Genetics of skin cancer

The post is suited to a Masters or PhD student with experience in genetic epidemiology, epidemiology, statistics or bioinformatics. Experience in the analysis and manipulation of large datasets and a good knowledge of computing is desirable. Experience in cancer genetics and/or molecular biology is advantageous but not essential. Non-statistical applicants must be able to demonstrate some knowledge of statistics. For applicants with a background solely in statistics, some knowledge of genetics is desirable.

BACKGROUND

Genetics, together with sun exposure, play an important role in the development of skin cancers. For the keratinocyte cancers basal cell carcinoma and squamous cell carcinoma immunosuppression, for example following organ transplant, can play also play an important role.

Our lab studies the genetics of these skin cancers. Melanoma is the deadliest skin cancer and is responsible for >1,900 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 (> $500 million p.a.). The goal of this project is to dissect the genetics of skin cancers and work out he we can use this information to improve health outcomes.

Our resources include large cohort studies based at QIMR Berghofer Medical Research Institute, 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 800,000 people [4-8].

Through this large resource, we are able to dissect the genetics of skin cancer and their risk factors, and use this information to better understand how to treat and manage these serious diseases.

AIM

• 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.

APPROACHES

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 [4-8]. The overlap of skin cancer and its risk factors will be used to identify new genetic risks common to all traits [6,7]. Fine-mapping, bioinformatics, and post-GWAS approaches (e.g. gene-based tests) will be used to fully interpret identified genetic variants [5]. 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 risk of skin cancer; this is particularly relevant with respect to skin cancer in organ transplant recipients [9,10]. Mendelian randomisation will be used to determine if potential risk factors associated with skin cancer are causal [11].

[1] Law MH et al. Hum Mol Genet. 2020. doi: 10.1093/hmg/ddaa156. https://pubmed.ncbi.nlm.nih.gov/32716505

[2] Law MH et al. J Invest Dermatol 2017. doi: 10.1016/j.jid.2017.04.026. https://pubmed.ncbi.nlm.nih.gov/28502801

[3] Olsen CM et al. Int J Epidemiol 2012. doi: 10.1093/ije/dys107. https://pubmed.ncbi.nlm.nih.gov/22933644

[4] Liyanage UE et al. Hum Mol Genet. 2019. doi: 10.1093/hmg/ddz121. https://pubmed.ncbi.nlm.nih.gov/31174203

[5] Landi MT et al. Nat Genet. 2020. doi: 10.1038/s41588-020-0611-8. https://pubmed.ncbi.nlm.nih.gov/32341527

[6] Liyanage U et al. J Invest Dermatol. 2021. doi: 10.1016/j.jid.2021.08.449. https://pubmed.ncbi.nlm.nih.gov/34813871

[7] Seviiri M et al., Nat Commun 2022. doi: 10.1038/s41467-022-35345-8. https://pubmed.ncbi.nlm.nih.gov/36496446

[8] Seviiri M et al. J Transl Med 2022. doi: 10.1186/s12967-022-03613-2. https://pubmed.ncbi.nlm.nih.gov/36064556

[9] Seviiri M et al. J Invest Dermatol. 2021. doi: 10.1016/j.jid.2021.03.034. https://pubmed.ncbi.nlm.nih.gov/34089721

[10] Seviiri M et al. J Invest Dermatol. 2021. doi: 10.1016/j.jid.2020.06.01. https://pubmed.ncbi.nlm.nih.gov/32615124

[11] Ingold N et al. J Invest Dermatol. 2021 doi: 10.1016/j.jid.2021.09.021. https://pubmed.ncbi.nlm.nih.gov/34656614