Communicating computational workflows in a regulatory environment

The volume of nucleic acid sequence data has exploded recently, amplifying the challenge of transforming data into meaningful information. Processing data can require an increasingly complex ecosystem of customized tools, which increases difficulty in communicating analyses in an understandable way yet is of sufficient detail to enable informed decisions or repeats. This can be of particular interest to institutions and companies communicating computations in a regulatory environment. BioCompute Objects (BCOs; an instance of pipeline documentation that conforms to the IEEE 2791-2020 standard) were developed as a standardized mechanism for analysis reporting. A suite of BCOs is presented, representing interconnected elements of a computation modeled after those that might be found in a regulatory submission but are shared publicly - in this case a pipeline designed to identify viral contaminants in biological manufacturing, such as for vaccines.

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

Consumption of sucralose- and acesulfame-potassium-containing diet soda alters the relative abundance of microbial taxa at the species level: findings of two pilot studies

Sucralose and acesulfame-potassium consumption alters gut microbiota in rodents, with unclear effects in humans. We examined effects of three-times daily sucralose- and acesulfame-potassium-containing diet soda consumption for 1 (n = 17) or 8 (n = 8) weeks on gut microbiota composition in young adults. After 8 weeks of diet soda consumption, the relative abundance of Proteobacteria, specifically Enterobacteriaceae, increased; and, increased abundance of two Proteobacteria taxa was also observed after 1 week of diet soda consumption compared with sparkling water. In addition, three taxa in the Bacteroides genus increased following 1 week of diet soda consumption compared with sparkling water. The clinical relevance of these findings and effects of sucralose and acesulfame-potassium consumption on human gut microbiota warrant further investigation in larger studies. Clinical trial registration: NCT02877186 and NCT03125356.

The Glycan Structure Dictionary-a dictionary describing commonly used glycan structure terms

Recent technological advances in glycobiology have resulted in a large influx of data and the publication of many papers describing discoveries in glycoscience. However, the terms used in describing glycan structural features are not standardized, making it difficult to harmonize data across biomolecular databases, hampering the harvesting of information across studies and hindering text mining and curation efforts. To address this shortcoming, the Glycan Structure Dictionary has been developed as a reference dictionary to provide a standardized list of widely used glycan terms that can help in the curation and mapping of glycan structures described in publications. Currently, the dictionary has 190 glycan structure terms with 297 synonyms linked to 3,332 publications. For a term to be included in the dictionary, it must be present in at least 2 peer-reviewed publications. Synonyms, annotations, and cross-references to GlyTouCan, GlycoMotif, and other relevant databases and resources are also provided when available. The purpose of this effort is to facilitate biocuration, assist in the development of text mining tools, improve the harmonization of search, and browse capabilities in glycoinformatics resources and help to map glycan structures to function and disease. It is also expected that authors will use these terms to describe glycan structures in their manuscripts over time. A mechanism is also provided for researchers to submit terms for potential incorporation. The dictionary is available at https://wiki.glygen.org/Glycan_structure_dictionary.

Worldwide Glycoscience Informatics Infrastructure: The GlySpace Alliance

The GlySpace Alliance was formed in 2018 among the principal investigators of three major glycoscience portals: Glyco@Expasy, GlyCosmos, and GlyGen, representing Europe, Asia, and the United States, respectively. While each of these portals has its unique user interface, the aim is to provide the same basic data set of glycan-related omics data. These portals will be introduced with the aim to enable users to find their target information in the most efficient manner, in particular, in terms of the chemical structures of glycans and their functions.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

POS1495-HPR COVID-19, INFLUENZA AND PNEUMOCOCCUS VACCINATION UPTAKE IN PATIENTS WITH RHEUMATIC DISEASE: A PROSPECTIVE AUDIT

Background

Patients with autoimmune inflammatory rheumatic diseases are susceptible to infections. This could be attributed to theimmunosuppressive effect of the underlying condition or the use of immunomodulatory medications. According to the Department of Health guidelines inthe UK and the European League Against Rheumatism (EULAR), patients who are immunosuppressed should be vaccinated against influenza and pneumococcal infection, as well as COVID-19 infection.

Objectives

Our aim was to explore the Pneumococcal, Influenza and COVID-19 vaccination uptake of our patients with different autoimmune inflammatory rheumatological conditions. In addition, to assess the side effects profile and the status of their underlying rheumatological diseases following COVID-19 vaccination.

Methods

We undertook a prospective audit of consecutive patients with regards to their vaccination update for influenza, pneumococcus, and COVID-19, utilizing a standard questionnaire and compared the results to our 2017 data.

Results

Some 81% of patients received the influenza vaccination (compared to 47% in 2017) representing a 172% improvement, p<0.001. Some 53% received the pneumococcus vaccination compared to 28% in 2017, indicating a 185% improvement, p=0.003. With regards to COVID-19 vaccination, 98/101(97%) of eligible patients received at least one dose and 66% received two doses. 47% received Astra Zeneca, 52% Pfizer and 1% unsure. 46% of patients mentioned, no one specifically discussed the COVID vaccine with them - got information via SMS/ from media, However, 37% of patients were informed by GP Doctor/ Nurse, 14% from the person giving the vaccine, and 7% from specialist hospital doctor. Safety concerns were indicated by all 3 patients who deferred vaccination.

Most side-effects were observed following the first dose (74 patients) vs. the second dose (13 patients) and were mainly mild (66%), but also moderate (19%) and severe (15%). The sore arm was the commonest side-effect, whilst the majority of side-effects resolved within two days. Crucially, 28% reported a flare of the rheumatological condition following the vaccination. No patients receiving at least one dose were diagnosed with COVID-19 infection subsequently.

Conclusion

Vaccination rates for influenza and pneumococcus have improved substantially since 2017, although the population with rheumatic diseases still has low uptake in pneumococcal vaccination. The COVID-19 vaccination uptake has been extremely high in this cohort.

Disclosure of Interests

None declared

Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers

Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data.

Nodding syndrome: A key role for sources of nutrition?

Nodding Syndrome (NS) has occurred among severely food-stressed communities in northern Uganda and several other East African populations that, with their forced physical displacement, have resorted to nutritional support from available wild plants and fungi, some of which have neurotoxic potential. Among the latter is an agaric mushroom with an unknown content of hydrazine-generating agaritine, namely Agaricus bingensis, the unusually wide consumption of which may relate to the low serum levels of vitamin B6 in Ugandan NS subjects relative to controls. Hydrazine-related compounds induce patterns of DNA damage that promote neuropathological changes (tauopathy) reminiscent of those associated with established NS. While the cause of this childhood brain disease is unknown, we encourage increased attention to the role of malnutrition and B6 hypovitaminosis in the etiology of this devastating brain disease.

•

Idiopathic epileptic encephalopathy with tauopathy (Nodding syndrome) impacts East African children

•

Associated factors include nematode infection, food insecurity, and food use of wild plants and fungi

•

Food use of hydrazinic fungi induces B6 hypovitaminosis, which may be marked in Nodding syndrome

•

Vitamin B6 deficiency promotes tau phosphorylation in mouse models of human tauopathy

•

Hydrazine generates carbon free radicals associated with DNA-damage and neurodegenerative disease

•

Increased research attention to nutritional practices associated with Nodding syndrome is merited.

Enhancing the interoperability of glycan data flow between ChEBI, PubChem, and GlyGen

Glycans play a vital role in health, disease, bioenergy, biomaterials, and bio-therapeutics. As a result, there is keen interest to identify and increase glycan data in bioinformatics databases like ChEBI and PubChem, and connecting them to resources at the EMBL-EBI and NCBI to facilitate access to important annotations at a global level. GlyTouCan is a comprehensive archival database that contains glycans obtained primarily through batch upload from glycan repositories, glycoprotein databases, and individual laboratories. In many instances, the glycan structures deposited in GlyTouCan may not be fully defined or have supporting experimental evidence and citations. Databases like ChEBI and PubChem were designed to accommodate complete atomistic structures with well-defined chemical linkages. As a result, they cannot easily accommodate the structural ambiguity inherent in glycan databases. Consequently, there is a need to improve the organization of glycan data coherently to enhance connectivity across the major NCBI, EMBL-EBI, and glycoscience databases. This paper outlines a workflow developed in collaboration between GlyGen, ChEBI, and PubChem to improve the visibility and connectivity of glycan data across these resources. GlyGen hosts a subset of glycans (~29,000) from the GlyTouCan database and has submitted valuable glycan annotations to the PubChem database and integrated over 10,500 (including ambiguously defined) glycans into the ChEBI database. The integrated glycans were prioritized based on links to PubChem and connectivity to glycoprotein data. The pipeline provides a blueprint for how glycan data can be harmonized between different resources. The current PubChem, ChEBI, and GlyTouCan mappings can be downloaded from GlyGen (https://data.glygen.org).

Abstract 1405: Colorectal carcinogenesis in Smad4/SPTBN1 mutants alter the intestinal microbiome and resistance to 5FU

Background/Aims: Emerging data shows a rise in colorectal cancer (CRC) incidence in young men and women that is often chemoresistant, with potential new risk factors including alterations in the microbiome that remain understudied. We have recently observed altered microbiome with modulation of the gut immune response through crosstalk between sensors, microbes, and the TGF-β signaling pathway. Interestingly, we observed that human CRC cell lines with disruption of TGF-β had increased sensitivity to cisplatin and other DNA cross-linking agents. Yet at the same time, human CRC with loss of Smad4 portends poor prognosis and resistance to chemotherapy. Therefore, it was unclear whether the epithelial loss of Smad4 or stromal loss of Smad4 expression could be responsible for chemoresistance. Here, we investigated the role of chemotherapy with disruption of TGF-β signaling and an altered intestinal microbiome in colorectal carcinogenesis.

Methods: CRCs induced by azoxymethane (AOM)/dextran sodium sulfate (DSS) in wild type (WT) and TGF-β signaling deficient mice (SKO: Smad4+/- and Smad4+/-/Sptbn1+/-) were treated with 5-Fluoro-Uracil (5FU). Shotgun metagenomic sequencing was performed in fecal samples from WT and SKO mice before and after treatment.

Results: Our analyses revealed that SKO mice are more susceptible to AOM/DSS induced CRC as demonstrated by increased multiplicity (SKO vs WT: 10.25±1.5 vs 5.5±1.4, p=0.01) and tumor size (SKO vs WT: 3.63±0.7mm vs 2.17±0.3mm, p=0.02). CRC that develops in mice with disrupted TGF-β signaling is chemoresistance to 5FU and progress to liver metastasis confirmed by histological and immunohistochemical analysis. Interestingly, SKO mice display a unique gut microbiome signature compared to the WT mice. SKO mice had significantly reduced abundances of beneficial species of B. vulgutus (0.056±0.0078 vs 0.018±0.0052) and P. distasonis (0.007±0.001 vs 0.003±0.001). In addition to E. Boltae, and Mordavella sp., the relative abundance of Bacteroides was significantly reduced in AOM/DSS induced tumors and recovered to basal levels after 5FU treatment in WT mice but not in SKO mice with deficient TGF-β signaling (e.g. Bacteroides dorei: WT basal vs WT-CRC vs WT-CRC-5FU: 0.28±0.08 vs 0.05 ±0.01 vs 0.45±0.17; SKO basal vs SKO CRC vs SKO-CRC-5FU: 0.13±0.02 vs 0.06 ±0.007 vs 0.07±0.01).

Conclusions: Our study identified unique gut microbiome species corresponding to 5FU resistance through interactions with key immune-related pathways such as TGF-β members that are implicated in CRC development and drug resistance. The in vivo studies suggest a cell non-autonomous role of the TGF-β pathway in CRC chemoresistance. Collectively, the altered microbiome composition from impaired TGF-β signaling could promote colorectal carcinogenesis and drug resistance. Manipulating these specific species associated with 5FU could potentially increase drug response.

Citation Format: Shuyun Rao, Zhuanhuai Wang, Xiaochun Yang, Lindsay M. Hopson, Stephanie S. Singleton, Wilma Jogunoori, Raja Mazumder, Vincent Obias, Paul Lin, Bao-Ngoc Nguyen, Michael Yao, Larry Miller, Jon White, Lopa Mishra. Colorectal carcinogenesis in Smad4/SPTBN1 mutants alter the intestinal microbiome and resistance to 5FU [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1405.

Mice with dysfunctional TGF-β signaling develop altered intestinal microbiome and colorectal cancer resistant to 5FU

Emerging data show a rise in colorectal cancer (CRC) incidence in young men and women that is often chemoresistant. One potential risk factor is an alteration in the microbiome. Here, we investigated the role of TGF-β signaling on the intestinal microbiome and the efficacy of chemotherapy for CRC induced by azoxymethane and dextran sodium sulfate in mice. We used two genotypes of TGF-β-signaling-deficient mice (Smad4^+/- and Smad4^+/-Sptbn1^+/-), which developed CRC with similar phenotypes and had similar alterations in the intestinal microbiome. Using these mice, we evaluated the intestinal microbiome and determined the effect of dysfunctional TGF-β signaling on the response to the chemotherapeutic agent 5-Fluoro-uracil (5FU) after induction of CRC. Using shotgun metagenomic sequencing, we determined gut microbiota composition in mice with CRC and found reduced amounts of beneficial species of Bacteroides and Parabacteroides in the mutants compared to the wild-type (WT) mice. Furthermore, the mutant mice with CRC were resistant to 5FU. Whereas the abundances of E. boltae, B.dorei, Lachnoclostridium sp., and Mordavella sp. were significantly reduced in mice with CRC, these species only recovered to basal amounts after 5FU treatment in WT mice, suggesting that the alterations in the intestinal microbiome resulting from compromised TGF-β signaling impaired the response to 5FU. These findings could have implications for inhibiting the TGF-β pathway in the treatment of CRC or other cancers.

COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model

In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors—compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike.

Bioinformatics tools developed to support BioCompute Objects

Developments in high-throughput sequencing (HTS) result in an exponential increase in the amount of data generated by sequencing experiments, an increase in the complexity of bioinformatics analysis reporting and an increase in the types of data generated. These increases in volume, diversity and complexity of the data generated and their analysis expose the necessity of a structured and standardized reporting template. BioCompute Objects (BCOs) provide the requisite support for communication of HTS data analysis that includes support for workflow, as well as data, curation, accessibility and reproducibility of communication. BCOs standardize how researchers report provenance and the established verification and validation protocols used in workflows while also being robust enough to convey content integration or curation in knowledge bases. BCOs that encapsulate tools, platforms, datasets and workflows are FAIR (findable, accessible, interoperable and reusable) compliant. Providing operational workflow and data information facilitates interoperability between platforms and incorporation of future dataset within an HTS analysis for use within industrial, academic and regulatory settings. Cloud-based platforms, including High-performance Integrated Virtual Environment (HIVE), Cancer Genomics Cloud (CGC) and Galaxy, support BCO generation for users. Given the 100K+ userbase between these platforms, BioCompute can be leveraged for workflow documentation. In this paper, we report the availability of platform-dependent and platform-independent BCO tools: HIVE BCO App, CGC BCO App, Galaxy BCO API Extension and BCO Portal. Community engagement was utilized to evaluate tool efficacy. We demonstrate that these tools further advance BCO creation from text editing approaches used in earlier releases of the standard. Moreover, we demonstrate that integrating BCO generation within existing analysis platforms greatly streamlines BCO creation while capturing granular workflow details. We also demonstrate that the BCO tools described in the paper provide an approach to solve the long-standing challenge of standardizing workflow descriptions that are both human and machine readable while accommodating manual and automated curation with evidence tagging. Database URL:  https://www.biocomputeobject.org/resources.

GlyGen data model and processing workflow

Summary

Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources.

Availability and implementation

GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Supplementary information

Supplementary data are available at Bioinformatics online.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data

PURPOSE

The purpose of OncoMX¹ knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types.

METHODS

Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.

RESULTS

OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.

CONCLUSION

OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

Bioinformatics and machine learning in gastrointestinal microbiome research and clinical application

The scientific community currently defines the human microbiome as all the bacteria, viruses, fungi, archaea, and eukaryotes that occupy the human body. When considering the variable locations, composition, diversity, and abundance of our microbial symbionts, the sheer volume of microorganisms reaches hundreds of trillions. With the onset of next generation sequencing (NGS), also known as high-throughput sequencing (HTS) technologies, the barriers to studying the human microbiome lowered significantly, making in-depth microbiome research accessible. Certain locations on the human body, such as the gastrointestinal, oral, nasal, and skin microbiomes have been heavily studied through community-focused projects like the Human Microbiome Project (HMP). In particular, the gastrointestinal microbiome (GM) has received significant attention due to links to neurological, immunological, and metabolic diseases, as well as cancer. Though HTS technologies allow deeper exploration of the GM, data informing the functional characteristics of microbiota and resulting effects on human function or disease are still sparse. This void is compounded by microbiome variability observed among humans through factors like genetics, environment, diet, metabolic activity, and even exercise; making GM research inherently difficult to study. This chapter describes an interdisciplinary approach to GM research with the goal of mitigating the hindrances of translating findings into a clinical setting. By applying tools and knowledge from microbiology, metagenomics, bioinformatics, machine learning, predictive modeling, and clinical study data from children with treatment-resistant epilepsy, we describe a proof-of-concept approach to clinical translation and precision application of GM research.

Scanning window analysis of non-coding regions within normal-tumor whole-genome sequence samples

Genomics has benefited from an explosion in affordable high-throughput technology for whole-genome sequencing. The regulatory and functional aspects in non-coding regions may be an important contributor to oncogenesis. Whole-genome tumor-normal paired alignments were used to examine the non-coding regions in five cancer types and two races. Both a sliding window and a binning strategy were introduced to uncover areas of higher than expected variation for additional study. We show that the majority of cancer associated mutations in 154 whole-genome sequences covering breast invasive carcinoma, colon adenocarcinoma, kidney renal papillary cell carcinoma, lung adenocarcinoma and uterine corpus endometrial carcinoma cancers and two races are found outside of the coding region (4 432 885 in non-gene regions versus 1 412 731 in gene regions). A pan-cancer analysis found significantly mutated windows (292 to 3881 in count) demonstrating that there are significant numbers of large mutated regions in the non-coding genome. The 59 significantly mutated windows were found in all studied races and cancers. These offer 16 regions ripe for additional study within 12 different chromosomes-2, 4, 5, 7, 10, 11, 16, 18, 20, 21 and X. Many of these regions were found in centromeric locations. The X chromosome had the largest set of universal windows that cluster almost exclusively in Xq11.1-an area linked to chromosomal instability and oncogenesis. Large consecutive clusters (super windows) were found (19 to 114 in count) providing further evidence that large mutated regions in the genome are influencing cancer development. We show remarkable similarity in highly mutated non-coding regions across both cancer and race.

COVID-19 Biomarkers in research: Extension of the OncoMX cancer biomarker data model to capture biomarker data from other diseases

Scientists, medical researchers, and health care workers have mobilized worldwide in response to the outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SCoV2). Preliminary data have captured a wide range of host responses, symptoms, and lingering problems post-recovery within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, co-morbidities, genetics, and other factors. As COVID-19-related data continue to accumulate from disparate groups, the heterogeneous nature of these datasets poses challenges for efficient extrapolation of meaningful observations, hindering translation of information into clinical applications. Attempts to utilize, analyze, or combine biomarker datasets from multiple sources have shown to be inefficient and complicated, without a unifying resource. As such, there is an urgent need within the research community for the rapid development of an integrated and harmonized COVID-19 Biomarker Knowledgebase. By leveraging data collection and integration methods, backed by a robust data model developed to capture cancer biomarker data we have rapidly crowdsourced the collection and harmonization of COVID-19 biomarkers. Our resource currently has 138 unique biomarkers. We found multiple instances of the same biomarker substance being suggested as multiple biomarker types during our extensive cross-validation and manual curation. As a result, our Knowledgebase currently has 265 biomarker type combinations. Every biomarker entry is made comprehensive by bringing in together ancillary data from multiple sources such as biomarker accessions (canonical UniProtKB accession, PubChem Compound ID, Cell Ontology ID, Protein Ontology ID, NCI Thesaurus Code, and Disease Ontology ID), BEST biomarker category, and specimen type (Uberon Anatomy Ontology) unified with ontology standards. Our preliminary observations show distinct trends in the collated biomarkers. Most biomarkers are related to the immune system (SAA,TNF-∝, and IP-10) or coagulopathies (D-dimer, antithrombin, and VWF) and a few have already been established as cancer biomarkers (ACE2, IL-6, IL-4 and IL-2). These trends align with proposed hypotheses of clinical manifestations compounding the complexity of COVID-19 pathobiology. We explore these trends as we put forth a COVID-19 biomarker resource that will help researchers and diagnosticians alike. All biomarker data are freely available from https://data.oncomx.org/covid19 .

AB1178 AN AUDIT OF ORIGINATOR ADALIMUMAB TO BIOSIMILAR SWITCH IN TWO HOSPITALS

Background:

Biological drugs have revolutionized the treatment of immune-mediated inflammatory diseases (IMIDs). Current guidelines reserve these drugs for patients with severe refractory disease.

Biologic drugs are expensive, but as they reach patent expiry, the introduction of lower-cost biosimilars reduces their impact on health care budgets. It is estimated that NHS England could save £300 million by 2021 following the recent launch of adalimumab biosimilars [1]. As part of this process, there has been a mandatory switch of originator adalimumab to biosimilar adalimumab throughout the U.K.

Objectives:

To evaluate the impact of the switch to biosimilar adalimumab in individuals with inflammatory arthritis at two NHS trusts in the East of England and calculate the proportion and reasons for switch back to originator adalimumab or a second biosimilar at 12 weeks.

Methods:

Both hospitals ran dedicated ‘switch’ clinics. All patient records were reviewed retrospectively.

Results:

855 patients with different IMID switched from originator to biosimilar over 13 months. At 12 weeks, 730 patients (85%) maintained the switch, 71 patients (8.7%) switched back to the originator, and 54 patients (6.3%) switched to other biosimilars of the same drug.

Table 1.

Primary outcome analysis of switching from originator to adalimumab biosimilar

DiagnosisTotal patient switched from originatorAverage duration (year) of use of originator before bio switch (for patients continue using bio switch)Total patients continuing (At 12 weeks)Average duration (year) of use of originator before bio switch (for patients switched back to originator)Total patients switched back to originator or other biosimilarRheumatoid Arthritis3567.9314 (88%)4.942 (12%)Axial Spondyloarthritis2606.4213 (82%)4.547 (18%)Psoriatic Arthritis2185.9187 (86%)2.931 (14%)Juvenile Arthritis163.714 (88%)4.52 (12%)Others52.22 (40%)0.83 (60%)Total8557.0730 (85%)4.2125 (15%)Table 2.

Reasons for back to originator or another biosimilar

Reasons for back to originator or another biosimilarNumber back for IntoleranceNumber back for InefficacyPainful injection69BASDAI/Spinal Pain13Pain/Others19TJC, SJC, VAS4Rash/Allergic reaction5DAS3Headache5PsARC2Nausea4No Detail1Total102Total23%82%18%

Conclusion:

Switching to a biosimilar was successful in the vast majority of patients and is associated with significant saving. The list prices for originator Adalimumab is £9,155/person/year and £8,238/person/year for biosimilar Adalimumab respectively [2]. By switching we will save approximately £719,402 per annum (9.2% cost reduction).

References:

[1]NHS England. NHS set to save record £300 million on the NHS’s highest drug spend 2018 [cited 2018 November 30].https://www.england.nhs.uk/2018/11/nhs-set-to-save-record-300-million-on-the-nhss-highest-drug-spend/

[2]https://bnf.nice.org.uk/medicinal-forms/adalimumab.html

Disclosure of Interests:

Rifat Mazumder: None declared, Marianne Loke: None declared, Chetan Mukhtyar: None declared, Karl Gaffney Grant/research support from: AbbVie, Celgene, MSD, Novartis, Pfizer, and UCB Pharma, Consultant of: AbbVie, Celgene, MSD, Novartis, Pfizer, and UCB Pharma, Speakers bureau: AbbVie, Celgene, MSD, Novartis, Pfizer, and UCB Pharma, Emese Balogh: None declared, Emerald Sekaran: None declared, Mushfika Sultana: None declared, Mabel Odonkor: None declared, Karen Miles: None declared

Mutated CEACAMs Disrupt Transforming Growth Factor Beta Signaling and Alter the Intestinal Microbiome to Promote Colorectal Carcinogenesis

BACKGROUND & AIMS

We studied interactions among proteins of the carcinoembryonic antigen-related cell adhesion molecule (CEACAM) family, which interact with microbes, and transforming growth factor beta (TGFB) signaling pathway, which is often altered in colorectal cancer cells. We investigated mechanisms by which CEACAM proteins inhibit TGFB signaling and alter the intestinal microbiome to promote colorectal carcinogenesis.

METHODS

We collected data on DNA sequences, messenger RNA expression levels, and patient survival times from 456 colorectal adenocarcinoma cases, and a separate set of 594 samples of colorectal adenocarcinomas, in The Cancer Genome Atlas. We performed shotgun metagenomic sequencing analyses of feces from wild-type mice and mice with defects in TGFB signaling (Sptbn1+/- and Smad4+/-/Sptbn1+/-) to identify changes in microbiota composition before development of colon tumors. CEACAM protein and its mutants were overexpressed in SW480 and HCT116 colorectal cancer cell lines, which were analyzed by immunoblotting and proliferation and colony formation assays.

RESULTS

In colorectal adenocarcinomas, high expression levels of genes encoding CEACAM proteins, especially CEACAM5, were associated with reduced survival times of patients. There was an inverse correlation between expression of CEACAM genes and expression of TGFB pathway genes (TGFBR1, TGFBR2, and SMAD3). In colorectal adenocarcinomas, we also found an inverse correlation between expression of genes in the TGFB signaling pathway and genes that regulate stem cell features of cells. We found mutations encoding L640I and A643T in the B3 domain of human CEACAM5 in colorectal adenocarcinomas; structural studies indicated that these mutations would alter the interaction between CEACAM5 and TGFBR1. Overexpression of these mutants in SW480 and HCT116 colorectal cancer cell lines increased their anchorage-independent growth and inhibited TGFB signaling to a greater extent than overexpression of wild-type CEACAM5, indicating that they are gain-of-function mutations. Compared with feces from wild-type mice, feces from mice with defects in TGFB signaling had increased abundance of bacterial species that have been associated with the development of colon tumors, including Clostridium septicum, and decreased amounts of beneficial bacteria, such as Bacteroides vulgatus and Parabacteroides distasonis.

CONCLUSION

We found expression of CEACAMs and genes that regulate stem cell features of cells to be increased in colorectal adenocarcinomas and inversely correlated with expression of TGFB pathway genes. We found colorectal adenocarcinomas to express mutant forms of CEACAM5 that inhibit TGFB signaling and increase proliferation and colony formation. We propose that CEACAM proteins disrupt TGFB signaling, which alters the composition of the intestinal microbiome to promote colorectal carcinogenesis.

Nodding syndrome phenotypes

Nodding syndrome (NS) is a progressive encephalopathy of children and adolescents characterized by seizures, including periodic vertical head nodding. Epidemic NS, which has affected parts of East Africa, appears to have clinical overlap with sub-Saharan Nakalanga syndrome (NLS), a brain disorder associated with pituitary dwarfism that appears to have a patchy distribution across sub-Sahara. Clinical stages of NS include inattention and blank stares, vertical head nodding, convulsive seizures, multiple impairments, and severe cognitive and motorsystem disability, including features suggesting parkinsonism. Head nodding episodes occur in clusters with an electrographic correlate of diffuse high-amplitude slow waves followed by an electrodecremental pattern with superimposed diffuse fast activity. Brain imaging reveals differing degrees of cerebral cortical and cerebellar atrophy. Brains of NS-affected children with mild frontotemporal cortical atrophy display neurofibrillary pathology and dystrophic neurites immunopositive for tau, consistent with a progressive neurodegenerative disorder. The etiology of NS and NLS appears to be dominated by environmental factors, including malnutrition, displacement, and nematode infection, but the specific cause is unknown.

Baseline human gut microbiota profile in healthy people and standard reporting template

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual’s microbiome to the growing knowledgebase of “normal” microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI’s Short Read Archive.

Abstract 2827: Identification of chemotherapeutic resistant microbiota profile in colorectal cancer

Background: Emerging data shows a rise in CRC incidence in men and women under 55 years of age, with a potential new risk factors including alterations in the microbiome. While recent advances have significantly reduced CRC morbidity and mortality, such as screening and new therapeutic strategies, how the microbiome modulates CRC chemotherapy response remains understudied. This alarming trend presents an urgent need for further understanding of the molecular mechanisms and “signatures” of the gut microbiome in CRC patients. Key driving pathways in CRC include WNT, RAS, RAF, APC and TGF-β signaling. Modulating the gut immune response includes multiple mechanisms, including sensors through carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) that serve as receptors to microbes, and crosstalk with signaling pathways such as TGF-β signaling pathway. Interestingly, disruption of TGF-β leads to increased sensitivity to cisplatin and other DNA cross-linking agents.

Hypothesis and Aims: Our hypothesis is that the gut microbiome plays a key role in modulating the TGF-β pathway by CEACAMs binding to specific microbial species, thereby impeding the TGF-β pathway and altering chemotherapy response. Our aims are to explore TCGA genomics, and functional interactions between gut microbiota, CEACAMs, and TGF-β signaling in processing chemotherapy response.

Methods and Results: We first analyzed microbiome signatures in normal and CRC patients, and identified a signature pattern. TCGA analyses in 9,125 human cancers reveal alterations of TGF-β with CEACAM members in over 40% of CRC and correlate with a cancer stem cell signature. Mechanistic studies reveal that CEACAMs inhibit TGF-β tumor suppressor signaling by blocking TGFBR1, and promoting invasive CRC. Likewise, our TGF-β signaling-deficient mouse models spontaneously develop CRC, as well as metastases, likely modulated in part by the microbiome. Furthermore, key defects in the TGF-β pathway alter sensitivity to 5-FU and DNA cross-linking agents, such as cisplatin, through disruption of DNA repair pathways.

Conclusion: The TGF-β pathway is important in maintaining colon epithelial cell homeostasis potentially through interactions with CEACAMs and gut microbiota. When the pathway is disrupted, epithelial cells are more susceptible to transformation and invasion, potentially identifying specific populations that are more sensitive to chemotherapy such as cisplatin and 5FU.

Citation Format: Shuyun Rao, Charles Hadley King, Raja Mazumder, Shoujun Gu, Richard Amdur, Bibhuti Mishra, Lopa Mishra. Identification of chemotherapeutic resistant microbiota profile in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2827.

Abstract 3156: Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer

In its early stages, HCC is curable; once advanced, HCC is associated with high mortality. HCC is associated with alterations in key driving pathways, including the Wnt pathway, TGF-β pathway, the p53 pathway, and pathways that regulate Myc activity. Members of the TGF-β superfamily regulate liver inflammation and can have tumor-suppressing or tumor-promoting activities that have been demonstrated to play key roles in liver and GI cancers through mouse models and human genomics. Yet, specific populations that are targetable with altered TGF-β remain unclear. Based upon our functional preclinical models and pilot studies in human liver disease samples, our hypothesis that aberrant expression of TGF-β pathways can lead to early detection of HCC and risk stratification. Methods and Results: Through preclinical studies that integrate analysis of human genomic data from The Cancer Genome Atlas (TCGA), mouse models, and human tissue/cell line studies, we sought to determine whether specific high-risk populations may be identified through a functional TGF-β pathway related biomarkers alterations. We observe the following: • Altered expression of TGF-β-Smad signaling genes is common in HCC (40%) • Somatic mutations in at least one gene encoding a member of the TGF-β-Smad pathway are common in HCC (38%) with the SMAD3 adaptor, β2SP (encoded by SPTBN1) having the highest frequency of mutation (6%), regardless of etiology, HBV or HCV infection or non-alcoholic steatohepatitis (NASH) • Disruption of the TGF-β-Smad pathway is associated with dysregulation of potentially targetable oncogenes that include MDM2, Telomerase, IGF2 and others. • TGF-β-Smad pathway activity clusters HCC patients into 4 groups. • HCC patients with a signature indicative of “inactivated” TGF-β-Smad signaling had shorter survival times than HCC patients with a signature indicative of “activated” TGF-β-Smad signaling reflecting a tumor suppressor role of TGF-β signaling in HCC. • TGF-β-Smad pathway activity correlates with genes in the DNA damage response and sirtuins. • Analysis of liver samples from normal subjects, patients with alcohol-induced cirrhosis, and alcohol-associated HCC showed that in the cirrhotic tissue TGF-β-Smad3 signaling and FANCD2 were highest and that in the HCC tissue these were lowest, suggesting a loss of this tumor-suppressing pathway in HCC • Mice deficient in either sirtuin activity (Sirt6-/-) or compromised TGF-β signaling (Sptbn+/-/Smad3+/-) are susceptible to liver injury, steatosis and spontaneously develop HCC • TGF-β-Smad pathway activity correlates with genes associated with hepatic fibrosis, immune cells, and the tumor microenvironment. Conclusions: Our data indicate that TGF-β-Smad members, particularly Smad3 and β2SP, and markers of DNA repair processes, particularly FANCD2 are potentially useful biomarkers of the loss from tumor-suppression by TGF-β signaling, which may be an early indicator of HCC.

Citation Format: Kirti Shetty, Chu-Xia Deng, Wilma S. Jogunoori, Richard Amdur, Linda S. Resar, Patricia Latham, Raja Mazumder, Anelia Horvath, Bao-Ngoc Nguyen, Shulin Li, Xifeng Wu, Herbert Yu, Linda L. Wong, Jon White, Sylvia Silver, Asif Rashid, Vikas Kundra, Xin Wei Wang, Lopa Mishra. Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3156.

Abstract 2463: Prototype open-access biomarker knowledgebase for genetic tests for breast cancer

Cancer biomarkers have become integral components of many clinical protocols and research studies. Nonetheless there still is an urgent need for more accurate, less invasive, and cost-effective biomarker tests to advance precision oncology. Despite considerable success in the preclinical setting, most candidate biomarker tests have yet to receive regulatory clearance or approval and gain market acceptance. To better understand the challenges facing modern biomarker research and facilitate access to biomarker-related information, this study aims to delineate the paths to approval of genetic tests for breast cancer. Public databases were searched to identify approved tests and respective biomarkers. A prototype format for unified biomarker information was developed iteratively due to the highly variable presentation of this data. A knowledgebase of FDA-approved genetic biomarkers for breast cancer was built as a pilot implementation of this format. This model is extensible to a larger database of biomarkers to be hosted in OncoMX (https://oncomx.org), which integrates cancer mutation, differential expression, literature mining, and pathway information. Fields in the prototype database were populated based on findings from the FDA website, PubMed, and other open-source databases. The resulting dataset is structured with the corresponding readme following the BioCompute Object (BCO) model. This BCO-compliant approach enables provenance capture and transparency of data processing, both of which are critical to large-scale data integration efforts. The adherence to BCO standards is expected to enhance the usability of the resulting data and to streamline the subsequent integration of multiple biomarker datasets. In the rapidly evolving field of oncologic biomarker research, this open-source biomarker knowledgebase offers clinicians and researchers a streamlined access to genetic tests information including corresponding genes, guides to evidence, clinical trial data, links to archival databases, and context for the behavior of the implicated gene in other disease and normal processes.

Citation Format: Dara Baker, Natalie Fedorova Abrams, Amanda Bell, Maureen Colbert, Hayley Dingerdissen, Evan Holmes, Samir Gupta, Robel Kahsay, Heather Kincaid, David Liu, A. S. M. Ashique Mahmood, Frédéric B. Bastian, Marc Robinson-Rechavi, Elena Schwartz, K. Vijay-Shanker, Daniel Crichton, Raja Mazumder. Prototype open-access biomarker knowledgebase for genetic tests for breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2463.

miCloud: A Plug-n-Play, Extensible, On-Premises Bioinformatics Cloud for Seamless Execution of Complex Next-Generation Sequencing Data Analysis Pipelines

The availability of low-cost small-factor sequencers, such as the Illumina MiSeq, MiniSeq, or iSeq, have paved the way for democratizing genomics sequencing, providing researchers in minority universities with access to the technology that was previously only affordable by institutions with large core facilities. However, these instruments are not bundled with software for performing bioinformatics data analysis, and the data analysis can be the main bottleneck for independent laboratories or even small clinical facilities that consider adopting genomic sequencing for medical applications. To address this issue, we have developed miCloud, a bioinformatics platform that enables genomic data analysis through a fully featured data analysis cloud, which seamlessly integrates with genome sequencers over the local network. The miCloud can be easily deployed without any prior bioinformatics expertise on any computing environment, from a laboratory computer workstation to a university computer cluster. Our platform not only provides access to a set of preconfigured RNA-Seq and CHIP-Seq bioinformatics pipelines, but also enables users to develop or install new preconfigured tools from the large selection available on open-source online Docker container repositories. The miCloud built-in analysis pipelines are also integrated with the Visual Omics Explorer framework (Kim et al., 2016), which provides rich interactive visualizations and publication-ready graphics from the next-generation sequencing data. Ultimately, the miCloud demonstrates a bioinformatics approach that can be adopted in the field for standardizing genomic data analysis, similarly to the way molecular biology sample preparation kits have standardized laboratory operations.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.

DEXTER: Disease-Expression Relation Extraction from Text

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

Analysis of Genomes and Transcriptomes of Hepatocellular Carcinomas Identifies Mutations and Gene Expression Changes in the Transforming Growth Factor-β Pathway

BACKGROUND & AIMS

Development of hepatocellular carcinoma (HCC) is associated with alterations in the transforming growth factor-beta (TGF-β) signaling pathway, which regulates liver inflammation and can have tumor suppressor or promoter activities. Little is known about the roles of specific members of this pathway at specific of HCC development. We took an integrated approach to identify and validate the effects of changes in this pathway in HCC and identify therapeutic targets.

METHODS

We performed transcriptome analyses for a total of 488 HCCs that include data from The Cancer Genome Atlas. We also screened 301 HCCs reported in the Catalogue of Somatic Mutations in Cancer and 202 from Cancer Genome Atlas for mutations in genome sequences. We expressed mutant forms of spectrin beta, non-erythrocytic 1 (SPTBN1) in HepG2, SNU398, and SNU475 cells and measured phosphorylation, nuclear translocation, and transcriptional activity of SMAD family member 3 (SMAD3).

RESULTS

We found somatic mutations in at least 1 gene whose product is a member of TGF-β signaling pathway in 38% of HCC samples. SPTBN1 was mutated in the largest proportion of samples (12 of 202, 6%). Unsupervised clustering of transcriptome data identified a group of HCCs with activation of the TGF-β signaling pathway (increased transcription of genes in the pathway) and a group of HCCs with inactivation of TGF-β signaling (reduced expression of genes in this pathway). Patients with tumors with inactivation of TGF-β signaling had shorter survival times than patients with tumors with activation of TGF-β signaling (P = .0129). Patterns of TGF-β signaling correlated with activation of the DNA damage response and sirtuin signaling pathways. HepG2, SNU398, and SNU475 cells that expressed the D1089Y mutant or with knockdown of SPTBN1 had increased sensitivity to DNA crosslinking agents and reduced survival compared with cells that expressed normal SPTBN1 (controls).

CONCLUSIONS

In genome and transcriptome analyses of HCC samples, we found mutations in genes in the TGF-β signaling pathway in almost 40% of samples. These correlated with changes in expression of genes in the pathways; up-regulation of genes in this pathway would contribute to inflammation and fibrosis, whereas down-regulation would indicate loss of TGF-β tumor suppressor activity. Our findings indicate that therapeutic agents for HCCs can be effective, based on genetic features of the TGF-β pathway; agents that block TGF-β should be used only in patients with specific types of HCCs.

Separation and assembly of deep sequencing data into discrete sub-population genomes

Sequence heterogeneity is a common characteristic of RNA viruses that is often referred to as sub-populations or quasispecies. Traditional techniques used for assembly of short sequence reads produced by deep sequencing, such as de-novo assemblers, ignore the underlying diversity. Here, we introduce a novel algorithm that simultaneously assembles discrete sequences of multiple genomes present in populations. Using in silico data we were able to detect populations at as low as 0.1% frequency with complete global genome reconstruction and in a single sample detected 16 resolved sequences with no mismatches. We also applied the algorithm to high throughput sequencing data obtained for viruses present in sewage samples and successfully detected multiple sub-populations and recombination events in these diverse mixtures. High sensitivity of the algorithm also enables genomic analysis of heterogeneous pathogen genomes from patient samples and accurate detection of intra-host diversity, enabling not just basic research in personalized medicine but also accurate diagnostics and monitoring drug therapies, which are critical in clinical and regulatory decision-making process.

Pubcast and Genecast: Browsing and Exploring Publications and Associated Curated Content in Biology Through Mobile Devices

Services such as Facebook, Amazon, and eBay were once solely accessed from stationary computers. These web services are now being used increasingly on mobile devices. We acknowledge this new reality by providing users a way to access publications and a curated cancer mutation database on their mobile device with daily automated updates.

AVAILABILITY

http://hive. biochemistry.gwu.edu/tools/HivePubcast.

Distribution bias analysis of germline and somatic single-nucleotide variations that impact protein functional site and neighboring amino acids

Single nucleotide variations (SNVs) can result in loss or gain of protein functional sites. We analyzed the effects of SNVs on enzyme active sites, ligand binding sites, and various types of post translational modification (PTM) sites. We found that, for most types of protein functional sites, the SNV pattern differs between germline and somatic mutations as well as between synonymous and non-synonymous mutations. From a total of 51,138 protein functional site affecting SNVs (pfsSNVs), a pan-cancer analysis revealed 142 somatic pfsSNVs in five or more cancer types. By leveraging patient information for somatic pfsSNVs, we identified 17 loss of functional site SNVs and 60 gain of functional site SNVs which are significantly enriched in patients with specific cancer types. Of the key pfsSNVs identified in our analysis above, we highlight 132 key pfsSNVs within 17 genes that are found in well-established cancer associated gene lists. For illustrating how key pfsSNVs can be prioritized further, we provide a use case where we performed survival analysis showing that a loss of phosphorylation site pfsSNV at position 105 in MEF2A is significantly associated with decreased pancreatic cancer patient survival rate. These 132 pfsSNVs can be used in developing genetic testing pipelines.

Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations

The unpredictability of actual physical, chemical, and biological experiments due to the multitude of environmental and procedural factors is well documented. What is systematically overlooked, however, is that computational biology algorithms are also affected by multiplicity of parameters and have no lesser volatility. The complexities of computation protocols and interpretation of outcomes is only a part of the challenge: There are also virtually no standardized and industry-accepted metadata schemas for reporting the computational objects that record the parameters used for computations together with the results of computations. Thus, it is often impossible to reproduce the results of a previously performed computation due to missing information on parameters, versions, arguments, conditions, and procedures of application launch. In this article we describe the concept of biocompute objects developed specifically to satisfy regulatory research needs for evaluation, validation, and verification of bioinformatics pipelines. We envision generalized versions of biocompute objects called biocompute templates that support a single class of analyses but can be adapted to meet unique needs. To make these templates widely usable, we outline a simple but powerful cross-platform implementation. We also discuss the reasoning and potential usability for such concept within the larger scientific community through the creation of a biocompute object database initially consisting of records relevant to the U.S. Food and Drug Administration. A biocompute object database record will be similar to a GenBank record in form; the difference being that instead of describing a sequence, the biocompute record will include information related to parameters, dependencies, usage, and other information related to specific computational instance. This mechanism will extend similar efforts and also serve as a collaborative ground to ensure interoperability between different platforms, industries, scientists, regulators, and other stakeholders interested in biocomputing.

DiMeX: A Text Mining System for Mutation-Disease Association Extraction

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.

The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed.

Database URL: https://hive.biochemistry.gwu.edu

Computational clustering for viral reference proteomes

MOTIVATION

The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies.

RESULTS

We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs.

AVAILABILITY AND IMPLEMENTATION

http://proteininformationresource.org/rps/viruses/

CONTACT

chenc@udel.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract 4875: HIVE Proteomics: Integrated, cloud-based RNA-Seq and proteomics analysis of prostate adenocarcinoma samples

Automated bottom-up proteomics workflows implemented with modern mass-spectrometry instrumentation can readily generate millions of peptide fragmentation spectra from cell-lines and clinically derived samples. The tandem mass-spectra promise to reveal wild-type and somatic mutations, insertions, and deletions and alternatively spliced isoforms translated to functional protein isoforms in tumor samples. However, the available reference proteomes represent a poor analysis substrate for the observation of protein evidence of genomic and transcript variation. The advent of cheap and fast RNA sequencing (RNA-Seq) provides an elegant solution to the lack of sample-specific reference proteomes. We anticipate that the falling cost of RNA-Seq will prompt an increasing number of proteomics labs to use contract next-gen sequencing (NGS) services to obtain RNA-Seq data to derive sample-specific reference proteomes. In contrast to public repositories of genomic variation, sample-specific RNA-Seq data captures transcribed rare, individual, cell-type, and sample-specific genomic variation. RNA-Seq-based transcripts also provide sample-specific information on observable proteins. Furthermore, paired RNA-Seq and proteomics data links gene expression and protein abundance, enabling the study of gene regulation linked to protein abundance dynamics. However, the analysis of multi-gigabyte paired mass-spectra and RNA-Seq datasets pose significant scientific and logistical challenges. Few proteomics labs have the personnel, archival data storage, computational resources, or informatics pipelines needed. The cloud-based genomics analysis platform High-performance Integrated Virtual Environment (HIVE) will provide turn-key integrated proteomics and RNA-Seq analyses to the wider proteomics community in a secure, trackable, sharable, and scalable computing platform. We will then use this integrated proteomics_RNA-Seq analysis pipeline to identify high-value mutations in prostate adenocarcinoma samples, thereby demonstrating the utility of the platform while also generating key data for future investigations.

Citation Format: Hayley Dingerdissen, Raja Mazumder. HIVE Proteomics: Integrated, cloud-based RNA-Seq and proteomics analysis of prostate adenocarcinoma samples. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4875. doi:10.1158/1538-7445.AM2015-4875