Leveraging patient-driven research to improve rational therapy selection in (ROS1+) non-small cell lung cancer.

Lung cancer is a heterogeneous disease with high prevalence and mortality. Despite improvements in treatment, lung cancer takes over 1,000 lives each day in Europe alone. The benefit of targeted therapy is illustrated for ALK-driven non-small cell lung cancer (NSCLC), with a median survival of 7 years, compared to <20% overall 5 year survival for lung cancer in general. As targeted inhibitors do not actively kill tumor cells, aberrant cells remain dormant in the patient. To tackle inevitable resistance and disease progression, novel generation drugs are needed to target the resistance mechanism. However, for ROS1-fusions, a relatively recently described oncogene representing 1-2% of NSCLC, only a single targeted drug is currently approved. Hence, patients resort to chemotherapy, off-label use or clinical studies on disease progression. Due to the scarcity of ROS1+ NSCLC, clinical decisions are guided by sporadic case reports and in vitro experiments based on synthetic setups in non-human cell models. Here, we couple modern genome engineering and computational prediction on drug/target interactions with patient-driven efforts to generate relevant disease models. We will introduce 13 known and predicted resistance mutations into 6 patient-derived cell lines, followed by experimental and computational evaluation of available targeted drugs. Complementing experimental and computational data results in an objective model to guide clinical decision making in rare cancer types.

Keywords:LUNG CANCER

Disciplines:Cancer therapy