=Paper=
{{Paper
|id=Vol-3939/short2
|storemode=property
|title=Semantic Representation of Preclinical Data in Radiation Oncology
|pdfUrl=https://ceur-ws.org/Vol-3939/short2.pdf
|volume=Vol-3939
|authors=Olga Giraldo,Abumansur Sabyrrakhim,Mareike Roscher,Rosemarie Euler-Lange,Michael Baumann,Ina Kurth,Wahyu Wijaya Hadiwikarta
|dblpUrl=https://dblp.org/rec/conf/icbo/GiraldoSRE0KH24
}}
==Semantic Representation of Preclinical Data in Radiation Oncology==
https://ceur-ws.org/Vol-3939/short2.pdf
Semantic Representation of Preclinical Data in
Radiation Oncology
Olga Giraldo1∗, Abumansur Sabyrrakhim1, Mareike Roscher2, Rosemarie Euler-Lange2,
Michael Baumann1,3,4, Ina Kurth1,2,3,4 and Wahyu Wijaya Hadiwikarta1,3
1
Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
2
Service Unit for Radiopharmaceuticals and Preclinical Studies, German Cancer Research Center (DKFZ), 69120
Heidelberg, Germany
3
German Cancer Consortium (DKTK), Core Center Heidelberg, 69120 Heidelberg, Germany
4
Heidelberg Institute of Radiation Oncology (HIRO), 69120 Heidelberg, Germany
Abstract
Background: In radiation oncology, the data generated from preclinical trials serve as initial
validation for treatment effectiveness and optimizing clinical approaches by unraveling molecular
mechanisms underlying different treatment responses. Therefore, it is important to standardize the
practice in managing preclinical trial data to ensure consistency and reproducibility across studies,
promoting collaboration, and facilitating regulatory review. The primary goal of this work is to
standardize the representation of data collected from preclinical radiobiology and radiation oncology
studies as a way to facilitate knowledge discovery. To achieve this goal, we combined ontology with
semantic Web techniques to publish mapped data and easily query them using SPARQL Protocol and
RDF Query Language (SPARQL). Results: We expanded the Radiation Oncology Ontology (ROO) to
include terminology related to the exposure of animal models to treatment, animal model’s
demographic characteristics; as well as clinical information in live animals. The extended ROO
contains 123 new entities (89 classes, 29 data properties and 5 object properties). We combined the
extended ontology with Semantic Web technologies to demonstrate how to integrate and query data
from different relational databases. Discussion: The use of ontologies and semantic web tools are a
way to comply to the FAIR principles. FAIR preclinical data improve collaboration, transparency, and
reproducibility in radiotherapy research.
1
1. Introduction
In radiation oncology, preclinical trials are conducted in animals prior to clinical trials to
evaluate the safety and efficacy of radiation therapy effects, taking into account various aspects
such as new radiation treatment techniques, radiation delivery methods, and novel therapeutic
agents. The data generated from preclinical trials are very important, because they serve as
initial validation for treatment effectiveness. Furthermore, considering the ethical and
economical aspects of performing animal studies, preclinical data are highly valuable.
15th International Conference on Biological and Biomedical Ontology, July 17-19 2024, Enschede, The Netherlands
∗
Corresponding author.
olga.giraldo@dkfz-heidelberg.de (O. Giraldo); sabyrrakhim.amd@gmail.com (A. Sabyrrakhim);
mareike.roscher@dkfz-heidelberg.de (M. Roscher); r.lange@dkfz-heidelberg.de (R. Euler-Lange);
michael.baumann@dkfz-heidelberg.de (M. Baumann); ina.kurth@dkfz-heidelberg.de (I. Kurth);
w.hadiwikarta@dkfz-hiedelberg.de (W. Hadiwikarta)
0000-0003-2978-8922 (O. Giraldo); 0000-0002-9340-974X (M. Baumann); 0000-0001-9261-5165 (I. Kurth); 0000-
0002-5909-4107 (W. Hadiwikarta)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Therefore, it is important to standardize the practice in managing preclinical trial data to
ensure consistency and reproducibility across studies, which is critical for advancing scientific
knowledge, promoting collaboration, and facilitating regulatory review.
Some efforts focused on standardization of preclinical data exist in the field. An example is
the Standard for Exchange of Nonclinical Data (SEND) from the Clinical Data Interchange
Standards Consortium (CDISC), aimed at standardizing collected individual animal data in
tabular data structures according to different nonclinical domains e.g., animal demographics,
laboratory test results, treatment procedures, etc.[1]. Conversely, ontologies such as the
Dependency Layered Ontology for Radiation Oncology (DLORO) [2], the Radiation Oncology
Structures Ontology (ROS) [3], and the Radiation Oncology Ontology (ROO) [4], were
developed for use cases in the clinical radiation oncology domain. Unfortunately, these
aforementioned ontologies were designed to support human clinical trials and their
relationships, therefore, unfortunately lack the required representation of preclinical data.
The primary goal of this work is to standardize the representation of data collected from
preclinical radiobiology and radiation oncology studies as a way to facilitate knowledge
discovery. Formalized preclinical data will serve as a critical basis for the conduct and
interpretation of clinical trial data stored in the database system RadPlanBio [5,7]. The data are
stored according to the CDISC SEND standard. To achieve this goal, we propose populating the
ROO with terminologies related to the exposure of animal models to treatment, animal model’s
demographic characteristics; as well as clinical information in live animals. We decided to reuse
and extend the ROO because this ontology contains classes that cover the most common
concepts in radiation oncology, including oncological diseases, cancer staging systems, and
oncological treatments. To reuse and extend an existing ontology is in principle aligned to the
open world assumption (OWA).
In this article we: i) present the materials and methods used to populate the ROO with
preclinical terminologies; ii) describe the validation and evaluation process of the ontology, iii)
show the current state of the extended ontology, and iv) conclude with the discussion and
outlook for future work.
2. Materials and Methods
2.1. Preclinical database
As a use case, we analyze information from databases collected in a study focused on
investigating the effect of nimorazole combination treatments on hypoxic tumor areas in mice
[6]. The databases are available in the German Cancer Consortium (DKTK) RadPlanBio
platform, a web-based platform which supports the collection and the exchange of radiotherapy
research data in clinical and preclinical studies [7].
The databases analyzed in this work include >2000 mice and contain information on: i)
demographic characteristics of each individual animal; ii) details of an animal’s exposure to
treatment; iii) body weights of animals during the study and at the end of the study; iv) diagnosis
of the cause of death of animals, and; v) laboratory test data per animal. The information in
these databases is organized and structured according to the format suggested by the SEND
standard for tabulation of nonclinical datasets.
Due to the heterogeneous nature of the data, they provide a good validation for the extended
ROO. The extended ROO was applied to represent each value in the database and to map them
through the concepts available in the ontology.
2.2. The ROO extension process
The process of enriching and extending the Radiation Oncology Ontology (ROO) with
preclinical concepts consists of three steps: i) collection of preclinical concepts; ii) semantic
analysis of existing vocabularies, and; iii) ontology extension.
In the first step, terms were collected from the preclinical databases mentioned in section
2.1. The next step was to identify reusable terminologies from other ontologies. BioPortal [8]
and the UMLS Metathesaurus Browser [9] were used throughout this stage to find references
and definitions for each terminology. In the last step, the ROO was extended with terminologies
that come primarily from the National Cancer Institute Thesaurus (NCIt) [10]. New terms that
are not coming from existing ontologies use the prefix ‘roo’ and a local ID that starts with the
letters DKFZ followed by 6-digit numbers; as an example, the identifier for the class ‘Animal
Identifier’ is roo:DKFZ000006. Protégé v. 5.6.1 [11] was used to create new concepts and manage
the ontology.
2.3. Ontology validation and evaluation
The ontology validation procedure ensures that the ontology can effectively represent and
capture the knowledge and data from the preclinical relational databases. This validation
process involves mapping the elements (rows, columns, and values) of the database to the
concepts and properties (predicates) in the ontology. Figure 1 shows a correspondence between
the columns in the relational database and the ontology entities. At the top (rectangle a) the
hierarchical structure of the extended ROO is illustrated. White boxes represent existing
concepts from original ROO. Grey boxes represent new concepts proposed in this work.
(a) Entity Extended ROO
sty:T071
Animal Identifier
sty:T008 ncit:25364
dkfz:Unique_Subject
Person Mouse _Identifier rdf:type Subject Unique Identifier
“N150a009”
ncit:C25190 ncit:C14238 ncit:C69256
ROO classes is a dkfz:Study_Identifier dkfz:Age dkfz:Age_Unit
New classes Property
“Xeno Nimo Hypox;FaDu” 74.0^^xsd:decimal “days”
Instances Mapping
(b)
Subject Id Study identifier Age Age Unit
N150a009 Xeno Nimo Hypox;FaDu 74.0 days
Preclinical database schema
…
(Mouse information)
Figure 1. Overview of the extended ROO structure and the relational database. The hierarchical
structure of extended ROO is presented in (a). The mapping performed to columns and values
in a database is presented in (b).
The added concept “Mouse (ncit:C14238)” is a subclass of “Animal (sty:T008)” in description
logic syntax, it can be expressed as Mouse ⊑ Animal. In addition, mouse or person are
animals (Mouse ⊔ Person ⊑ Animal) and mouse is not a person (Mouse ⊑ ¬ Person).
Boxes with rounded corners represent instances or individuals. Hierarchical relationships
(“is subclass of”) between classes, are represented by dotted arrows. Properties are represented
with arrows; they connect classes or instances between each other.
At the bottom (rectangle b) demographic information about the mouse (e.g., age, age unit)
and the study identifier of which the animal was enrolled are presented as examples. In the
extended ROO, the column “Subject Id” is mapped to the concept “Subject Unique Identifier
(ncit:C69256)”. The link between a mouse and the subject identifier is the property “Unique
Subject Identifier (DKFZ000009)”. In description logic syntax, any mouse that has a unique
subject identifier can be expressed as Mouse ⊓ ∃ Unique_Subject_Identifier.⊤.
Several languages and software tools are available to perform the mapping procedure from
relational databases to RDF triples [12]. We use RDF Mapping Language (RML), an extension
of R2RML to map columns and rows of preclinical databases and our ontology. R2RML is a W3C
standard for mapping relational databases to RDF. RML follows exactly the same syntax as
R2RML; therefore, RML mappings are themselves RDF graphs [13]. The stages we implemented
to generate linked data between the extended ROO and our preclinical relational databases are
illustrated in Figure 2 and explained below.
Starting phase Linked data generation
List of SPARQL RMLMapper.
queries, Expected
outcomes, and
Ontology validation.
Tables transformed
into CSV files,
Creation of turtle
files specifying the 30/09/2022
expected triples. List of RDF files.
Preparation Output
Figure 2. Linked data generation process.
maSMPs at DaMaLOS 2023 Page 6
2.3.1. Starting phase
In the first stage, we gathered a set of SPARQL queries and the corresponding expected
outcomes (triples and query result). We focused on the functional aspects that we wanted the
ontology to represent. The queries we gathered include, “retrieve the Subject Unique Identifier of
the animals tested”.
2.3.2. Preparation
As preparation, we exported the analyzed preclinical databases to CSV formats. Then, we
created turtle files specifying expected triples. Some of the expected triples we specified include,
rdf:type ; study identifier
(roo:DKFZ000008) .
2.3.3. Linked data generation
To generate linked data, we use the RMLMapper [14] which executes RML rules to achieve its
task. We used Docker [15] to run RMLMapper and storing data.
2.3.4. Output
An ontology validation process was considered done with valid result if the generated outcomes
are not different from the expected outcomes. Therefore, in this stage the ontology evaluation
is done and we compare the generated triples with the expected triples specified in the
preparation step.
3. Results
3.1. Extended Radiation Oncology Ontology (ROO)
The extended ROO contains 123 new entities (89 classes, 29 data properties and 5 object
properties). The new terminologies represent: i) attributes that are common across the used
databases e.g., “subject unique identifier (ncit:C69256)”, study identifier ( roo:DKFZ000008); ii)
demographic characteristics e.g., “strain (roo:DKFZ000041)”; iii) findings or information
collected during a study e.g., “body weight (roo:DKFZ000017)”, “cause of death
(roo:DKFZ000021)” and “clinical observation (roo:DKFZ000036)”; iv) exposure information e.g.,
“treatment name (roo:DKFZ000013)”, “route of administration (roo:DKFZ000011)” and
“treatment vehicle (roo:DKFZ00012)”. We followed the design principles from ROO. The
extended ROO is saved as OWL and available on GitHub [16].
3.2. Ontology validation and evaluation
3.2.1. SPARQL queries
The ontology represents and captures the knowledge and data from the preclinical relational
databases. The expected SPARQL queries were executed by using a Protégé desktop plug-in
that provides support for writing and executing SPARQL queries. All the queries returned the
expected results. The complete list of the queries is available on GitHub [17].
3.2.2. Linked data
Based on Linked Data principles, an ontology enables semantic interoperability across
preclinical data available in relational databases. Our ontology facilitates data sharing and
transparent access to data. Figure 3 shows a database transformed into CSV format and the RDF
triples produced for the first and second subject Ids of the transformed database. The database
presents demographic information of two mice. The first mouse has the unique subject
identifier “N150a009”. The second mouse has the unique subject identifier “N150a011”. Both
were registered in the same study “Xeno Nimo Hypox.FaDu”. Each mouse was given an
identifier used within the study; “9” is the study identifier for the first mouse and “11” for the
second mouse. The age is available for the first mouse “74.0”; the age unit is “days”. Both mice
are male (represented as “0”) and belong to the strain/substrain “Nude Mouse”. As seen in Figure
3, the RDF triples obtained after running RMLMapper capture the data described above.
CSV tables reflecting the content of the databases, the Turtle files specifying expected triples,
and the RDF files generated by running RMLMapper are available on GitHub [18].
RDF triples produced for the first Subject Id of the database.
Database transformed into CSV
RDF triples produced for the second Subject Id of the database.
Figure 3. RDF triples capturing demographic characteristics from mice.
4. Discussion and future work
Semantic representation of preclinical data in radiobiology and radiation oncology involves
structuring and encoding information about demographic characteristics of animals, findings
and treatments in a machine-readable format that facilitates data integration, analysis, and
interpretation of outcomes, such as, overall survival or toxicities after treatment.
To achieve this goal, we have expanded the ROO to describe preclinical data [16]. This
ensures semantic interoperability and enabling integration with other datasets and knowledge
resources. Our extended ontology supports publishing preclinical data as linked data using RDF
to enable integration and interoperability with other datasets. The use of ontologies and
semantic web tools are a way of adhering to the FAIR principles [19]. FAIR preclinical data
enhances collaborations, transparency, and reproducibility in preclinical research.
In this work, we were able to map all the entities present in the analysed databases with
concepts and properties from the extended ROO. Nevertheless, it is not without limitations. The
extended ontology should be validated against other preclinical data to ensure robustness.
Additionally, improving the ontology extension strategy is crucial, e.g., by utilizing
owl:imports. Currently, the extension was performed manually, while preserving the existing
ROO entities to maintain the organizational structure of preclinical terminology derived from
our relational databases. Improving the extension strategy will address issues such as the lack
of unique URIs for the preclinical entities from the analyzed RDBs, and enabling to index the
ontology on a repository such as BioPortal. Further step includes testing our ontology against
competency questions that retrieve information from two or more databases and establish
interconnections. For example, “survival of mice when are exposed to a particular treatment such
as cisplatin”. Then will be to integrate the ontology to the semantic layer of the RadPlanBio
platform, through a knowledge graph to allow semantic querying, reasoning, and inference.
The final step will be to develop a plan to maintain the ontology over time. This plan will
involves addressing issues such as ontology evolution, version control, and alignment with
evolving domain knowledge.
Acknowledgements
The first author of this paper has received funding from the European Federation for Cancer
Images (EUCAIM), Project ID: 101100633. The authors acknowledge the DKTK funding for the
operation of RadPlanBio platform in DKFZ. We also acknowledge Dr. Freddy Priyatna for his
support in the creation of mapping files and in the transformation/generation of triples. The
authors also wish to thank to Thomas Früchtel, Myta Pristanty and Betül Çakir, for the
digitization and standardization of the preclinical data, and the reviewers of this article for their
valuable comments.
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