Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis

Abstract

Background: The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity of cancer vaccine data significantly impedes effective integration and analysis. While CanVaxKB serves as a pioneering database for over 670 manually annotated cancer vaccines, it is important to distinguish that a database, on its own, does not offer the structured relationships and standardized definitions found in an ontology. Recognizing this, we expanded the Vaccine Ontology (VO) to include those cancer vaccines present in CanVaxKB that were not initially covered, enhancing VO's capacity to systematically define and interrelate cancer vaccines.

Results: An ontology design pattern (ODP) was first developed and applied to semantically represent various cancer vaccines, capturing their associated entities and relations. By applying the ODP, we generated a cancer vaccine template in a tabular format and converted it into the RDF/OWL format for generation of cancer vaccine terms in the VO. '12MP vaccine' was used as an example of cancer vaccines to demonstrate the application of the ODP. VO also reuses reference ontology terms to represent entities such as cancer diseases and vaccine hosts. Description Logic (DL) and SPARQL query scripts were developed and used to query for cancer vaccines based on different vaccine's features and to demonstrate the versatility of the VO representation. Additionally, ontological modeling was applied to illustrate cancer vaccine related concepts and studies for in-depth cancer vaccine analysis. A cancer vaccine-specific VO view, referred to as "CVO," was generated, and it contains 928 classes including 704 cancer vaccines. The CVO OWL file is publicly available on: http://purl.obolibrary.org/obo/vo/cvo.owl , for sharing and applications.

Conclusion: To facilitate the standardization, integration, and analysis of cancer vaccine data, we expanded the Vaccine Ontology (VO) to systematically model and represent cancer vaccines. We also developed a pipeline to automate the inclusion of cancer vaccines and associated terms in the VO. This not only enriches the data's standardization and integration, but also leverages ontological modeling to deepen the analysis of cancer vaccine information, maximizing benefits for researchers and clinicians.

Availability: The VO-cancer GitHub website is: https://github.com/vaccineontology/VO/tree/master/CVO .

Keywords: CanVaxKB; Cancer vaccine; Ontology design pattern; Ontology modeling; Vaccine ontology.

Fig. 1

Cancer vaccine ontology design pattern (ODP). The pattern represented the collected cancer vaccine data (shown in box as VO classes) and the relations (as VO object properties) between them

Fig. 2

Ontological modeling of canvaxgens. The model illustrated the distinction between canvaxgen and antigen

Fig. 3

Protégé screenshot of a specific cancer vaccine in VO. The figure shows a specific cancer vaccine ‘12MP vaccine’ in VO that was generated based on ODP. It includes the term label, definition, definition source, status of vaccine development status in addition to logic axioms representing targeted organism and cancer type, vaccine platform, and vaccine administration route

Fig. 4

Protégé screenshot of a DL query in VO. The DL query is used to retrieve all the cancer vaccines that immunize against reproductive system cancer. The query is shown at the top right of the screenshot, while the results are shown at the bottom right

Fig. 5

Hierarchical classification of cancers and cancer vaccines for specific cancers. The cancer hierarchy was generated using OntoFox tool and visualized using Protégé, and the numbers next to the cancer types were obtained using DL query for VO

Fig. 6

Ontological modeling of cancer vaccine clinical trials. The model represented main processes in a clinical trial study and showed how cancer vaccine activity and efficacy were evaluated using RECIST criteria. The terms with prefix indicate the source of the ontologies. All ontologies used in the model are OBO Foundry ontologies. CTO: Clinical Trial Ontology, DOID: human Disease Ontology, IAO: Information Artifact Ontology, OBI: Ontology for Biomedical Investigations, and VO: Vaccine Ontology

Fig. 7

Occurrence of different patient outcome endpoints measurements of melanoma vaccines. The results generated from clinical trial endpoints analysis of 53 annotated melanoma cancer vaccine data. The endpoints were measured and categorized based on RECIST. The numbers located next to the bars indicated the frequency occurred for corresponding endpoint