OLCIR: Optimization of Lung Cancer Therapy with Ionizing Radiation

Ionizing radiation (IR) leads to DNA double-strand breaks and can therefore be used as cancer treatment. However, some tumors are less responsive to radiation treatment because of their underlying molecular profile. Our ultimate goal is to understand the reaction of lung cancer cells with different phenotypes to IR on a molecular level to then provide the best therapy options for lung cancer patients with IR therapy.

Various effective therapies exist for specific cancer types, but naturally, some tumors respond better to certain therapies than others based on their underlying molecular profile. For instance, lung cancers with mutations in the Serine/Threonine-Protein Kinase LKB1 gene are known to be less sensitive to ionizing radiation (IR) and, thus, have a worse clinical outcome. In our BMBF-funded (German Federal Ministry for Education and Research) project, we will analyze how lung cancer cells with different molecular profiles will act on different doses of IR and the resulting DNA double-strand breaks (DSBs) on a cellular level. We want to investigate relevant DSB repair mechanism switches, including underlying genetic or regulatory changes that appear in those pathways dependent on different molecular phenotypes and IR doses.
We will analyze lung cancer samples from openly accessible databases (e.g., TCGA, GEO) to obtain further in-depth insights into their molecular profiles, such as the presence of pathogenic variants in cancer-associated genes or the overexpression of MYC or RTK genes. Furthermore, we will integrate data from our project partners in Essen and Frankfurt, who will perform wet lab experiments on lung cancer cell lines and mice with varying doses of IR. We will perform variant calling, expression, and splicing analysis on RNA data with different tools to identify differentially expressed genes, allele-specific expression, and aberrant transcripts. After these steps, we will do an enrichment analysis of the differentially expressed transcripts and identify enriched signaling pathways. To prioritize biologically relevant transcripts, we will utilize various Machine Learning (ML) models. TCGA clinical and imaging data will enhance the informative value of the ML approach. From there on, we will develop molecular interaction networks using systems biology standards.
With our work, we will contribute towards the more in-depth investigation of the molecular genetic mechanisms of lung cancer cells and analyze the network of DSB recognition proteins and signal transduction cascades. This will support us in predicting the response of tumors to specific therapies, which may ultimately pave the way for more personalized therapies.