Measuring immunomodulators availability predicts response to immune checkpoint inhibitors treatment in cancer patients

The development of immune checkpoint inhibitors (ICIs) signaled a breakthrough in the fight against cancer and tremendously moved forward the field of immune oncology. Since 2011, seven different antibodies have been approved by the FDA for treatment of more than 25 cancer types. While 43.5% of cancer patients eligible to treatment with ICI, the overall response rate is still low in many of cancer types (12.5% pan-cancer) and over 60% of patients will suffer from severe adverse effects without achieving clinical benefit from the ICI treatment. Currently, response predicting biomarker to ICI therapies are based on gene and protein expression but do not provide the precise information of the protein function.

To overcome those challenges, we have developed a functional system that can mimic T-cell interactions with tumor associated IC ligands and quantify their abundance. By implementing the current ICI therapies into our system, we can precisely measure the functionality of each individual tumor associated ligand. Initially, we have demonstrated the precision of our system in both cell lines and PDX studies. As of today, in an ongoing retrospective clinical trial, we have shown that our system is superior to current prediction methods and can predict response to ICI treatment with high sensitivity (93%) and specificity (95%) in variety of cancer types. Moreover, we can use our system to screen novel potential ICI therapeutics and fit personalized treatments for individual patients.

Utilizing the advantages of our novel system will greatly contribute to the clinical benefits from ICI treatments.