High-Resolution DNA Copy Number Profiling of Malignant Peripheral Nerve Sheath Tumors Using Targeted Microarray-Based Comparative Genomic Hybridization
Bibliographic record
Abstract
PURPOSE: Neurofibromatosis type 1 (NF1) is an autosomal dominant condition that predisposes to benign and malignant tumors. The lifetime risk of a malignant peripheral nerve sheath tumor (MPNST) in NF1 is approximately 10%. These tumors have a poor survival rate and their molecular basis remains unclear. We report the first comprehensive investigation of DNA copy number across multitude of genes in NF1 tumors using high-resolution array comparative genomic hybridization (CGH), with the aim to identify molecular signatures that delineate malignant from benign NF1 tumors. EXPERIMENTAL DESIGN: We constructed an exon-level resolution microarray encompassing 57 selected genes and profiled DNA from 35 MPNSTs, 16 plexiform, and 8 dermal neurofibromas. Bioinformatic analysis was done on array CGH data to identify concurrent aberrations in malignant tumors. RESULTS: The array CGH profiles of MPNSTs and neurofibromas were markedly different. A number of MPNST-specific alterations were identified, including amplifications of ITGB4, PDGFRA, MET, TP73, and HGF plus deletions in NF1, HMMR/RHAMM, MMP13, L1CAM2, p16INK4A/CDKN2A, and TP53. Copy number changes of HMMR/RHAMM, MMP13, p16INK4A/CDKN2A, and ITGB4 were observed in 46%, 43%, 39%, and 32%, respectively of the malignant tumors, implicating these genes in MPNST pathogenesis. Concomitant amplifications of HGF, MET, and PDGFRA genes were also revealed in MPNSTs, suggesting the putative role of p70S6K pathway in NF1 tumor progression. CONCLUSIONS: This study highlights the potential of array CGH in identifying novel diagnostic markers for MPNSTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".