Novel genomic imbalances in embryonal rhabdomyosarcoma revealed by comparative genomic hybridization and fluorescence in situ hybridization: An Intergroup Rhabdomyosarcoma Study
Bibliographic record
Abstract
A comparative genomic hybridization (CGH) approach provides identification of genomic gains and losses in a tumor specimen in a single experiment. Only 11 embryonal rhabdomyosarcomas (E-RMS) have previously been subjected to CGH. The underlying genetic events in this histologic subtype are not well defined. In this investigation, 12 E-RMS specimens from 10 patients entered into Intergroup Rhabdomyosarcoma Study (IRS) I-IV and two local patients were analyzed by CGH and fluorescence in situ hybridization (FISH). Gains of chromosomes or chromosomal regions 2 (50%), 7 (42%), 8 (67%), 11 (42%), 12 (58%), 13q21 (33%), and 20 (33%) and losses of 1p35-36.3 (42%), 6 (33%), 9q22 (33%), 14q21-32 (25%), and 17 (25%) were most prominent. Chromosomal regions 1p35-36.3 and 9q22 represent novel regions of loss. Importantly, loss of 9q22 corresponds to the locus of a putative tumor suppressor gene (PTCH), which has been shown to play a role in rhabdomyosarcoma in a mouse model of Gorlin syndrome. Loss of 1p36 corresponds to the locus for PAX7, a paired box containing gene characteristically altered in alveolar rhabdomyosarcoma. Moreover, loss of 1p36 is prominent in another common pediatric soft tissue tumor, neuroblastoma. Gains of 2, 7, 8, 12, and 13 and loss of 14 were seen in the sole prior E-RMS CGH series; thus, these data provide important confirmatory results. In contrast to this previous study, however loss, not gain, of chromosome 17 was observed in the current study. Chromosome 17 loss correlates well with previous descriptions of frequent allelic loss of 17p (TP53) in E-RMS. In summary, CGH and FISH analyses of 12 E-RMS specimens revealed novel genomic imbalances that may be useful in directing further molecular studies for the determination of E-RMS critically involved genes.
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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".