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@ARTICLE{Ustjanzew:307440,
      author       = {A. Ustjanzew and F. Marini and S. Wagner and A. Wingerter
                      and R. Sandhoff$^*$ and J. Faber$^*$ and C. Paret$^*$},
      title        = {{P}redicting {GD}2 expression across cancer types by the
                      integration of pathway topology and transcriptome data.},
      journal      = {Frontiers in bioinformatics},
      volume       = {5},
      issn         = {2673-7647},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2025-03039},
      pages        = {1705930},
      year         = {2025},
      abstract     = {The disialoganglioside GD2 is a key cancer therapy target
                      due to its overexpression in several cancers and limited
                      presence in normal tissues. However, experimental assessment
                      is technically challenging and not routinely available. We
                      developed a computational framework that integrates reaction
                      activity derived from transcriptomic data with the
                      glycosphingolipid biosynthesis pathway to predict GD2
                      expression.We computed Reaction Activity Scores from
                      transcriptomic data and weighted the reactions of a
                      glycosphingolipid metabolic network, refining edge weights
                      with topology-based transition probabilities to account for
                      enzyme promiscuity. Cumulative activities of GD2-promoting
                      and -mitigating reactions served as features in a Support
                      Vector Machine (SVM) to model GD2-associated differences
                      between neuroblastoma and normal tissue. SVM decision values
                      were used as a continuous proxy for GD2 expression. We
                      validated the predicted GD2 scores across independent
                      datasets by comparing them with literature-reported values
                      and flow-cytometric confirmation of a model-predicted
                      high-GD2 tumor. Copy-number alteration (CNA) data were
                      integrated to identify candidate genomic biomarkers of
                      GD2-positive samples.Our SVM-based GD2 score achieved
                      balanced accuracy of 0.80 with a linear kernel, selected due
                      to reduced overfitting risk and interpretability, while
                      matching the accuracy of more complex kernels. The model
                      transferred reliably across six independent RNA-seq datasets
                      and reproduced known GD2 expression patterns, outperforming
                      a two-gene signature in capturing subtype-specific
                      heterogeneity and avoiding overestimation in normal brain
                      tissue. Pan-cancer analyses revealed heterogeneous GD2
                      expression in several cancer subtypes. Notably, we
                      experimentally confirmed high GD2 expression in clear cell
                      sarcoma of the kidney, consistent with model predictions.
                      CNA analysis implicated B4GALNT1 amplification as a
                      GD2-promoting factor in dedifferentiated liposarcoma. To
                      facilitate adoption of our approach, we developed GD2Viz, an
                      R package with an interactive Shiny application for score
                      computation, visualization, and analysis of user 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.},
      keywords     = {GD2 prediction (Other) / biomarker (Other) / cancer
                      subtypes (Other) / ganglioside (Other) / metabolic network
                      (Other) / reaction activity score (Other) / support vector
                      machine (Other) / transcriptome analysis (Other)},
      cin          = {A411 / FM01},
      ddc          = {570},
      cid          = {I:(DE-He78)A411-20160331 / I:(DE-He78)FM01-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41424948},
      pmc          = {pmc:PMC12711791},
      doi          = {10.3389/fbinf.2025.1705930},
      url          = {https://inrepo02.dkfz.de/record/307440},
}