Project Funding Details

This proposal is to be considered for funding from NHMRC and PdCCRS. Funding from NHMRC is sought for a project addressing Aims 1, 2 and 3. Funding from the PdCCRS is alternatively sought for the same project modified to one year. In the one year timeframe the project will only address Aim 1. As we enter the era of precision medicine, powered in part by the advancements in the field of immuno-oncology, rather than killing cancer cells directly using chemo/radiation therapy, immunotherapy aims to harness the body’s own immune system to eliminate the cancer and its ability to spread to distant organs. Immunotherapies have revolutionised the field of oncology, however there remains a critical need to identify patients who are likely to respond to targeted and cost intensive therapy (AUD $150K/patient/year). Immunotherapies, in particular check point inhibitors, have proven to be a game changer in the treatment of head and neck cancer (HNC) and non-small cell lung cancer (NSCLC). With the emerging success of immune checkpoint blockade (anti PD-1/PD-L1 therapy e.g. Nivolumab/Pembroluzimab) leading to durable responses and prolonged survival in approximately 10-40% of patients, there is now a need, more than ever, for predictive biomarkers to guide patient selection for highly targeted therapies, such as immunotherapies. There are currently no means by which to determine whether a patient would respond to anti PD-1/ PD-L1 immunotherapy. Identifying patients that are likely to respond to checkpoint inhibitors is a necessity to avoid substantial toxicities which are often cost intensive. Studies in the tumour microenvironment (TME) have demonstrated that a high degree of T-cell infiltration provides fertile grounds for effective immunotherapy. As such, the immune contexture have been associated with prediction of treatment response. In parallel, the genomic landscape of the tumour has been shown to be predictive of outcome to immunotherapy, with highly mutated tumours (high tumour mutation burden – TMB) being more sensitive to treatment. I hypothesise that the assessment of the TME and TMB, sampled by tissue biopsy, will indicate which patients are likely to benefit from immunotherapy. I will address this hypothesis by characterising the TME contexture and TMB of the tumour as a way of comprehensively predicting response to immune checkpoint therapies. This project is ground breaking, in that, the comprehensive characterisation of the TME and TMB in solid tumour under the stressors of treatment has the potential to indicate cell populations involved in treatment resistance/poor response. Neither patient nor healthcare funders can afford to use drugs that are unlikely to be effective and targeting treatment to those likely to achieve the greatest benefit is an absolute requirement. The project has been amended to focus on Aim 1 of the NHMRC proposal where the spatial and mutational profiles of tumour tissue will be generated: Spatial and genomic profiles will be generated per tumour type – head and neck cancer (HNC, n=10) and non-small cell lung cancer (NSCLC, n=10). The additional aims to profile liquid biopsy (Aims 2 and 3) have been removed to maintain feasibility for a 1-year study. The budget has been amended to profile tumour tissue only and includes bioinformatics assistance to generate a tumour microenvironment and mutational burden predictor. Salary has been reduced to PSP2 (1.5 days/week) and a contribution towards a shared trials nurse (PSP2, 1 day/week). In this study, tumour tissue (spatial and genomic profiling of tissue biopsy) will be compared to clinical outcome following immunotherapy. The spatially revolved profiles and mutational burden assessments will be assessed for independent and combinatorial prediction of outcome to immunotherapy. In so doing, this will form one of the first studies to use the compliment of spatial proteomic and mutational data to predict outcome to immunotherapy.

Immunotherapies have been hailed as a game changer in the treatment of solid tumours. However there are currently no means by which to identify whether a patient will respond to therapy. In this project, I aim to assess the tumour spatially and genetically, over the course of therapy to develop a means by which to predict response to immunotherapy. In so doing, targeting therapies to those that are likely to achieve the greatest benefit.