Evidence map›Paper›PMID 41741430›Full record

ArticleNature communications2026

Benchmarking EGF signaling pathway inference using phosphoproteomics and kinase-substrate interactions.

Martin Garrido-Rodriguez, Clement Potel, Mira Lea Burtscher, Isabelle Becher, Pablo Rodriguez-Mier, Sophia Müller-Dott, Mikhail M Savitski, Julio Saez-Rodriguez

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Martin Garrido-Rodriguez *Molecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID 0000-0003-4125-5643
Clement Potel *Molecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID 0000-0003-3655-4665
Mira Lea BurtscherMolecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID 0000-0003-2628-0795
Isabelle BecherMolecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.ORCID 0000-0001-7170-2235
Pablo Rodriguez-MierHeidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany.ORCID 0000-0002-4938-4418
Sophia Müller-DottHeidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany.ORCID 0000-0002-9710-1865
Mikhail M SavitskiMolecular Systems Biology unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. mikhail.savitski@embl.de.ORCID 0000-0003-2011-9247
Julio Saez-RodriguezHeidelberg University, Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany. saezlab@ebi.ac.uk.ORCID 0000-0002-8552-8976

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Signaling pathways are useful models for interpreting molecular data, but their coverage has long been constrained by classic biochemistry methods. The growing corpus of kinase-substrate interactions, coupled to phosphoproteomics improvements, pave the way to revisit classic signaling pathways. In this study, we explore context-specific signaling pathway inference from phosphoproteomics and kinase-substrate networks. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis and generate three datasets representing the most comprehensive characterization of the EGF response to date. We infer kinase-kinase pathways and compare them to different ground truth sets. Literature-curated networks consistently yield the highest recovery of ground-truth interactions, with modest gains from network propagation methods. Up to 90% of interactions are absent from current ground truth sets, indicating many unexplored interactions supported by data and knowledge. Our results demonstrate the limitations of traditional views on signaling pathways and point to opportunities for generating better mechanistic hypotheses.

Indexed as

Epidermal Growth FactorPhosphoproteinsProtein KinasesProteomicsSignal TransductionAnimalsBenchmarkingHumansPhosphorylationEpidermal Growth FactorPhosphoproteinsProtein Kinases

Identifiers

PMID41741430
PMCPMC12949236

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.