Patrick Micke – Cartography of the lung cancer microenvironment to improve therapy

The introduction of immunotherapy gives advanced lung cancer patients, for the first time, the chance of long-term survival. However, only a few patients experience response to checkpoint inhibitors, and patient stratification for available therapy options presents a major clinical challenge.

Based on deep-spatial analyses of patient tissue, we aim to understand relevant molecular mechanisms that drive tumorigenesis and mediate sensitivity or resistance to therapy. The cancer tissue is evaluated comprehensively by RNA and DNA sequencing in combination with advanced in situ methods, including multiplex immune fluorescence and in situ sequencing techniques.

Analysis of multidimensional data

We generate data sets with multidimensional images that can describe immune cell densities, patterns, and interactions in patient cancer tissue. To evaluate these multidimensional data, we apply analysis pipelines based on deep learning methods. Recently, we developed proximity ligations assays to identify in situ protein interaction. This allows locating checkpoint PD-PD-L1 interaction) and signal pathway activation (e.g., PDGFRB/GRB2) on cellular levels.

This analysis adds unique dimensions of diagnostic information to explain the response to immune checkpoint inhibition, but also provides an original diagnostic tool to guide targeted therapy in the absence of driver mutations or situations of resistance. Ultimately, we hope our studies will improve clinical diagnosis and suggest candidate targets for novel treatment strategies.