Integrated prediction of lung cancer histology and molecular profiles from small bronchoscopic tumor specimens

Introduction Decisions regarding lung cancer treatment rely on comprehensive molecular and histological characterization, however, limited tumor tissue availability poses major challenges for such analysis. This proof-of-concept study investigated whether preserving small bronchoscopic tumor specimens in RNAlater could enable integrated molecular and histological assessment using a multicomponent tool. Methods Bronchoscopic small tumor specimens, including bronchial forceps biopsies and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), were collected during initial diagnostic work-up for suspected lung cancer. After sampling for routine diagnostics, additional research specimens from the same tumor lesion were obtained and preserved in RNAlater. DNA, RNA, and proteins were extracted for further analyses of 45 cases with confirmed non-resectable lung cancer. RNA was analyzed using a NanoString gene expression assay and RNA sequencing to assess treatment-predictive fusion gene status, METex14 skipping events and histological subtypes. Mutation detection was performed using massive parallel sequencing and proteome-based non-small cell lung cancer (NSCLC) subtypes were further evaluated using mass spectrometry. Results High-quality nucleic acids and proteins were successfully retrieved for the entire cohort. Forty-four tumors were successfully analyzed for gene fusions. The histological subtypes retrieved based on gene expression were adequate in relation to the clinical pathologist's diagnosis, although with some discrepancies. Mutation detection demonstrated total concordance with clinical routine testing and comprehensive proteomics data were successfully acquired. Conclusion RNAlater-preserved small samples provide high-quality nucleic acids and proteins for multi-omics analyses, enabling comprehensive tumor profiling from a single small bronchoscopic specimen.