Predicting GD2 expression across cancer types by the integration of pathway topology and transcriptome data
Our computational framework provides a robust, interpretable, biologically grounded predictor of GD2 expression, offering greater consistency and clinical interpretability over existing gene-based signatures. Importantly, with over 20 GD2-directed trials ongoing, our approach may help prioritize tumor entities with high GD2 levels, delineate candidate patient subgroups, and generate testable hypotheses in underexplored cancers, thereby supporting patient stratification and eligibility screening for clinical trials.
Authors
- Saskia Wagner
- Arthur Wingerter (ORCID: https://orcid.org/0000-0002-9547-6532)
- Arsenij Ustjanzew (ORCID: https://orcid.org/0000-0002-1014-4521)
- Claudia Paret (ORCID: https://orcid.org/0000-0002-9094-1634)
- Roger Sandhoff (ORCID: https://orcid.org/0000-0003-3794-9458)
- Fédérico Marini (ORCID: https://orcid.org/0000-0003-3252-7758)
- Jörg Faber
Institutions
- German Cancer Research Center (DE)
- Johannes Gutenberg University Mainz (DE)
- Heidelberg University (DE)
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- Quantitative BioSciences (US)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-12-04
- DOI
- https://doi.org/10.3389/fbinf.2025.1705930
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft