MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease statuses. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

Survival analysis of posterior composite restorations in National Dental PBRN general dentistry practices

OBJECTIVE

Quantify the survival of posterior composite restorations (PCR) placed during the study period in permanent teeth in United States (US) general dental community practices and factors predictive of that survival.

METHODS

A retrospective cohort study was conducted utilizing de-identified electronic dental record (EDR) data of patients who received a PCR in 99 general dentistry practices in the National Dental Practice-Based Research Network (Network). The final analyzed data set included 700,885 PCRs from 200,988 patients. Descriptive statistics and Kaplan Meier (product limit) estimator were performed to estimate the survival rate (defined as the PCR not receiving any subsequent treatment) after the first PCR was observed in the EDR during the study time. The Cox proportional hazards model was done to account for patient- and tooth-specific covariates.

RESULTS

The overall median survival time was 13.3 years. The annual failure rates were 4.5-5.8 % for years 1-5; 5.3-5.7 %, 4.9-5.5 %, and 3.3-5.2 % for years 6-10, 11-15, and 16-20, respectively. The failure descriptions recorded for < 7 % failures were mostly caries (54 %) and broken or fractured tooth/restorations (23 %). The following variables significantly predicted PCR survival: number of surfaces that comprised the PCR; having at least one interproximal surface; tooth type; type of prior treatment received on the tooth; Network region; patient age and sex. Based on the magnitude of the multivariable estimates, no single factor predominated.

CONCLUSIONS

This study of Network practices geographically distributed across the US observed PCR survival rates and predictive factors comparable to studies done in academic settings and outside the US.

CLINICAL SIGNIFICANCE

Specific baseline factors significantly predict the survival of PCRs done in US community dental practices.

Aligning Standards Communities for Omics Biodiversity Data: Sustainable Darwin Core-MIxS Interoperability

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

Improving the Quality and Utility of Electronic Health Record Data through Ontologies

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors' rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs.

MRIO: The Magnetic Resonance Imaging Acquisition and Analysis Ontology

Objective

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition.

Materials and Methods

To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Results

MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types.

Discussion

MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principals and has contributed several terms to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains.

Conclusion

MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond

BACKGROUND

Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery.

METHODS AND FINDINGS

ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS).

CONCLUSIONS

We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

Background

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020.

Results

As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment.

Conclusion

CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13326-022-00279-z.

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community. Database URL: https://github.com/INCATools/ontology-development-kit.

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

A community effort for COVID-19 Ontology Harmonization

Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge.

Challenges in Bioinformatics Workflows for Processing Microbiome Omics Data at Scale

The nascent field of microbiome science is transitioning from a descriptive approach of cataloging taxa and functions present in an environment to applying multi-omics methods to investigate microbiome dynamics and function. A large number of new tools and algorithms have been designed and used for very specific purposes on samples collected by individual investigators or groups. While these developments have been quite instructive, the ability to compare microbiome data generated by many groups of researchers is impeded by the lack of standardized application of bioinformatics methods. Additionally, there are few examples of broad bioinformatics workflows that can process metagenome, metatranscriptome, metaproteome and metabolomic data at scale, and no central hub that allows processing, or provides varied omics data that are findable, accessible, interoperable and reusable (FAIR). Here, we review some of the challenges that exist in analyzing omics data within the microbiome research sphere, and provide context on how the National Microbiome Data Collaborative has adopted a standardized and open access approach to address such challenges.

OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies

Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR).

Database URL http://obofoundry.org/

Structuring, reuse and analysis of electronic dental data using the Oral Health and Disease Ontology

Background

A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines’ EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe.

To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems.

The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework.

Results

The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice’s EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance – a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies.

Conclusions

This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

Leveraging Electronic Dental Record Data for Clinical Research in the National Dental PBRN Practices

OBJECTIVES

The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR).

METHODS

Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location.

RESULTS

Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset.

CONCLUSION

This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice.

Daily Time of Radiation Treatment Is Associated with Subsequent Oral Mucositis Severity during Radiotherapy in Head and Neck Cancer Patients

BACKGROUND

Limited treatment options are available for oral mucositis, a common, debilitating complication of cancer therapy. We examined the association between daily delivery time of radiotherapy and the severity of oral mucositis in patients with head and neck cancer.

METHODS

We used electronic medical records of 190 patients with head and neck squamous cell carcinoma who completed radiotherapy, with or without concurrent chemotherapy, at Roswell Park Comprehensive Cancer Center (Buffalo, NY) between 2015 and 2017. Throughout a 7-week treatment course, patient mouth and throat soreness (MTS) was self-reported weekly using a validated oral mucositis questionnaire, with responses 0 (no) to 4 (extreme). Average treatment times from day 1 until the day before each mucositis survey were categorized into seven groups. Multivariable-adjusted marginal average scores (LSmeans) were estimated for the repeated- and maximum-MTS, using a linear-mixed model and generalized-linear model, respectively.

RESULTS

Radiation treatment time was significantly associated with oral mucositis severity using both repeated-MTS (n = 1,156; P = 0.02) and maximum-MTS (n = 190; P = 0.04), with consistent patterns. The severity was lowest for patients treated during 8:30 to <9:30 am (LSmeans for maximum-MTS = 2.24; SE = 0.15), increased at later treatment times and peaked at early afternoon (11:30 am to <3:00 pm, LSmeans = 2.66-2.71; SEs = 0.16/0.17), and then decreased substantially after 3 pm.

CONCLUSIONS

We report a significant association between radiation treatment time and oral mucositis severity in patients with head and neck cancer.

IMPACT

Although additional studies are needed, these data suggest a potential simple treatment time solution to limit severity of oral mucositis during radiotherapy without increasing cost.

Abstract 4860: Association between timing of radiotherapy and severity of oral mucositis in head-neck cancer patients

Background: Oral mucositis is a very painful and debilitating complication of radiotherapy, occurring in nearly all head and neck cancer patients. It can severely affect quality of life and lead to treatment interruptions that subsequently impact clinical outcomes. With limited treatment options, there is an urgent need to develop new strategies for mucositis prevention.

Animal studies have shown that sensitivity to genotoxic stress strongly depends on the time of a day therapy is administered. Therefore, radiotherapy could be conducted at optimal times of a day associated with best tolerability, to reduce mucosal damage. This hypothesis is partially supported by two clinical trials that reported less severe oral mucositis in head and neck cancer patients treated in the morning vs. late afternoon; but results were not significant and times of a day not fully covered.

Methods: This study examined the associations between timing of radiotherapy and development of oral mucositis in 219 head neck cancer patients treated at Roswell Park Comprehensive Cancer Center, using data from Electronic Medical Records. Patients’ treatment time was consistent throughout a 6/7-week course, and mucositis survey was self-reported weekly during treatment, providing a unique resource for this study. The primary endpoint is “soreness quality score” (SQS), a five-level score “0-no, 1-a little, 2-moderate, 3-quite a lot, and 4-extreme” in assessing patients’ soreness severity for mouth and throat in the past 24 hours. We used a generalized linear model approach to assess the association between radiotherapy time and the maximum SQS (MSQS) reported during treatment, adjusting for age, smoking, treatment type, survey week and cancer site.

Results: Multivariate analyses revealed treatment time to be significantly associated with MSQS (p-value=0.025), with lowest MSQS (lsmean=2.26, ste=0.18) seen for patients treated in the early morning (8:30-&lt;9:30am). MSQS increased in patients treated at later times, peaking in the early afternoon (lsmean=2.92 (ste 0.21) and 2.84 (ste 0.18) for patients treated at 12:00-&lt;13:30pm and 13:30-&lt;15:00pm, respectively), then decreased for patients treated in late afternoon (lsmean=2.37, ste=0.25). Among patients treated between 8:30-&lt;9:30am, 43.2% developed severe oral mucositis (SQS grade 3 or 4), compared to 69.2% among those treated between 13:30-&lt;15:00pm.

Conclusion: To our knowledge, this is the first study to find a significant variation of oral mucositis severity by treatment hour of radiotherapy; the variation shows an arc shape with the peak in the early afternoon. Our results suggest that identifying an optimal time of a day for radiotherapy may substantially prevent severe oral mucositis in head and neck cancer patients. Further studies are worthwhile to confirm our findings, and to find optimal treatment times for individual patients.

Citation Format: Fangyi Gu, William D. Duncan, YingDong Feng, Austin Miller, Nicolas Schlecht, Alan Hutson, Anurag K. Singh. Association between timing of radiotherapy and severity of oral mucositis in head-neck cancer patients [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 4860.

An ontology for representing hematologic malignancies: the cancer cell ontology

BACKGROUND

Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.

RESULTS

We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies.

CONCLUSION

We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.

OSCI: standardized stem cell ontology representation and use cases for stem cell investigation

Background

Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively.

Results

To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI.

Conclusions

OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.

Routine surveillance scanning in HNSCC: Lung screening CT scans have value but head and neck scans do not

OBJECTIVES

To examine the utility of computed tomography (CT) imaging during routine surveillance for the detection of recurrent head and neck squamous cell carcinoma (HNSCC).

MATERIALS/METHODS

Clinical characteristics of HNSCC patients treated between 2008 and 2017 with radiation therapy or concurrent chemoradiation were abstracted from medical records. In patients who achieved a complete response to treatment by positron emission tomography scan, surveillance CT scans were conducted to the maxillofacial area, neck, and chest every 3 months in year 1, every 6 months in year 2, and every 12 months in years 3 and beyond.

RESULTS

Within the entire cohort (n = 534), complete response was achieved in 446 patients (83.5%); of these, 84 (15.7%) patients had a recurrence. Among the 84 patients with disease recurrence, 25 (30%) patients remained alive, of which 15 (18%) underwent successful salvage treatment and became free of disease. Lung screening CT scans detected failure in 8 of these successfully salvaged patients. Among the 8 patients successfully salvaged for locoregional recurrence, 3 failures were asymptomatic at onset and detected by laryngoscope or dental exam. The remaining 5 failures were symptomatic and detected upon work up prompted by symptoms. Maxillofacial and neck surveillance CT imaging failed to detect any successfully salvaged patients.

CONCLUSIONS

Routine surveillance for HNSCC patients with lung CT imaging had value but routine head and neck CT scans failed to identify any successfully salvaged patients. Given this finding, routine CT imaging surveillance in HNSCC patients should be restricted to annual lung screening with low-dose chest CT.

Protein Ontology (PRO): enhancing and scaling up the representation of protein entities

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.

The bisphosphonate zoledronic acid regulates key angiogenesis-related genes in primary human gingival fibroblasts

BACKGROUND

Osteonecrosis of the jaws is recognised as a serious complication for patients receiving bisphosphonates. The anti-angiogenic effects of bisphosphonates have been implicated in the pathogenesis of bisphosphonate-related osteonecrosis of the jaw (BRONJ). The purpose of this study was to determine the effects of zoledronic acid on cultured human gingival fibroblasts in relation to the modulation of genes associated with angiogenic regulation.

METHODS

Primary cultures of fibroblasts were developed from gingival tissues excised during crown-lengthening surgery from three patients. Cells were cultured with and without 30μM zoledronic acid for 6, 12 and 24h and cellular proliferation and migration investigated using CellTiter-Blue and scratch wound assays, respectively. Gene expression was determined using semi-quantitative PCR array technology that allowed the analysis of 84 pathway-focused genes known to be important in the regulation of angiogenesis.

RESULTS

Zoledronic acid increased the proliferation of the gingival fibroblasts in a dose dependent manner with 12 and 24h of exposure. Scratch wounding of the human gingival fibroblasts and treatment with increasing doses and time exposure to zoledronic acid (ZA) inhibited their migration. Statistically significant increases in gene expression were found for RHOB, VEGFA, CD55 and BMP2 (p≤0.05) in response to 30μM zoledronic acid. CCL2 and IL6 genes were significantly downregulated (p≤0.05).

CONCLUSIONS

The regulation of the prenylated protein RHOB in this study was consistent with the known effects of zoledronic acid on the mevalonate pathway. The down regulation of CCL2 and IL6 and the upregulation of CD55 may be associated with suppression of inflammation. An increase in VEGFA and BMP2 gene expression suggests that fibroblasts respond to zoledronic acid by producing a proangiogenic environment.

Radiological safety at a broad scope radioactive materials license: Texas A&M University

Operating a radiological safety program under a broad-scope license at a major research university introduces many elements not encountered by general or industrial licenses. This article outlines elements of the radiological safety program in place at Texas A&M University.

Electrical and optical measurements on the first SCUBA-2 prototype 1280 pixel submillimeter superconducting bolometer array

SCUBA-2 is a submillimeter camera being built for the James Clerk Maxwell Telescope in Hawaii. Bringing CCD style imaging to the submillimeter for the first time, with over 10000 pixels, it will provide a revolutionary improvement in sensitivity and mapping speed. We present results of the first tests on a prototype 1280 pixel SCUBA-2 subarray; the full instrument will be made up of eight such subarrays. The array is made up of transition edge sensor (TES) detectors, with Mo/Cu bilayers as the sensing element. To keep the number of wires reasonable, a multiplexed readout is used. Unlike previous TES arrays, an in-focal plane multiplexer configuration is used, in which the multiplexing elements are located beneath each pixel. To achieve the required performance, the detectors are operated at a temperature of approximately 120 mK. We describe the results of a basic electrical and optical characterization of the array, demonstrating that it is fully operational. Noise measurements were made on several pixels and gave a noise equivalent power below 2.5 x 10(-17) W HZ(-0.5), within the requirements for SCUBA-2. The construction of the testbed used to carry out these measurements is also described.