Genetic Testing in Familial Melanoma: Epidemiologic/Genetic Assessment of Risks and Role ofCDKN2A Analysis
Bibliographic record
Abstract
The first description of familial melanoma in the English literature appeared in 1820, when Norris (1) reported: It is remarkable that this gentleman's father, about thirty years ago, died of a similar disease.... This tumour, I have remarked, originated in a mole, and it is worth mentioning, that not only my patient and his children had many moles on various parts of their bodies, but also his own father and brothers had many of them.... These facts, together with a case that has come under my notice, rather similar, would incline me to believe that this disease is hereditary. Since then, many families with a predisposition to melanoma have been described worldwide (2-5). For purposes of case definition, our laboratory curently defines familial melanoma (FMM) as a family containing >2 affected first-degree relatives with melanoma and/or pancreatic carcinoma. According to this definition, about 8-12% of melanoma is inherited as an autosomal dominant trait with variable penetrance. Affected members (AFM) of these FMM kindreds may develop multiple primary melanoma (6) and/or pancreatic cancer (7) and typically present at an earlier age than do patients with sporadic disease. In a subset of such individuals and kindreds, germline mutations of the CDKN2A gene (also known as p16INK4A and MTS1) cosegregate with cases of melanoma (2-5).We have hypothesized that the identification of mutation carriers may in the future allow us to direct resources to the prevention and surveillance of mela noma in high-risk individuals and families. This chapter provides an overview of melanoma genetics, as well as the indications, drawbacks, and methods of germline CDKN2A mutation screening by polymerase chain reaction (PCR) amplification and automated sequencing of genomic DNA.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".