Decoding the lung cancer microenvironment through spatial approaches

Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related deaths worldwide, with an estimated 47,000 people being diagnosed with the condition every year in the UK. Non-small cell lung cancer (NSCLC) accounts for the majority of cases, with over half of people diagnosed dying within one year of diagnosis and the 5-year survival being less than 18%.

A revolution in cancer treatment has emerged from the use of antibody-based immunotherapies that modulate immune responses against tumours. Immune-checkpoint inhibitors (ICI) work by blocking receptor ligand interactions of molecules, such as Programmed Death-1 checkpoint (PD-1), that are involved in regulating T cell activation or function, clonal expansion, and systemic T cell distribution after binding counter ligands (e.g., PD-L1/2). Since its first approval in 2015, ICI therapy targeting PD-1/PD-L1 has been established as the standard of care for patients with NSCLC. However, ICI therapies show significant clinical benefit for only a minority of patients that experience durable responses.

Thus, working towards personalised medicine approaches for treating this disease is of paramount importance. This project will bring together two emerging methodologies to better develop individualized treatment options for cancer patients. 3D culture of cancer cells and patient tissue will be combined with spatial biology approaches, offering a cutting-edge platform for testing targeted therapies preclinically.

By developing an analysis pipeline to explore emerging, publicly available spatial transcriptomics datasets and integrating single-cell RNA-seq reference datasets for spatial deconvolution, we will generate a standardised approach that can be applied to various samples. In that manner, making use of a biobank of pre-invasive lesions of different severities will be essential when trying to understand the early stages of lung cancer and improve early detection. Moreover, the use of clinically relevant 3D models of lung cancer will allow the discovery and validation of biomarkers involved in disease progression and could have the potential to assist clinical decisions.