RNA-Based Assay for Next-Generation Sequencing of Clinically Relevant Gene Fusions in Non-Small Cell Lung Cancer.
Caterina De LucaFrancesco PepeAntonino IaccarinoPasquale PisapiaLuisella RighiAngela ListìLorenza GrecoGianluca GragnanoSevero CampioneGianfranco De DominicisFabio PagniRoberta SgarigliaMariantonia NacchioRossella TufanoFloriana ConticelliElena VigliarClaudio BellevicineDiego Luigi CortinovisSilvia NovelloMiguel Ángel Molina-VilaRafael RosellGiancarlo TronconeUmberto MalapellePublished in: Cancers (2021)
Gene fusions represent novel predictive biomarkers for advanced non-small cell lung cancer (NSCLC). In this study, we validated a narrow NGS gene panel able to cover therapeutically-relevant gene fusions and splicing events in advanced-stage NSCLC patients. To this aim, we first assessed minimal complementary DNA (cDNA) input and the limit of detection (LoD) in different cell lines. Then, to evaluate the feasibility of applying our panel to routine clinical samples, we retrospectively selected archived lung adenocarcinoma histological and cytological (cell blocks) samples. Overall, our SiRe RNA fusion panel was able to detect all fusions and a splicing event harbored in a RNA pool diluted up to 2 ng/µL. It also successfully analyzed 46 (95.8%) out of 48 samples. Among these, 43 (93.5%) out of 46 samples reproduced the same results as those obtained with conventional techniques. Intriguingly, the three discordant results were confirmed by a CE-IVD automated real-time polymerase chain reaction (RT-PCR) analysis (Easy PGX platform, Diatech Pharmacogenetics, Jesi, Italy). Based on these findings, we conclude that our new SiRe RNA fusion panel is a valid and robust tool for the detection of clinically relevant gene fusions and splicing events in advanced NSCLC.
Keyphrases
- advanced non small cell lung cancer
- copy number
- small cell lung cancer
- genome wide
- genome wide identification
- high throughput
- newly diagnosed
- epidermal growth factor receptor
- end stage renal disease
- machine learning
- single cell
- ejection fraction
- real time pcr
- dna methylation
- peritoneal dialysis
- deep learning
- transcription factor
- patient reported outcomes
- gene expression
- circulating tumor
- tyrosine kinase