=Paper=
{{Paper
|id=Vol-2180/paper-59
|storemode=property
|title=Ontology-enabled Breast Cancer Characterization
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-59.pdf
|volume=Vol-2180
|authors=Oshani Seneviratne,Sabbir Rashid,Shruthi Chari,Jim McCusker,Kristin Bennett,Jim Hendler,Deborah McGuinness
|dblpUrl=https://dblp.org/rec/conf/semweb/SeneviratneRCMB18a
}}
==Ontology-enabled Breast Cancer Characterization==
https://ceur-ws.org/Vol-2180/paper-59.pdf
Ontology-enabled Breast Cancer
Characterization
Oshani Seneviratne, Sabbir M. Rashid, Shruthi Chari, James P. McCusker,
Kristin P. Bennett, James A. Hendler, and Deborah L. McGuinness
Rensselaer Polytechnic Institute, Troy NY 12180, USA
Abstract. We address the problem of characterizing breast cancer, which
today is done using staging guidelines. Our demo will show different
breast cancer staging results that leverage the Whyis semantic nanopub-
lication knowledge graph framework [8]. The system we developed is able
to ingest breast cancer characterization guidelines in a semi-automated
manner and then use our deductive inferencer to generate new informa-
tion based on those guidelines as described in our ISWC resource track
paper ‘Knowledge Integration for Disease Characterization: A Breast
Cancer Example’ [11]. In this paper we demonstrate the versatility of
our framework using a synthetic patient profile.
1 Introduction
The most recent authoritative cancer staging guideline, i.e. the American Joint
Committee on Cancer (AJCC) staging 8th edition [1], includes additional data
streams not previously used, thus characterizing cancer differently from the pre-
vious editions. We constructed the cancer staging ontology for the AJCC 7th
and 8th editions to model the guidelines. In order to address data heterogene-
ity and to improve interoperability of the data sources used in cancer staging
and treatment, we used nanopublications in RDF [9] as the underlying knowl-
edge representation model. These nanopublications are generated utilizing the
Semantic Data Dictionary (SDD) methodology [10], thus enabling the nanop-
ublications in the knowledge graph to be updated in a semi-automated manner.
Our deductive inferencer is used to characterize (or re-characterize) the stage of
a tumor in a given patient and present treatment options extracted from a por-
tion of the National Comprehensive Cancer Network’s biomarker compendium
[6]. Crowd sourced resources such as Clinical Interpretations of Variants in Can-
cer (CIViC) [4] are used to identify drugs that may interact with the biomarkers,
as well as related articles and their trust ratings are captured as provenance.
2 Related Work
There are existing ontological representations for cancer characterization based
on previous AJCC cancer staging editions [7,2]. The cancer characterization in
these ontologies is different from ours due to the inclusion of additional biomark-
ers as per the AJCC 8th edition staging criteria. Unlike previous ontologies,
our ontologies also include mappings from breast cancer terms to community-
accepted terms from the National Cancer Institute thesaurus (NCIt) [3], and
incorporate recommended tests and treatment plans from the openly reusable
CIViC database [4]. Furthermore, we include terms that are not included in NCIt
or AJCC, such as more specific subclasses of tumor characteristics (T1, T1 as,
T1 am, T1NOS, etc.) that are available in the Surveillance, Epidemiology, and
End Results (SEER) dataset [5]. Additionally, we provide an end-to-end system
that demonstrates our ontology’s utility for breast cancer characterization.
3 Prototype System Demonstration
Some patients who do not respond as expected to traditional treatment paths
may require the physician to consider additional testing measures, new sources,
and/or alternative treatment plans. We will exemplify the interplay of disease
characterization and personalized medicine being offered by our cancer charac-
terization tool, powered by semantic web tools, in our demonstration.
Let’s consider the patient profile with the biomarkers1 given in Table 1. Sup-
pose there is a physician considering this multitude of parameters related to
tumor biology as well as standard pathology to inform a diagnosis, treatment,
and monitoring plan for this patient. With the utilization of the SDD method-
ology, the data in Table 1 is converted to the nanopublication format in the
knowledge graph. Specific to this use case, our cancer staging ontologies include
the axioms in Listings 3.1 and 3.2 that were extracted from the AJCC 7th and
8th editions respectively.
Tumor In lymph Metastasized? Tumor HER2 ER PR
Size nodes? Grade
5cm (T3) Yes (N1) No (M0) Aggressive Positive Positive Positive
(Grade3)
Table 1. Patient Profile for Triple Positive Large Tumor
@prefix cst : < http :// purl . bioontology . org / ontology / CST > .
[] a owl : Class ; rdfs : subClassOf cst : R7_Stage_IIIA ;
owl : intersectionOf ( cst : T3 cst : N1 cst : M0 ).
Listing 3.1. OWL axiom for a tumor to be classified as Stage IIIA in the 7th guideline
[] a owl : Class ; rdfs : subClassOf cst : R8_Stage_IIB ;
owl : intersectionOf ( cst : T3 cst : N1 cst : M0
cst : Grade3 cst : HER2_Pos cst : ER_Pos cst : PR_Pos ).
Listing 3.2. OWL axiom for a tumor to be classified as Stage IIB in the 8th guideline
1
The abbreviations used include: HER2 (Human Epidermal Growth factor receptor
2 ), ER (Estrogen Receptor), PR (Progesterone Receptor).
Once these axioms, along with the other 400+ staging axioms, are applied
on the patient nanopublication data, the stages as per the 7th and 8th editions
are determined (IIIA in 7th and IIB in 8th ). Because of the additional data
streams considered in the 8th guideline (i.e. Tumor Grade, HER2, ER and PR),
the patient now has improved prognosis. The visualization tool in Fig. 1 shows
the changes to the treatment and monitoring options based on the new stage.
Fig. 1. Example of Downstaged Breast Cancer Characterization
More information on our system, including a video of the system demonstra-
tion, is available at https://cancer-staging-ontology.github.io.
4 Conclusion
With new understanding of cancer biology, guidelines are expected to increase in
complexity and personalized care is more sought after as a component of treat-
ment. Similarly, as the data streams for diagnosing and treating cancer patients
becomes complicated, physicians may have to consult many different trusted
sources and use knowledge from clinical trials and literature to decide on alter-
native treatment options which can take a great deal of the doctors’ and the
patients’ precious time. Using our cancer characterization tool, physicians have
access to patient records in RDF nanopublication format, and can infer the stage,
as per the cancer staging ontology, that models the new staging guidelines, and
then can investigate alternative, evidence-based treatment options. Our visual-
izations present evidence-based, updated staging determinations and treatment
options, along with provenance. Physician-facing software applications can use
our cancer characterization tool to provide physicians with an efficient way to
investigate alternative treatment options based on different staging guidelines.
In the future, new guidelines for cancer staging are expected to incorporate
genomic test results analyzed in the context of the patient’s history. Many predict
a rapid influx of information related to cancer from clinical trials, as well as
from basic science research. Leveraging all these heterogeneous data sources and
making connections to understand the data is of utmost importance. Our system
demonstration shows how semantics are being used to support this fast changing
landscape.
Acknowledgements
This work is partially supported by IBM Research AI through the AI Horizons
Network. We thank our colleagues from IBM (Amar Das, Ching-Hua Chen) and
RPI (John Erickson, Alexander New, Rebecca Cowan) who provided insight and
expertise that greatly assisted the research.
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