Targets Of Approved Drugs Research Articles

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine.

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g., BCL2, ERBB2, EGFR, ESR1, MYC , while revealing new targets confirmed by both mRNA- and protein-expression driven dependency. Notably, the identified genes show an overall 3.8-fold enrichment for approved drug targets and enrich for druggable oncology targets by 7 to 10-fold. We experimentally validate that the depletion of GRHL2 , TP63 , and PAX5 effectively reduce tumor cell growth and survival in their dependent cells. Overall, we present the catalog of express-driven dependency targets as a resource for identifying novel therapeutic targets in precision oncology.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves’ disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

A chromatographic method for pursuing potential GPCR ligands with the capacity to characterize their intrinsic activities of regulating downstream signaling pathway

GPCRs are dominant targets for approved drugs and the discovery of lead compound targeting them is still challengeable. Affinity-based screening technique is a promising platform to uncover GPCR ligands. However, the intrinsic activities of them are seldom simultaneously determined during the screening. Taking beta2-adrenoceptor (β2AR) as a probe, this work created a strategy for screening GPCR ligands with simultaneous characterization of their downstream G protein binding responses associated with GTP. The strategy included (i) the design and expression of a protein miniature formed by β2AR and G protein α-subunit (Gαs) using circularly permuted HaloTag (cpHalo) as a flexible linker; (ii) immobilization of the miniature onto silica gel by a click dehalogenation reaction; (iii) systematic characterization of the immobilized miniature by fluorescent and chromatographic studies, and (iv) simulating of ligand-induced β2AR-Gαs signaling cascade by chromatographic assays using GTP as an indicator. The immobilized miniature exhibited specificity to β2AR and Gαs antibodies and ligands. The specificity is stable at least within fifteen days with the variation less than 1%. The intrinsic activities of β2AR ligands were distinguished by the changes of GTP chromatographic behaviors on Gαs-cpHalo-β2AR column. Agonists strengthened the binding affinity and kinetics of GTP with Gαs, while antagonist did not give any effect on them. With the intrinsic activity evaluation, we believe, it will improve the attributes of chromatographic methods for drug discovery efforts with minimizing false-positive results.

The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery

BackgroundRetinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP.MethodsBy mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa.ResultsA mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa.ConclusionsThe possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.

Pharmacology education in the medical curriculum: Challenges and opportunities for improvement.

The knowledge and application of pharmacology is essential for safe prescribing and administration of drugs. In this narrative review, the challenges to pharmacology education in the medical curricula were broadly identified to include issues around content and pedagogies. The increasing number of approved drugs and drug targets, expanding field of pharmacology and the often-changing treatment guidelines and board-defined competencies can make pharmacology education in the medical curriculum daunting. There has been a consensus around the deployment of innovative medical curricula with emphasis on vertical and horizontal integration. This strategy, effective as it has been, presents new challenges to pharmacology education. As a discipline often perceived by students to be hard-to-learn, the future of pharmacology education must include heavy reliance on active learning strategies. The continuing utilization of problem-based, team-based and case-based learning can be complemented with personalized learning which aims to identify the learning gaps in individual students. Technology-inspired student engagement can foster pharmacology learning and retention. Early exposure to pharmacology from premedical preparation through an enduring across-the-level integration can be an effective way to enhance pharmacology learning in the medical curricula.

Identifying actionable druggable targets for breast cancer: Mendelian randomization and population-based analyses

Drug repurposing provides a cost-effective approach to address the need for breast cancer prevention and therapeutics. We aimed to identify actionable druggable targets using Mendelian randomization (MR) and then validate the candidate drugs using population-based analyses. We identified genetic instruments for 1406 actionable targets of approved non-oncological drugs based on gene expression, DNA methylation, and protein expression quantitative trait loci (eQTL, mQTL, and pQTL, respectively). Genome-wide association study (GWAS) summary statistics were obtained from the Breast Cancer Association Consortium (122,977 cases, 105,974 controls). We further conducted a nested case-control study using data retrieved from Swedish registers to validate the candidate drugs that were identified from MR analyses. We identified six significant MR associations with gene expression levels (TUBB, MDM2, CSK, ULK3, MC1R and KCNN4) and two significant associations with gene methylation levels across 21 CpG islands (RPS23 and MAPT). Results from the nested case-control study showed that the use of raloxifene (targeting MAPT) was associated with 35% reduced breast cancer risk (odds ratio, OR, 0.65; 95% confidence interval, CI, 0.51-0.83). However, usage of estradiol, tolterodine, and nitrofurantoin (also targeting MAPT) was associated with increased breast cancer risk, with adjusted ORs and 95% CI of 1.10 (1.07-1.13), 1.16 (1.09-1.24), and 1.09 (1.05-1.13), respectively. The effect of raloxifene and nitrofurantoin lost significance in further validation analyses using active-comparator and new-user design. This large-scale MR analysis, combined with population-based validation, identified eight druggable target genes for breast cancer and suggested that raloxifene is an effective chemoprevention against breast cancer. Swedish Research Council, Cancerfonden, Crafoordska Stiftelsen, Allmänna Sjukhusets i Malmö Stiftelsen för bekämpande av cancer, 111 Project and MAS cancer.

Novel insights into the whole-blood DNA methylome of asthma in ethnically diverse children and youth.

The epigenetic mechanisms of asthma remain largely understudied in African Americans and Hispanics/Latinos, two populations disproportionately affected by asthma. We aimed to identify markers, regions and processes with differential patterns of DNA methylation (DNAm) in whole blood by asthma status in ethnically diverse children and youth, and to assess their functional consequences. DNAm levels were profiled with the Infinium MethylationEPIC or HumanMethylation450 BeadChip arrays among 1226 African Americans or Hispanics/Latinos and assessed for differential methylation per asthma status at the CpG and region (differentially methylated region (DMR)) level. Novel associations were validated in blood and/or nasal epithelium from ethnically diverse children and youth. The functional and biological implications of the markers identified were investigated by combining epigenomics with transcriptomics from study participants. 128 CpGs and 196 DMRs were differentially methylated after multiple testing corrections, including 92.3% and 92.8% novel associations, respectively. 41 CpGs were replicated in other Hispanics/Latinos, prioritising cg17647904 (NCOR2) and cg16412914 (AXIN1) as asthma DNAm markers. Significant DNAm markers were enriched in previous associations for asthma, fractional exhaled nitric oxide, bacterial infections, immune regulation or eosinophilia. Functional annotation highlighted epigenetically regulated gene networks involved in corticosteroid response, host defence and immune regulation. Several implicated genes are targets for approved or experimental drugs, including TNNC1 and NDUFA12. Many differentially methylated loci previously associated with asthma were validated in our study. We report novel whole-blood DNAm markers for asthma underlying key processes of the disease pathophysiology and confirm the transferability of previous asthma DNAm associations to ethnically diverse populations.

Neuroprotection afforded by targeting G protein-coupled receptors in heteromers and by heteromer-selective drugs.

G protein-coupled receptors (GPCRs) are the target of hundreds of approved drugs. Although these drugs were designed to target individual receptors, it is becoming increasingly apparent that GPCRs interact with each other to form heteromers. Approved drug targets are often part of a GPCR heteromer, and therefore new drugs can be developed with heteromers in mind. This review presents several strategies to selectively target GPCRs in heteromeric contexts, namely, taking advantage of i) heteromer-mediated biased agonism/signalling, ii) discovery of drugs with higher affinity for the receptor if it is part of a heteromer (heteromer selective drugs), iii) allosteric compounds directed against the interacting transmembrane domains and, eventually, iv) antagonists that block both GPCRs in a heteromer. Heteromers provide unique allosteric sites that should help designing a new type of drug that by definition would be a heteromer selective drug. The review also provides examples of rhodopsin-like class A receptors in heteromers that could be targeted to neuroprotect and/or delay the progression of diseases such as Parkinson's and Alzheimer's. GPCRs in heteromers (GriH) with the potential to address dyskinesias, a common complication of dopaminergic replacement therapy in parkinsonian patients, are also described.

Mendelian Randomization Using the Druggable Genome Reveals Genetically Supported Drug Targets for Psychiatric Disorders.

Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis. We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence. By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD. Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.

Multi-layered genetic approaches to identify approved drug targets

Drugs targeting genes linked to disease via evidence from human genetics have increased odds of approval. Approaches to prioritize such genes include genome-wide association studies (GWASs), rare variant burden tests in exome sequencing studies (Exome), or integration of a GWAS with expression/protein quantitative trait loci (eQTL/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 clinically relevant traits and benchmark their ability to recover drug targets. Across traits, prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81, and 1.31 for the GWAS, eQTL-GWAS, Exome, and pQTL-GWAS methods, respectively. Adjusting for differences in testable genes and sample sizes, GWAS outperforms e/pQTL-GWAS, but not the Exome approach. Furthermore, performance increased through gene network diffusion, although the node degree, being the best predictor (OR= 8.7), revealed strong bias in literature-curated networks. In conclusion, we systematically assessed strategies to prioritize drug target genes, highlighting the promises and pitfalls of current approaches.

Abstract 3115: Predicting in-depth mechanism of action of cancer drugs

Abstract Knowing a drug’s mechanism of action (MOA) is essential for its clinical success by selecting the best indications, likely responders, and combinations. Yet knowledge of many drugs’ MOA remains lacking. Here we present DeepTarget, a computational tool for deep characterization of cancer drugs’ MOA by integrating existing large-scale genetic and drug screens. Spanning ∼1500 drugs across ∼18K possible target genes, DeepTarget predicts: (1) a drug’s primary target(s), (2) whether it specifically targets the wild-type or mutated target forms, and (3) the secondary target(s) that mediate its response when the primary target is not expressed. We first tested and successfully validated DeepTarget in a total of eleven unseen gold-standard datasets, with an average AUC of 0.82, 0.77, and 0.92 for the above three prediction abilities, respectively. We then proceed to use it in a wide range of applications: First, we find that DeepTarget’s predicted specificity of a drug to its target is strongly associated with its success in clinical trials and is higher in its FDA-approved indications. Second, DeepTarget predicts candidate drugs for targeting currently undruggable cancer oncogenes and their mutant forms. Finally, DeepTarget predicts new targets for drugs with unknown MOA and new secondary targets of approved drugs. Taken together, DeepTarget is a new computational framework for accelerating drug prioritization and its target discovery by leveraging large-scale genetic and drug screens. Citation Format: Sanju Sinha, Neelam Sinha, Eytan Ruppin. Predicting in-depth mechanism of action of cancer drugs [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 3115.

Molecules promoting circulating clusters of cancer cells suggest novel therapeutic targets for treatment of metastatic cancers.

Treatment of metastatic disease remains among the most challenging tasks in oncology. One of the early events that predicts a poor prognosis and precedes the development of metastasis is the occurrence of clusters of cancer cells in the blood flow. Moreover, the presence of heterogeneous clusters of cancerous and noncancerous cells in the circulation is even more dangerous. Review of pathological mechanisms and biological molecules directly involved in the formation and pathogenesis of the heterotypic circulating tumor cell (CTC) clusters revealed their common properties, which include increased adhesiveness, combined epithelial-mesenchymal phenotype, CTC-white blood cell interaction, and polyploidy. Several molecules involved in the heterotypic CTC interactions and their metastatic properties, including IL6R, CXCR4 and EPCAM, are targets of approved or experimental anticancer drugs. Accordingly, analysis of patient survival data from the published literature and public datasets revealed that the expression of several molecules affecting the formation of CTC clusters predicts patient survival in multiple cancer types. Thus, targeting of molecules involved in CTC heterotypic interactions might be a valuable strategy for the treatment of metastatic cancers.

Network expansion of genetic associations defines a pleiotropy map of human cell biology

Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.

Sub-pocket-focused designing of tacrine derivatives as potential acetylcholinesterase inhibitors

Human acetylcholinesterase (hAChE) has a potential role in the management of acetylcholine, one of the neurotransmitters that modulate the overall activity of cholinergic system, AChE inhibitors have a greater impact in the therapeutics. Though the atomic structure of hAChE has been extensively studied, the precise active site geometry upon binding to different ligands are yet to be explored. In the present study, an extensive structural analysis of our recently reported hAChE-tacrine complex has carried out and revealed the presence of two prominent sub-pockets located at the vicinity of the hAChE active site. Structural bioinformatics assisted studies designed 132 putative sub-pockets focused tacrine derivatives (SPFTDs), their molecular docking, free energy estimations revealed that they are stronger than tacrine in terms of binding affinity. Our in vitro studies also supported the in silico findings, all these SPFTDs are having better potencies than tacrine. Cytotoxic nature of these SPFTDs on HepG2 and Neuro-2a cell lines, diminishes the possibilities for future in vivo studies. However, the identification of these sub pockets and the SPFTDs paved a new way to the future drug discovery especially since AChE is one of the promising and approved drug targets in treatment of AD drug discovery.

Deep learning identification of cardiac transporters as targets for approved drugs.

We re-tasked a novel ligand-centered deep learning drug discovery method to identify molecular targets for approved drugs in the heart. This approach uses information about small-molecule effectors to map and probe the pharmacological space of functionally relevant targets in the heart. Here we studied the calcium pump (Sarcoplasmic reticulum Ca2+-ATPase, SERCA) as a proof-of-principle target to test our method. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in normal and pathological muscle, and it represents a major pharmacological target in the heart. We applied this method to demonstrate that SERCA is a pharmacological target for statins, a group of FDA-approved HMGCoA inhibitors used as lipid-lowering medications. We used in situ enzymatic assays and atomistic simulations to demonstrate that that these approved drugs are SERCA inhibitors at micromolar concentrations, inhibiting the pump by binding to two different effector sites. These proof-of-concept studies support the applicability of our approach for off-target identification and drug repurposing, ultimately minimizing the translational gap in drug development targeting the heart.

Macrophage activity at the site of tumor ablation can promote murine urothelial cancer via transforming growth factor-β1.

Cell death and injury at the site of tumor ablation attracts macrophages. We sought to understand the status and activity of these cells while focusing on transforming growth factor-β1 (TGF-β1), a potent immunosuppressive and tumorigenic cytokine. Patients with urothelial cancer who underwent ablation using electrocautery or laser demonstrated increased infiltration and numbers of CD8+ T cells, along with FoxP3+ regulatory T cells, CD68+ macrophages and elevated levels of TGF-β1 in recurrent tumors. Similar findings were reproduced in a mouse model of urothelial cancer (MB49) by partial tumor ablation with irreversible electroporation (IRE). Stimulation of bone marrow derived macrophages with MB49 cell debris produced using IRE elicited strong M2 polarization, with exuberant secretion of TGF-β1. The motility, phenotypic markers and cytokine secretion by macrophages could be muted by treatment with Pirfenidone (PFD), a clinically approved drug targeting TGF-β1 signaling. MB49 cancer cells exposed to TGF-β1 exhibited increased migration, invasiveness and upregulation of epithelial-mesenchymal transition markers α-Smooth Muscle Actin and Vimentin. Such changes in MB49 cells were reduced by treatment with PFD even during stimulation with TGF-β1. IRE alone yielded better local tumor control when compared with control or PFD alone, while also reducing the overall number of lung metastases. Adjuvant PFD treatment did not provide additional benefit under in vivo conditions.

Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates.

The COVID-19 pandemic is an acute and rapidly evolving global health crisis. To better understand this disease's molecular basis and design therapeutic strategies, we built upon the recently proposed concept of an integrated cell, iCell, fusing three omics, tissue-specific human molecular interaction networks. We applied this methodology to construct infected and control iCells using gene expression data from patient samples and three cell lines. We found large differences between patient-based and cell line-based iCells (both infected and control), suggesting that cell lines are ill-suited to studying this disease. We compared patient-based infected and control iCells and uncovered genes whose functioning (wiring patterns in iCells) is altered by the disease. We validated in the literature that 18 out of the top 20 of the most rewired genes are indeed COVID-19-related. Since only three of these genes are targets of approved drugs, we applied another data fusion step to predict drugs for re-purposing. We confirmed with molecular docking that the predicted drugs can bind to their predicted targets. Our most interesting prediction is artenimol, an antimalarial agent targeting ZFP62, one of our newly identified COVID-19-related genes. This drug is a derivative of artemisinin drugs that are already under clinical investigation for their potential role in the treatment of COVID-19. Our results demonstrate further applicability of the iCell framework for integrative comparative studies of human diseases.

Mutational Profiling of Lung Cancer Using Next Generation Sequencing: A Malaysian Real-World Clinical Diagnostic Experience

Lung cancer is one of the most common cancers and a leading cause of cancer-related mortality in Malaysia. This analysis aimed to evaluate the prevalence of actionable and common mutations, as well as co-mutations frequently occurring with EGFR variants in lung cancer. Mutational profiling of lung tumour samples was performed using next generation sequencing (NGS) panels at the Subang Jaya Medical Centre laboratory. A total of 469 lung tumour samples referred from several medical facilities in Malaysia were analysed and 84% were of the adenocarcinoma subtype. The three most frequent mutations found were EGFR (46.5%), TP53 (37.5%) and KRAS (14.3%). Actionable mutations with approved drug targets for lung cancer were detected in 63.5% of patient samples. Among patients with EGFR mutations, deletions in exon 19 were detected in 44.5% and p.L858R in 38.5% of samples. The most common co-mutations for samples with EGFR mutations were found in the TP53 gene (38.1%). A median turnaround time (TAT) of 3 working days was achievable with an automated NGS platform. NGS testing can provide valuable information on the mutational landscape and the prevalence of common or actionable mutations present in lung cancer patients. This real-world experience demonstrates the high percentage of actionable mutations detected and highlights the value of NGS testing in a clinical diagnostic setting.

Dissection of hubs and bottlenecks in a protein-protein interaction network

Analysis of degree centrality in conjunction with betweenness centrality of proteins in a human protein-protein interaction network revealed three categories of centrally important proteins: a) proteins with high degree and betweenness (hub-bottlenecks denoted as MX), b) proteins with high betweenness and low degree (non-hub-bottlenecks/pure bottlenecks denoted as PB) and c) proteins with high degree and low betweenness (hub-non-bottlenecks/pure hubs denoted as PH). When subjected to a detailed statistical analysis of their molecular-level properties, the proteins belonging to each of these categories were found to be associated with distinct canonical molecular properties, i.e., "molecular markers". The MX proteins are a) conformationally versatile, mainly comprising of essential proteins, b) the targets for interactions by the proteins of viral and bacterial pathogens, c) evolutionally constrained, involved in multiple pathways, enriched with disease genes and d) involved in the functions such as protein stabilization, phosphorylation, and mRNA slicing processes. PB proteins are a) enriched with extracellular and cancer-related proteins, b) enriched with the approved drug targets and c) involved in cell-cell signaling processes. Finally, PH are a) structurally versatile, b) enriched with essential proteins primarily involved in housekeeping processes (transcription and replication). The fact that the proteins belonging to these three categories form three distinct sets in terms of their molecular properties reveals the existence of trichotomy among hubs and bottlenecks, and this knowledge is of paramount importance while prioritizing protein targets for further studies such as drug design and disease association studies based on their network centrality values.