BACKGROUND
Genetics, together with sun exposure, play an important role in the development of skin cancers. The Genetics and Skin Cancer lab studies the genetics of these skin cancers. Melanoma is the deadliest skin cancer and is responsible for >1,800 deaths a year in Australia. While keratinocyte cancers are rarely deadly, their high incidence still results in ~600 deaths a year, and that same high incidence means overall they are the most expensive cancer in Australia. The goal of this project is to dissect the genetics of skin cancers and work out how we can use this information to improve health outcomes.
Our resources include large cohort studies based at QIMR Berghofer, including the Queensland Study of Melanoma: Environmental and Genetic Associations [1], the Queensland Twin Registry [2], and the QSkin Sun and Health Study [3] with genetic data on over > 40,000 people across the cohorts. Through access to large public datasets like the UK Biobank and international collaborations, we have data linking genetics to skin cancer risk and outcomes for over 1,500,000 people [4-7].
Through these large-scale resources, we are able to dissect the genetics of skin cancers and their risk factors, and use this information to better understand how to predict, manage, and treat these serious diseases.
AIMS
- To use computational statistics approaches to dissect the genetics of melanoma, keratinocyte cancers, and their risk factors.
- To use this genetic information in risk prediction models and to identify factors important for outcome and prognosis.
- To use this genetic data to understand how genetic differences cause skin cancer.
METHODS
The project will focus on characterizing the role of germline genetic variation in skin cancer. Genome-wide genetic information will be married with data on cancer susceptibility traits and cancer outcomes [4-7]. The overlap of skin cancer and its risk factors will be used to identify new genetic risks common to all traits [4, 8, 9]. Fine-mapping, bioinformatics, and post-GWAS approaches (e.g., transcriptomics data) will be used to fully interpret identified genetic variants [6, 10, 11]. The resulting genetic data will be used to develop prediction models, and these models will be calibrated against in-house datasets such as QSkin to determine how they can best help predict disease risk [12-15]. Mendelian randomisation may be used to determine if potential risk factors associated with skin cancer are causal [16].
References
1. Law, M.H., et al., Multiplex melanoma families are enriched for polygenic risk. Hum Mol Genet, 2020. 29(17): p. 2976-2985.
2. Ingold, N., et al., Counting nevi on the outer arm provides an accurate and feasible alternative to total body nevus count. Journal of the European Academy of Dermatology and Venereology, 2023. 37(11): p. E1302-E1304.
3. Olsen, C.M., et al., Cohort profile: the QSkin Sun and Health Study. Int J Epidemiol, 2012. 41(4): p. 929-929i.
4. Jayasinghe, G.J.M.S.R., et al., A large-scale genome-wide association meta-analysis for nevus count provides direct insights into the genetics of melanoma. medRxiv, 2025.
5. Seviiri, M., et al., Higher polygenic risk for melanoma is associated with improved survival. medRxiv, 2022.
6. Landi, M.T., et al., Genome-wide association meta-analyses combining multiple risk phenotypes provide insights into the genetic architecture of cutaneous melanoma susceptibility. Nat Genet, 2020. 52(5): p. 494-504.
7. Liyanage, U.E., et al., Multi-Trait Genetic Analysis Identifies Autoimmune Loci Associated with Cutaneous Melanoma. J Invest Dermatol, 2022. 142(6): p. 1607-1616.
8. Helder, M., et al., Dissecting the Genetic Architecture of Tanning and Sunburn as Skin Cancer Risk Factors. J Invest Dermatol, 2025.
9. Ingold, N., et al., Exploring the Germline Genetics of In Situ and Invasive Cutaneous Melanoma: A Genome-Wide Association Study Meta-Analysis. JAMA Dermatol, 2024. 160(9): p. 964-971.
10. Kong, H., et al., Functional characterization of a multi-cancer risk locus on chromosome band 2q33.1 near CASP8. bioRxiv, 2026.
11. Thakur, R., et al., Mapping chromatin interactions at melanoma susceptibility loci uncovers distant cis-regulatory gene targets. Am J Hum Genet, 2025. 112(7): p. 1625-1648.
12. Tanha, H.M., et al., Performance of different polygenic risk scores for breast cancer risk prediction: in-depth evaluations across large UK and Australian cohorts. Eur J Hum Genet, 2026. 34(2): p. 278-287.
13. Tanha, H.M., et al., Polygenic risk scores for prostate cancer: Comparative evaluations in UK and Australian cohorts. HGG Adv, 2025. 6(4): p. 100477.
14. Olsen, C.M., et al., Risk Factors for Incident Nevus-Associated vs De Novo Invasive Melanoma. JAMA Dermatol, 2025. 161(9): p. 941-949.
15. Whiteman, D.C., et al., A Risk Prediction Tool for Invasive Melanoma. JAMA Dermatol, 2025. 161(11): p. 1123-1131.
16. Helder, M., et al., No Evidence that Genetically Predicted Circulating Retinol Is Protective for Skin Cancer. J Invest Dermatol, 2026. 146(1): p. 264-267 e3.