Skin cancer risk in hereditary mixed cancer syndromes
Bibliographic record
Abstract
Hereditary cancer syndromes are genetic conditions that increase an individual's risk for multiple cancer types, often due to mutations that affect critical cellular processes such as DNA repair and cell cycle regulation. Skin cancers, including malignant melanoma (MM), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and related precancerous lesions may be underrecognized in some hereditary cancer syndromes, as suggested by underlying biological mechanisms and their underreporting in studies. In this narrative review, we examine the skin cancer risks associated with the most prevalent hereditary cancer syndromes, including Li-Fraumeni syndrome (LFS), Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), ATM-associated hereditary cancer syndrome, CHEK2-associated hereditary cancer syndrome, BRIP1-associated cancer predisposition, and hereditary leiomyomatosis and renal cell carcinoma (HLRCC). This review consolidates existing evidence and suggests that mixed cancer syndromes, especially LFS, LS, and HBOC but also pathogenic ATM and CHEK2 variants may predispose individuals to skin cancers, warranting tailored screening and preventive measures. On the basis of emerging evidence, we recommend dermatologic evaluation and individualized UV protection strategies for patients with reviewed hereditary cancer syndromes to reduce skin cancer risk and enhance early detection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".