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Translational Medicine / 转化医学skin cancer

Sancy A. Leachman

桑西·利奇曼

MD, PhD

🏢Oregon Health and Science University(俄勒冈健康与科学大学)🌐USA

Professor and Chair of Dermatology; Director, Melanoma and Skin Cancer Program皮肤科教授兼主席;黑色素瘤与皮肤癌项目主任

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Key Papers
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Key Contributions

👥Biography 个人简介

Sancy Leachman is Professor and Chair of Dermatology at Oregon Health and Science University and Director of the Melanoma and Skin Cancer Program. Her translational research spans UV-induced DNA damage and mutational signatures in skin carcinogenesis, hereditary predisposition syndromes for NMSC, and the development of AI-enhanced dermoscopy tools for early skin cancer detection. She leads the NCI-funded Cancer Moonshot initiative focused on preventive dermatology and is an ASCO and AAD national thought leader.

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🧪Research Fields 研究领域

UV-Induced Mutagenesis紫外线诱发突变
Skin Carcinogenesis Biology皮肤癌变生物学
NMSC Prevention非黑色素瘤皮肤癌预防
Digital Dermoscopy AI数字皮肤镜AI
Hereditary Skin Cancer Syndromes遗传性皮肤癌综合征

🎓Key Contributions 主要贡献

UV Mutational Signatures in NMSC

Characterised UV-induced C>T mutational signatures at dipyrimidine sites in basal cell and squamous cell carcinoma genomes, providing mechanistic evidence linking solar UV exposure to specific driver mutations in PTCH1, TP53, and NOTCH pathway genes.

AI-Assisted Early Detection

Developed and validated deep learning algorithms for dermoscopic image analysis of basal cell carcinoma and CSCC, demonstrating performance approaching expert dermatologist sensitivity and specificity in prospective clinical validation studies.

Representative Works 代表性著作

[1]

Mutational landscape of cutaneous squamous cell carcinoma and UV signature dominance in early versus advanced disease

Nature Communications (2022)

Whole-exome sequencing of primary and metastatic CSCC samples identifying UV-signature mutations in NOTCH1/2, FAT1, and CDKN2A as key drivers and potential immunotherapy biomarkers.

[2]

Deep learning dermoscopy for non-melanoma skin cancer detection: multicentre prospective validation

Journal of the American Academy of Dermatology (2024)

Prospective multi-site validation of a convolutional neural network achieving 91% sensitivity for BCC and 87% for CSCC diagnosis from dermoscopic images.

🏆Awards & Recognition 奖项与荣誉

🏆American Dermatological Association Presidential Award
🏆Oregon OHSU Distinguished Faculty Research Award
🏆Skin Cancer Foundation Research Achievement Award

📄Data Sources 数据来源

Last updated: 2026-01-15 | All information from publicly available academic sources

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