A temporal (phospho-)proteomic dataset of neurotrophic receptor tyrosine kinase signalling in neuroblastoma
Bibliographic record
Abstract
Neurotrophic receptor tyrosine kinases (TrkA, TrkB, TrkC), despite their homology, contribute to the clinical heterogeneity of the childhood cancer neuroblastoma. TrkA expression is associated with low-stage disease and is often seen with spontaneous tumour regression. Conversely, TrkB is present in unfavourable neuroblastomas that often harbour amplification of the MYCN oncogene. The role of TrkC is less clearly defined, although some studies suggest its association with a favourable outcome. Understanding the differences in activity of Trk receptors that drive divergent clinical phenotypes as well as the influence of MYCN amplification on downstream Trk receptor signalling remains poorly understood. Here, we present a comprehensive label-free mass spectrometry-based total proteomics and phosphoproteomics dataset (432 raw files with FragPipe search outputs; available on PRIDE with accession number PXD054441) where we identified and quantified 4,907 proteins, 16,744 phosphosites and 5,084 phosphoproteins, derived from NGF/BDNF/NT-3 treated TrkA/B/C-overexpressing neuroblastoma cells with differential MYCN status. Analysing our dataset offers valuable insights into TrkA/B/C receptor signalling in neuroblastoma and its modulation by MYCN status; and holds potential for advancing therapeutic strategies in this challenging childhood cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".