Comprehensive analysis of the ErbB receptor family in pediatric nervous system tumors and rhabdomyosarcoma
Bibliographic record
Abstract
BACKGROUND: There is a paucity of knowledge regarding pediatric biomarkers, including the relevance of ErbB pathway aberrations in pediatric tumors. We investigated the occurrence of ErbB receptor aberrations across different pediatric malignancies, to identify patterns of ErbB dysregulation and define biomarkers suitable for patient enrichment in clinical studies. PROCEDURE: Tissue samples from 297 patients with nervous system tumors and rhabdomyosarcoma were analyzed for immunohistochemical expression or gene amplification of epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2). Exploratory analyses of HER3/HER4 expression, and mRNA expression of ErbB receptors/ligands (NanoString) were performed. Assay validation followed general procedures, with additional validation to address Clinical Laboratory Improvement Amendments (CLIA) requirements. RESULTS: In most tumor types, samples with high ErbB receptor expression were found with heterogeneous distribution. We considered increased/aberrant ErbB pathway activation when greater than or equal to two EGFR/HER2 markers were simultaneously upregulated. ErbB pathway dysregulation was identified in ∼20%-30% of samples for most tumor types (medulloblastoma/primitive neuroectodermal tumors 31.1%, high-grade glioma 27.1%, neuroblastoma 22.7%, rhabdomyosarcoma 23.1%, ependymoma 18.8%), 4.2% of diffuse intrinsic pontine gliomas, and no recurrent or refractory low-grade astrocytomas. In medulloblastoma/primitive neuroectodermal tumors and neuroblastoma, this was attributed mainly to high EGFR polysomy/HER2 amplification, whereas EGFR gene amplification was observed in some high-grade glioma samples. EGFR/HER2 overexpression was most prevalent in ependymoma. CONCLUSIONS: Overexpression and/or amplification of EGFR/HER2 were identified as potential enrichment biomarkers for clinical trials of ErbB-targeted drugs.
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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 | 0.001 |
| 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".