# Finding personalized drugs from the integration of clinical and molecular data into a semantic knowledge graph

Matthieu Najm, Marko Baric, Altti Ilari Maarala, Sebastian Lobentanzer,
Jaana Oikkonen, Edwin Carreno, Federico Bolelli, Vittorio Pipoli, Veli-Matti
Isoviita, Ekaterina Gaydukova, et al.

► To cite this version:

Matthieu Najm, Marko Baric, Altti Ilari Maarala, Sebastian Lobentanzer, Jaana Oikkonen, et al. Finding personalized drugs from the integration of clinical and molecular data into a semantic knowledge graph. Molecular Analysis for Precision Oncology Congress, ESMO European Society For Medical Oncology, Sep 2025, Paris, France. pasteur-05121971

HAL Id: pasteur-05121971

https://pasteur.hal.science/pasteur-05121971v1

Submitted on 20 Jun 2025

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## Abstract 257

Finding personalized drugs from the integration of clinical and molecular data into a semantic knowledge graph

Type: Abstract

Topic: Artificial intelligence (AI)

Authors: Matthieu Najm¹, M. Baric¹, T. Muranen², A.I. Maarala², S. Lobentanzer³, J. Oikkonen², E.

Carreño⁴, F. Bolelli⁵, V. Pipoli⁵, V.-M. Isoviita⁶, E. Gaydukova¹, E. Quaglio¹, I. Ulusoy⁴, J.

Hynninen⁷, B. Schwikowski¹, J. Dreo¹; ¹Computational Systems Biomedicine, Institut

Pasteur, Paris, France, ²Research Program of Systems Oncology, University of Helsinki -

Faculty of medicine, Helsinki, Finland, ³Institute of Computational Biology, Helmholtz

Munich, Neuherberg, Germany, ⁴Scientific Software Center, Interdisciplinary Center of

Scientific Computing, Heidelberg University, Heidelberg, Germany, ⁵Engineering

Department "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy,

⁶University of Helsinki - Faculty of medicine, Helsinki, Finland, ⁷Gynecology Department,

Turku University Hospital TYKS, Turku, Finland

## Background

Personalized medicine for ovarian high-grade serous carcinoma (HGSC) is challenging due to its heterogeneous genomic profile, characterized by few recurring driver aberrations. Effective identification of targeted therapies involves the curation and analysis of molecular and clinical data, along with existing biological knowledge. A scalable approach is essential to integrate and interpret this data and support clinicians in decision making.

## Methods

We propose the integration of this data into a Semantic Knowledge Graph (SKG). SKGs are specialized databases that represent data in the form of nodes and relationships. To build this SKG, we leveraged two tools developed within the DECIDER consortium, BioCypher and OntoWeaver, which allow the fully automated and FAIR (Findable, Accessible, Interoperable, and Reusable) creation of SKGs from a configuration that is easy for an end-user to read and write.

## Results

We present OncodashKB, an SKG that integrates clinical and genome-scale DNA and RNA sequencing data from the DECIDER observational trial cohort of HSCG patients. Additionally, prior knowledge concerning actionable aberrations, drugs and their targeted proteins were collected from external databases such as OncoKB, OpenTargets and OmniPath and integrated to OncodashKB.

OncodashKB allows molecular tumor boards to access relevant patient data in an interactive graphical format. The graph database schema ensures that each interaction has a causal meaning, enabling users to query paths of evidence that connect patient-specific alterations to drug indications. Nodes and relationships in the graph are annotated with confidence or proof scores, such as ESMO Scale of Clinical Actionability for molecular Targets (ESCAT), which facilitates the prioritization of queried paths.

## Conclusions

OncodashKB integrates patient clinical and molecular data along with prior knowledge into a graph database. We expect this to enable novel personalized drug indications and systematic cross checks of predictions made by predictive “black box” methods on the basis of mechanistic hypothesis and their supporting evidence.

## Editorial acknowledgement
During the preparation of this work the author(s) used Mistral Small 3.1 in order to rephrase certain sentences. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Clinical trial identification

NCT04846933

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