Non-invasive prediction of immune-therapy response in lung cancer patients (NIPIT)

01 October 2020 → Ongoing
Funding by bilateral agreement (private and foundations)
Research disciplines
  • Medical and health sciences
    • Computational transcriptomics and epigenomics
    • Single-cell data analysis
    • Cancer therapy
lung cancer immunotherpay non-invasive test single-cell RNA sequencing predictive biomarker deconvolution
Project description

An increasing number of patients with, amongst others, non-small cell lung cancer (NSCLC), melanoma and bladder cancer, undergoes treatment with immune checkpoint inhibitors. Globally speaking, 20 to 45% of patients will respond to this class of medicines in mono- or combination therapy. Nevertheless, existing predictive biomarkers cannot accurately separate non-responders from responders and require invasive tumor tissue sampling. Lately, evidence accumulates that tumor regressions under immune checkpoint inhibitors are reflected by quantitative and qualitative changes in specific immune cells in peripheral blood. We aim to develop a blood-based test by using these systemic immune profile signatures. For this project, we can rely on a biobank of longitudinally prelevated blood samples from immune checkpoint inhibitor treated lung cancer patients. Using these samples, we will apply a stepwise, transcriptomics-based approach to define immunological events that are associated with response or disease progression under immune checkpoint inhibitor treatment. To this end, we will use single cell RNA-sequencing that will help us to catalogue, in an unbiased way, the immune cell content of responders and non-responders. Given the complex workload and high costs of single-cell RNA sequencing, we will subsequently optimize and evaluate computational deconvolution of bulk RNA sequencing of blood cells to map the peripheral blood immune-reactome. To further reduce costs of the final test, the most predictive transcript-level modules defining specific immune events will inform the design of capture RNA sequencing or PCR methods as well as focused flow cytometrybased marker panels for cross-validation on the same cohort. Moreover, as DNA methylation is known to be more celltype specific then RNA expression levels, we will generate bulk DNA methylation profiles to deconvolve and define the immune-cell content in an independent way. In a final validation of the different predictive models on prospectively
collected blood samples, we will compare the performance of the different approaches. In conclusion, this research project will bring us closer to deliver non-invasive theranostic tests for immunotherapy-treated lung cancer patients. The results will be generated in the context of immune checkpoint inhibitor treated lung cancer patients, but can be extrapolated and validated in other adult cancer types treated with these same inhibitors. Besides, if successful, this approach might be valuable for predictive biomarker identification for other immunotherapy approaches.