Abstract E-selectin is a cell adhesion glycoprotein expressed on endothelial cells and participates in the development of environmental-mediated drug resistance and poor clinical outcome in cancer. The E-selectin antagonist, uproleselan, interrupts leukemic cell homing to the vascular niche and increases susceptibility to cytotoxic therapies. Recent clinical data demonstrate a correlation between leukemic cell surface E-selectin ligands and response to uproleselan, linking E-selectin ligand expression to response. Multiple genes involved in the glycan synthesis of E-selectin ligands are highly expressed in pediatric AML. Expression levels of two of these genes, ST3GAL4 and FUT7 are associated with poor outcome and are associated with cell surface E-selectin ligand expression. In the current studies we extend transcriptome profiling of E-selectin ligand forming glycosylation genes with an emphasis on ST3GAL4 and FUT7 in different cancers. Initially, we investigated expression levels of ST3GAL4 and FUT7 in 10,258 samples covering 33 cancer types from the TCGA PanCanAtlas. ST3GAL4 and FUT7 were consistently expressed in the majority of cancers evaluated. The cancer types that expressed ST3GAL4 most highly were melanoma (uveal and skin), kidney chromophobe, adrenocortical carcinoma and bladder urothelial carcinoma, while for FUT7, it was AML, diffuse large B-cell lymphoma, thymoma, testicular germ cell tumors and head and neck squamous cell carcinoma. Of particular interest was the identification of adult AML for the highest expression of FUT7 and high expression of ST3GAL4 (mean log2 gene expression = 8.1 and 9.4, respectively). Augmented expression of FUT7 and ST3GAL4 were also observed in an analysis of 39 AML cell lines among the 1,457 cell lines comprising the Cancer Cell Line Encyclopedia RNAseq data set. The prognostic significance of FUT7 and ST3GAL4 in adult AML was further assessed using the TCGA-LAML RNAseq dataset for differential expression and associations with overall survival (OS). The data set included 151 RNAseq profiles of bone marrow samples from adult patients with AML, and within this data set the status of the FMS-like tyrosine kinase 3 (FLT3) proto-oncogene was considered. Mutational alterations of FLT3 are associated with higher risk of relapse and shorter OS compared with wild-type FLT3. ST3GAL4 and FUT7 were both identified as being upregulated (fold-change = 1.73 and 1.40, respectively) in the mutated FLT3 subset (n=46) as compared to wild type FLT-3 (p=0.000033 and 0.046, respectively). Notably in the mutated FLT-3 subset expression of FUT7 was significantly associated with a poor prognosis and decreased OS (HR = 4.56, p= 0.015). Collectively, these studies extend the prognostic importance of the E-selectin ligand glycosylation genes, ST3GAL4 and FUT7, to adult AML where these genes may be useful as predictive biomarkers. In addition, these studies suggest potential additional tumor types beyond AML where treatment protocols with uproleselan may have therapeutic benefits. Citation Format: William E. Fogler, Vince Deng, David Stewart, Michael Jarman, Simone Daminelli, Adrian Carr, John L. Magnani. Transcriptome profiling of ST3GAL4 and FUT7 in multiple tumor types and prognostic value in adult acute myeloid leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5867.

Background: Histone acetyl transferases E1A binding protein (p300) and CREB binding protein (CBP) are known co-activators of several key transcription factors that contribute to tumor progression including HIF1a, BRCA-1, p53, c-myc and androgen receptor (AR). A large proportion of AR regulated gene expression has been shown to be dependent on p300 either through direct regulation of AR interaction with promoters of AR regulated genes or subsequent histone modification events. Both p300 and CBP are highly expressed in advanced prostate cancer and androgen deprivation leads to upregulation of both proteins. CCS1477 is a potent, selective inhibitor of the bromodomain in CBP/p300 that has been shown to inhibit prostate tumor cell proliferation in vitro and tumor growth in vivo. Methods: 22Rv1 prostate tumor cells that express both AR and AR variants were transplanted in nude mice. Established tumors were treated with CCS1477, 20mg per kg, once daily p.o. for 28 days. CCS1477 treatment resulted in virtually complete inhibition of tumor growth that was maintained up to 24 days after cessation of treatment by which time tumor recurrence was evident. Tumors were excised from vehicle and CCS1477 treated animals at day 7, day 28, and day 52; mRNA was isolated and gene expression analysis was carried using Affymetrix Clarion D microarrays. Differential gene expression based on fold change (FC) >1.5 and FDR-adjusted p-value <0.05 identified a number of genes with significant FC in CCS1477 vs vehicle treated control tumors. Results: Although ~1.5 fold downregulation of AR was maintained from day 7 to day 52, downregulation of AR target genes ETS2, TMPRSS2 and NKX3.1 recovered after treatment cessation. Similarly, expression of c-myc was significantly reduced at day 7 (-2.7 FC) and recovered by day 28. Of note, among the top downregulated genes were CIART and BHLHE40 circadian clock regulated genes that provide negative feedback loops that in conjunction with downregulation of AR and c-myc would disrupt circadian gene regulation. VEGFA mRNA that was downregulated >1.5 fold at all time points, together with c-myc and p300 are all under circadian regulation. Expression of the histone demethylase KDM3A was reduced >1.5 fold at all time points. KDM3A is known to function as an AR coactivator of key AR target genes including NKX3.1 and c-myc. Conclusions: Gene expression analysis of CCS1477 treated prostate tumors suggests an underlying mechanism involving the inhibition of key drivers of prostate cancer progression including AR and c-myc and a network of interacting pathways. Citation Format: Paul Elvin, Neil Pegg, Simone Daminelli, Izabela Eden, Barbara Young, Amy Prosser, Jenny Worthington, Nigel Brooks. P300/CBP inhibitor CCS1477 targets 22Rv1 prostate tumor AR and c-myc gene expression in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1019.

Introduction: Prostate cancer (PrCa) is the second leading cause of cancer death in men. Despite having a lower tumor mutation burden (TMB) than most tumor types (1), multiple gene fusions such as TMPRSS2-ERG have been characterized in prostate cancer and have been linked to aggressive disease (1). Individual tumor samples may contain multiple fusions and it is unknown whether these fusions could increase tumor immunogenicity. Here we investigated the effects of fusion burden on the expression of key molecular and immune effectors in prostate cancer specimens representing different stages of disease progression and androgen sensitivity (hormone sensitive vs metastatic castrate resistant prostate cancer (mCRPC)). Methods: 187 prostate samples taken from different stages of PrCa including Early/intermediate (n:30), late (n:112) and metastatic disease (n:9) were interrogated by RNASeq and assessed for mutation burden (mutations/MB of the genome) and fusion burden (number of fusions/10,000 genes profiled in each sample). To characterize the immune microenvironment, stromal TILS, % immune infiltrate, CD8+ T-cells, and PDL-1 expression were assessed by immunohistochemistry (IHC). Key molecular and immune gene signatures derived from the expression data were clustered according to disease stage, mutation or fusion burden, and hormone sensitive vs mCRPC disease (determined by mCRPC score from (2)). Results and Conclusion: TMB was very low across all samples analyzed (0.01-1.2 mutations per MB) while fusion burden ranged from 0 to 34 fusions/10,000 genes (up to 70 fusion(s) per sample) and was inversely correlated to TMB and not associated with disease stage. High fusion burden across samples correlated with high cell cycle progression and AR signaling (P<0.0001), ERG and ETS transcriptional activity (P<0.005) along with a modest increase in stromal TILs, % immune infiltrate and PDL-1 expression on immune cells. Fusion burden also correlated with immune signatures representing activation of M1 macrophages, checkpoint inhibitors, and T-cell activity (IFNγ-induced and T-effector signatures) (P<0.0001) whilst inversely correlating with an immune suppressor signature (P<0.01). High fusion burden also correlated with a high mCRPC score (P<0.0001), representing the most aggressive disease. Samples with a high mCRPC score also had gene signature scores suggesting high AR signaling, PI3K signaling, cell cycle progression and class I antigen presentation, and low expression of neuroendocrine tumor markers, NK cell and immune suppressor signatures. Our data suggest that high fusion burden may be associated more closely with immunogenicity and disease prognosis than TMB in PrCa and that tumors with high fusion burden could be potential candidates for immunotherapy. References(1) Chalmers et al. (2017), Genome Medicine Apr 19: 9(1):34 9:34.(2) Sharma N.L et al. (2013), Cancer Cell: Jan 14: 23(1):35-47. Note: This abstract was not presented at the meeting. Citation Format: Marie Wagle, Kobe Yuen, Edward E. Kadel, Thomas Holcomb, Shrividhya Srinivasan, Joseph Castillo, Dan Halligan, Adrian Carr, Max Bylesjo, Julian Augley, Simone Daminelli, Mark Kockx, Yannick Waumans, Jennifer Giltnane, Zineb Mounir. Association of tumor fusion burden with immune presence and androgen sensitivity in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2149.

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

Drug repositioning identifies new indications for known drugs. Here we report repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. While most repositioning efforts emerge through serendipity, we have devised a computational approach, which exploits interaction patterns shared between compounds. As a test case, we took the anti-viral drug brivudine (BVDU), which also has anti-cancer activity, and defined ten interaction patterns using our tool PLIP. These patterns characterise BVDU's interaction with its target s. Using PLIP we performed an in silico screen of all structural data currently available and identified the FDA approved malaria drug amodiaquine as a promising repositioning candidate. We validated our prediction by showing that amodiaquine suppresses chemoresistance in a multiple myeloma cancer cell line by inhibiting the chaperone function of the cancer target Hsp27. This work proves that PLIP interaction patterns are viable tools for computational repositioning and can provide search query information from a given drug and its target to identify structurally unrelated candidates, including drugs approved by the FDA, with a known safety and pharmacology profile. This approach has the potential to reduce costs and risks in drug development by predicting novel indications for known drugs and drug candidates.

Drug discovery is usually focused on a single protein target; in this process, existing compounds that bind to related proteins are often ignored. We describe ProBiS plugin, extension of our earlier ProBiS-ligands approach, which for a given protein structure allows prediction of its binding sites and, for each binding site, the ligands from similar binding sites in the Protein Data Bank. We developed a new database of precalculated binding site comparisons of about 290000 proteins to allow fast prediction of binding sites in existing proteins. The plugin enables advanced viewing of predicted binding sites, ligands' poses, and their interactions in three-dimensional graphics. Using the InhA query protein, an enoyl reductase enzyme in the Mycobacterium tuberculosis fatty acid biosynthesis pathway, we predicted its possible ligands and assessed their inhibitory activity experimentally. This resulted in three previously unrecognized inhibitors with novel scaffolds, demonstrating the plugin's utility in the early drug discovery process.

BACKGROUND:Drug repositioning aims to identify novel indications for existing drugs. One approach to repositioning exploits shared binding sites between the drug targets and other proteins. Here, we review the principle and algorithms of such target hopping and illustrate them in Chagas disease, an in Latin America widely spread, but neglected disease.CONCLUSION:We demonstrate how target hopping recovers known treatments for Chagas disease and predicts novel drugs, such as the antiviral foscarnet, which we predict to target Farnesyl Pyrophosphate Synthase in Trypanosoma cruzi, the causative agent of Chagas disease.

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

Detection of remote binding site similarity in proteins plays an important role for drug repositioning and off-target effect prediction. Various non-covalent interactions such as hydrogen bonds and van-der-Waals forces drive ligands' molecular recognition by binding sites in proteins. The increasing amount of available structures of protein-small molecule complexes enabled the development of comparative approaches. Several methods have been developed to characterize and compare protein ligand interaction patterns. Usually implemented as fingerprints, these are mainly used for post processing docking scores and (off-)target prediction. In the latter application, interaction profiles detect similarities in the bound interactions of different ligands and thus identify essential interactions between a protein and its small molecule ligands. Interaction pattern similarity correlates with binding site similarity and is thus contributing to a higher precision in binding site similarity assessment of proteins with distinct global structure. This renders it valuable for existing drug repositioning approaches in structural bioinformatics.Current methods to characterize and compare structure-based interaction patterns both for protein-small-molecule and protein protein interactions as well as their potential in target prediction will be reviewed in this article. The question of how the set of interaction types, flexibility or water-mediated interactions, influence the comparison of interaction patterns will be discussed. Due to the wealth of protein ligand structures available today, predicted targets can be ranked by comparing their ligand interaction pattern to patterns of the known target. Such knowledge-based methods offer high precision in comparison to methods comparing whole binding sites based on shape and amino acid physicochemical similarity. (C) 2014 Elsevier Ltd. All rights reserved.

Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) - a drug's ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity. Based on large compound screens, hydrophobicity and molecular weight have been suggested as key reasons. However, the results are sometimes contradictory and leave space for further analysis. Protein structures offer a structural dimension to explain promiscuity: Can a drug bind multiple targets because the drug is flexible or because the targets are structurally similar or even share similar binding sites? We present a systematic study of drug promiscuity based on structural data of PDB target proteins with a set of 164 promiscuous drugs. We show that there is no correlation between the degree of promiscuity and ligand properties such as hydrophobicity or molecular weight but a weak correlation to conformational flexibility. However, we do find a correlation between promiscuity and structural similarity as well as binding site similarity of protein targets. In particular, 71% of the drugs have at least two targets with similar binding sites. In order to overcome issues in detection of remotely similar binding sites, we employed a score for binding site similarity: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary structural binding site alignments. Three representative examples, namely the anti-cancer drug methotrexate, the natural product quercetin and the anti-diabetic drug acarbose are discussed in detail. Our findings suggest that global structural and binding site similarity play a more important role to explain the observed drug promiscuity in the PDB than physicochemical drug properties like hydrophobicity or molecular weight. Additionally, we find ligand flexibility to have a minor influence.

Recently, there has been much interest in gene-disease networks and polypharmacology as a basis for drug repositioning. Here, we integrate data from structural and chemical databases to create a drug-target-disease network for 147 promiscuous drugs, their 553 protein targets, and 44 disease indications. Visualizing and analyzing such complex networks is still an open problem. We approach it by mining the network for network motifs of bi-cliques. In our case, a bi-clique is a subnetwork in which every drug is linked to every target and disease. Since the data are incomplete, we identify incomplete bi-cliques, whose completion introduces novel, predicted links from drugs to targets and diseases. We demonstrate the power of this approach by repositioning cardiovascular drugs to parasitic diseases, by predicting the cancer-related kinase PIK3CG as a novel target of resveratrol, and by identifying for five drugs a shared binding site in four serine proteases and novel links to cancer, cardiovascular, and parasitic diseases.

Analytical chemistry is playing a critical role in many scientific disciplines and certainly pharmaceutical and biomedical sciences are among the most important.Just a quick glance in the international literature can easily prove this statement.On this basis it is a pleasure to introduce you to this book under the title "Reviews in Pharmaceutical and Biomedical Analysis".The topic of this book is so wide that it would have been a utopia to expect to cover all aspects of pharmaceutical and biomedical analysis in one single volume.However, the reader can find ten very interesting chapters that cover important fields ranging from sample preparation to metabolomics.The authors of the chapters of the book are distributed in a large number of countries and they certainly are well-respected and experienced researchers.I strongly believe that this ebook will be a valuable assistance to a variety of scientists and of course to students that are involved to the field of Analytical Chemistry and I strongly recommend it.

In recent years mass spectrometry-based proteomics became very important and now it is the leading approach employed in high-throughput analysis. Its relevance increased thanks to availability of genome-sequence database and the development of high sensitivity instruments allows a rapid and automated proteins profiling. The need to analyze complex biological samples at a large-scale level required the development of computational tools to analyze and statistically evaluate data generated from mass spectrometry (MS) experiments. These aspects have stimulated the young emerging field of bioinformatics in proteomics to introduce new software and algorithms to handle large and heterogeneous data sets and to improve the knowledge of discovery process. This review discusses of the most recent progresses in bioinformatics tools useful in mass spectrometry-based proteomics. In particular we will be focusing on software applications applied to proteomics profiling biomarker discovery and cluster analysis. Finally since most known mechanisms leading to biological processes involve different molecules here are reported the most recent methodologies to investigate biological systems through their underlying interactions with particular attention to protein-protein interaction.

Since red blood cells (RBCs) lack nuclei and organelles, cell membrane is their main load-bearing component and, according to a dynamic interaction with the cytoskeleton compartment, plays a pivotal role in their functioning. Even if erythrocyte membranes are available in large quantities, the low abundance and the hydrophobic nature of cell membrane proteins complicate their purification and detection by conventional 2D gel-based proteomic approaches. So, in order to increase the efficiency of RBC membrane proteome identification, here we took advantage of a simple and reproducible membrane sub-fractionation method coupled to Multidimensional Protein Identification Technology (MudPIT). In addition, the adoption of a stringent RBC filtration strategy from the whole blood, permitted to remove exhaustively contaminants, such as platelets and white blood cells, and to identify a total of 275 proteins in the three RBC membrane fractions collected and analysed. Finally, by means of software for the elaboration of the great quantity of data obtained and programs for statistical analysis and protein classification, it was possible to determine the validity of the entire system workflow and to assign the proper sub-cellular localization and function for the greatest number of the identified proteins.