Predicting therapeutic outcomes to immune checkpoint inhibitors used in cancer treatment_012022

Immune checkpoint inhibitors (anticancer medicines that aim to help your immune system fight cancer) are important emerging treatments option for cancer which are resulting in significant benefit for many patients. However, response and toxicity to immune checkpoint inhibitors is highly unpredictable. For example, up to 40% of patients who initiate immune checkpoint inhibitors do not respond, while 40% may experience serious toxicity.

Using the diverse range of data collected from clinical trials, it is possible to unlock clinical predictions that enable an improved understanding of the therapeutic and adverse outcomes from medicines. Specifically, this program will use data science techniques to assess the association between available clinicopathological data with therapeutic and adverse events outcomes from immune checkpoint inhibitors. Evaluated predictors will be prioritised according to biological/clinical plausibility and prior evidence. This includes emerging evidence that outcomes following immune checkpoint inhibitor initiation are based on an individual’s demographic, clinical, laboratory, disease, and genetic characteristics. Using this data from a diverse range of patients may enable expected response and adverse effect profiles to be predicted, which will help facilitate patients and clinicians to make better decisions regarding whether to commence certain immune checkpoint inhibitor treatments.