Globally, lung cancer is the leading cause of cancer-related death. The majority of non-small cell lung cancer (NSCLC) tumours express epidermal growth factor receptor (EGFR), which allows for precise and targeted therapy in these patients. The dysregulation of EGFR in solid epithelial cancers has two distinct mechanisms: either a kinase-activating mutation in EGFR (EGFR-mutant) and/or an overexpression of wild-type EGFR (wt-EGFR). The underlying mechanism of EGFR dysregulation influences the efficacy of anti-EGFR therapy as well as the nature of resistance patterns and secondary mutations. This review will critically analyse the mechanisms of EGFR expression in NSCLC, its relevance to currently approved targeted treatment options, and the complex nature of secondary mutations and intrinsic and acquired resistance patterns in NSCLC.

PURPOSE: Cystic fibrosis (CF), caused by pathogenic variants in the CF transmembrane conductance regulator (CFTR), affects multiple organs including the exocrine pancreas, which is a causal contributor to cystic fibrosis-related diabetes (CFRD). Untreated CFRD causes increased CF-related mortality whereas early detection can improve outcomes. METHODS: Using genetic and easily accessible clinical measures available at birth, we constructed a CFRD prediction model using the Canadian CF Gene Modifier Study (CGS; n = 1,958) and validated it in the French CF Gene Modifier Study (FGMS; n = 1,003). We investigated genetic variants shown to associate with CF disease severity across multiple organs in genome-wide association studies. RESULTS: The strongest predictors included sex, CFTR severity score, and several genetic variants including one annotated to PRSS1, which encodes cationic trypsinogen. The final model defined in the CGS shows excellent agreement when validated on the FGMS, and the risk classifier shows slightly better performance at predicting CFRD risk later in life in both studies. CONCLUSION: We demonstrated clinical utility by comparing CFRD prevalence rates between the top 10% of individuals with the highest risk and the bottom 10% with the lowest risk. A web-based application was developed to provide practitioners with patientspecific CFRD risk to guide CFRD monitoring and treatment.

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