# Exploring Explainable Treatments Hypotheses with the Oncodash Decision Support System

Johann Dreo, Altti Ilari Maarala, Taru Muranen, Matthieu Najm, Federico Bolelli, Vittorio Pipoli, Marko Baric, Rachel Torchet, Simon Malesys, Oskari Lehtonen, et al.

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## To cite this version:

Johann Dreo, Altti Ilari Maarala, Taru Muranen, Matthieu Najm, Federico Bolelli, et al. Exploring Explainable Treatments Hypotheses with the Oncodash Decision Support System. Molecular Analysis for Precision Oncology Congress, ESMO European Society For Medical Oncology, Sep 2025, Paris, France. pasteur-05121991

HAL Id: pasteur-05121991

https://pasteur.hal.science/pasteur-05121991v1

Submitted on 20 Jun 2025

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## Abstract 395

Exploring Explainable Treatments Hypotheses with the Oncodash Decision Support System

Type: Abstract

Topic: Clinical practice

Authors: Johann Dreo¹, A.I. Maarala², T. Muranen², M. Najm³, F. Bolelli⁴, V. Pipoli⁴, M. Baric³, R. Torchet⁵, S. Malesys⁵, O. Lehtonen², V.-M. Isoviita⁶, S. Lobentanzer⁷, E. Gaydukova³, E. Carreño⁸, J. Oikkonen⁶, K. Lavikka⁶, J. Hynninen⁹, I. Ulusoy⁸, B. Schwikowski³, ¹Computational Systems Biomedicine lab., Computational Biology Dept., Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, France, ²Research Program of Systems Oncology, University of Helsinki - Faculty of medicine, Helsinki, Finland, ³Computational Systems Biomedicine, Institut Pasteur, Paris, France, ⁴Engineering Department "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy, ⁵Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, France, ⁶University of Helsinki - Faculty of medicine, Helsinki, Finland, ⁷Institute of Computational Biology, Helmotz Centre Munich, Neuherberg, Germany, ⁸Scientific Software Center, Interdisciplinary Center of Scientific Computing, Heidelberg University, Heidelberg, Germany, ⁹Gynecology Department, Turku University Hospital TYKS, Turku, Finland

## Background

Curated oncogenomic knowledge is fragmented across many databases, slowing therapeutic decision-making in tumor boards.

One of the few open-source systems of that kind is the Platform for Oncogenomic Reporting and Interpretation, primarily based on an innovative type of database —a knowledge graph (KG). It is no longer maintained, and its code was not designed to be modular, easy to Find, Accessible, Interoperable, and Reusable (“FAIR”).

## Methods

We introduce Oncodash, a decision support system for tumor boards. The main innovation of Oncodash is that its KG represents broad cancer biology knowledge, particularly its molecular aspects. As a result, Oncodash allows users to explore causal hypotheses about personalized treatment options. This "causal" KG can present the scientific evidence that support oncologists' reasoning. This evidence is essential for clinical applications "based on scientific data".

Oncodash is being developed by the DECIDER project, involving doctors and patients. It was designed to maximize the “FAIRness” of the studies in which it is used.

## Results

Oncodash is currently used in pre-clinical research to fight high-grade serous ovarian cancer. It has been used to discuss several patients' from the Turku Hospital, during "pre-virtual tumor boards".

It currently shows patient selection, clinical, and genomics views, and its modular architecture easily allow for more. Each of them allows displaying all data of interest in single pages instead of the user having to browse many websites and sheets containing patient and genomics data. It currently handles a cohort of ≈290 patients, and integrates information about ≈88k objects (genes, proteins, pathways, etc.), ≈343k relationships and ≈906k properties.

## Conclusions

Oncodash helps tumor boards systematically explore vast amount of patient-specific data and knowledge. At its core, Oncodash enables the integration and exploration of heterogeneous information related to cancer biology. More generally, Oncodash aims at exploring a systematic collection of evidence supporting working hypotheses about treatments.

## Editorial acknowledgement

### Clinical trial identification

NCT04846933
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