Feeds:
Posts
Comments

Posts Tagged ‘Proteomics’

From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Curator: Stephen J. Williams, PhD

Marc W. Kirschner*

Department of Systems Biology
Harvard Medical School

Boston, Massachusetts 02115

With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like spe­cies, arise by descent with modification, so in their ear­liest forms even the founders of great dynasties are only marginally different than their sister fields and spe­cies. It is only in retrospect that we can recognize the significant founding events. Before embarking on a def­inition of systems biology, it may be worth remember­ing that confusion and controversy surrounded the in­troduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retro­spect molecular biology was new and different. It intro­duced both new subject matter and new technological approaches, in addition to a new style.

As a point of departure for systems biology, consider the quintessential experiment in the founding of molec­ular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonle­thal mutation in these genes in a multicellular organ­ism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiologi­cal. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previ­ous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.

That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been per­fected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new con­tinent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the sim­plistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organ­isms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combina­tions, something that is not assured in chemistry. It also downplays the significant regulatory features that in­volve interactions between gene products, their local­ization, binding, posttranslational modification, degra­dation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the con­served genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different pheno­types in different tissues of metazoan organisms. These circuits may have certain robustness, but more impor­tant they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes them­selves. Among other things it loads the deck in evolu­tionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.

Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the pheno­type. One aspect of systems biology is the develop­ment of techniques to examine broadly the level of pro­tein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.

High-throughput biology has opened up another im­portant area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and repro­ducible environment. The real world of ecology, evolu­tion, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later ex­tended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some ge­neticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and pro­tein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quan­titative effects, partially masked or accentuated by other genetic and environmental conditions. To under­stand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environ­mental variation.

Extracts and explants are relatively accessible to syn­thetic manipulation. Next there is the explicit recon­struction of circuits within cells or the deliberate modifi­cation of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of de­scribing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, pro­teins, cells in tissues, and whole organisms in their en­vironment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.

You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biologi­cal organization and processes in terms of the molecu­lar constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the impor­tance of defining a succession of physiological states in that process, and on evolutionary biology and ecol­ogy for the appreciation that all aspects of the organ­ism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology gen­erates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of sys­tems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.

Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005

Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn 

Molecules 202025(23), 5702; https://doi.org/10.3390/molecules25235702

Abstract

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

5.1.5. Large-Scale Proteomics

While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.

All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.

5.2. Genetic Approaches

Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.

Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.

5.2.1. Resistance Cloning

The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.

In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].

While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].

When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].

While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.

5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens

When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).

An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].

The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].

Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].

An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].

From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.

SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 202113(13), 3148; https://doi.org/10.3390/cancers13133148

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question

1. Introduction

The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.

Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)

Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).

Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16]. 

2. Systems Biology in Cancer Research

Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].

In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.

2.1. Biological Network Analysis for Biomarker Validation

The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].

2.2. De Novo Construction of Biological Networks

While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].

2.3. Network Based Machine Learning

A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].

In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.

3. Network-Based Learning in Cancer Research

As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.

3.1. Molecular Characterization with Network Information

Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].

Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.

3.2. Tumor Heterogeneity Study with Network Information

The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].

In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].

3.3. Drug Target Identification with Network Information

In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].

Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.

4. Deep Learning in Cancer Research

DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].

In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].

4.1. Challenges for Deep Learning in Cancer Research

Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].

Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).

4.2. Molecular Charactization with Network and DNN Model

DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].

Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].

4.3. Tumor Heterogeneity with Network and DNN Model

As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].

4.4. Drug Target Identification with Networks and DNN Models

In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].

The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].

4.5. Graph Neural Network Model

In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].

In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].

4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge

The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].

As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].

Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.

“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”

References

  1. Janes, K.A.; Yaffe, M.B. Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 20067, 820–828. [Google Scholar] [CrossRef] [PubMed]
  2. Kreeger, P.K.; Lauffenburger, D.A. Cancer systems biology: A network modeling perspective. Carcinogenesis 201031, 2–8. [Google Scholar] [CrossRef] [PubMed]
  3. Vucic, E.A.; Thu, K.L.; Robison, K.; Rybaczyk, L.A.; Chari, R.; Alvarez, C.E.; Lam, W.L. Translating cancer ‘omics’ to improved outcomes. Genome Res. 201222, 188–195. [Google Scholar] [CrossRef]
  4. Hoadley, K.A.; Yau, C.; Wolf, D.M.; Cherniack, A.D.; Tamborero, D.; Ng, S.; Leiserson, M.D.; Niu, B.; McLellan, M.D.; Uzunangelov, V.; et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 2014158, 929–944. [Google Scholar] [CrossRef] [PubMed]
  5. Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell 2018173, 283–285. [Google Scholar] [CrossRef]
  6. Chuang, H.Y.; Lee, E.; Liu, Y.T.; Lee, D.; Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 20073, 140. [Google Scholar] [CrossRef]
  7. Zhang, W.; Chien, J.; Yong, J.; Kuang, R. Network-based machine learning and graph theory algorithms for precision oncology. NPJ Precis. Oncol. 20171, 25. [Google Scholar] [CrossRef] [PubMed]
  8. Ngiam, K.Y.; Khor, W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 201920, e262–e273. [Google Scholar] [CrossRef]
  9. Creixell, P.; Reimand, J.; Haider, S.; Wu, G.; Shibata, T.; Vazquez, M.; Mustonen, V.; Gonzalez-Perez, A.; Pearson, J.; Sander, C.; et al. Pathway and network analysis of cancer genomes. Nat. Methods 201512, 615. [Google Scholar]
  10. Reyna, M.A.; Haan, D.; Paczkowska, M.; Verbeke, L.P.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 202011, 729. [Google Scholar] [CrossRef]
  11. Luo, P.; Ding, Y.; Lei, X.; Wu, F.X. deepDriver: Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks. Front. Genet. 201910, 13. [Google Scholar] [CrossRef]
  12. Jiao, W.; Atwal, G.; Polak, P.; Karlic, R.; Cuppen, E.; Danyi, A.; De Ridder, J.; van Herpen, C.; Lolkema, M.P.; Steeghs, N.; et al. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns. Nat. Commun. 202011, 728. [Google Scholar] [CrossRef]
  13. Chaudhary, K.; Poirion, O.B.; Lu, L.; Garmire, L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 201824, 1248–1259. [Google Scholar] [CrossRef]
  14. Gao, F.; Wang, W.; Tan, M.; Zhu, L.; Zhang, Y.; Fessler, E.; Vermeulen, L.; Wang, X. DeepCC: A novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 20198, 44. [Google Scholar] [CrossRef]
  15. Zeng, X.; Zhu, S.; Liu, X.; Zhou, Y.; Nussinov, R.; Cheng, F. deepDR: A network-based deep learning approach to in silico drug repositioning. Bioinformatics 201935, 5191–5198. [Google Scholar] [CrossRef]
  16. Issa, N.T.; Stathias, V.; Schürer, S.; Dakshanamurthy, S. Machine and deep learning approaches for cancer drug repurposing. In Seminars in Cancer Biology; Elsevier: Amsterdam, The Netherlands, 2020. [Google Scholar]
  17. Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.M.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M.; Network, C.G.A.R.; et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 201345, 1113. [Google Scholar] [CrossRef]
  18. The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 2020578, 82. [Google Scholar] [CrossRef] [PubMed]
  19. King, M.C.; Marks, J.H.; Mandell, J.B. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science 2003302, 643–646. [Google Scholar] [CrossRef] [PubMed]
  20. Courtney, K.D.; Corcoran, R.B.; Engelman, J.A. The PI3K pathway as drug target in human cancer. J. Clin. Oncol. 201028, 1075. [Google Scholar] [CrossRef] [PubMed]
  21. Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 200927, 1160. [Google Scholar] [CrossRef]
  22. Yersal, O.; Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 20145, 412. [Google Scholar] [CrossRef] [PubMed]
  23. Zhao, L.; Lee, V.H.; Ng, M.K.; Yan, H.; Bijlsma, M.F. Molecular subtyping of cancer: Current status and moving toward clinical applications. Brief. Bioinform. 201920, 572–584. [Google Scholar] [CrossRef] [PubMed]
  24. Jones, P.A.; Issa, J.P.J.; Baylin, S. Targeting the cancer epigenome for therapy. Nat. Rev. Genet. 201617, 630. [Google Scholar] [CrossRef] [PubMed]
  25. Huang, S.; Chaudhary, K.; Garmire, L.X. More is better: Recent progress in multi-omics data integration methods. Front. Genet. 20178, 84. [Google Scholar] [CrossRef]
  26. Chin, L.; Andersen, J.N.; Futreal, P.A. Cancer genomics: From discovery science to personalized medicine. Nat. Med. 201117, 297. [Google Scholar] [CrossRef] [PubMed]

Use of Systems Biology in Anti-Microbial Drug Development

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965

In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Genome Sequences and Proteomic Structural Databases

In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.

Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.

There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.

We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.

FIGURE 2

Figure 2. (A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).

Examples of Understanding and Combatting Resistance

The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.

FIGURE 3

Figure 3. (A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).

Other articles related to Computational Biology, Systems Biology, and Bioinformatics on this online journal include:

20th Anniversary and the Evolution of Computational Biology – International Society for Computational Biology

Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

Quantum Biology And Computational Medicine

Systems Biology Analysis of Transcription Networks, Artificial Intelligence, and High-End Computing Coming to Fruition in Personalized Oncology

Read Full Post »

Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Read Full Post »

Structure-guided Drug Discovery: (1) The Coronavirus 3CL hydrolase (Mpro) enzyme (main protease) essential for proteolytic maturation of the virus and (2) viral protease, the RNA polymerase, the viral spike protein, a viral RNA as promising two targets for discovery of cleavage inhibitors of the viral spike polyprotein preventing the Coronavirus Virion the spread of infection

 

Curators and Reporters: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

 

Therapeutical options to coronavirus (2019-nCoV) include consideration of the following:

(a) Monoclonal and polyclonal antibodies

(b)  Vaccines

(c)  Small molecule treatments (e.g., chloroquinolone and derivatives), including compounds already approved for other indications 

(d)  Immuno-therapies derived from human or other sources

 

 

Structure of the nCoV trimeric spike

The World Health Organization has declared the outbreak of a novel coronavirus (2019-nCoV) to be a public health emergency of international concern. The virus binds to host cells through its trimeric spike glycoprotein, making this protein a key target for potential therapies and diagnostics. Wrapp et al. determined a 3.5-angstrom-resolution structure of the 2019-nCoV trimeric spike protein by cryo–electron microscopy. Using biophysical assays, the authors show that this protein binds at least 10 times more tightly than the corresponding spike protein of severe acute respiratory syndrome (SARS)–CoV to their common host cell receptor. They also tested three antibodies known to bind to the SARS-CoV spike protein but did not detect binding to the 2019-nCoV spike protein. These studies provide valuable information to guide the development of medical counter-measures for 2019-nCoV. [Bold Face Added by ALA]

Science, this issue p. 1260

Abstract

The outbreak of a novel coronavirus (2019-nCoV) represents a pandemic threat that has been declared a public health emergency of international concern. The CoV spike (S) glycoprotein is a key target for vaccines, therapeutic antibodies, and diagnostics. To facilitate medical countermeasure development, we determined a 3.5-angstrom-resolution cryo–electron microscopy structure of the 2019-nCoV S trimer in the prefusion conformation. The predominant state of the trimer has one of the three receptor-binding domains (RBDs) rotated up in a receptor-accessible conformation. We also provide biophysical and structural evidence that the 2019-nCoV S protein binds angiotensin-converting enzyme 2 (ACE2) with higher affinity than does severe acute respiratory syndrome (SARS)-CoV S. Additionally, we tested several published SARS-CoV RBD-specific monoclonal antibodies and found that they do not have appreciable binding to 2019-nCoV S, suggesting that antibody cross-reactivity may be limited between the two RBDs. The structure of 2019-nCoV S should enable the rapid development and evaluation of medical countermeasures to address the ongoing public health crisis.

SOURCE
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
  1. Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA.

  2. 2Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
  1. Corresponding author. Email: jmclellan@austin.utexas.edu
  1. * These authors contributed equally to this work.

Science  13 Mar 2020:
Vol. 367, Issue 6483, pp. 1260-1263
DOI: 10.1126/science.abb2507

 

02/04/2020

New Coronavirus Protease Structure Available

PDB data provide a starting point for structure-guided drug discovery

A high-resolution crystal structure of COVID-19 (2019-nCoV) coronavirus 3CL hydrolase (Mpro) has been determined by Zihe Rao and Haitao Yang’s research team at ShanghaiTech University. Rapid public release of this structure of the main protease of the virus (PDB 6lu7) will enable research on this newly-recognized human pathogen.

Recent emergence of the COVID-19 coronavirus has resulted in a WHO-declared public health emergency of international concern. Research efforts around the world are working towards establishing a greater understanding of this particular virus and developing treatments and vaccines to prevent further spread.

While PDB entry 6lu7 is currently the only public-domain 3D structure from this specific coronavirus, the PDB contains structures of the corresponding enzyme from other coronaviruses. The 2003 outbreak of the closely-related Severe Acute Respiratory Syndrome-related coronavirus (SARS) led to the first 3D structures, and today there are more than 200 PDB structures of SARS proteins. Structural information from these related proteins could be vital in furthering our understanding of coronaviruses and in discovery and development of new treatments and vaccines to contain the current outbreak.

The coronavirus 3CL hydrolase (Mpro) enzyme, also known as the main protease, is essential for proteolytic maturation of the virus. It is thought to be a promising target for discovery of small-molecule drugs that would inhibit cleavage of the viral polyprotein and prevent spread of the infection.

Comparison of the protein sequence of the COVID-19 coronavirus 3CL hydrolase (Mpro) against the PDB archive identified 95 PDB proteins with at least 90% sequence identity. Furthermore, these related protein structures contain approximately 30 distinct small molecule inhibitors, which could guide discovery of new drugs. Of particular significance for drug discovery is the very high amino acid sequence identity (96%) between the COVID-19 coronavirus 3CL hydrolase (Mpro) and the SARS virus main protease (PDB 1q2w). Summary data about these closely-related PDB structures are available (CSV) to help researchers more easily find this information. In addition, the PDB houses 3D structure data for more than 20 unique SARS proteins represented in more than 200 PDB structures, including a second viral protease, the RNA polymerase, the viral spike protein, a viral RNA, and other proteins (CSV).

Public release of the COVID-19 coronavirus 3CL hydrolase (Mpro), at a time when this information can prove most vital and valuable, highlights the importance of open and timely availability of scientific data. The wwPDB strives to ensure that 3D biological structure data remain freely accessible for all, while maintaining as comprehensive and accurate an archive as possible. We hope that this new structure, and those from related viruses, will help researchers and clinicians address the COVID-19 coronavirus global public health emergency.

Update: Released COVID-19-related PDB structures include

  • PDB structure 6lu7 (X. Liu, B. Zhang, Z. Jin, H. Yang, Z. Rao Crystal structure of COVID-19 main protease in complex with an inhibitor N3 doi: 10.2210/pdb6lu7/pdb) Released 2020-02-05
  • PDB structure 6vsb (D. Wrapp, N. Wang, K.S. Corbett, J.A. Goldsmith, C.-L. Hsieh, O. Abiona, B.S. Graham, J.S. McLellan (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation Science doi: 10.1126/science.abb2507) Released 2020-02-26
  • PDB structure 6lxt (Y. Zhu, F. Sun Structure of post fusion core of 2019-nCoV S2 subunit doi: 10.2210/pdb6lxt/pdb) Released 2020-02-26
  • PDB structure 6lvn (Y. Zhu, F. Sun Structure of the 2019-nCoV HR2 Domain doi: 10.2210/pdb6lvn/pdb) Released 2020-02-26
  • PDB structure 6vw1
    J. Shang, G. Ye, K. Shi, Y.S. Wan, H. Aihara, F. Li Structural basis for receptor recognition by the novel coronavirus from Wuhan doi: 10.2210/pdb6vw1/pdb
    Released 2020-03-04
  • PDB structure 6vww
    Y. Kim, R. Jedrzejczak, N. Maltseva, M. Endres, A. Godzik, K. Michalska, A. Joachimiak, Center for Structural Genomics of Infectious Diseases Crystal Structure of NSP15 Endoribonuclease from SARS CoV-2 doi: 10.2210/pdb6vww/pdb
    Released 2020-03-04
  • PDB structure 6y2e
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure of the free enzyme of the SARS-CoV-2 (2019-nCoV) main protease doi: 10.2210/pdb6y2e/pdb
    Released 2020-03-04
  • PDB structure 6y2f
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure (monoclinic form) of the complex resulting from the reaction between SARS-CoV-2 (2019-nCoV) main protease and tert-butyl (1-((S)-1-(((S)-4-(benzylamino)-3,4-dioxo-1-((S)-2-oxopyrrolidin-3-yl)butan-2-yl)amino)-3-cyclopropyl-1-oxopropan-2-yl)-2-oxo-1,2-dihydropyridin-3-yl)carbamate (alpha-ketoamide 13b) doi: 10.2210/pdb6y2f/pdb
    Released 2020-03-04
  • PDB structure 6y2g
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure (orthorhombic form) of the complex resulting from the reaction between SARS-CoV-2 (2019-nCoV) main protease and tert-butyl (1-((S)-1-(((S)-4-(benzylamino)-3,4-dioxo-1-((S)-2-oxopyrrolidin-3-yl)butan-2-yl)amino)-3-cyclopropyl-1-oxopropan-2-yl)-2-oxo-1,2-dihydropyridin-3-yl)carbamate (alpha-ketoamide 13b) doi: 10.2210/pdb6y2g/pdb
    Released 2020-03-04
First page image

Abstract

Coronavirus disease 2019 (COVID-19) is a global pandemic impacting nearly 170 countries/regions and more than 285,000 patients worldwide. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which invades cells through the angiotensin converting enzyme 2 (ACE2) receptor. Among those with COVID-19, there is a higher prevalence of cardiovascular disease and more than 7% of patients suffer myocardial injury from the infection (22% of the critically ill). Despite ACE2 serving as the portal for infection, the role of ACE inhibitors or angiotensin receptor blockers requires further investigation. COVID-19 poses a challenge for heart transplantation, impacting donor selection, immunosuppression, and post-transplant management. Thankfully there are a number of promising therapies under active investigation to both treat and prevent COVID-19. Key Words: COVID-19; myocardial injury; pandemic; heart transplant

SOURCE

https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.120.046941

ACE2

  • Towler P, Staker B, Prasad SG, Menon S, Tang J, Parsons T, Ryan D, Fisher M, Williams D, Dales NA, Patane MA, Pantoliano MW (Apr 2004). “ACE2 X-ray structures reveal a large hinge-bending motion important for inhibitor binding and catalysis”The Journal of Biological Chemistry279 (17): 17996–8007. doi:10.1074/jbc.M311191200PMID 14754895.

 

  • Turner AJ, Tipnis SR, Guy JL, Rice G, Hooper NM (Apr 2002). “ACEH/ACE2 is a novel mammalian metallocarboxypeptidase and a homologue of angiotensin-converting enzyme insensitive to ACE inhibitors”Canadian Journal of Physiology and Pharmacology80 (4): 346–53. doi:10.1139/y02-021PMID 12025971.

 

  •  Zhang, Haibo; Penninger, Josef M.; Li, Yimin; Zhong, Nanshan; Slutsky, Arthur S. (3 March 2020). “Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target”Intensive Care Medicine. Springer Science and Business Media LLC. doi:10.1007/s00134-020-05985-9ISSN 0342-4642PMID 32125455.

 

  • ^ Gurwitz, David (2020). “Angiotensin receptor blockers as tentative SARS‐CoV‐2 therapeutics”Drug Development Researchdoi:10.1002/ddr.21656PMID 32129518.

 

Angiotensin converting enzyme 2 (ACE2)

is an exopeptidase that catalyses the conversion of angiotensin I to the nonapeptide angiotensin[1-9][5] or the conversion of angiotensin II to angiotensin 1-7.[6][7] ACE2 has direct effects on cardiac functiona and is expressed predominantly in vascular endothelial cells of the heart and the kidneys.[8] ACE2 is not sensitive to the ACE inhibitor drugs used to treat hypertension.[9]

ACE2 receptors have been shown to be the entry point into human cells for some coronaviruses, including the SARS virus.[10] A number of studies have identified that the entry point is the same for SARS-CoV-2,[11] the virus that causes COVID-19.[12][13][14][15]

Some have suggested that a decrease in ACE2 could be protective against Covid-19 disease[16], but others have suggested the opposite, that Angiotensin II receptor blocker drugs could be protective against Covid-19 disease via increasing ACE2, and that these hypotheses need to be tested by datamining of clinical patient records.[17]

REFERENCES

https://en.wikipedia.org/wiki/Angiotensin-converting_enzyme_2

 

FOLDING@HOME TAKES UP THE FIGHT AGAINST COVID-19 / 2019-NCOV

We need your help! Folding@home is joining researchers around the world working to better understand the 2019 Coronavirus (2019-nCoV) to accelerate the open science effort to develop new life-saving therapies. By downloading Folding@Home, you can donate your unused computational resources to the Folding@home Consortium, where researchers working to advance our understanding of the structures of potential drug targets for 2019-nCoV that could aid in the design of new therapies. The data you help us generate will be quickly and openly disseminated as part of an open science collaboration of multiple laboratories around the world, giving researchers new tools that may unlock new opportunities for developing lifesaving drugs.

2019-nCoV is a close cousin to SARS coronavirus (SARS-CoV), and acts in a similar way. For both coronaviruses, the first step of infection occurs in the lungs, when a protein on the surface  of the virus binds to a receptor protein on a lung cell. This viral protein is called the spike protein, depicted in red in the image below, and the receptor is known as ACE2. A therapeutic antibody is a type of protein that can block the viral protein from binding to its receptor, therefore preventing the virus from infecting the lung cell. A therapeutic antibody has already been developed for SARS-CoV, but to develop therapeutic antibodies or small molecules for 2019-nCoV, scientists need to better understand the structure of the viral spike protein and how it binds to the human ACE2 receptor required for viral entry into human cells.

Proteins are not stagnant—they wiggle and fold and unfold to take on numerous shapes.  We need to study not only one shape of the viral spike protein, but all the ways the protein wiggles and folds into alternative shapes in order to best understand how it interacts with the ACE2 receptor, so that an antibody can be designed. Low-resolution structures of the SARS-CoV spike protein exist and we know the mutations that differ between SARS-CoV and 2019-nCoV.  Given this information, we are uniquely positioned to help model the structure of the 2019-nCoV spike protein and identify sites that can be targeted by a therapeutic antibody. We can build computational models that accomplish this goal, but it takes a lot of computing power.

This is where you come in! With many computers working towards the same goal, we aim to help develop a therapeutic remedy as quickly as possible. By downloading Folding@home here [LINK] and selecting to contribute to “Any Disease”, you can help provide us with the computational power required to tackle this problem. One protein from 2019-nCoV, a protease encoded by the viral RNA, has already been crystallized. Although the 2019-nCoV spike protein of interest has not yet been resolved bound to ACE2, our objective is to use the homologous structure of the SARS-CoV spike protein to identify therapeutic antibody targets.

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

Structures of the closely related SARS-CoV spike protein bound by therapeutic antibodies may help rapidly design better therapies. The three monomers of the SARS-CoV spike protein are shown in different shades of red; the antibody is depicted in green. [PDB: 6NB7 https://www.rcsb.org/structure/6nb7]

(post authored by Ariana Brenner Clerkin)

References:

PDB 6lu7 structure summary ‹ Protein Data Bank in Europe (PDBe) ‹ EMBL-EBI https://www.ebi.ac.uk/pdbe/entry/pdb/6lu7 (accessed Feb 5, 2020).

Tian, X.; Li, C.; Huang, A.; Xia, S.; Lu, S.; Shi, Z.; Lu, L.; Jiang, S.; Yang, Z.; Wu, Y.; et al. Potent Binding of 2019 Novel Coronavirus Spike Protein by a SARS Coronavirus-Specific Human Monoclonal Antibody; preprint; Microbiology, 2020. https://doi.org/10.1101/2020.01.28.923011.

Walls, A. C.; Xiong, X.; Park, Y. J.; Tortorici, M. A.; Snijder, J.; Quispe, J.; Cameroni, E.; Gopal, R.; Dai, M.; Lanzavecchia, A.; et al. Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion. Cell 2019176, 1026-1039.e15. https://doi.org/10.2210/pdb6nb7/pdb.

SOURCE

https://foldingathome.org/2020/02/27/foldinghome-takes-up-the-fight-against-covid-19-2019-ncov/

UPDATED 3/13/2020

I am reposting the following Science blog post from Derrick Lowe as is and ask people go browse through the comments on his Science blog In the Pipeline because, as Dr. Lowe states that in this current crisis it is important to disseminate good information as quickly as possible so wanted the readers here to have the ability to read his great posting on this matter of Covid-19.  Also i would like to direct readers to the journal Science opinion letter concerning how important it is to rebuild the trust in good science and the scientific process.  The full link for the following In the Pipeline post is: https://blogs.sciencemag.org/pipeline/archives/2020/03/06/covid-19-small-molecule-therapies-reviewed

A Summary of current potential repurposed therapeutics for COVID-19 Infection from In The Pipeline: A Science blog from Derick Lowe

Covid-19 Small Molecule Therapies Reviewed

Let’s take inventory on the therapies that are being developed for the coronavirus epidemic. Here is a very thorough list of at Biocentury, and I should note that (like Stat and several other organizations) they’re making all their Covid-19 content free to all readers during this crisis. I’d like to zoom in today on the potential small-molecule therapies, since some of these have the most immediate prospects for use in the real world.

The ones at the front of the line are repurposed drugs that are already approved for human use, for a lot of obvious reasons. The Biocentury list doesn’t cover these, but here’s an article at Nature Biotechnology that goes into detail. Clinical trials are a huge time sink – they sort of have to be, in most cases, if they’re going to be any good – and if you’ve already done all that stuff it’s a huge leg up, even if the drug itself is not exactly a perfect fit for the disease. So what do we have? The compound that is most advanced is probably remdesivir from Gilead, at right. This has been in development for a few years as an RNA virus therapy – it was originally developed for Ebola, and has been tried out against a whole list of single-strand RNA viruses. That includes the related coronaviruses SARS and MERS, so Covid-19 was an obvious fit.

The compound is a prodrug – that phosphoramide gets cleaved off completely, leaving the active 5-OH compound GS-44-1524. It mechanism of action is to get incorporated into viral RNA, since it’s taken up by RNA polymerase and it largely seems to evade proofreading. This causes RNA termination trouble later on, since that alpha-nitrile C-nucleoside is not exactly what the virus is expecting in its genome at that point, and thus viral replication is inhibited.

There are five clinical trials underway (here’s an overview at Biocentury). The NIH has an adaptive-design Phase II trial that has already started in Nebraska, with doses to be changed according to Bayesian readouts along the way. There are two Phase III trials underway at China-Japan Friendship Hospital in Hubei, double-blinded and placebo-controlled (since placebo is, as far as drug therapy goes, the current standard of care). And Gilead themselves are starting two open-label trials, one with no control arm and one with an (unblinded) standard-of-care comparison arm. Those might read out first, depending on when they get off the ground, but will be only rough readouts due to the fast-and-loose trial design. The two Hubei trials and the NIH one will add some rigor to the process, but I’m not sure when they’re going to report. My personal opinion is that I like the chances of this drug more than anything else on this list, but it’s still unlikely to be a game-changer.

There’s an RNA polymerase inhibitor (favipiravir) from Toyama, at right, that’s in a trial in China. It’s a thought – a broad-spectrum agent of this sort would be the sort of thing to try. But unfortunately, from what I can see, it has already turned up as ineffective in in vitro tests. The human trial that’s underway is honestly the sort of thing that would only happen under circumstances like the present: a developing epidemic with a new pathogen and no real standard of care. I hold out little hope for this one, but given that there’s nothing else at present, it probably should be tried. As you’ll see, this is far from the only situation like this.

One of the screens of known drugs in China that also flagged remdesivir noted that the old antimalarial drug chloroquine seemed to be effective in vitro. It had been reported some years back as a possible antiviral, working through more than one mechanism, probably both at viral entry and intracellularly thereafter. That part shouldn’t be surprising – chloroquine’s actual mode(s) of action against malaria parasites are still not completely worked out, either, and some of what people thought they knew about it has turned out to be wrong. There are several trials underway with it at Chinese facilities, some in combination with other agents like remdesivir. Chloroquine has of course been taken for many decades as an antimalarial, but it has a number of liabilities, including seizures, hearing damage, retinopathy and sudden effects on blood glucose. So it’s going to be important to establish just how effective it is and what doses will be needed. Just as with vaccine candidates, it’s possible to do more harm with a rushed treatment than the disease is doing itself

There are several other known antiviral drugs are being tried in China, but I don’t have too much hope for those, either. The neuraminidase inhibitors such as oseltamivir (better known as Tamiflu) were tried against SARS and were ineffective; there is no reason to expect anything versus Covid-19 although these drugs are a component of some drug cocktail trials. The HIV protease therapies such as darunavir and the combination therapy Kaletra are in trials, but that’s also a rather desperate long shot, since there’s no particular reason to think that they will have any such protease inhibition against what this new virus has to offer (and indeed, such agents weren’t much help against SARS in the end, either). The classic interferon/ribavirin combination seems to have had some activity against SARS and MERS, and is in two trials from what I can see. That’s not an awful idea by any means, but it’s not a great one, either: if your viral disease has interferon/ribavirin as a front line therapy, it generally means that there’s nothing really good available. No, unless we get really lucky none of these ideas are going to slow the disease down much.

There are a few other repurposed-protease-inhibitors ideas out there, such as this one. (Edit: I had seen this paper but couldn’t track it down, so thanks to those who sent it along). This paper suggests that the TMPRSS2 protease is important for viral entry on the human-cell-side of the process, a pathway that has been noted for other coronaviruses. And it points out that there is a an approved inhibitor (in Japan) for this enzyme (camostat), so that would definitely seem to be worth a trial, probably in combination with remdesivir.

That’s about it for the existing small molecules, from what I can see. What about new ones? Don’t hold your breath, is all I can say. A drug discovery program from scratch against a new pathogen is, as many readers here well know, not a trivial exercise. As this Bloomberg article details, many such efforts in the past (small molecules and vaccines alike) have come to grief because by the time they had anything to deliver the epidemic itself had passed. Indeed, Gilead’s remdesivir had already been dropped as a potential Ebola therapy.

You will either need to have a target in mind up front or go phenotypic. For the former, what you’d see are better characterizations of the viral protease and more extensive screens against it. Two other big target areas are viral entry (which involves the “spike” proteins on the virus surface and the ACE2 protein on human cells) and viral replication. To the former, it’s worth quickly noting that ACE2 is so much unlike the more familiar ACE protein that none of the cardiovascular ACE inhibitors do anything to it at all. And targeting the latter mechanisms is how remdesivir was developed as a possible Ebola agent, but as you can see, that took time, too. Phenotypic screens are perfectly reasonable against viral pathogens as well, but you’ll need to put time and effort into that assay up front, just as with any phenotypic effort, because as anyone who does that sort of work will tell you, a bad phenotypic screen is a complete waste of everyone’s time.

One of the key steps for either route is identifying an animal model. While animal models of infectious disease can be extremely well translated to human therapy, that doesn’t happen by accident: you need to choose the right animal. Viruses in general (and coronaviruses are no exception) vary widely in their effects in different species, and not just across the gaps of bird/reptile/human and the like. No, you’ll run into things where even the usual set of small mammals are acting differently from each other, with some of them not even getting sick at all. This current virus may well have gone through a couple of other mammalian species before landing on us, but you’ll note that dogs (to pick one) don’t seem to have any problem with it.

All this means that any new-target new-chemical-matter effort against Covid-19 (or any new pathogen) is going to take years, and there is just no way around that. Update: see here for just such an effort to start finding fragment hits for the viral protease. This puts small molecules in a very bimodal distribution: you have the existing drugs that might be repurposed, and are presumably available right now. Nothing else is! At the other end, for completely new therapies you have the usual prospects of drug discovery: years from now, lots of money, low success rate, good luck to all of us. The gap between these two could in theory be filled by vaccines and antibody therapies (if everything goes really, really well) but those are very much their own area and will be dealt with in a separate post.

Either way, the odds are that we (and I mean “we as a species” here) are going to be fighting this epidemic without any particularly amazing pharmacological weapons. Eventually we’ll have some, but I would advise people, pundits, and politicians not to get all excited about the prospects for some new therapies to come riding up over the hill to help us out. The odds of that happening in time to do anything about the current outbreak are very small. We will be going for months, years, with the therapeutic options we have right now. Look around you: what we have today is what we have to work with.

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

Reporters: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/06/group-of-researchers-solve-covid-19-structure-and-map-potential-therapeutic/

Predicting the Protein Structure of Coronavirus: Inhibition of Nsp15 can slow viral replication and Cryo-EM – Spike protein structure (experimentally verified) vs AI-predicted protein structures (not experimentally verified) of DeepMind (Parent: Google) aka AlphaFold

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/08/predicting-the-protein-structure-of-coronavirus-inhibition-of-nsp15-can-slow-viral-replication-and-cryo-em-spike-protein-structure-experimentally-verified-vs-ai-predicted-protein-structures-not/

 

Coronavirus facility opens at Rambam Hospital using new Israeli tech

https://www.jpost.com/Israel-News/Coronavirus-facility-opens-at-Rambam-Hospital-using-new-Israeli-tech-619681

 

Read Full Post »

Live Conference Coverage @MedCity news Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

Read Full Post »

Protein profiling in cancer and metabolic diseases

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Deep Protein Profiling Key

Company has encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment.

http://www.technologynetworks.com/Proteomics/news.aspx?ID=190145

Proteome Sciences plc has strongly encouraged by two recent reports that emphasise the importance of protein profiling to improve outcomes in cancer treatment. These highlight the growing need for more detailed, personal assessment of protein profiles to improve the management of cancer treatment.

In the first study two groups from University College London and Cancer Research UK demonstrated that genetic mutations in cancer can lead to changes in the proteins on the cell surface1. These are new sequences which are seen as foreign by the body’s immune system and, with appropriate immunotherapy, the level of response in lung cancer was greatly enhanced.

However many of the patients with these types of mutations unfortunately still did not respond which highlighted the need for deeper analysis of the protein expression in tumours in order to better appreciate the mechanisms that contribute to treatment failure.

The second study, led by Professor Nigel Bundred of Manchester University, reported that use of two drugs that act on the same breast cancer target, an over-expressing protein called Her-2, were able to eradicate detectable tumours in around 10% of those treated in just 11 days, with 87% of those treated having a proteomic change indicating cells had stopped growing and/or cell death had increased2.

Whilst these results appear very promising it is worth noting that the over-expressing Her-2 target is only present in about 20% of breast tumours meaning this combination therapy was successful in clearing tumours in just 2% of the total breast cancer population.

Dr. Ian Pike, Chief Operating Officer of Proteome Sciences commented, “Both these recent studies should rightly be recognised as important steps forward towards better cancer treatment. However, in order to overcome the limitations of current drug therapy programs, a much deeper and more comprehensive analysis of the complex protein networks that regulate tumour growth and survival is required and will be essential to achieve a major advance in the battle to treat cancer.

“Our SysQuant® workflows provide that solution. As an example, in pancreatic cancer3 we have successfully mapped the complex network of regulatory processes and demonstrate the ability to devise personalised treatment combinations on an individual basis for each patient. A retrospective study with SysQuant® to predict response to the targeted drug Sorafenib in liver cancer is in process and we are planning further prospective trials to guide personalised treatment selection in liver cancer.

“We are already delivering systems-wide biology solutions through SysQuant® and TMTcalibrator™ programs to our clients that are generating novel biological data and results using more sensitive profiling that are helping them to better understand their drug development programs and to provide new biomarkers for tracking patient response in clinical trials.

“We are strongly positioned to deliver more comprehensive analysis of proteins and cellular pathways across other areas of disease and in particular to extend the use of SysQuant® with other leading cancer research groups in liver and other cancers.”

Proteome Sciences has also expanded its offering in personalised medicine through the use of its TMTcalibrator™ technology to uniquely identify protein biomarkers that reveal active cancer and other disease processes in body fluid samples. The importance of these ‘mechanistic’ biomarkers is that they are essential to monitor that drugs are being effective and that they can be used as early biomarkers of disease recurrence.

Using SysQuant® and TMTcalibrator™, Proteome Sciences can deliver more comprehensive analysis and provide unparalleled levels of sensitivity and breadth of coverage of the proteome, enabling faster, more efficient drug development and more accurate disease diagnosis.

 

Discovering ‘Outlier’ Enzymes

Researchers at TSRI and Salk Institute have discovered ‘Outlier’ enzymes that could offer new targets to treat type 2 diabetes and inflammatory disorders.

A team led by scientists at The Scripps Research Institute (TSRI) and the Salk Institute for Biological Studies have discovered two enzymes that appear to play a role in metabolism and inflammation—and might someday be targeted with drugs to treat type 2 diabetes and inflammatory disorders. The discovery is unusual because the enzymes do not bear a resemblance—in their structures or amino-acid sequences—to any known class of enzymes.

The team of scientists nevertheless identified them as “outlier” members of the serine/threonine hydrolase class, using newer techniques that detect biochemical activity. “A huge fraction of the human ‘proteome’ remains uncharacterized, and this paper shows how chemical approaches can be used to uncover proteins of a given functionality that have eluded classification based on sequence or predicted structure,” said co-senior author Benjamin F. Cravatt, chair of TSRI’s Department of Chemical Physiology.

“In this study, we found two genes that control levels of lipids with anti-diabetic and anti-inflammatory activity, suggesting exciting targets for diabetes and inflammatory diseases,” said co-senior author Alan Saghatelian, who holds the Dr. Frederik Paulsen Chair at the Salk Institute. The study, which appeared as a Nature Chemical Biology Advance Online Publication on March 28, 2016, began as an effort in the Cravatt laboratory to discover and characterize new serine/threonine hydrolases using fluorophosphonate (FP) probes—molecules that selectively bind and, in effect, label the active sites of these enzymes.

Pulling FP-binding proteins out of the entire proteome of test cells and identifying them using mass spectrometry techniques, the team matched nearly all to known hydrolases. The major outlier was a protein called androgen-induced gene 1 protein (AIG1). The only other one was a distant cousin in terms of sequence, a protein called ADTRP. “Neither of these proteins had been characterized as an enzyme; in fact, there had been little functional characterization of them at all,” said William H. Parsons, a research associate in the Cravatt laboratory who was co-first author of the study.

Experiments on AIG1 and ADTRP revealed that they do their enzymatic work in a unique way. “It looks like they have an active site that is novel—it had never been described in the literature,” said Parsons. Initial tests with panels of different enzyme inhibitors showed that AIG1 and ADTRP are moderately inhibited by inhibitors of lipases—enzymes that break down fats and other lipids. But on what specific lipids do these newly discovered outlier enzymes normally work?

At the Salk Institute, the Saghatelian laboratory was investigating a class of lipids it had discovered in 2014. Known as fatty acid esters of hydroxy fatty acids (FAHFAs), these molecules showed strong therapeutic potential. Saghatelian and his colleagues had found that boosting the levels of one key FAHFA lipid normalizes glucose levels in diabetic mice and also reduces inflammation.

“[Ben Cravatt’s] lab was screening panels of lipids to find the ones that their new enzymes work on,” said Saghatelian, who is a former research associate in the Cravatt laboratory. “We suggested they throw FAHFAs in there—and these turned out to be very good substrates.” The Cravatt laboratory soon developed powerful inhibitors of the newly discovered enzymes, and the two labs began working together, using the inhibitors and genetic techniques to explore the enzymes’ functions in vitro and in cultured cells.

Co-first author Matthew J. Kolar, an MD-PhD student, performed most of the experiments in the Saghatelian lab. The team concluded that AIG1 and ADTRP, at least in the cell types tested, appear to work mainly to break down FAHFAs and not any other major class of lipid. In principle, inhibitors of AIG1 and ADTRP could be developed into FAHFA-boosting therapies.

“Our prediction,” said Saghatelian, “is that if FAHFAs do what we think they’re doing, then using an enzyme inhibitor to block their degradation would make FAHFA levels go up and should thus reduce inflammation as well as improve glucose levels and insulin sensitivity.” The two labs are now collaborating on further studies of the new enzymes—and the potential benefits of inhibiting them—in mouse models of diabetes, inflammation and autoimmune disease.

“One of the neat things this study shows,” said Cravatt, “is that even for enzyme classes as well studied as the hydrolases, there may still be hidden members that, presumably by convergent evolution, arrived at that basic enzyme mechanism despite sharing no sequence or structural homology.”

Other co-authors of the study, “AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs,” were Siddhesh S. Kamat, Armand B. Cognetta III, Jonathan J. Hulce and Enrique Saez, of TSRI; and co-senior author Barbara B. Kahn of Beth Israel Deaconess Medical Center and Harvard Medical School

 

New Weapon Against Breast Cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says.

Harvard Stem Cell Institute (HSCI) researchers at Dana-Farber Cancer Institute (DFCI) and collaborators at Brigham and Women’s Hospital (BWH) have identified a molecular marker in normal breast tissue that can predict a woman’s risk for developing breast cancer, the leading cause of death in women with cancer worldwide.

The work, led by HSCI principal faculty member Kornelia Polyak and Rulla Tamimi of BWH, was published in an early online release and in the April 1 issue of Cancer Research.

The study builds on Polyak’s earlier research finding that women already identified as having a high risk of developing cancer — namely those with a mutation called BRCA1 or BRCA2 — or women who did not give birth before their 30s had a higher number of mammary gland progenitor cells.

In the latest study, Polyak, Tamimi, and their colleagues examined biopsies, some taken as many as four decades ago, from 302 participants in the Nurses’ Health Study and the Nurses’ Health Study II who had been diagnosed with benign breast disease. The researchers compared tissue from the 69 women who later developed cancer to the tissue from the 233 women who did not. They found that women were five times as likely to develop cancer if they had a higher percentage of Ki67, a molecular marker that identifies proliferating cells, in the cells that line the mammary ducts and milk-producing lobules. These cells, called the mammary epithelium, undergo drastic changes throughout a woman’s life, and the majority of breast cancers originate in these tissues.

Doctors already test breast tumors for Ki67 levels, which can inform decisions about treatment, but this is the first time scientists have been able to link Ki67 to precancerous tissue and use it as a predictive tool.

“Instead of only telling women that they don’t have cancer, we could test the biopsies and tell women if they were at high risk or low risk for developing breast cancer in the future,” said Polyak, a breast cancer researcher at Dana-Farber and co-senior author of the paper.

“Currently, we are not able to do a very good job at distinguishing women at high and low risk of breast cancer,” added co-senior author Tamimi, an associate professor at the Harvard T.H. Chan School of Public Health and Harvard Medical School. “By identifying women at high risk of breast cancer, we can better develop individualized screening and also target risk reducing strategies.”

To date, mammograms are the best tool for the early detection, but there are risks associated with screening. False positive and negative results and over-diagnosis could cause psychological distress, delay treatment, or lead to overtreatment, according to the National Cancer Institute (NCI).

Mammography machines also use low doses of radiation. While a single mammogram is unlikely to cause harm, repeated screening can potentially cause cancer, though the NCI writes that the benefits “nearly always outweigh the risks.”

“If we can minimize unnecessary radiation for women at low risk, that would be good,” said Tamimi.

Screening for Ki67 levels would “be easy to apply in the current setting,” said Polyak, though the researchers first want to reproduce the results in an independent cohort of women.

 

AIG1 and ADTRP are atypical integral membrane hydrolases that degrade bioactive FAHFAs

William H ParsonsMatthew J Kolar, …., Barbara B KahnAlan Saghatelian & Benjamin F Cravatt

Nature Chemical Biology 28 March 2016                    http://dx.doi.org:/10.1038/nchembio.2051

Enzyme classes may contain outlier members that share mechanistic, but not sequence or structural, relatedness with more common representatives. The functional annotation of such exceptional proteins can be challenging. Here, we use activity-based profiling to discover that the poorly characterized multipass transmembrane proteins AIG1 and ADTRP are atypical hydrolytic enzymes that depend on conserved threonine and histidine residues for catalysis. Both AIG1 and ADTRP hydrolyze bioactive fatty acid esters of hydroxy fatty acids (FAHFAs) but not other major classes of lipids. We identify multiple cell-active, covalent inhibitors of AIG1 and show that these agents block FAHFA hydrolysis in mammalian cells. These results indicate that AIG1 and ADTRP are founding members of an evolutionarily conserved class of transmembrane threonine hydrolases involved in bioactive lipid metabolism. More generally, our findings demonstrate how chemical proteomics can excavate potential cases of convergent or parallel protein evolution that defy conventional sequence- and structure-based predictions.

Figure 1: Discovery and characterization of AIG1 and ADTRP as FP-reactive proteins in the human proteome.

 

http://www.nature.com/nchembio/journal/vaop/ncurrent/carousel/nchembio.2051-F1.jpg

(a) Competitive ABPP-SILAC analysis to identify FP-alkyne-inhibited proteins, in which protein enrichment and inhibition were measured in proteomic lysates from SKOV3 cells treated with FP-alkyne (20 μM, 1 h) or DMSO using the FP-biotin…

 

  1. Willems, L.I., Overkleeft, H.S. & van Kasteren, S.I. Current developments in activity-based protein profiling. Bioconjug. Chem. 25, 11811191 (2014).
  2. Niphakis, M.J. & Cravatt, B.F. Enzyme inhibitor discovery by activity-based protein profiling.Annu. Rev. Biochem. 83, 341377 (2014).
  3. Berger, A.B., Vitorino, P.M. & Bogyo, M. Activity-based protein profiling: applications to biomarker discovery, in vivo imaging and drug discovery. Am. J. Pharmacogenomics 4,371381 (2004).
  4. Liu, Y., Patricelli, M.P. & Cravatt, B.F. Activity-based protein profiling: the serine hydrolases.Proc. Natl. Acad. Sci. USA 96, 1469414699 (1999).
  5. Simon, G.M. & Cravatt, B.F. Activity-based proteomics of enzyme superfamilies: serine hydrolases as a case study. J. Biol. Chem. 285, 1105111055 (2010).
  6. Bachovchin, D.A. et al. Superfamily-wide portrait of serine hydrolase inhibition achieved by library-versus-library screening. Proc. Natl. Acad. Sci. USA 107, 2094120946 (2010).
  7. Jessani, N. et al. A streamlined platform for high-content functional proteomics of primary human specimens. Nat. Methods 2, 691697 (2005).
  8. Higa, H.H., Diaz, S. & Varki, A. Biochemical and genetic evidence for distinct membrane-bound and cytosolic sialic acid O-acetyl-esterases: serine-active-site enzymes. Biochem. Biophys. Res. Commun. 144, 10991108 (1987).

Academic cross-fertilization by public screening yields a remarkable class of protein phosphatase methylesteras-1 inhibitors

Proc Natl Acad Sci U S A. 2011 Apr 26; 108(17): 6811–6816.    doi:  10.1073/pnas.1015248108
National Institutes of Health (NIH)-sponsored screening centers provide academic researchers with a special opportunity to pursue small-molecule probes for protein targets that are outside the current interest of, or beyond the standard technologies employed by, the pharmaceutical industry. Here, we describe the outcome of an inhibitor screen for one such target, the enzyme protein phosphatase methylesterase-1 (PME-1), which regulates the methylesterification state of protein phosphatase 2A (PP2A) and is implicated in cancer and neurodegeneration. Inhibitors of PME-1 have not yet been described, which we attribute, at least in part, to a dearth of substrate assays compatible with high-throughput screening. We show that PME-1 is assayable by fluorescence polarization-activity-based protein profiling (fluopol-ABPP) and use this platform to screen the 300,000+ member NIH small-molecule library. This screen identified an unusual class of compounds, the aza-β-lactams (ABLs), as potent (IC50 values of approximately 10 nM), covalent PME-1 inhibitors. Interestingly, ABLs did not derive from a commercial vendor but rather an academic contribution to the public library. We show using competitive-ABPP that ABLs are exquisitely selective for PME-1 in living cells and mice, where enzyme inactivation leads to substantial reductions in demethylated PP2A. In summary, we have combined advanced synthetic and chemoproteomic methods to discover a class of ABL inhibitors that can be used to selectively perturb PME-1 activity in diverse biological systems. More generally, these results illustrate how public screening centers can serve as hubs to create spontaneous collaborative opportunities between synthetic chemistry and chemical biology labs interested in creating first-in-class pharmacological probes for challenging protein targets.

Protein phosphorylation is a pervasive and dynamic posttranslational protein modification in eukaryotic cells. In mammals, more than 500 protein kinases catalyze the phosphorylation of serine, threonine, and tyrosine residues on proteins (1). A much more limited number of phosphatases are responsible for reversing these phosphorylation events (2). For instance, protein phosphatase 2A (PP2A) and PP1 are thought to be responsible together for > 90% of the total serine/threonine phosphatase activity in mammalian cells (3). Specificity is imparted on PP2A activity by multiple mechanisms, including dynamic interactions between the catalytic subunit (C) and different protein-binding partners (B subunits), as well as a variety of posttranslational chemical modifications (2, 4). Within the latter category is an unusual methylesterification event found at the C terminus of the catalytic subunit of PP2A that is introduced and removed by a specific methyltransferase (leucine carbxoylmethyltransferase-1 or LCMT1) (5, 6) and methylesterase (protein phosphatase methylesterase-1 or PME-1) (7), respectively (Fig. 1A). PP2A carboxymethylation (hereafter referred to as “methylation”) has been proposed to regulate PP2A activity, at least in part, by modulating the binding interaction of the C subunit with various regulatory B subunits (810). A predicted outcome of these shifts in subunit association is the targeting of PP2A to different protein substrates in cells. PME-1 has also been hypothesized to stabilize inactive forms of nuclear PP2A (11), and recent structural studies have shed light on the physical interactions between PME-1 and the PP2A holoenzyme (12).

There were several keys to the success of our probe development effort. First, screening for inhibitors of PME-1 benefited from the fluopol-ABPP technology, which circumvented the limited throughput of previously described substrate assays for this enzyme. Second, we were fortunate that the NIH compound library contained several members of the ABL class of small molecules. These chiral compounds, which represent an academic contribution to the NIH library, occupy an unusual portion of structural space that is poorly accessed by commercial compound collections. Although at the time of their original synthesis (23) it may not have been possible to predict whether these ABLs would show specific biological activity, their incorporation into the NIH library provided a forum for screening against many proteins and cellular targets, culminating in their identification as PME-1 inhibitors. We then used advanced chemoproteomic assays to confirm the remarkable selectivity displayed by ABLs for PME-1 across (and beyond) the serine hydrolase superfamily. That the mechanism for PME-1 inhibition involves acylation of the enzyme’s conserved serine nucleophile (Fig. 3) suggests that exploration of a more structurally diverse set of ABLs might uncover inhibitors for other serine hydrolases. In this way, the chemical information gained from a single high-throughput screen may be leveraged to initiate probe development programs for additional enzyme targets.

Projecting forward, this research provides an example of how public small-molecule screening centers can serve as a portal for spawning academic collaborations between chemical biology and synthetic chemistry labs. By continuing to develop versatile high-throughput screens and combining them with a small-molecule library of expanding structural diversity conferred by advanced synthetic methodologies, academic biologists and chemists are well-positioned to collaboratively deliver pharmacological probes for a wide range of proteins and pathways in cell biology.

 

New weapon against breast cancer

Molecular marker in healthy tissue can predict a woman’s risk of getting the disease, research says

April 6, 2016 | Popular
BRC_Cancer605

 

New Group of Aging-Related Proteins Discovered

http://www.genengnews.com/gen-news-highlights/new-group-of-aging-related-proteins-discovered/81252599/

Scientists have discovered a group of six proteins that may help to divulge secrets of how we age, potentially unlocking new insights into diabetes, Alzheimer’s, cancer, and other aging-related diseases.

The proteins appear to play several roles in our bodies’ cells, from decreasing the amount of damaging free radicals and controlling the rate at which cells die to boosting metabolism and helping tissues throughout the body respond better to insulin. The naturally occurring amounts of each protein decrease with age, leading investigators to believe that they play an important role in the aging process and the onset of diseases linked to older age.

The research team led by Pinchas Cohen, M.D., dean and professor of the University of Southern California Leonard Davis School of Gerontology, identified the proteins and observed their origin from mitochondria and their game-changing roles in metabolism and cell survival. This latest finding builds upon prior research by Dr. Cohen and his team that uncovered two significant proteins, humanin and MOTS-c, hormones that appear to have significant roles in metabolism and diseases of aging.

Unlike most other proteins, humanin and MOTS-c are encoded in mitochondria. Dr. Cohen’s team used computer analysis to see if the part of the mitochondrial genome that provides the code for humanin was coding for other proteins as well. The analysis uncovered the genes for six new proteins, which were dubbed small humanin-like peptides, or SHLPs, 1 through 6 (pronounced “schlep”).

After identifying the six SHLPs and successfully developing antibodies to test for several of them, the team examined both mouse tissues and human cells to determine their abundance in different organs as well as their functions. The proteins were distributed quite differently among organs, which suggests that the proteins have varying functions based on where they are in the body. Of particular interest is SHLP 2, according to Dr. Cohen.  The protein appears to have insulin-sensitizing, antidiabetic effects as well as neuroprotective activity that may emerge as a strategy to combat Alzheimer’s disease. He added that SHLP 6 is also intriguing, with a unique ability to promote cancer cell death and thus potentially target malignant diseases.

Proteins That May Protect Against Age Related Illnesses Discovered

 

The cell proliferation antigen Ki-67 organises heterochromatin

 Michal Sobecki, 

Antigen Ki-67 is a nuclear protein expressed in proliferating mammalian cells. It is widely used in cancer histopathology but its functions remain unclear. Here, we show that Ki-67 controls heterochromatin organisation. Altering Ki-67 expression levels did not significantly affect cell proliferation in vivo. Ki-67 mutant mice developed normally and cells lacking Ki-67 proliferated efficiently. Conversely, upregulation of Ki-67 expression in differentiated tissues did not prevent cell cycle arrest. Ki-67 interactors included proteins involved in nucleolar processes and chromatin regulators. Ki-67 depletion disrupted nucleologenesis but did not inhibit pre-rRNA processing. In contrast, it altered gene expression. Ki-67 silencing also had wide-ranging effects on chromatin organisation, disrupting heterochromatin compaction and long-range genomic interactions. Trimethylation of histone H3K9 and H4K20 was relocalised within the nucleus. Finally, overexpression of human or Xenopus Ki-67 induced ectopic heterochromatin formation. Altogether, our results suggest that Ki-67 expression in proliferating cells spatially organises heterochromatin, thereby controlling gene expression.

 

A protein called Ki-67 is only produced in actively dividing cells, where it is located in the nucleus – the structure that contains most of the cell’s DNA. Researchers often use Ki-67 as a marker to identify which cells are actively dividing in tissue samples from cancer patients, and previous studies indicated that Ki-67 is needed for cells to divide. However, the exact role of this protein was not clear. Before cells can divide they need to make large amounts of new proteins using molecular machines called ribosomes and it has been suggested that Ki-67 helps to produce ribosomes.

Now, Sobecki et al. used genetic techniques to study the role of Ki-67 in mice. The experiments show that Ki-67 is not required for cells to divide in the laboratory or to make ribosomes. Instead, Ki-67 alters the way that DNA is packaged in the nucleus. Loss of Ki-67 from mice cells resulted in DNA becoming less compact, which in turn altered the activity of genes in those cells.

Read Full Post »

BioMEMS The Market aspects of Oligonucleotide-Chips, Products and Applications, Competition, January 21, 2016

Curator: Gérard LOISEAU, ESQ

 

BioMEMS

The Market aspects of Oligonucleotide-Chips, Products, Applications, Competition 

January 21, 2016

2015-2020

The oligonucleotide synthesis market is expected to reach USD 1.918.6Billion at a CAGR of 10.1% by 2020 from USD 1.078.1Billion in 2015.

SOURCE

MARKETSANDMARKETS marketsandmarkets.com/

 

PLAYERS

  • Agilent Technologies Inc.
  • BioAutomation Corp.
  • Biosearch Technologies
  • Gen9 Inc.
  • GenScript Inc.
  • Illumina Inc.
  • Integrated DNA Technologies
  • New England Biolabs Inc.
  • Nitto Denko Avecia Inc.
  • OriGene Technologies Inc.
  • Sigma-Aldrich Corporation
  • Thermo Fisher Scientific Inc.
  • TriLink Biotechnologies

 

Agilent Technologies
 CA NYSE :A


http://www.agilent.com/

  • Agilent was created as a spin off from Hewlett-Packard Company in 1999.
  • Agilent Technologies Inc. is engaged in the life sciences, diagnostics and applied chemical markets. The Company provides application focused solutions that include instruments, software, services and consumables for the entire laboratory workflow. The Company has three business segments:

the life sciences and applied markets business,

the diagnostics and genomics business, and

the Agilent Cross Lab business

  • The Company’s life sciences and applied markets business segment brings together the Company’s analytical laboratory instrumentation and informatics.
  • The Company’s diagnostics and genomics business segment consists of three businesses: the Dako business, the genomics business and the nucleic acid solutions business.
  • The Company’s Agilent Cross Lab business segment combines its analytical laboratory services and consumables business

SOURCE

http://reuters.com/

PRODUCTS AND SERVICES

https://www.agilent.com/en-us/default#collapse-0

  • October 09, 2015 03:21 PM Eastern Daylight Time
  • CARPINTERIA, Calif.–(BUSINESS WIRE)–Dako, an Agilent Technologies company and a worldwide provider of cancer diagnostics, today announced the U.S. Food and Drug Administration has approved a new test that can identify PD-L1 expression levels on the surface of non-small cell lung cancer tumor cells and provide information on the survival benefit with OPDIVO® (nivolumab) for patients with non-squamous NSCLC.

SOURCE

BUSINESS WIRE busibesswire.com/

 

BioAutomation Corp.

 TX


 

http://bioautomation.com/

          PRODUCTS AND SERVICES

  • DNA and RNA synthesis reagents for the MerMades

 

Note: The MerMade 192E Oligonucleotide synthesizer is designed to synthesize DNA, RNA & LNA oligonucleotides in a column format

          PARTNERSHIPS

  • HONGENE BIOTECH : BIOAUTOMATION is the exclusive distributor for the Americas
  • EMD MILLIPORE
  • BIOSEARCH TECHNOLOGIES

 

DISTRIBUTORS

  • LINK TECHNOLOGIES : UK
  • AME BIOSCIENCE : UK
  • BOSUNG SCIENCE : KOREA
  • DNA CHEM : CHINA
  • WAKO : JAPAN
  • ACE PROBE : INDIA

SOURCE

bioautomation.com/

 

Biosearch Technologies
 CA


http://biosearchtech.com/

          PRODUCTS

  • qPCR & SNP Genotyping
  • Custom Oligonucleotides
  • – highly sophisticated oligonucleotides
  • – simple PCR primers
  • Oligos in Plates
  • RNA FISH
  • Synthesis Reagents
  • Immunochemicals
  • Primers
  • Probes
  • Large-Scale Synthesis Oligos
  • Intermediate-Scale Synthesis Oligos

          SERVICES

  • GMP & Commercial Services
  • OEM & Kit Manufacturing
  • qPCR Design Collaborations

          DISTRIBUTORS

Argentina | Australia | Austria | Brazil | Canada |Chile | China | Colombia | Czech Republic | Denmark | Ecuador | Finland | Germany |Hong Kong | Israel | Italy | Japan | Korea | Malaysia | Mexico | New Zealand | Norway | Paraguay | Peru| Philippines | Poland | Romania | Singapore | South Africa | Spain | Sweden |Switzerland | Taiwan ROC | Thailand | Turkey | United Kingdom | Uruguay | Vietnam

SOURCE

biosearchtech.com/

 

Gen9 Inc.
 MA 


http://www.gen9bio.com/

          PRODUCTS

Gen9 is building on advances in synthetic biology to power a scalable fabrication capability that will significantly increase the world’s capacity to produce DNA content. The privately held company’s next-generation gene synthesis technology allows for the high-throughput, automated production of DNA constructs at lower cost and higher accuracy than previous methods on the market. Founded by world leaders in synthetic biology, Gen9 aims to ensure the constructive application of synthetic biology in industries ranging from enzyme and chemical production to pharmaceuticals and biofuels.

          SERVICES

  • Synthetic Biology
  • Gene Synthesis Services
  • Variant Libraries
  • Gene Sequence Design Services

         INVESTORS

  • Agilent Technologies : Private Equity
  • CAMBRIDGE, Mass. and SANTA CLARA, Calif. — April 24, 2013 —Gen9 Receives $21 Million Strategic Investment from Agilent Technologies

SOURCE

gen9bio.com/

 

GenScript Inc.
 NJ 


http://www.genscript.com/

  • GenScript is the largest gene synthesis provider in the USA
  • GenScript Corporation, a biology contract research organization, provides biological research and drug discovery services to pharmaceutical companies, biotech firms, and research institutions in the United States, Europe, and Japan. It offers bio-reagent, custom molecular biology, custom peptide, protein production, custom antibody production, drug candidates testing, assay development and screening, lead optimization, antibody drug development, gene synthesis, and assay-ready cell line production services.
  • The company also offers molecular biology, peptide, protein, immunoassay, chemicals, and cell biology products. It offers its products through distributors in Tokyo, Japan; and Seoul, Korea. GenScript Corporation has a strategic partnership with Immunologix, Inc. The company was founded in 2002 and is based in Piscataway, New Jersey. It has subsidiaries in France, Japan, and China.

 

Note: As of October 24, 2011, Immunologix, Inc. was acquired by Intrexon Corporation. Immunologix, Inc. develops and produces antibody-based therapeutics for various biological targets. It produces human monoclonal antibodies against viral, bacterial, and tumor antigens, as well as human auto antigens.

Intrexon Corporation, founded in 1998, is a leader in synthetic biology focused on collaborating with companies in Health, Food, Energy, Environment and Consumer sectors to create biologically based products that improve quality of life and the health of the planet.

 

 

             PRODUCTS AND SERVICES

  • Gene synthesis
  • Antibody services
  • Protein Services
  • Peptide services

 

               INVESTORS


Note: The Balloch Group (‘TBG’) was established in 2001 by Howard Balloch (Canada‘s ambassador to China from 1996 to 2001). TBG has since grown from a market-entry consultancy working with North American clients in China to a leading advisory and merchant banking firm serving both domestic Chinese companies and multinational corporations. TBG was ranked as the number one boutique investment bank in China by ChinaVenture in 2008.

Kleiner, Perkins, Caufield and Byers

 

Illumina
Inc. CA


http://illumina.com/

 

Monica Heger : SAN FRANCISCO (GenomeWeb) – Illumina today announced two new next-generation sequencing platforms, a targeted sequencing system called MiniSeq and a semiconductor sequencer that is still under development.

Illumina disclosed the initiatives during a presentation at the JP Morgan Healthcare conference held here today. During the presentation, Illumina CEO Jay Flatley also announced a new genotyping array called Infinium XT; a partnership with Bio-Rad to develop a single-cell sequencing workflow; preliminary estimates of its fourth-quarter 2015 revenues; and an update on existing products. The presentation followed the company’s announcement on Sunday that it has launched a new company called Grail to develop a next-generation sequencing test for early cancer detection from patient blood samples.

The MiniSeq system, which is based on Illumina’s current sequencing technology, will begin shipping early this quarter and has a list price of $49,500. It can perform a variety of targeted DNA and RNA applications, from single-gene to pathway sequencing, and promises “all-in” prices, including library prep and sequencing, of $200 to $300 per sample, Flatley said during the JP Morgan presentation.

SOURCES

https://www.genomeweb.com/sequencing-technology/illumina-unveils-mini-targeted-sequencer-semiconductor-sequencing-project-jp

http://investor.biospace.com/biospace/quote?Symbol=ILMN

 

              PRODUCTS AND SERVICES

  •               Mid to large scale manufacturing assets
  •               Analytical Labs
  •               Pre-clinical
  •               Clinical
  •               Launched products

 

              COMPETITORS

https://finance.yahoo.com/q/co?s=ILMN+Competitors Tue, Feb 2, 2016, 2:16pm EST – US Markets

ILMN PVT1 AFFX LMNX Industry
Market Cap: 22.75B N/A 1.13B 835.66M 134.14M
Employees: 3,700 10,000 1,200 745 45.00
Qtrly Rev Growth (yoy): 0.14 N/A -0.01 0.07 0.18
Revenue (ttm): 2.14B 3.80B1 357.74M 235.37M 8.47M
Gross Margin (ttm): 0.73 N/A 0.63 0.71 0.58
EBITDA (ttm): 770.84M N/A 46.64M 52.99M -12.31M
Operating Margin (ttm): 0.30 N/A 0.08 0.17 -1.62
Net Income (ttm): 510.36M 430.90M1 11.22M 39.29M N/A
EPS (ttm): 3.42 N/A 0.13 0.93 -0.34
P/E (ttm): 45.43 N/A 104.40 20.91 25.33
PEG (5 yr expected): 2.68 N/A 4.66 0.55 N/A
P/S (ttm): 10.87 N/A 3.13 3.45 13.65

 

Pvt1 = Life Technologies Corporation (privately held)

AFFX = Affymetrix Inc.

LMNX = Luminex Corporation

 

 

Integrated DNA Technologies (IDT)
IOWA + CA

http://www.com/

 

Integrated DNA Technologies, Inc. (IDT), the global leader in nucleic acid synthesis, serving all areas of life sciences research and development, offers products for a broad range of genomics applications. IDT’s primary business is the production of custom, synthetic nucleic acids for molecular biology applications, including qPCR, sequencing, synthetic biology, and functional genomics. The company manufactures and ships an average of 44,000 custom nucleic acids per day to more than 82,000 customers worldwide. For more information, visit idtdna.com.

 

               PRODUCTS AND SERVICES

               https://eu.idtdna.com/site

  • DNA & RNA Synthesis
  • Custom DNA Oligos 96- & 384-Well Plates Ultramer Oligos Custom RNA Oligos SameDay Oligos HotPlates ReadyMade Primers Oligo Modifications Freedom
  • Dyes GMP for Molecular Diagnostics Large Scale Oligo Synthesis

 

Note : Skokie, IL – December 1, 2015. Integrated DNA Technologies Inc. (“IDT”), the global leader in custom nucleic acid synthesis, has entered into a definitive agreement to acquire the oligonucleotide synthesis business of AITbiotech Pte. Ltd. in Singapore (“AITbiotech”). With this acquisition, IDT expands its customer base across Southeast Asia making it possible for these additional customers to now have access to its broad range of products for genomic applications. AITbiotech will continue operations in its other core business areas.

 

New England Biolabs Inc.
 MA 


http://www.neb.com/

 

                PRODUCTS AND SERVICES

  •                 Restriction Endonucleases
  •                 PCR, Polymerases & Amplification Technologies
  •                 DNA Modifying Enzymes
  •                 Library Preparation for Next Generation Sequencing
  •                 Nucleic Acid Purification
  •                 Markers & Ladders
  •                 RNA Reagents
  •                 Gene Expression
  •                 Cellular Analysis

SOURCE

neb.com/

 

Nitto Denko Avecia Inc.
 MA


http://avecia.com/

 

With over 20 years of experience in oligonucleotide development and production, and over 1000 sequences manufactured, Avecia has played an integral role in the advancing oligo therapeutic market. Our mission is to continue to build value for our customers, as they progress through drug development into commercialization. And as a member of the Nitto Denko Corporation (nitto.com), Avecia is committed to the future of the oligonucleotide market. We are driven by innovative ideas and flexible solutions, designed to provide our customers with the best in service, quality, and technology.

 

SOURCE

http://avecia.com/

 

Note : 1918 Nitto Electric Industrial Co., Ltd. forms in Ohsaki, Tokyo, to produce electrical insulating materials in Japan.

2011 Acquires Avecia Biotechnology Inc. in the U.S.A.

 

 

OriGene Technologies Inc.
 CA

http://www.com/

 

OriGene Technologies, Inc. develops, manufactures, and sells genome wide research and diagnostic products for pharmaceutical, biotechnology, and academic research applications. The company offers cDNA clones, including TrueORF cDNA, viral ORF, destination vectors, TrueClones (human), TrueClones (mouse), organelle marker plasmids, MicroRNA tools, mutant and variant clones, plasmid purification kits, transfection reagents, and gene synthesis service; and HuSH shRNA, siRNA, miRNA, qPCR reagents, plasmid purification products, transfection reagents, PolyA+ and total RNA products, first-strand cDNA synthesis, and CRISPR/Cas9 genome products. It also provides proteins and lysates, such as purified human proteins, over-expression cell lysates, mass spectrometry standard proteins, and protein purification reagents; UltraMAB IHC antibodies, TrueMAB primary antibodies, anti-tag and fluorescent proteins, ELISA antibodies, luminex antibodies, secondary antibodies, and controls and others; and anatomic pathology products, including IHC antibodies, detection systems, and IHC accessories

The company offers luminex and ELISA antibody pairs, autoantibody profiling arrays, ELISA kits, cell assay kits, assay reagents, custom development, and fluorogenic cell assays; TissueFocus search tools; tissue sections; tissue microarrays, cancer protein lysate arrays, TissueScan cDNA arrays, tissue blocks, and quality control products, as well as tissue RNA, DNA, and protein lysates; and lab essentials. Its research areas include cancer biomarker research, RNAi, pathology IHC, stem cell research, ion channels, and protein kinase products. The company provides gene synthesis and molecular biology services, genome editing, custom cloning, custom shRNA, purified protein, monoclonal antibody development, and assay development. It sells its products through distributors worldwide, as well as online. OriGene Technologies, Inc. was incorporated in 1995 and is based in Rockville, Maryland.

SOURCE

http://BLOOMBERG.com

               PRODUCTS AND SERVICES

  •                cDNA Clones
Human, mouse, rat
Expression validated
  •                RNAi
shRNA, siRNA
microRNA & 3’UTR clones
  •                Gene Synthesis
Codon optimization
Variant libraries
  •                Real-time PCR
Primer pairs, panels
SYBR green reagents
  •                Lab Essentials
DNA/RNA purification kits
Transfection reagents
  •                Anatomic Pathology
UltraMAB antibodies
Specificity validated
  •                Recombinant Proteins
10,000 human proteins
from mammalian system
  •                Antibodies
TrueMAB primary antibodies
Anti-tag antibodies
  •                Assays and Kits
ELISA & Luminex antibodies
Autoantibody Profiling Array
  •                Cancer & Normal Tissues
Pathologist verified
gDNA, RNA, sections, arrays

SOURCE

origene.com/

 

Sigma-Aldrich Corporation 
MI 


http://www.sigmaaldrich.com/

Louis, MO – November 18, 2015 Merck KGaA, Darmstadt, Germany, Completes Sigma-Aldrich Acquisition

Merck KGaA today announced the completion of its $17 billion acquisition of Sigma-Aldrich, creating one of the leaders in the $130 billion global industry to help solve the toughest problems in life science.

Press Release: 18-Nov-2015

Letter to our Life Science Customers from Dr. Udit Batra

The life science business of Merck KGaA, Darmstadt, Germany brings together the world-class products and services, innovative capabilities and exceptional talent of EMD Millipore and Sigma-Aldrich to create a global leader in the life science industry.

Everything we do starts with our shared purpose – to solve the toughest problems in life science by collaborating with the global scientific community. 

This combination is built on complementary strengths, which will enable us to serve you even better as one organization than either company could alone.

This means providing a broader portfolio with a catalog of more than 300,000 products, including many of the most respected brands in the industry, greater geographic reach, and an unmatched combination of industry-leading capabilities.

                PRODUCTS AND SERVICES

                http://www.sigmaaldrich.com/configurator/servlet/DesignCenter?btnOpen_0.x=1

                http://www.sigmaaldrich.com/content/dam/sigma-aldrich/common/quality-products.jpg

 

Thermo Fisher Scientific Inc.
 MA 
NYSE :TMO


http://thermofisher.com/

Thermo Fisher Scientific Inc. is a provider of analytical instruments, equipment, reagents and consumables, software and services for research, manufacturing, analysis, discovery and diagnostics. The company operates through four segments: Life Sciences Solutions, provides reagents, instruments and consumables used in biological and medical research, discovery and production of new drugs and vaccines as well as diagnosis of disease; Analytical Instruments, provides instruments, consumables, software and services that are used in the laboratory; Specialty Diagnostics, offers diagnostic test kits, reagents, culture media, instruments and associated products, and Laboratory Products and Services, offers self-manufactured and sourced products for the laboratory.

SOURCE

http://REUTERS.com

 

                PRODUCTS AND SERVICES

  •                 Oligos Value – Standard – Plate
  •                 Primers
  •                 Probes
  •                 Nucleotides

 

                BRANDS

  1.                THERMO SCIENTIFIC
  2.                 APPLIED BIOSYSTEMS
  3.                 INVITROGEN
  4.                 FISHER SCIENTIFIC
  5.                 UNITY LAB SERVICES

 

                 PARTNERSHIPS

AFFYMETRIX : NASDAQ : AFFX : affymetrix.com/

WALTHAM, Mass. & SANTA CLARA, Calif.–(BUSINESS WIRE)–Jan. 8, 2016– Thermo Fisher Scientific Inc. (NYSE:TMO), the world leader in serving science, and Affymetrix Inc. (NASDAQ:AFFX), a leading provider of cellular and genetic analysis products, today announced that their boards of directors have unanimously approved Thermo Fisher’s acquisition of Affymetrix for $14.00 per share in cash. The transaction represents a purchase price of approximately $1.3 billion.

SOURCE

http://BUSINESSWIRE.com

 

TriLink Biotechnologies
 CA 


http://www.com/

 

              PRODUCTS

              Oligonucleotides

  •               DNA Oligos
  •               RNA Oligos
  •               Modified Oligos
  •               Specialty Oligos

              Nucleotides

  •               NTPs (Nucleoside Triphosphates)
  •               Biphosphates
  •               Monophosphates

 

              SERVICES

  •              Custom Chemistry
  •              Reagents
  •              Aptamers

 

             PARTNERSHIPS

  • LIFE TECHNOLOGIES,
  • TERMO FISHER SCIENTIFIC since July 2015 thermofisher.com/
  • GENMARK genmarkdx.com/

SOURCE

http://trilinkbiotech.com/

 

Other related articles published in this Open Access Online Scientific Journal include the following:

Gene Editing: The Role of Oligonucleotide Chips

http://pharmaceuticalintelligence.com/2016/01/07/gene-editing-the-role-of-oligonucleotide-chips/

Gene Editing for Exon 51: Why CRISPR Snipping might be better than Exon Skipping for DMD

http://pharmaceuticalintelligence.com/2016/01/23/gene-editing-for-exon-51-why-crispr-snipping-might-be-better-than-exon-skipping-for-dmd/

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Read Full Post »

Antibody alternatives in specific aptamer 3-D scaffold binding

Curator: Larry H. Bernstein, MD, FCAP

 

 

New Proteomics Tools to Open Up Drug Discovery

Dr. Paul Ko Ferrigno, Chief Scientific Officer, and Dr. Jane McLeod, of Avacta Life Sciences

http://www.dddmag.com/articles/2016/01/new-proteomics-tools-open-drug-discovery

 

Size matters: Smaller molecules allow a tighter packing density on solid surfaces for improved signal-to-noise in assays, such as SPR or ELISA.

http://www.dddmag.com/sites/dddmag.com/files/AvactaAffimer%20stills_00006.jpg

Size matters: Smaller molecules allow a tighter packing density on solid surfaces for improved signal-to-noise in assays, such as SPR or ELISA.

 

In the current genomic era of very accurate DNA analyses by in situ hybridization, DNA chip analyses, and deep sequencing, it is often assumed that antibodies have an analogous ability to identify molecular targets accurately. Nothing could be further from the truth. Proteomics as a field is still lagging behind its genomic counterpart in the level of detail we can achieve, the level of data we can collect and the overall levels of accuracy and reliability that the collected data represent.

From the estimated 20,000 human genes 100,000 different possible proteins have been predicted. What is more, the variation achieved via the post-translational modification of these proteins brings another layer of complexity to cellular signalling. All this means that while studying the genetic blueprint can offer insights into the cell, it is only through examining the functional protein units that we can comprehensively map the dynamic interactions that occur within the cell to drive an organism or disease process.

 

Why not use antibodies?

Antibodies form the basis of molecular recognition in proteomics, whether this is to identify a protein within a complex mixture or label a specific protein within a cell. Both their target specificity and high binding capacity have made these molecules fantastically useful tools within diagnostics. However, the large majority of commercially available antibodies are for use as reagents in research and development, where they are simply not as well validated and issues with their manufacture have created problems that have hindered drug development.

It has proven next to impossible to develop antibodies to certain targets. This may be because of their high homology to the host protein, so the immune system fails to recognise them as different, or due to antigen processing resulting in the loss of post-translational modifications or discontinuous epitopes. However, without the necessary antibodies to investigate these targets the corresponding research avenues have remained closed and key drug targets may have been missed.

Almost worse than lacking the necessary research tools is the problem of antibody reproducibility. Matthias Uhlen revealed that of the 5,436 antibodies tested as part of the Protein Atlas project over 50 percent failed to recognize their target in at least one of two commonly used assays. Antibodies that are not specific to their target or do not recognize their target at all are responsible for increasing the cost of biological research, with an estimated $800 million spent globally every year on bad antibodies. Many published studies have had to be retracted due to antibody-derived error and those that remain unidentified in the literature will continue to lead researchers down blind alleys. This level of misinformation in the published literature is not just hindering the progression of the field, but possibly even sending it backwards and costing more than is needed — an issue not to be taken lightly when so many research budgets are coming under the knife.

More recently there has been a call from a number of leading scientists for the use of polyclonal antibodies, considered to be the worst offenders in terms of batch-to-batch irreproducibility, to be abandoned. They suggest a move towards recombinant systems of production, which would remove the restrictions of the immune system on antibody production. Yet, they state that $1 billion dollars investment would be required to re-route antibody production down this path and suggest that a period of five to ten years may be required to bring about these changes.

Simply producing recombinant antibodies rather than animal-derived affinity reagents will still leave us with a number of problems regarding their use. Antibodies are simply too large to target many smaller, hidden epitopes and the presence of key disulphide bonds within their structure makes them all too susceptible to reduction within the cell, rendering them useless for applications such as live cell imaging of molecules within the cytoplasm. Moreover, can we afford to wait another decade for a solution to this problem before we pursue protein targets for basic understanding and drug development?

 

 

 

 

Antibody alternatives for new targets and techniques

Antibody alternatives are already available to researchers in the life sciences field. They are produced either from nucleic acid or protein molecules. Aptamers are short, single-stranded RNA or DNA molecules that fold to form 3D scaffolds, which can present a specific interaction surface to allow specific binding to its target molecule. Protein scaffolds are formed from parts of or whole proteins modified to present a peptide sequence. This peptide sequence works in a similar manner to present a specific interaction surface for specific binding to a desired target.

These antibody alternatives are produced in recombinant systems, minimizing batch-to-batch variation and allowing them to be produced to theoretically any target. Additionally, as they do not use animals in their production they are generally less expensive to produce than traditional antibodies.

 

 

 

 

While companies such as Affibody and Avacta Life Sciences are aiming to open up the drug pipeline, by offering these alternative affinity reagents to previously inaccessible targets for use in research and development, many have moved into exploiting their therapeutic potential. Noxxon produce an RNA-based scaffold, Spiegelmers, which are currently in phase 2 clinical trials for diabetic nephropathy, and Molecular Partners have reached phase 3 clinical trials with their protein scaffold DARPins, for wet age-related macular degeneration.

These new antibody alternatives are smaller than traditional antibodies. This opens up the use of new technologies, such as super resolution microscopy. While the diffraction limit remained at about 250 nmm the length of the antibody at 15 nm was of little importance, tagging your molecule as accurately as necessary. Removing this limit in super resolution microscopy has meant that antibodies are now too large to provide the required level of accuracy. Instead, using an antibody alternative of only 2nm to tag your protein of interest opens up this new technique offering greater precision to intracellular localization.

 

 

 

As these scaffolds have been engineered to be fit-for-purpose many contain no intramolecular disulphide bonds and so are not sensitive to the reducing environment of the cell. This function enables their use as intracellular reporters of molecular conformation, as well as in standard assays like IHC or western blotting, so allowing scientists to use the same reagent across both intracellular and biochemical assays, thus bridging the gap between cell biology and biochemical studies.

Offering increased reproducibility, access to an increased range of applications, and the opportunity to hit previously inaccessible targets, antibody alternatives are opening up potential new avenues of drug discovery.

 

 

About the Authors

Dr. Paul Ko Ferrigno is Chief Scientific Officer at Avacta Life Sciences and a Visiting Professor at the University of Leeds. He has been working on peptide aptamers since 2001 in Leeds and at the MRC Cancer Cell Unit in Cambridge, UK where his team developed the Affimer scaffold technology.

Dr. Jane McLeod is a Scientific Writer at Avacta Life Sciences.

 

 

 

 

Read Full Post »

Development of Chemoresistance to Targeted Therapies: Alterations of Cell Signaling, & the Kinome [11.4.1.2]

 

Curator, Reporter: Stephen J. Williams, Ph.D.

The advent of molecular targeted therapies like Imatinib (Gleevec), and other tyrosine kinase inhibitors (TKI) has been transformative to cancer therapy. However, as with all chemotherapeutics, including radiation therapy, the development of chemo-resistance toward personalized, molecular therapies has been disastrous to the successful treatment of cancer. The fact that chemo-resistance develops to personalized therapies was a serious disappointment to clinicians (although most expected this to be the case) but more surprisingly it was the rapidity of onset and speed of early reported cases which may have been the biggest shocker.

A post on resistance to other TKIs (to EGFR and ALK) can be seen here: http://pharmaceuticalintelligence.com/2013/11/01/resistance-to-receptor-of-tyrosine-kinase/

History of Development of Resistance to Imatinib (Gleevec)

The Melo group published a paper in Blood showing that short exposure to STI571 (imatinib; trade name Gleevec®) could result in drug resistant clones

Selection and characterization of BCR-ABL positive cell lines with differential sensitivity to the tyrosine kinase inhibitor STI571: diverse mechanisms of resistance. Blood. 2000 Aug 1;96(3):1070-9.

Mahon FX1, Deininger MW, Schultheis B, Chabrol J, Reiffers J, Goldman JM, Melo JV.

Abstract

Targeting the tyrosine kinase activity of Bcr-Abl with STI571 is an attractive therapeutic strategy in chronic myelogenous leukemia (CML). A few CML cell lines and primary progenitors are, however, resistant to this compound. We investigated the mechanism of this resistance in clones of the murine BaF/3 cells transfected with BCR-ABL and in 4 human cell lines from which sensitive (s) and resistant (r) clones were generated by various methods. Although the resistant cells were able to survive in the presence of STI571, their proliferation was approximately 30% lower than that of their sensitive counterparts in the absence of the compound. The concentration of STI571 needed for a 50% reduction in viable cells after a 3-day exposure was on average 10 times higher in the resistant (2-3 micromol/L) than in the sensitive (0.2-0.25 micromol/L) clones. The mechanism of resistance to STI571 varied among the cell lines. Thus, in Baf/BCR-ABL-r, LAMA84-r, and AR230-r, there was up-regulation of the Bcr-Abl protein associated with amplification of the BCR-ABL gene. In K562-r, there was no Bcr-Abl overexpression, but the IC(50) for the inhibition of Bcr-Abl autophosphorylation was increased in the resistant clones. Sequencing of the Abl kinase domain revealed no mutations. The multidrug resistance P-glycoprotein (Pgp) was overexpressed in LAMA84-r, indicating that at least 2 mechanisms of resistance operate in this cell line. KCL22-r showed neither Bcr-Abl up-regulation nor a higher threshold for tyrosine kinase inhibition by STI571. We conclude that BCR-ABL-positive cells can evade the inhibitory effect of STI571 by different mechanisms, such as Bcr-Abl overexpression, reduced intake mediated by Pgp, and, possibly, acquisition of compensatory mutations in genes other than BCR-ABL.

mellobcrablresistamplification

FISH analysis of AR230 and LAMA84 sensitive and resistant clones, with probes for the ABL (red signal) and theBCR (green signal) genes. BCR-ABL is identified as a red–green or yellow fused signal. Adapted from Mahon et al., Blood 2000; 96(3):1070-9.

This rapid onset of imatinib resistance also see in the clinic and more prominent in advance disease

From NCCN 2nd Annual Congress: Hematologic Malignancies – Update on Primary Therapy, Second-Line Therapy, and New Agents for Chronic Myelogenous Leukemia (Slides with Transcript)

http://www.medscape.org/viewarticle/564097

There is some evidence that even looking earlier makes some sense in determining what the prognosis is. This is from Timothy Hughes’ group in Adelaide, and he is looking at an earlier molecular time point, 3 months after initiation of therapy. And what you have done here is you have taken the 3-month mark and you have said, “Well, based on your response at 3 months, what is your likelihood that in the future you will either get a major molecular response or become resistant?”

3monthimitanibresist

If you look at the accumulation of imatinib resistance to find if it is either initially not responding or becoming resistant after a good response, it goes up with type of disease and phase of disease. So if you look at patients who have early chronic phase disease — that is, they start getting imatinib less than a year from the diagnosis — their chance of failure is pretty low. With later disease — they are in a chronic phase but they have had disease more than a year before they get imatinib — it is higher. If you see patients with accelerated phase or blast crisis, the chances are that they will fail sometime in the future.

speed of imitinib resistance

Therefore, because not all resistant samples show gene amplification of Bcr/Abl and the rapidity of onset of resistance, many feel that there are other mechanisms of resistance at play, like kinome plasticity.

Kinome Plasticity Contributes to TKI resistance

Beyond gene amplification, other mechanisms of imitanib and other tyrosine kinase inhibitors (TKI) include alterations in compensatory signaling pathways. This can be referred to as kinome plasticity and is explained in the following abstracts from the AACR 2015 meeting.

Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML

Georg E Winter, Uwe Rix, Scott M Carlson, Karoline V Gleixner, Florian Grebien, Manuela Gridling, André C Müller, Florian P Breitwieser, Martin Bilban, Jacques Colinge, Peter Valent, Keiryn L Bennett, Forest M White & Giulio Superti-Furga. Nature Chemical Biology 8,905–912(2012)

Occurrence of the BCR-ABLT315I gatekeeper mutation is among the most pressing challenges in the therapy of chronic myeloid leukemia (CML). Several BCR-ABL inhibitors have multiple targets and pleiotropic effects that could be exploited for their synergistic potential. Testing combinations of such kinase inhibitors identified a strong synergy between danusertib and bosutinib that exclusively affected CML cells harboring BCR-ABLT315I. To elucidate the underlying mechanisms, we applied a systems-level approach comprising phosphoproteomics, transcriptomics and chemical proteomics. Data integration revealed that both compounds targeted Mapk pathways downstream of BCR-ABL, resulting in impaired activity of c-Myc. Using pharmacological validation, we assessed that the relative contributions of danusertib and bosutinib could be mimicked individually by Mapk inhibitors and collectively by downregulation of c-Myc through Brd4 inhibition. Thus, integration of genome- and proteome-wide technologies enabled the elucidation of the mechanism by which a new drug synergy targets the dependency of BCR-ABLT315I CML cells on c-Myc through nonobvious off targets.

nchembio.1085-F2kinomegleevecresistance

Please see VIDEO and SLIDESHARE of a roundtable Expert Discussion on CML

Curated Content From the 2015 AACR National Meeting on Drug Resistance Mechanisms and tyrosine kinase inhibitors

Session Title: Mechanisms of Resistance: From Signaling Pathways to Stem Cells
Session Type: Major Symposium
Session Start/End Time: Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
Location: Terrace Ballroom II-III (400 Level), Pennsylvania Convention Center
CME: CME-Designated
CME/CE Hours: 2
Session Description: Even the most effective cancer therapies are limited due to the development of one or more resistance mechanisms. Acquired resistance to targeted therapies can, in some cases, be attributed to the selective propagation of a small population of intrinsically resistant cells. However, there is also evidence that cancer drugs themselves can drive resistance by triggering the biochemical- or genetic-reprogramming of cells within the tumor or its microenvironment. Therefore, understanding drug resistance at the molecular and biological levels may enable the selection of specific drug combinations to counteract these adaptive responses. This symposium will explore some of the recent advances addressing the molecular basis of cancer cell drug resistance. We will address how tumor cell signaling pathways become rewired to facilitate tumor cell survival in the face of some of our most promising cancer drugs. Another topic to be discussed involves how drugs select for or induce the reprogramming of tumor cells toward a stem-like, drug resistant fate. By targeting the molecular driver(s) of rewired signaling pathways and/or cancer stemness it may be possible to select drug combinations that prevent the reprogramming of tumors and thereby delay or eliminate the onset of drug resistance.
Presentations:
Chairperson
Tuesday, Apr 21, 2015, 10:30 AM -12:30 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA
Introduction
Tuesday, Apr 21, 2015, 10:30 AM -10:40 AM
Resistance to tyrosine kinase inhibitors: Heterogeneity and therapeutic strategies.
Tuesday, Apr 21, 2015, 10:40 AM -10:55 AM
Jeffrey A. Engelman. Massachusetts General Hospital, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 10:55 AM -11:00 AM
NG04: Clinical acquired resistance to RAF inhibitor combinations in BRAF mutant colorectal cancer through MAPK pathway alterations
Tuesday, Apr 21, 2015, 11:00 AM -11:15 AM
Ryan B. Corcoran, Leanne G. Ahronian, Eliezer Van Allen, Erin M. Coffee, Nikhil Wagle, Eunice L. Kwak, Jason E. Faris, A. John Iafrate, Levi A. Garraway, Jeffrey A. Engelman. Massachusetts General Hospital Cancer Center, Boston, MA, Dana-Farber Cancer Institute, Boston, MA
Discussion
Tuesday, Apr 21, 2015, 11:15 AM -11:20 AM
SY27-02: Tumour heterogeneity and therapy resistance in melanoma
Tuesday, Apr 21, 2015, 11:20 AM -11:35 AM
Claudia Wellbrock. Univ. of Manchester, Manchester, United Kingdom
Discussion
Tuesday, Apr 21, 2015, 11:35 AM -11:40 AM
SY27-03: Breast cancer stem cell state transitions mediate therapeutic resistance
Tuesday, Apr 21, 2015, 11:40 AM -11:55 AM
Max S. Wicha. University of Michigan, Comprehensive Cancer Center, Ann Arbor, MI
Discussion
Tuesday, Apr 21, 2015, 11:55 AM -12:00 PM
SY27-04: Induction of cancer stemness and drug resistance by EGFR blockade
Tuesday, Apr 21, 2015, 12:00 PM -12:15 PM
David A. Cheresh. UCSD Moores Cancer Center, La Jolla, CA
Discussion
Tuesday, Apr 21, 2015, 12:15 PM -12:20 PM
General Discussion
Tuesday, Apr 21, 2015, 12:20 PM -12:30 PM

Targeting Macromolecular Signaling Complexes 
Room 115, Pennsylvania Convention Center

Drug Resistance 
Hall A (200 Level), Pennsylvania Convention Center
Resistance to Pathway-Targeted Therapeutics 1 
Section 33

Molecular Mechanisms of Sensitivity or Resistance to Pathway-Targeted Agents 
Room 118, Pennsylvania Convention Center

Targeting Signaling Pathways in Cancer 
Room 204, Pennsylvania Convention Center
Exploiting the MAPK Pathway in Cancer 
Room 115, Pennsylvania Convention Center

PLEASE see the attached WORD file which includes ALL abstracts, posters, and talks on this subject from the AACR 2015 national meeting BELOW

 AACR2015resistancekinome

Other posts related to, Cancer, Chemotherapy, Gleevec and Resistance on this Open Access Journal Include

Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

Treatments for Acute Leukemias [2.4.4A]

Therapeutic Implications for Targeted Therapy from the Resurgence of Warburg ‘Hypothesis’

Hematologic Malignancies [6.2]

Overview of Posttranslational Modification (PTM)

Novel Modeling Methods for Genomic Data Analysis & Evolutionary Systems Biology to Design Dosing Regimens to Minimize Resistance

Mechanisms of Drug Resistance

Using RNA-seq and targeted nucleases to identify mechanisms of drug resistance in acute myeloid leukemia

An alternative approach to overcoming the apoptotic resistance of pancreatic cancer

Resistance to Receptor of Tyrosine Kinase

Read Full Post »

Heroes in Basic Medical Research – Leroy Hood

Larry H Bernstein, MD, FCAP, Curator

Leaders in Pharmaceutical Intelligence

Series E. 2; 4.5

Leroy Hood, MD, PhD

Dr. Hood created the technological foundation for the sciences of genomics (study of genes) and proteomics (study of proteins) through the invention of five groundbreaking instruments and by explicating the potentialities of genome and proteome research into the future through his pioneering of the fields of systems biology and systems medicine. Hood’s instruments not only pioneered the deciphering of biological information, but also introduced the concept of high throughput data accumulation through automation and parallelization of the protein and DNA chemistries.

The first four instruments were commercialized by Applied Biosystems, Inc., a company founded by Dr. Hood in 1981, and the ink-jet technology was commercialized by Agilent Technologies, thus making these instruments immediately available to the world-community of scientists.

The first two instruments transformed the field of proteomics. The protein sequencer allowed scientists to read and analyze proteins that had not previously been accessible, resulting in the characterization of a series of new proteins whose genes could then be cloned and analyzed. These discoveries led to significant ramifications for biology, medicine, and pharmacology. The second instrument, the protein synthesizer, synthesized proteins and peptides in sufficient quantities to begin characterizing their functions. The DNA synthesizer, the first of three instruments for genomic analyses, was used to synthesize DNA fragments for DNA mapping and gene cloning. The most notable of Hood’s inventions, the automated DNA sequencer developed in 1986, made possible high-speed sequencing of human genomes and was the key technology enabling the Human Genome Project.

In the early 1990s Hood and his colleagues developed the ink-jet DNA synthesis technology for creating DNA arrays with tens of thousands of gene fragments, one of the first of the so-called DNA chips, which enabled measuring the levels of 10,000s of expressed genes. This instrument has also transformed genomics, biology, and medicine.

In 1992, Hood created the first cross-disciplinary biology department, Molecular Biotechnology, at the University of Washington. In 2000, he left the UW to co-found Institute for Systems Biology, the first of its kind. He has pioneered systems medicine the years since ISB’s founding.

In 2000, Hood and two colleagues founded the Institute for Systems Biology (ISB), a nonprofit research institute integrating biology, technology, computation and medicine to take a systems (holistic) approach to studying the complexity of biology and medicine by analyzing all elements in a biological system rather than studying them one gene or protein at a time (an atomistic approach).

Hood has made many seminal discoveries in the fields of immunology, neurobiology and biotechnology and, most recently, has been a leader in the development of systems biology, its applications to cancer, neurodegenerative disease, and the linkage of systems biology to personalized medicine.

Hood’s efforts in a systems approach to disease have led him to pioneer a new approach to medicine that he coined P4 Medicine in 2003. His view is that P4 medicine will transform the practice of medicine over the next decade, moving it from a largely reactive discipline to a proactive one.

Dr. Hood’s outstanding contributions have had a resounding effect on the advancement of science since the 1960s. Throughout his career, he has adhered to the advice of his mentor, Dr. William J. Dreyer: “If you want to practice biology, do it on the leading edge, and if you want to be on the leading edge, invent new tools for deciphering biological information.”

 

Hood is now pioneering new approaches to P4 medicine

Co-founder and Chairman P4 Medicine institute

—predictive, preventive, personalized and participatory, and most recently, has embarked on creating a P4 pilot project on 100,000 well individuals, that is transforming healthcare.

In addition to his ground-breaking research, Hood has published 750 papers, received 36 patents, 17 honorary degrees and more than 100 awards and honors. He is one of only 15 individuals elected to all three National Academies—the National Academy of Science, the National Academy of Engineering, and the Institute of Medicine. Hood has founded or co-founded 15 different biotechnology companies.

 

http://www.youtube.com/watch%3Fv%3D5aE8tgbsl9U Feb 18, 2015 Dr. Leroy Hood, President and Co-founder, Institute for Systems Biology, gives a talk entitled “Systems Medicine and a Longitudinal, …

http://www.youtube.com/watch%3Fv%3DaYGTLj02sx0  Nov 19, 2014 … of Healthcare? A Personal View of Biological Complexity, Paradigm Changes, Systems Biology and Systems Medicine .Speaker: Leroy Hood.

http://www.youtube.com/watch%3Fv%3DnT1MvnH6j8Q Sep 26, 2014 Dr. Leroy Hood discusses how P4 (Predictive, Preventive, … EMBC 2014 Theme Keynote Lecture with Dr. Emery Brown – Duration: 58:49. by …

Read Full Post »

Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Metabolic Genomics and Pharmaceutics, Vol. 1 of BioMed Series D available on Amazon Kindle

Reporter: Stephen S Williams, PhD

 

Leaders in Pharmaceutical Business Intelligence would like to announce the First volume of their BioMedical E-Book Series D:

Metabolic Genomics & Pharmaceutics, Vol. I

SACHS FLYER 2014 Metabolomics SeriesDindividualred-page2

which is now available on Amazon Kindle at

http://www.amazon.com/dp/B012BB0ZF0.

This e-Book is a comprehensive review of recent Original Research on  METABOLOMICS and related opportunities for Targeted Therapy written by Experts, Authors, Writers. This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time baccalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates.

We invite e-Readers to write an Article Reviews on Amazon for this e-Book on Amazon.

All forthcoming BioMed e-Book Titles can be viewed at:

http://pharmaceuticalintelligence.com/biomed-e-books/

Leaders in Pharmaceutical Business Intelligence, launched in April 2012 an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. The venture operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and for curation and reporting on frontiers in biomedical, biological sciences, healthcare economics, pharmacology, pharmaceuticals & medicine. In addition the venture publishes a Medical E-book Series available on Amazon’s Kindle platform.

Analyzing and sharing the vast and rapidly expanding volume of scientific knowledge has never been so crucial to innovation in the medical field. WE are addressing need of overcoming this scientific information overload by:

  • delivering curation and summary interpretations of latest findings and innovations on an open-access, Web 2.0 platform with future goals of providing primarily concept-driven search in the near future
  • providing a social platform for scientists and clinicians to enter into discussion using social media
  • compiling recent discoveries and issues in yearly-updated Medical E-book Series on Amazon’s mobile Kindle platform

This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Table of Contents for Metabolic Genomics & Pharmaceutics, Vol. I

Chapter 1: Metabolic Pathways

Chapter 2: Lipid Metabolism

Chapter 3: Cell Signaling

Chapter 4: Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8:  Impairments in Pathological States: Endocrine Disorders; Stress

                   Hypermetabolism and Cancer

Chapter 9: Genomic Expression in Health and Disease 

 

Summary 

Epilogue

 

 

Read Full Post »

Older Posts »