Methods for actionable gene fusion detection in lung cancer: now and in the future.
Pasquale PisapiaFrancesco PepeRoberta SgarigliaMariantonia NacchioGianluca RussoGianluca GragnanoFloriana ConticelliMaria SalatielloCaterina De LucaIlaria GirolamiAlbino EccherAntonino IaccarinoClaudio BellevicineElena VigliarUmberto MalapelleGiancarlo TronconePublished in: Pharmacogenomics (2021)
Although gene fusions occur rarely in non-small-cell lung cancer (NSCLC) patients, they represent a relevant target in treatment decision algorithms. To date, immunohistochemistry and fluorescence in situ hybridization are the two principal methods used in clinical trials. However, using these methods in routine clinical practice is often impractical and time consuming because they can only analyze single genes and the quantity of tissue material is often insufficient. Thus, novel technologies, able to test multiple genes in a single run with minimal sample input, are being under investigation. Here, we discuss the utility of next-generation sequencing and nCounter technologies in detecting simultaneous gene fusions in NSCLC patients.
Keyphrases
- end stage renal disease
- genome wide
- clinical trial
- clinical practice
- genome wide identification
- chronic kidney disease
- copy number
- small cell lung cancer
- ejection fraction
- machine learning
- prognostic factors
- dna methylation
- peritoneal dialysis
- randomized controlled trial
- transcription factor
- advanced non small cell lung cancer
- patient reported outcomes
- deep learning
- study protocol
- current status
- phase ii
- open label
- circulating tumor cells
- patient reported