA compared to CA tissues revealed differences in immune cells and cells associated with tumor microenvironment. These integative multiomic approach reveals new cell identities that may contribute to the aggressive pathogenicity of PC in AA compared to CA. CRISPR models and small molecule inhibitors of our top candidates will be used in investigate druggability of these DEGs. This study lays the groundwork for potential therapeutics targeted towards PC in AA men. Citation Format: Felipe Dezem, Aiza Akhtar, Sungyong You, So Young Kim, Jasmine Plummer, Everardo Macias. A multiomic approach towards identifying actionable targets to combat prostate cancer health disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr IA047.

Abstract B025: A multi-omic, spatial approach using patient prostatectomies to identify actionable drug targets for combatting prostate cancer health disparities

Abstract Prostate cancer (PC) is the most commonly diagnosed cancer among African American men (AA) and continues to be the largest difference in absolute incidence and cancer-specific deaths between AA and Caucasian men (CA). AA have a 70% higher incidence of PC than CA and over a 2 times higher mortality rate. Among men undergoing prostate biopsy In an equal access setting (i.e. the VA), AA are ~50% more likely to have PC and ~80% more likely to have high-grade PC than CA. The frequency of PC-associated genetic alterations and differences in oncogene expression in PCs is altered from CA and AA. While social economic status and environmental factors may play a role in PC health disparities, there are inherent genetic/molecular differences that account for the differences in PC outcomes between CA and AA. We sought to test our hypothesis that there is a subset of actionable gene products that are essential for enhanced PC progression in AA. A focused and clinically informed CRISPR knockout screen was conducted to identify and functionally validate race-specific actionable genes that contribute to the increased risks of fatal PC in AA. The CRISPR screen was designed leveraging a radical prostatectomy transcriptomic dataset from the Durham VA Medical Center that was linked with long-term clinical outcome data. This unique dataset consists of ~600-profiled radical prostatectomy samples of which over 50% were from AA patients. A Cox hazard analysis revealed differentially expressed genes (DEGs) which are positively associated with an increased risk of metastatic disease unique to AA PC patients. To identify drug-targetable genes, these DEGs were filtered against the drug-gene interaction database (DGIdb). This approach yielded 89 genes positively associated with an increased risk of metastatic outcome in AA and 236 genes in CA only. The CRISPR KO screen resulted in 19 genes significantly depleted genes. Of these 19 hits, 11 genes were genes associated with metastasis in AA, while 8 hits were CA genes, with 5 of 11 AA hits having documented small molecule inhibitors. To further prioritize our hits, we have conducted a spatial transcriptomic analysis of radical prostatectomy tissues from AA and CA PCs. By standard H and E staining these tumors look quite similar. But spatial analysis of AA compared to CA tissues revealed differences in immune cells and cells associated with the tumor microenvironment. This integrative multi-omic approach reveals new cell identities that may contribute to the aggressive pathogenicity of PC in AA compared to CA. CRISPR models and small molecule inhibitors of our top candidates will be used to investigate the druggability of these DEGs. This study lays the groundwork for potential therapeutics targeted toward PC in AA men. Citation Format: Felipe Segato Dezem, Aiza Akhtar, Arjumand Wanj, Sunyoung You, Hannah Chasteen, So Young Kim, Jasmine Plummer, Everardo Macias. A multi-omic, spatial approach using patient prostatectomies to identify actionable drug targets for combatting prostate cancer health disparities [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B025.

Single-Cell Multi-Ome Analysis Reveals Novel Molecular Mechanisms Underlying Subclonal Response to Survivin Inhibition in Relapsed/Refractory Multiple Myeloma

Multiple myeloma is the second-most common hematological malignancy in the US that remains a challenging disease to cure despite recent advancements in treatment strategies. Drug resistance is a major cause of concern in myeloma chemotherapy. We have created an optimization-regularization-based computational prediction method called secDrug that identified several novel secondary drug (‘secDrug‘) combinations against drug-resistant myeloma (relapsed/refractory multiple myeloma or RRMM). Top among our predicted secDrugs includes the survivin inhibitor, YM155. Survivin is a member of the inhibitor of apoptosis protein (IAP) family that has dual roles in mitosis and apoptosis. YM155 inhibits survivin at the transcriptional level by inhibiting the survivin gene promoter. Several studies have demonstrated the efficacy of YM155 in solid tumors and heme malignancies, including myeloma. However, the precise mechanism of action is not yet known. MM cell lines express high levels of IAPs. We observed high in vitro cytotoxicity of YM155 as a single agent (IC 50 range 0.41 to 12.85nM) as well as in combination with the primary drugs like proteasome inhibitors and Immunomodulatory drugs (IMiDs) against &amp;gt;15 human myeloma cell lines representing innate/refractory and acquired/emerging (relapse) resistance. Next, we explored the molecular mechanisms underlying the efficacy of YM155 using pre-vs-post-treatment tumor whole-transcriptome analysis (RNAseq). The top differentially regulated genes include DDIT3 (CHOP), ATF3, TXNDC5 SEC24D, and DHX15 ( Figure 1A). Ingenuity Pathway Analysis (IPA) predicted that, in addition to regulating apoptosis, survivin inhibition in myeloma significantly upregulated unfolded protein response and downregulated vehicle trafficking and genes associated with immune cell function. Further, we performed single-cell transcriptomics (scRNAseq; 10X Genomics) analysis to explore treatment-induced changes to the subclonal architecture. Our scRNAseq results revealed a distinct shift in clusters representing myeloma single-cell subpopulations following YM155 treatment ( Figure 1B). For example, Cluster 1 (53.11%), characterized by high expression of cell survival markers Ki67 and MYC, was pre-dominant in untreated myeloma cells, while Cluster 4 (52.19%) was the primary subclone in post-treatment tumors. Cluster 3 was shared between the untreated and treated groups. Detailed distinctions between each single-cell cluster will be presented. Furthermore, our scRNAseq data alluded to the benefits of combining survivin-inhibitors and Hsp90-inhibitors based on the expression of drug target genes. Hsp90 inhibitors indirectly target survivin by disrupting the interaction between survivin and Hsp90, resulting in a destabilized survivin protein that ultimately results in survivin degradation. In fact, we have earlier shown the effectiveness of Hsp90 inhibitors (17-AAG, AICAR, and CCT018159) as potent secDrugs against RRMM. Finally, high-dimensional mass cytometry or CyTOF analysis using heavy metal-conjugated proteins in patient bone marrow-derived primary myeloma cells ( ex vivo model system) showed that YM155 induced dose-dependent reduction of several protein markers implicated in cell survival and chemotherapeutic resistance in multiple myeloma, including myc, Ki-67, CD28, CD81, and CD274 (PD-L1). Previous studies have shown that CD28 and CD81 are high-risk immunophenotypes associated with significantly worse prognosis in patients with myeloma. PD-L1 (Programmed death-ligand 1) is a cell-surface glycoprotein expressed in a large number of malignancies that is involved in the termination of immune response and immune evasion. An earlier study has shown that YM155 enhances daratumumab-mediated cellular lysis of multiple myeloma cells. Our RNAseq and CyTOF results thus suggest a potential basis of synergy between YM155 with immunotherapies approved for myeloma, which we will test further. Our approach thus integrates in silico prediction with single-cell multi-ome analysis to identify molecular mechanisms potentially underlying subclonal response to novel combination therapy candidates (secDrugs) for the treatment of RRMM.

Association between antihypertensive drugs and oral cancer: a drug target Mendelian randomization study

Background: Recent reports have suggested that antihypertensive drugs may play an oncogenic role in common cancers, but it is still uncertain whether this could influence the risk of oral cancer. Through two-sample Mendelian randomization (MR), we sought to assess the causal effect of antihypertensive drugs on oral cancer outcomes.Methods: To proxy the exposure of antihypertensive drugs, we utilized two genetic instruments, including expression quantitative trait loci of drug target genes and genetic variants within or around drug target genes related to blood pressure from genome-wide association studies. Inverse-variance-weighted MR (IVW-MR) and summary-data-based MR (SMR) were employed to compute the instrument effect estimates.Results: It was observed through IVW-MR analysis that there is a positive relationship between KCNH2 (target of beta-adrenoceptor blockers)–mediated blood pressure and oral cancer (odds ratio [OR] = 1.197, 95% confidence interval [CI] = 1.028–1.394). Similarly, SMR analysis demonstrated that a higher expression of KCNH2 (target of beta-adrenoceptor blockers) was linked to a greater risk of oral cancer (OR = 2.223, 95% CI = 1.094–4.516). Both analyses yielded no consistent evidence of other associations.Conclusion: This two-sample MR study proposed a latent causal association between KCNH2 (target of beta-adrenoceptor blockers) inhibition and diminished risk of oral cancer.

Molecular mechanism of Achyranthis bidentatae radix and Morindae officinalis radix in osteoporosis therapy: An investigation based on network pharmacology, molecular docking, and molecular dynamics simulations

ObjectiveTo investigate the targets and mechanism of Achyranthis bidentatae radix and Morindae officinalis radix (ABR-MOR) for the treatment of osteoporosis (OP) by utilizing network pharmacology, molecular docking technology (MDT) and molecular dynamics simulation (MDS). MethodsThe main drug active ingredients (DAIs) and target genes of ABR-MOR were screened by the TCMSP database. The relevant targets of OP were obtained from GeneCards, DisGeNET, and CTD databases. Venny mapping is used to determine the potential target of ABR-MOR in the treatment of OP. The potential targets were analyzed using a protein‒protein interaction network and the MCODE module, and were subjected to GO and KEGG enrichment analysis. The binding sites and conditions of potential key DAIs and core targets were verified through MDT and MDS. ResultThe 32 DAIs and 212 targets of ABR-MOR were screened; 1453 OP-related targets were obtained, and 118 targets were mapped. The results of GO and KEGG enrichment analysis showed that the targets of DAIs-OP were mainly enriched in biological processes such as response to hormones, peptides, oxygen levels and reactive oxygen species, and positive regulation of cell migration. The main signaling pathways enriched in the regulation of the immune-inflammatory response, cell proliferation, senescence, apoptosis, angiogenesis, and estrogen signaling pathway. Additionally, the targets were also enriched in bone metabolism-related cell differentiation biological processes and the osteoclast differentiation signaling pathway. MDT and MDS results showed that wogonin, beta-sitosterol, and americanin A, the core DAIs of ABR-MOR, were able to form good ligand‒protein complexes with key targets such as PTGS2, PTGS1, PRKACA, PGR, MAPK1, AKT1, and RELA. ConclusionThis study preliminarily investigated the key targets, biological processes, and signaling pathways involved in the combined application of ABR and MOR for the treatment of OP. The results revealed that ABR-MOR may play a therapeutic role mainly by regulating immune-inflammatory responses, cellular biological processes, and osteoblast differentiation, which provides a theoretical basis for further experimental validation and a new strategy for the treatment of OP.

STEM-02. SYNGN: A NOVEL EPIGENETIC PLATFORM FOR PATIENT-SPECIFIC DRUG-MATCHING IN GLIOBLASTOMA

Abstract The alteration of the glioblastoma genome by epigenetic mechanisms that share functions with normal developmental processes, such as self-renewal and fate specification of NSC, is a key piece of evidence that links brain cancer pathogenesis with dysregulated stem cell functions. A patient-specific comparison of glioblastoma cells with NSC, a putative cell of origin of at least a proportion of these tumours, is not feasible as patient-matched endogenous NSC are not surgically accessible and all epigenetic studies in glioblastoma have so far compared epigenetic changes of different tumours with each other or to comparators obtained from foetal brains or an unrelated donor. We reasoned that availability of syngeneic GSC and NSC pairs would allow to identify crucial epigenetic differences on a patient-specific basis and would provide essential therapeutic contrast to define disease-and patient-intrinsic biomarkers of drug response that are less confounded by germline variation. We have derived GSC from IDH-wildtype glioblastoma and harnessed state-of-the-art stem cell technologies to generate patient-matched fibroblast-derived EPSC, which were induced to NSC (iNSC). We demonstrate that integrated analysis of the transcriptome and DNA methylome of GSC/iNSC pairs identifies druggable target genes (PTGER4, ALDH3B1, NTRK2) in a proportion of patients and we validate this patient-specific prediction of drug response at pre-clinical level in 3D synGLICO and in in vivo models. Integration of small RNA regulation to the pipeline allows refined in silico prediction of drug responses on a patient-specific basis. Most recently we have added assessment of chromatin remodelling (H3K4me3, H3K27ac, H3K3me3, H3K27me3 and ATACSeq) to the pipeline and shown that redistribution of selected histone marks as well as shifts in chromatin states across the genome identifies known and novel druggable regulatory mechanisms in GSC, which are specific of the neoplastic context. In conclusion, SYNGN is an epigenetic platform which can identify patient-specific druggable targets in glioblastoma.