Molecular Characterization of Pediatric Gastrointestinal Stromal Tumors
Bibliographic record
Abstract
PURPOSE: Pediatric gastrointestinal stromal tumors (GIST) are rare and occur preferentially in females as multifocal gastric tumors, typically lacking mutations in KIT and PDGFRA. As KIT oncoprotein is consistently overexpressed in pediatric GIST, we sought to investigate the activation of KIT downstream targets and alterations of KIT/PDGFRA gene copy number, mine novel therapeutic targets by gene expression, and test tyrosine kinase receptor activation by proteomic profiling. EXPERIMENTAL DESIGN: Seventeen pediatric GISTs were investigated for KIT/PDGFRA genotype and biochemical activation of KIT downstream targets. The transcriptional profile of 13 nodules from 8 pediatric patients was compared with 8 adult wild-type (WT) GISTs, including 3 young adults. The drug sensitivity of second-generation kinase inhibitors was tested in murine Ba/F3 cells expressing human WT KIT, as well as in short-term culture of explants of WT GIST cells. RESULTS: A KIT/PDGFRA WT genotype was identified in all 12 female patients, whereas two of five males had either a KIT exon 11 or PDGFRA exon 18 mutation. KIT downstream targets were consistently activated. Pediatric GISTs showed a distinct transcriptional signature, with overexpression of BAALC, PLAG1, IGF1R, FGF4, and NELL1. In vitro studies showed that nilotinib, sunitinib, dasatinib, and sorafenib are more effective than imatinib against WT KIT. CONCLUSIONS: Rare cases of pediatric GIST may occur in male patients and harbor activating KIT/PDGFRA mutations. Pediatric GISTs show distinct transcriptional signature, suggesting a different biology than WT GIST in adults. In vitro drug screening showed that second-generation kinase inhibitors may provide greater clinical benefit in pediatric GIST.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".