The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Cancer immunotherapy has greatly improved the quality of life of cancer patients and it hinges on the discovery of novel cancer antigens that could be targeted to improve disease outcomes. The creation of databases such as IEDB, SysteMHC, TANTIGEN, caAtlas, HLA Ligand Atlas, Cancer Antigenic Peptide Database, SPENCER and IEAtlas support the immunopeptidomics community in understanding the landscape of antigen presentation. We have developed a pan-cancer, pan-HLA, and pan-tissue database containing immunopeptidomics data mapped to transcriptomic, genomic, immunological and biochemical data. The database was generated from 80 different publicly available immunopeptidomics mass spectrometry datasets collected between 2015-2022 (76 cancer and 4 normal datasets), covering 15 different types of cancers and 152 different HLA-I alleles. The peptides contained in our database were obtained by a combination of closed, open and de novo searches using an in-house developed computational pipeline. Following rigorous false discovery rate estimation at 1% and a second-round search to eliminate any false signals that may not have been detected in the previous round of FDR estimation, we obtained a list of 11.2 million peptide-HLA combinations comprising both coding and non-coding regions of the genome as well as bacterial peptides. These peptides have been mapped to chromosomal coordinates to facilitate adoption by the genomics community of this useful resource on antigen presentation. Pathway/biochemical analysis of each peptide was performed using the rWikiPathways package. Finally, mutations associated with each peptide were annotated using COSMIC and dbSNP resources. Our database includes a FAIR knowledge graph which contextualizes and enriches the data to enable clinicians to take effective therapeutic decisions on the appropriate form of treatment for cancer immunotherapy with the case study of clear cell renal cell carcinoma (ccRCC). We will continue to expand our database with new data over the next two years and expand the scope of its applications to facilitate uptake by the larger scientific community. Citation Format: Ashwin Adrian Kallor, Michał Waleron, Georges Bedran, Patrícia Eugénio, Catia Pesquita, Daniel Faria, Fabio Massimo Zanzotto, Christophe Battail, Ajitha Rajan, Javier Alfaro. CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6577.