Array Comparative Genomic Hybridization in Osteosarcoma
Bibliographic record
Abstract
Osteosarcoma, the most frequent primary bone tumor, is a malignant mesenchymal sarcoma with a peak incidence in young children and adolescents. Left untreated, it progresses relentlessly to local and systemic disease, ultimately leading to death within months. Genomically, osteosarcomas are aneuploid with chaotic karyotypes, lacking the pathognomonic genetic rearrangements characteristic of most sarcomas. The familial genetics of osteosarcoma helped in elucidating some of the etiological molecular disruptions, such as the tumor suppressor genes RB1 in retinoblastoma and TP53 in Li-Fraumeni, and RECQL4 involved in DNA repair/replication in Rothmund-Thomson syndrome. Genomic profiling approaches such as array comparative genomic hybridization (aCGH) have provided additional insights concerning the mechanisms responsible for generating complex osteosarcoma genomes. This chapter provides a brief introduction to the clinical features of conventional osteosarcoma, the predominant subtypes, and a general overview of materials and analytical methods of osteosarcoma aCGH, followed by a more detailed literature overview of aCGH studies and a discussion of emerging genes, molecular mechanisms, and their clinical implications, as well as more recent application of integrative genomics in osteosarcoma. aCHG is helping elucidate genomic events leading to tumor development and evolution as well as identification of prognostic markers and therapeutic targets in osteosarcoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".