High-resolution Genome-wide Copy-number Analyses Identify Localized Copy-number Alterations in Ewing Sarcoma
Bibliographic record
Abstract
Ewing sarcoma family tumors are aggressive sarcomas of childhood and adolescence with continuing poor outcomes. Decades of research on the characteristics of the often solitary-known oncogenic-genomic aberration in Ewing sarcoma family tumors, namely a TET-ETS fusion, have provided little advancement in the understanding of the molecular pathogenesis of Ewing sarcoma or treatment thereof. In this study, the high-resolution single-nucleotide polymorphism technology was used to identify additional/secondary copy-number alterations (CNAs) in Ewing sarcoma that might elucidate the aggressive biology of this sarcoma. We compared paired constitutional and tumor DNA samples. Commonly known genomic alterations including gain of 1q and chromosome 8 were the most frequently detected changes in this study. In addition, deletions and loss of heterozygosity were identified in 10q, 11p, and 17p. Furthermore, tumor-specific CNAs were identified not only in genes previously known to be of interest, including CDKN2A, but also in genes not previously associated with Ewing sarcoma, including SOX6 and PTEN. Selected array-based findings were confirmed by fluorescence in situ hybridization, immunohistochemical studies, or sequencing. The results highlight an unexpected level of cytogenetic complexity associated with several of the samples, 2 of which contained TP53 mutations. In summary, our high-resolution genome-wide copy-number data identify several novel CNAs associated with Ewing sarcoma, which are promising targets for novel therapeutic strategies in this aggressive sarcoma.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".