High-resolution cDNA microarray CGH mapping of genomic imbalances in osteosarcoma using formalin-fixed paraffin-embedded tissue
Bibliographic record
Abstract
Formalin-fixed paraffin embedded (FFPE) tumor tissue provides an opportunity to perform retrospective genomic studies of tumors in which chromosomal imbalances are strongly associated with oncogenesis. The application of comparative genomic hybridization (CGH) has led to the rapid accumulation of cytogenetic information on osteosarcoma (OS); however, the limited resolving power of metaphase CGH does not permit precise mapping of imbalances. Array CGH allows quantitative detection and more precise delineation of copy number aberrations in tumors. Unfortunately the high cost and lower density of BACs on available commercial arrays has limited the ability to comprehensively profile copy number changes in tumors such as OS that are recurrently subject to genomic imbalance. In this study a cDNA/EST microarray including 18,980 human cDNAs (which represent all 22 pairs of autosomal chromosomes and chromosome X) was used for CGH analysis of eight OS FFPE. Chromosomes 1, 12, 17, and X harbored the most imbalances. Gain/amplification of X was observed in 4/8 OS, and in keeping with other recent genomic analyses of OS, gain/amplification of 17p11.2 was often accompanied by a distal deletion in the region of the p53 gene. Gain/amplification of the X chromosome was verified using interphase FISH carried out on a subset of OS FFPE sections and OS tissue arrays.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".