Melanoma treatment has been revolutionised by the advent of immune check point inhibitors (ICIs). However, only 50% of patients will respond to the treatment. A promising alternative for those who ICI is ineffective is tumour infiltrating lymphocyte (TIL) therapy, an innovative form of immunotherapy that involves extraction, expansion and infusion of a patient’s specific immune cells (T cells). In an Australian first, the PERTIL trial, led by Prof Elin Gray, will be treating ICI resistant melanoma patients with TIL therapy. Predicting who will benefit from TIL therapy will be an important complementary work to the PERTIL trial.
Our team has shown the value of circulating tumour DNA (ctDNA) as a blood-based biomarker for melanoma. Plasma-derived ctDNA can be distinguished from normal cell free DNA (cfDNA) by differences in biology, including fragment length and tumour-induced DNA lesions. Thus, this project will leverage ctDNA genomics and bioinformatics to predict patients that would benefit from TIL therapy.
Specific Aims: 1) Construct a machine learning model of cfDNA features which can predict response to TIL therapy at baseline. 2) Longitudinally monitor patients for markers of treatment response and resistance using cfDNA.
Expected Outcomes and Impact: The development of a novel biomarker that could be used to predict which patients will benefit from TIL therapy. The successful completion of the project will provide clinicians with more information regarding the success of TIL therapy prior to administration.
Preferred applicant: We are looking for a HDR applicant with extensive wet lab experience in cfDNA extraction, library preparation and next generation sequencing. We also expect that the student is proficient in both windows and linux operating systems, as well as having previous experience with bioinformatics analysis using R, python and command line.
A scholarship may be available for this project.
Contact: Dr Aaron Beasley