PDO as Translational Model for Biomarker Discovery in NSCLC

This study leverages treatment-naïve patient-derived organoids (PDOs) as a powerful translational model to drive biomarker discovery in non-small cell lung cancer (NSCLC). Current biomarker discovery efforts rely predominantly on publicly available datasets that predict cell surface localization at the RNA level. By focusing on the proteomic characterization of the cell surface, specifically targeting N-glycosylated proteins, we aim to capture the dynamic landscape of membrane proteins that play key roles in tumor biology.We performed a surfaceome screening on a cohort of 15 NSCLC organoids and 5 matched normal lung organoids after in vitro treatment with saline, cisplatin and B7H3 ADC. Live-cell biotin labeling followed by enrichment enabled isolation of glycosylated cell surface proteins. Downstream LC-MS/MS-based proteomics allowed quantitative profiling of both enriched and total protein fractions. Concurrently, transcriptomic profiling via RNA-seq and WGS will enable cross-comparative analysis. This proteogenomic approach will be critical given that transcript-level data do not always reflect actual protein expression, particularly for membrane proteins that may be subject to complex post-translational modifications and regulation. Comprehensive multiomic in silico analyses are currently ongoing to integrate our experimental data with external databases, including CPTAC, TCGA, TEMPUS, and GTEx. These computational analyses are designed to refine our list of candidate biomarkers based on high protein expression and differential expression pre- and post-treatment.Our approach underscores the potential of utilizing advanced proteomic and transcriptomic methodologies in tandem with cutting-edge in silico analyses to better understand tumor biology. Further analysis and validation studies will be needed to ensure that the biomarkers we identify have the highest translational potential and can be used as effective diagnostic tools.