How expert-curated cancer data from COSMIC and HSMD can help biopharmaceutical researchers identify and validate targets faster and optimize clinical trial design.
The Catalogue Of Somatic Mutations In Cancer (COSMIC) and the Human Somatic Mutation Database (HSMD) are two expert-curated somatic databases exclusively licensed through QIAGEN that enable biopharmaceutical researchers to avoid pitfalls in early cancer drug discovery, confidently qualify candidate drug targets, and accelerate indication expansion and repurposing of existing cancer therapies.
While similar, both databases differ in their applications. Whereas COSMIC is a downloadable database best suited for exploratory research that can be integrated into your pipeline to identify and validate candidate drug targets, HSMD is a searchable database best suited for finding real-world data to support clinical development and post-market research.
In this blog, we provide an in-depth look at how you can use both COSMIC and HSMD throughout your cancer drug discovery and development pipeline.
While similar, COSMIC and HSMD differ in their applications for cancer drug discovery and development. As a result, biopharmaceutical researchers can use both databases to support different workflow stages.
COSMIC and HSMD differ in terms of content, curation, function and application. The below graphic provides a high-level overview of the two databases.
How COSMIC supports exploratory research in cancer drug discovery
Developed and maintained by Wellcome Sanger Institute, the latest release, COSMIC v99 (December 2023), includes over 6 million coding mutations across 1.5 million tumor samples, curated from over 29,000 publications. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations.
Data is translated into a standardized format and available through downloadable datasets and user-friendly data exploration tools.
How HSMD supports cancer drug clinical development and post-market research
A relatively new somatic mutation database from QIAGEN (released in 2019), HSMD combines over two decades of expert curation and data from scientific literature, on- and off-label therapies and clinical trials, and real-world clinical oncology cases. In the latest release, HSMD 3.0 (November 2023), the database contains manually curated, detailed molecular information on over 1.8 million somatic variants, with more than 430,000 observed in real clinical cases, as well as data from over 545,000 real-world clinical oncology cases.
COSMIC and HSMD are two expert-curated databases licensed exclusively through QIAGEN that enable biopharmaceutical companies to improve the drug discovery process, develop more effective clinical trials, and enhance the treatment of rare cancers. To learn more about how your research team can use COSMIC and HSMD, visit our product webpage or click the button below for a free trial and personal consultation with our biopharmaceutical research experts.
COSMIC & HSMD FOR BIOPHARMA
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How expert-curated cancer data from COSMIC and HSMD can help biopharmaceutical researchers identify and validate targets faster and optimize clinical trial design.
In cancer drug discovery and development, data is king. From identifying potential molecular targets to helping predict drug toxicity and optimizing clinical trial design, high-quality data can significantly improve the efficiency and success rate of bringing new cancer therapies to market.
The Catalogue Of Somatic Mutations In Cancer (COSMIC) and the Human Somatic Mutation Database (HSMD) are two expert-curated somatic databases exclusively licensed through QIAGEN that enable biopharmaceutical researchers to avoid pitfalls in early cancer drug discovery, confidently qualify candidate drug targets, and accelerate indication expansion and repurposing of existing cancer therapies.
In this blog, we take a closer look at COSMIC and HSMD for biopharmaceutical research, providing an overview of the expert curation processes, what types of data can be found in each database, and examples of how this data can be applied through the cancer drug discovery and development pipeline.
COSMIC is an expert-curated knowledge base providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer and biopharmaceutical researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies.
COSMIC integrates somatic data from multiple sources published around the world and allows researchers to access and scrutinize information about somatic mutations and their impact in cancer. Over the past two decades, COSMIC has been diligently collecting, cleaning, and organizing genomic data and associated metadata from cancer studies published in scientific literature and various bioinformatics sources. This data is then translated into a standardized format, integrated, and made available to the research community through well-structured datasets and user-friendly data exploration websites and tools.
In addition to the main catalogue of somatic mutations, a further 6 accompanying resources focus on different aspects of oncology (Figure 1). The Cancer Gene Census (CGC) and Cancer Mutation Census (CMC) provide additional annotations regarding the roles of genes and mutations in oncogenesis, which are based on a defined set of rules and sufficient evidence obtained through dedicated literature curation and analysis of the content of the core catalogue.
→ View the complete database numbers in the latest COSMIC v99 (December 2023) here.
Figure 1. COSMIC’s 7 key resources for understanding cancer and improving cancer patient care. The main catalogue of somatic mutations is supported by further six resources that together lay additional layers of knowledge helping to interpret the impact of somatic mutations on cancer development and presenting available therapeutic options (graphic from Sondka et al. 2024).
COSMIC’s workflows to manually curate cancer genetic data have been built to deliver high-quality, biologically and clinically-relevant data to the research community. Different data sources and types of curated data require different approaches (Figure 2). However, in each case there are common core elements.
Figure 2. COSMIC data curation flowchart. Depending on the data source and curation objectives, there are three main curation paths in COSMIC (graphic from Sondka et al. 2024).
HSMD is a web-based application that allows biopharmaceutical researchers and clinical NGS testing labs to harness genetic insights from QIAGEN’s real-world oncology dataset combined with knowledge from two decades of expert curation.
In the latest version of HSMD, the resource focuses on providing deep insight into small variants, such as SNVs, indels, frameshifts, fusions and copy number variants that have been clinically observed or curated from scientific literature to help users better understand and define precise function and actionability. This expert-curated resource contains content from over 547,000 real-world clinical oncology cases combined with content from the QIAGEN Knowledge Base (QKB), providing gene-level, alteration-level, and disease-level information.
HSMD enables users to easily search and explore mutational characteristics across genes, synthesize key findings from drug labels, clinical trials, and professional guidelines, and receive detailed annotations for each observed variant (Figure 3).
Figure 3. HSMD home screen. HSMD enables users to search by gene, alteration, disease, drugs, and clinical trials.
HSMD leverages variant content from two sources: expert-curated content from the QIAGEN Knowledge Base (QKB) and data from real-world oncology cases sourced from our professional clinical interpretation services (Figure 4).
When a variant has been “clinically observed,” it means our professional clinical interpretation service has encountered this alteration in a real-world clinical case. For these variants, QIAGEN's team has assessed the clinical and biological relevance and calculated the gene and variant prevalence across observed tumor types. Conversely, content from the QKB is proactively curated from scientific literature; therefore, not all variants have yet been directly clinically observed by our professional clinical interpretation services.
Figure 4. HSMD curation workflow. HSMD contains content from the QKB, which pulls information from all public and proprietary databases, clinical articles for the most relevant cancer genes, and thousands of clinical articles for somatic genes. Curation then occurs by artificial intelligence (AI) approaches, manual curation, or a combination of both. All content then goes through rigorous quality control to ensure consistency, accuracy, and reproducibility. In addition, HSMD contains content from over 500,000 somatic mutations submitted to QIAGEN's professional variant interpretation service, QCI Precision Insights (formerly N-of-One). This is de-identified patient data that provides even greater insight into real-world clinical cases.
COSMIC and HSMD are two expert-curated databases licensed exclusively through QIAGEN that enable biopharmaceutical companies to improve the drug discovery process, develop more effective clinical trials, and enhance the treatment of rare cancers. To learn more about how your research team can use COSMIC and HSMD, visit our product webpage or click the button below for a free trial and personal consultation with our biopharmaceutical research experts.
COSMIC & HSMD FOR BIOPHARMA
REQUEST FREE TRIAL
Two expert-curated databases exclusively licensed through QIAGEN link sequence-level somatic mutation data to detailed molecular information about functional and clinical impacts, as well as implications for druggability and relevant clinical trials. The two databases, the Catalogue Of Somatic Mutations In Cancer (COSMIC) and the Human Somatic Mutation Database (HSMD), enable biopharmaceutical researchers to avoid pitfalls in early cancer drug discovery and development, confidently qualify candidate drug targets, and accelerate indication expansion and repurposing of existing cancer therapies.
In this blog, learn more about the high-level applications of using COSMIC and HSMD in cancer drug discovery and development pipelines.
The Catalogue Of Somatic Mutations In Cancer (COSMIC) is the most detailed and comprehensive resource for exploring the effect of somatic mutations in human cancer. Developed and maintained by Wellcome Sanger Institute, the latest release, COSMIC v99 (December 2023), includes over 6 million coding mutations across 1.5 million tumor samples, curated from over 29,000 publications. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations.
COSMIC integrates somatic data from multiple sources published around the world and allows researchers to access and scrutinize information about somatic mutations and their impact in cancer. Over the past two decades, COSMIC, through predominantly manual curation workflows, has been diligently collecting, cleaning, and organizing genomic data and associated metadata from cancer studies published in scientific literature and various bioinformatics sources. This data is then translated into a standardized format, integrated, and made available to the research community through well-structured datasets and user-friendly data exploration websites and tools.
The Human Somatic Mutation Database (HSMD) is a relatively new somatic mutation database from QIAGEN (released in 2019) that combines over two decades of expert curation and data from scientific literature, on- and off-label therapies and clinical trials, and real-world clinical oncology cases. In the latest release, HSMD 3.0 (November 2023), the database contains manually curated, detailed molecular information on over 1.8 million somatic variants, with more than 430,000 observed in real clinical cases, as well as data from over 545,000 real-world clinical oncology cases.
Unique to HSMD is the availability of data from clinically observed variants. When a variant has been “clinically observed,” it means QIAGEN’s professional clinical interpretation service (previously N-of-One) has encountered this alteration in a real-world clinical case. For these variants, QIAGEN assesses the clinical and biological relevance and calculates the gene and variant prevalence across observed tumor types.
Easy to search with new content added weekly, HSMD enables researchers to explore key genes or mutations with driving properties or clinical relevance and search for associated treatment options, off-label therapies, resistance markers, and regional and/or disease-specific clinical trials.
While similar, COSMIC and HSMD differ in their applications for cancer drug discovery and development. As a result, biopharmaceutical researchers can use both databases to support different workflow stages.
COSMIC is a valuable resource for cancer researchers and drug discovery efforts. Here are several ways in which the COSMIC database can be used to support exploratory research in cancer drug discovery:
HSMD is a valuable resource for biopharmaceutical researchers, facilitating the confident evaluation of cancer-related genetic variations by granting access to real-world data. Here are several ways in which HSMD supports cancer drug clinical development and post-market research.
COSMIC and HSMD are two expert-curated databases licensed exclusively through QIAGEN that enable biopharmaceutical companies to improve the drug discovery process, develop more effective clinical trials, and enhance the treatment of rare cancers. To learn more about how your research team can use COSMIC and HSMD, visit our product webpage or click the button below for a free trial and personal consultation with our biopharmaceutical research experts.
COSMIC & HSMD FOR BIOPHARMA
REQUEST FREE TRIAL
Progranulin (PGRN) is a growth factor and immune regulatory protein involved in the regulation of host-defense signaling pathways during infection and inflammation. It is critical in innate immunity against bacteria and targets TLR4 which recognizes LPS (1–3). Progranulin deficiency in animal models leads to increased vulnerability to LPS-induced septic shock and high mortality (1). Increased progranulin plasma levels have been described in in patients with sepsis (4).
Exciting research on progranulin as a novel biomarker was recently presented at the Sepsis Update 2019 conference which took place on September 11–13 in Weimar, Germany. QIAGEN’s Senior Principal Scientist for Bioinformatics, Dr. Jean-Noel Billaud, collaborated on this research with Dr. Gustav Schelling’s team from Klinikum der Universität München, who presented their progranulin research findings at the conference. The aim of their research was to study the performance characteristics of progranulin as a potential biomarker for sepsis, compared to established markers such as procalcitonin (PCT), and to delineate molecular networks involved in upregulating progranulin in sepsis.
To achieve this, the team used QIAGEN bioinformatics software OmicSoft ArrayStudio to obtain the differentiation profile after DESEq2 analysis, and performed biological interpretation using Ingenuity Pathway Analysis (IPA). NGS data from sepsis patient samples were used to identify the canonical gene network (targeted miRNA-mRNA network) involved in the early antimicrobial response of progranulin, followed by RT-qPCR confirmation.
NGS revealed significantly upregulated mRNA transcripts of GRN from human blood cell samples (the progranulin gene) (log2FC = 2.23, padj=3.46E-8) and SORT1 (sortilin, an important regulator of progranulin) (log2FC = 5.56, padj=1.38E-8), whereas comprehensive NGS did not detect any transcripts of CALC-1 (PCT) in blood cells. Filtering and pairing of NGS miRNA/mRNA data using IPA revealed a network (Figure 1) including TP53 and TLR4 as well as progranulin and sortilin, shown to be regulated by miR-16, miR-150 and others. The miRNAs and mRNAs from the network, including progranulin and sortilin, were confirmed by RT-qPCR.
Figure 1. Upregulation of progranulin (GRN gene transcript) in a molecular network activated during early antimicrobial response in septic shock. The network was constructed using high-throughput sequencing (NGS) followed by RT-qPCR confirmation. Red indicates upregulation of the respective molecules and green indicates downregulation.
This research performed using QIAGEN bioinformatics solutions indicates how progranulin is part of a key blood-cell derived network involved in early antimicrobial response in sepsis, and performs just as well as other more established biomarkers for the differentiation between systemic inflammatory response syndrome (SIRS) and sepsis. Based on this research progranulin represents a novel and sensitive biomarker for sepsis.
References.
1. Jian J, Konopka J and Liu C. (2013) Insights into the role of progranulin in immunity, infection, and inflammation. J Leukoc Biol 93: 199–208.
2. McIsaac SM, Stadnyk AW and Lin TJ. (2012) Toll-like receptors in the host defense against Pseudomonas aeruginosa respiratory infection and cystic fibrosis. J Leukoc Biol 92: 977–985.
3. Abella V, et al (2016). The novel adipokine progranulin counteracts IL-1 and TLR4-driven inflammatory response in human and murine chondrocytes via TNFR1. Sci Rep 6: 20356.
4. Yan W, et al. (2016) Progranulin Controls Sepsis via C/EBPalpha-Regulated Il10 Transcription and Ubiquitin Ligase/Proteasome-Mediated Protein Degradation. J Immunol 197: 3393–3405.
As a researcher, it’s an enormous task to acquire knowledge and insight from the sea of biological data and complex interactions involved in a specific research topic. With QIAGEN Bioinformatics’ Ingenuity Pathway Analysis (IPA), we make it easier.
With the comprehensive, manually curated content of the Ingenuity Knowledge Base, combined with powerful algorithms, IPA provides advanced analysis capabilities to help scientists understand the biological context of expression analysis experiments. With IPA, you can identify the most significant pathways, and discover novel regulatory networks and causal relationships associated with your experimental data.
In the past several months there have been over 500 citations for Ingenuity Pathway Analysis, demonstrating how this tool helps put biological data in context to gain insight. Here, we round up just a few of them to offer a sense of the diverse research for which Ingenuity Pathway Analysis makes a difference.
Increasing evidence indicates that miR-301a is a potential oncogenic microRNA and that its genetic ablation reduces Kras-driven lung tumorigenesis in mice. A recent Molecular Cancer paper describes how researchers from China studied the role of miR-301a on host antitumor immunity.
After differentially expressed genes (DEGs) of two mouse models (with or without miR-301a) were identified from RNA-seq data, IPA was used to identify gene networks. The five most highly implicated IPA networks related to cell cycle and immune response were merged, and it was discovered that IFNG (INF-γ) and CTNNB1 (β-catenin) were in the core modules within the entire network. This discovery led to further investigation of these genes, which enabled the researchers to find that miR-301a deficiency recruits immune cells to the tumor microenvironment, resulting in higher IFN-γ expression in early lung tumorigenesis. Additionally, miR-301a directly targets Runx3 mRNA, a negative regulator of the β-catenin pathway. After further experiments, the authors conclude that miR-301a facilitates antitumor immunity in the tumor microenvironment via Runx3 suppression during lung tumorigenesis.
In a Nature Communications paper, scientists from the Broad Institute and the Worchester Polytechnic Institute looked into transcriptional dynamics of macrophages infected with Candida albicans. IPA was used to investigate biological relationships, canonical pathways and upstream regulators of differentially expressed genes in macrophages either exposed to or infected with C. albicans.
Using IPA, the group was able to assess the overlap between significantly DEGs and an extensively curated database of target genes for each of several hundred known regulatory proteins. The researchers found that transcriptomes of infected macrophages and phagocytosed C. albicans displayed tightly coordinated shifts in gene expression, and they established an approach for studying host-pathogen trajectories to resolve heterogeneity in dynamic populations.
A group of collaborating researchers from China recently published their findings on the signaling pathway network profile of human ovarian cancers. They used IPA to mine signaling pathway networks with nearly 1200 differentially expressed mitochondrial proteins, and they compared the pathway and network changes between ovarian cancers and controls. Their results were experimentally validated using qRT-PCR and Western blot. The scientific data generated in this study may lead to the discovery of pathway- and network-based disease and treatment biomarkers for ovarian cancers, and potentially novel molecular mechanisms and therapeutic targets for this disease.
Metal oxide nanoparticles (NPs) are widely used in industry despite little knowledge about the cellular pathways involved in their potential toxicity. Collaborating scientists from France and Ireland published in Cell Biology and Toxicology results of their gene expression study, showing expression changes in rat macrophages upon exposure to metal oxide NPs. IPA was used to identify top canonical pathways influenced by the exposure, notably eIF2 signaling involved in protein homeostasis.
If QIAGEN’s IPA is helping you make strides in your research, we would love to hear about it. Please contact us to share your story, or just to request a free trial!
Every year, 7.9 million infants are born with a serious birth defect of genetic or partially genetic origin.1
Inherited disorders affect millions of people globally, often at a very early age, with debilitating or fatal effects. There is an urgent need for better understanding of the diseases, their causes and prevalence. Next-generation sequencing (NGS) has made great strides in unraveling the underlying mechanisms of genetic-related disorders, leading to the discovery of novel disease-associated genes.
Today, multi-disease, pan-ethnic expanded carrier screening (ECS) uses NGS to probe thousands of bases per gene, detecting rare mutations and providing valuable insight into disease mutation carrier risks and rates for the global population.
ECS requires the integration of multiple workflows, an understanding of rare variants, and the ability to evaluate NGS data from large cohort samples efficiently, consistently and accurately.
The QIAseq Expanded Carrier Screening Solution features a comprehensive workflow for the identification of targets, genes and other regions of interest responsible for more than 200 disease indications. Running on any leading NGS platform, the QIAseq panel is integrated with QIAGEN’s comprehensive CLC Genomics Workbench and QIAGEN Clinical Insight (QCI)-Interpret for QIAseq to provide evidence-based, actionable insights.
Read the press release here.
QIAseq Expanded Carrier Screening Panel - NGS sequencer-agnostic solution based on QIAGEN’s proprietary QIAseq Enrichment Technology and use of unique molecular indices (UMI) to deliver a digital sequencing approach that detects all mutation types, including copy number variants (CNVs) and low-frequency variants
CLC Genomics Workbench - Secondary NGS analysis package that combines quality control steps, adapter trimming, read mapping, variant detection and cascade filtering into one, scalable pipeline.
QCI-Interpret for QIAseq - Clinical research software that reproducibly translates highly complex NGS data into detailed variant reports, using current scientific evidence and expert guidelines
Find out here
From Hong Kong to Spain and Korea, QIAGEN’s Human Gene Mutation Database (HGMD) is helping researchers make strides in their work. The papers highlighted below provide only a few of the many recent instances in which HGMD was identified in the scientific literature as a resource for mutations related to disease and the interpretation of clinical test results.
Integrating functional analysis in the next generation sequencing diagnostic pipeline of RASopathies
First author: Gordon K. C. Leung
The results of a recent study in Nature’s Scientific Reports focused on variants of unknown significance (VUSs), which pose challenges during clinical interpretation and genetic counseling. A team from the University of Hong Kong investigated the potential of a diagnostic pipeline combining NGS and the functional assessment of variants, for which they used HGMD. They aimed to diagnose RASopathies, which are heterogeneous conditions caused by germline mutations in RAS/MAPK signaling pathway genes. As a result of their findings – classifying two genetic variants as likely pathogenic mutations – the report argues that establishing functional analysis for genetic syndromes would aid in long-term decision-making and lend itself to comprehensive interpretation of new variants.
Primary osteoporosis in young adults: genetic basis and identification of novel variants in causal genes
First author: Corinne Collet
To determine the causal genes of idiopathic osteoporosis in adulthood, a French team used HGMD to search for known pathogenic mutations in a cohort of 123 young/middle-aged adults. Their report, published in the Journal of Bone and Mineral Research, found that the clinical phenotype of patients carrying causal gene variants was indistinguishable. They did determine, however, that molecular screening of young osteoporotic adults revealed several variants, which might prove useful to identify susceptibility genes for personalizing treatment; this is particularly important for treatment with anabolic drugs.
Functional analyses of a novel splice variant in the CHD7 gene, found by next generation sequencing, confirm its pathogenicity in a Spanish patient and diagnose him with CHARGE syndrome
First author: Olatz Villate
Frontiers in Genetics recently published a report completed by a Spanish team who researched the functional consequences of a novel splice mutation in the CHD7 gene, which is largely responsible for CHARGE syndrome (an acronym for Coloboma, Heart defects, Atresia of the choanae, Retardation of growth and development, Genital hypoplasia and Ear abnormalities). Many of the features and symptoms of CHARGE syndrome are common to those of other syndromes, making it difficult to diagnose. The team used HGMD to place the incidence of splice mutations at 12%, enriching their understanding of the genetic causes of CHARGE syndrome, which will in turn aid in improving diagnosis and genetic counseling efforts in the future.
Detection of familial hypercholesterolemia using next generation sequencing in two population-based cohorts
First author: Hee Nam Kim
Researchers from Korea looked at familial hypercholesterolaemia (FH), a common autosomal dominant disorder. They classified pathogenic mutations by comparing all non-synonymous variants to HGMD. Though the team confirmed 17 mutations in 23 of their subjects, they also discovered that those with high total cholesterol levels had a low prevalence of FH mutations, leading them to conclude that that NGS-based testing at population levels would not be cost-effective.
When reading the scientific literature, we love to read reports of how our solutions contribute to furthering science and medicine. If you have a story of your own that you’d be willing to share with us, please get in touch! For a trial of HGMD, just click here.
Scientific literature frequently provides inspiration and insight when we see how our customers are advancing their knowledge of the world. QIAGEN’s Ingenuity® Pathway Analysis (IPA®) is a powerful analysis and interpretation tool for uncovering network interactions and identifying new targets or candidate biomarkers within the context of biological systems. Here, we take a brief look at some recent papers that cite IPA used during the research process.
Liver proteomic analysis of postpartum Holstein cows exposed to heat stress or cooling conditions during the dry period
First author: Amy L. Skibiel
A group of US and Israeli scientists studied the impact of heat-related stress on cow performance and disease outcomes, and published their results in The Journal of Dairy Science. The team focused the metabolic rates and pathways within the liver proteome of postpartum cows as they transitioned from gestation to lactation, providing either hot or cool conditions for the animals. They used QIAGEN’s IPA to analyze proteins to determine the most relevant pathways, physiological functions and networks, and identified 75 out of more than 3000 proteins that were differentially expressed between hot and cool cows. The results suggest that keeping cows cool might improve production, reduce oxidative stress, increase milk yield and decrease susceptibility to disease.
Inflammatory gene expression signatures in idiopathic intracranial hypertension: Possible implications in microgravity-induced ICP elevation
First author: Susana B. Zanello
The Microgravity journal recently ran a report submitted by a group of scientists based in Texas and Massachusetts. The team investigated idiopathic intracranial hypertension (IIH), a neuro-ophthalmologic condition suffered by many astronauts returning from extensive missions in outer space, symptoms of which include visual impairment and intracranial pressure. They applied QIAGEN IPA’s Target Filter Analysis to the differentially expressed gene data set to study molecular pathways associated with the elevation of IIH-related intracranial pressure. The report confirms that neurophysiological alterations and neuro-immunomodulation are present in IIH and suggests further research on the topic.
Impaired IFN-α-mediated signal in dendritic cells differentiates active from latent tuberculosis
First author: Stefania Parlato
PLoS One recently reported a study by Italian researchers who wanted to understand how active tuberculosis (TB) might develop in individuals exposed to Mycobacterium tuberculosis (Mtb), which can also remain latent. The team used IPA to identify biological functions, gene networks and canonical pathways. They found that active TB patients have an impaired IFN-α signal in their dendritic cells, which are significant in dictating antibacterial immunity, and which might account for an inability to generate a T-cell response against Mtb.
Uncovering a predictive molecular signature for the onset of NASH-related fibrosis in a translational NASH mouse model
First author: Arianne van Koppen
A team of researchers from The Netherlands, the US and Japan collaborated to gain insight on the increasing prevalence of nonalcoholic steatohepatitis (NASH), which is the progressive form of nonalcoholic fatty liver disease – the most common chronic liver disease found in developed countries. The researchers looked at the key molecular processes typically involved, ranking early markers for hepatic fibrosis. The team used QIAGEN IPA’s Path Explorer tool to calculate the most efficient path between signature genes and four defined key processes. The resulting report, which was published in Cellular and Molecular Gastroenterology and Hepatology, identified an early predictive model signature that marked the early onset of histopathologic fibrosis. This can aid in early detection of the onset of NASH – in addition to new blood-based biomarkers which may aid in the development of new therapeutics and help cut down on (pre)clinical experimental timeframes.
Genome re-sequencing to identify single nucleotide polymorphism markers for muscle color traits in broiler chickens
First author: H. R. Kong
Using genetic selection, efficient production systems, improved nutrition and regular veterinary attention, the poultry industry has successfully improved growth and yield of chickens used for broiler meat. This very success may also have had a negative impact on meat qualities – including muscle color – so researchers from the University of Arkansas investigated the specific genetic elements that regulate muscle color in chicken meat. Their study, published in the Asian-Australasian Journal of Sciences, used IPA to analyze functional interpretation of chicken genes retaining SNPs. The results helped identify a link between meat color in chickens with chromosomal DNA stability, the functions of ubiquitylation and the quality and quantity of collagen subtypes.
Got a bioinformatics story you’d like to share? Interested in a trial of Ingenuity Pathway Analysis? Get in touch!
This post is authored by Gnosis Data Analysis I.K.E.
We are proud to announce a new release for BioSignature Discoverer, the plugin specifically devised for identifying collection of biomarkers in omics data.
The new version of the plug-in comes with several important improvements, including:
The new release immediately follows the first scientific publication demonstrating the applicability of BioSignature Discoverer in practice:
Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower.
This work employs BioSignature Discoverer for identifying biomarkers characterizing leaf senescence in sunflower plants. The biomarkers allow a net separation across the senescence stages of the plants, and were identified by integratively analyzing transcriptomics and metabolomics information.
Gnosis Data Analysis I.K.E. is a university spin-off of the University of Crete whose mission is empowering companies and research institutions with powerful data analysis solutions and services.
For more details about the BioSignature Discoverer plug-in, please visit the dedicated plug-in page.
For more information about Gnosis Data Analysis, please visit their website.
We are thrilled to be part of the announcement at ASHG 2016, detailing the new QIAseq® cfDNA All-in-One Kit — including the market’s first bioinformatics workflow for cell-free DNA.
With this streamlined testing solution researchers can now analyze cell-free DNA quickly, conveniently, and reliably to obtain accurate and meaningful results using any major sequencing platform. Researchers who are applying liquid biopsy methods in their work with NIPT or with hereditary and rare diseases can use this solution to address NGS bottlenecks while increasing the accuracy and sensitivity of their variant identification efforts.
We are proud to be furthering the use case for liquid biopsy, and are looking forward to hearing how others are using this tool in innovative ways at ASHG. Please stop by our booth #1234, where we’ll be hosting several presentations on Wednesday and Thursday.
Furthermore, QIAGEN unveiled an enhanced bioinformatics workflow for hereditary and rare diseases, offering unique capabilities for research using liquid biopsies in non-invasive prenatal testing (NIPT) as well as cancer biomarker discovery. QIAGEN is rolling out the solutions at the American Society of Human Genetics ASHG 2016 Annual Meeting in Vancouver.
“The new All-in-One Kit for extraction and library preparation delivers a powerful solution for researchers to maximize their discovery potential and accuracy of results from liquid biopsies, achieving breakthroughs in NGS detection of even the rarest variants. In tandem with new dedicated bioinformatics, our cfDNA kit is creating a true Sample to Insight experience for liquid biopsy analysis, efficiently delivering, accurate and meaningful results with any major sequencing platform,” said Brad Crutchfield, Senior Vice President of QIAGEN’s Life Sciences Business Area. “Also at ASHG 2016, QIAGEN Bioinformatics is introducing an enhanced analysis and interpretation workflow for identification of disease-causing variants in hereditary and rare diseases, useful in both NIPT and cancer research. NGS users are increasingly relying on QIAGEN’s growing portfolio of Sample to Insight solutions.”
The QIAseq cfDNA kit provides a complete solution, from plasma to NGS-ready libraries, to maximize cell-free DNA conversion and discovery potential for translational research using liquid biopsies. Building on proven QIAamp technology, the gold standard in cfDNA extraction, the All-in-One kit is the first kit to combine extraction and library preparation, making library prep more convenient, efficient and accurate for the demands of exome or whole genome sequencing.
QIAGEN also is unveiling its enhanced hereditary disease solution to provide a streamlined, easy-to-use analysis and interpretation workflow for NGS data from liquid biopsies. Use of small blood samples to detect disease-causing variants in circulating cfDNA offers advantages in neonatal testing or monitoring of cancer patients for translational research, but the NGS data can be difficult to analyze. QIAGEN’s bioinformatics solution enables labs to achieve more accurate detection and the highest sensitivity in identifying variants. Integrating Biomedical Genomics Workbench, Ingenuity Variant Analysis and other components, the solution addresses NGS bottlenecks and ensures that no pathogenic variant is missed.
QIAGEN will exhibit at booth #1234 during ASHG 2016, demonstrating the cfDNA Sample to Insight workflow, the enhanced hereditary disease solution and other tools. The company also will present an educational session, “Sample-to-Insight NGS Solutions: Multimodal Liquid Biopsy WGS, Trio and Family Analyses, and RNA-sequencing Analysis and Interpretation,” from 1:00 p.m. to 2:30 p.m. on Thursday October 20 in Room 13, Convention Centre East Building. For information please visit https://digitalinsights.qiagen.com.
About QIAGEN
QIAGEN N.V., a Netherlands-based holding company, is the leading global provider of Sample to Insight solutions that enable customers to gain valuable molecular insights from samples containing the building blocks of life. Our sample technologies isolate and process DNA, RNA and proteins from blood, tissue and other materials. Assay technologies make these biomolecules visible and ready for analysis. Bioinformatics software and knowledge bases interpret data to report relevant, actionable insights. Automation solutions tie these together in seamless and cost-effective workflows. QIAGEN provides solutions to more than 500,000 customers around the world in Molecular Diagnostics (human healthcare), Applied Testing (forensics, veterinary testing and food safety), Pharma (pharma and biotech companies) and Academia (life sciences research). As of June 30, 2016, QIAGEN employed approximately 4,600 people in over 35 locations worldwide. Further information can be found at https://www.qiagen.com.
Certain statements contained in this press release may be considered forward-looking statements within the meaning of Section 27A of the U.S. Securities Act of 1933, as amended, and Section 21E of the U.S. Securities Exchange Act of 1934, as amended. To the extent that any of the statements contained herein relating to QIAGEN's products, collaborations, markets, strategy or operating results, including without limitation its expected adjusted net sales and adjusted diluted earnings results, are forward-looking, such statements are based on current expectations and assumptions that involve a number of uncertainties and risks. Such uncertainties and risks include, but are not limited to, risks associated with management of growth and international operations (including the effects of currency fluctuations, regulatory processes and dependence on logistics), variability of operating results and allocations between customer classes, the commercial development of markets for our products to customers in academia, pharma, applied testing and molecular diagnostics; changing relationships with customers, suppliers and strategic partners; competition; rapid or unexpected changes in technologies; fluctuations in demand for QIAGEN's products (including fluctuations due to general economic conditions, the level and timing of customers' funding, budgets and other factors); our ability to obtain regulatory approval of our products; difficulties in successfully adapting QIAGEN's products to integrated solutions and producing such products; the ability of QIAGEN to identify and develop new products and to differentiate and protect our products from competitors' products; market acceptance of QIAGEN's new products and the integration of acquired technologies and businesses. For further information, please refer to the discussions in reports that QIAGEN has filed with, or furnished to, the U.S. Securities and Exchange Commission (SEC).
