Skin Cancer Risk Is Increased by Somatic Mutations Detected Noninvasively in Healthy-Appearing Sun-Exposed Skin
Bibliographic record
Abstract
Skin cancer risk is increased by exposure to ultraviolet radiation (UVR). Since UVR exposure accumulates over time and lighter skin is more susceptible to UVR, age and skin tone are risk factors for skin cancer. However, measurements of somatic mutations in healthy-appearing skin have not been used to calculate skin cancer risk. Herein, we developed a noninvasive test that quantifies somatic mutations in healthy-appearing sun-exposed skin and applied it to a 1,038-subject cohort. Somatic mutations were combined with other known skin cancer risk factors to train a model to calculate risk. The final model (DNA-Skin Cancer Assessment of Risk, DNA-SCAR) was trained to predict personal history of skin cancer from age, family history, skin tone and mutation count. The addition of mutation count significantly improved model performance (OR=1.3 95%CI:1.14–1.48; p-value=5.3x10-6) and made a more significant contribution than skin tone. Calculations of skin cancer risk matched the known U.S. population prevalence, indicating DNA-SCAR was well-calibrated. In conclusion, somatic mutations in healthy-appearing sun-exposed skin increase skin cancer risk and mutations capture risk information that is not accounted for by other risk factors. Clinical utility is supported by the noninvasive nature of skin sample collection via adhesive patches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".