Medical and health sciences
- Analysis of next-generation sequence data
- Development of bioinformatics software, tools and databases
- Single-cell data analysis
- Molecular diagnostics
- Cancer biology
An increasing number of lung cancer patients undergoes treatment with immune checkpoint inhibitors (ICIs), hence, only 20 to 45% of patients will respond. Nevertheless, existing predictive biomarkers cannot accurately separate non-responders from responders and require invasive tumor tissue sampling. Lately, evidence accumulates that tumor regressions under ICIs are reflected by changes in specific immune cells in peripheral blood. We aim to develop a blood-based test that interrogates these systemic immune profile signatures. For this project. In a first step, I will use single cell RNA-sequencing that will help to catalog, in an unbiased way, the immune cell content of responders and non-responders. This catalog will form the basis to optimize and evaluate deconvolution of bulk RNA sequencing of blood cells to predict the peripheral blood immune content. The predicted immune content will subsequently be used as an input to train predictive models for immunotherapy response. Importantly, I will also test our assay on prospectively collected blood samples and compare its performance with currently used predictive biomarkers. By combined sampling of tumor, lymph nodes and blood we will decipher the observed differences. In conclusion, this research project will bring us closer to deliver a non-invasive test for immunotherapy-treated lung cancer patients. The results can be extrapolated and validated in other adult cancer types treated with these same inhibitors.