Dissecting karyotypic patterns in malignant melanomas: Temporal clustering of losses and gains in melanoma karyotypic evolution
Bibliographic record
Abstract
Malignant melanomas can be divided into two major subtypes, involving either the skin or eye melanomas. Both tumor forms exhibit highly complex karyotypes with nonrandom recurrent chromosomal imbalances. Loss of chromosome 3, the short arm of chromosome 1, and gain of 8q have been suggested to be associated with eye melanomas, whereas gain of 6p and loss of 6q have been more often seen in skin melanomas. Imbalances implicated in tumor progression include among others, -10 and +7. In spite of the abundance of cytogenetic information, with more than 300 published karyotypes, very little is known about the mode of karyotypic evolution or of the presence of possible cytogenetic pathways. In our investigation, we have used 362 melanoma karyotypes, including both the skin and eye subtypes, to identify the most frequently occurring imbalances. Tumor cases were then classified with respect to the presence or absence of these imbalances and statistically analyzed in order to assess the order of appearance of chromosomal imbalances, the presence of karyotypic pathways, as well as possible cytogenetic subtypes. We show that the melanomas develop through one mode of karyotypic evolution, common to both low and high complexity karyotypes, and we establish the temporal order by which the different imbalances occur. By applying several statistical methods, we show that at least two cytogenetic pathways of clonal evolution exist in malignant melanomas, one initiated with -3 and one with +6p, and that these pathways operate in both skin and eye melanomas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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".