Genetics of skin cancer

The project is suited to a PhD student with experience in genetic epidemiology, epidemiology, biostatistics or bioinformatics. Experience in the analysis and manipulation of large datasets and a good knowledge of computing is desirable. Experience in cancer genetics 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

Skin cancers, including melanoma and keratinocyte cancers (KCs), are the most common cancers in Australia leading to significant morbidity, mortality and health costs. Each year over 15,300 Australians are diagnosed with melanoma, the deadliest skin cancer with over 1,400 Australians succumbing to advanced or metastatic disease every year. Early diagnosis and appropriate treatment are crucial in improving survival outcomes. Over the last decade, new drug therapies known as immunotherapy have drastically improved treatment outcomes in patients with metastatic melanoma. Despite this success, there is significant variability in response to treatment amongst patients, with 59% patients experiencing life threatening immune-related adverse events or toxicities, while a third acquire complete remission. The biology underlying why some people do or do not develop immunotherapy-related adverse events, or why others do or do not acquire remission, is poorly understand.

Due to immunosuppression, transplant patients have up to 100-fold risk of developing KC compared to the general population, with the majority (57%) of recipients developing multiple KCs. Unlike in the general population, for transplant patients KC is very aggressive, and highly metastatic. It is also a major cause of death in transplant patients accounting for 15% of cancer deaths, a 51-fold increase compared to mortality in the general population. There is an increasing need to effectively manage these cancers in transplant patients

AIM

• Explore the genetic predisposition to poor immunotherapy efficacy in patients with metastatic melanoma.
• Assess genetic-based prediction of immunotherapy efficacy in metastatic-melanoma patients.
• Explore putative causal factors for immunotherapy response in patients with metastatic melanoma.
• Explore clinical translation of genetic risk prediction of skin cancers in transplant patients.

APPROACHES

For Part 1 the candidate will use statistical genetics approaches (e.g. Genome-wide association study techniques), multi-omics data (DNA, gene expression, metabolomics), and clinical data to uncover the genetic risk for developing severe adverse events, and poor treatment response/efficacy (disease progression/remission). They will use this genetic data with clinical information to generate genetic risk prediction models for adverse events, and treatment response. They will also use other approaches such as Mendelian randomisation to investigative putative causal factors (e.g. gut microbiome, diet, etc) for treatment response.

In Part 2 the candidate will use large genetic data (from >1 million people) to develop genetic risk prediction models for KC to help identify transplant patients at the highest risk of developing multiple/invasive skin cancers. They will also explore the efficacy of using genetic risk prediction models to triage transplant patients for personalized early chemoprevention, improved screening, and modulation of immunosuppressive medication.

PROJECT POTENTIAL

We have large-scale genetic data sets available in the lab for skin cancer risk, treatment, and treatment outcomes. We also have access to other national and international biobanks, as well as deeply phenotyped data sets for transplant patients. The candidate will use a range of statistical genetic approaches to interrogate these data and to determine the genes and pathways underlying melanoma treatment response and use these in prediction models. They will also use these data sets to develop and apply genetic prediction models for skin cancer in transplant patients. The project may also consider similar gene-mapping and prediction analysis for other complex traits such as other cancers e.g. colorectal carcinoma, and glaucoma in non-European ancestries.