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  • Research home > Beat Cancer Project > Dynamic Prediction of Immune Checkpoint Inhibitor Response and Toxicity

    Dynamic Prediction of Immune Checkpoint Inhibitor Response and Toxicity

    Associate Professor Michael Sorich

    Our research

    Medicines that help the immune system to destroy cancer cells (known as immunotherapy) represent one of the most important recent advances in cancer therapy. While these medicines are able to greatly improve survival for some patients, a substantial proportion of patients treated with immunotherapy do not respond. For some patients who do eventually benefit from therapy it is difficult for doctors to work out if they are benefiting from therapy during the early period of treatment. Additionally, immunotherapy use may be associated with a spectrum of unique and potentially severe toxicities.

    Preliminary evidence indicates that some routinely collected clinical and laboratory characteristics may help predict in advance which patients will benefit from therapy and which patients are at greatest risk of toxicity. Although promising, validation of these findings is required before they can be used in practice. This project will utilise the combined data from a large number of completed clinical trials to develop a better way to provide valuable insight into a patient’s likelihood of response, survival and severe toxicities at key time points in the course of treatment with immunotherapy.

    What we aim to achieve

    If successful the outcome of this research will be improved ability of doctors and patients to predict whether a specific patient is likely to benefit or likely to experiencing severe toxicity if they use a specific form of immunotherapy. This will allow patients and doctors to be better informed with respect to decisions on whether to start, continue, restart and stop therapy.

    Our next steps and milestones

    There are a range of different immunotherapies which are now used for a range of different cancers. The next steps will be to better understand how prediction of response and toxicity will differ between different forms of immunotherapy and between different forms of cancer.

    What motivates me

    Using patient and tumour characteristics to better inform decisions on how best to use cancer medicines has great potential to improve benefit and reduce toxicity of cancer medicines, but its current use in practice has not yet lived up to its potential. My background is in pharmacy and I am motivated by the potential to make meaningful improvements in how cancer medicines are used. To undertake research that has a meaningful impact in a complex area such as cancer requires many individuals contributing their specific key area of expertise. Working with other expert researchers and clinicians who are also dedicated to improving cancer outcomes is also an important component of what motivates me to pursue cancer research.

    My message to supporters

    The research proposed involves the analysis of a very large amount of complex data that will be extracted from a large number of existing clinical trials. Because the clinical trials are already completed and the data is already collected, this is a relatively quick and cost-effective way of answering important research questions required to better guide how these medicines are used. However, combining this complex data from many distinct clinical trials requires the assistance of an individual with specific expertise in managing complex datasets. The subsequent analyses planned for analysing the combined data are also complex and requiring specific skills in statistical analysis to ensure that the results are both meaningful and valid. The funding provided by Cancer Council SA is critical to facilitating personal with these critical skills to assist in the key elements of the project that are required for it to be successful.



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