Chromosomal imbalances in primary and metastatic melanomas
Bibliographic record
Abstract
Comparative genomic hybridization was used to map copy number abnormalities in 48 short-term cell cultures established from different stages and types of human melanoma. A variety of random and non-random chromosomal alterations were detected, with gains within chromosomes 20q, 7q, 7p, 20p, 6p and 17q and losses in 9p, 10q, 6q, 10p, 4q, and 11q being the most common observations. In addition, several other chromosomal loci were over- or under-represented in subgroups of melanomas. For example, sequences on 3q26 were over-represented in 33% and on 5p15.33 in 27% of cell cultures, reaching the level of amplification in 12% and 22%, respectively. These regions harbour the two essential genes for the enzyme telomerase: the telomerase reverse transcriptase gene (hTERT) on 5p15.33 and the telomerase RNA component gene (hTERC) on 3q26. Using fluorescence in situ hybridization and Southern blot analysis, both genes were shown to be over-represented or amplified in several melanomas. Interestingly, hTERT amplification was abundant in superficial spreading primary melanomas, subcutaneous metastases and malignant effusion-derived cells, but completely absent or very rare in primary nodular melanomas as well as brain, bone and lymph node metastases. Several chromosomes or chromosomal regions harbouring telomerase-suppressing activities (3p, 4, 6 and 10p) were frequently under-represented in melanomas. Our data suggest that genetic alterations at several chromosomal loci might facilitate activation of telomerase during the development of cutaneous malignant melanoma.
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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) | 0.002 | 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".