Whole transcriptome targeted gene quantification provides new insights on pulmonary sarcomatoid carcinomas.
Greta AlìRossella BrunoAnello Marcello PomaOrnella AffinitoAntonella MonticelliPaolo PiaggiSara RicciardiMarco LucchiFranca MelfiAntonio ChellaSergio CocozzaGabriella FontaniniPublished in: Scientific reports (2019)
Pulmonary sarcomatoid carcinomas (PSC) are a rare group of lung cancer with a median overall survival of 9-12 months. PSC are divided into five histotypes, challenging to diagnose and treat. The identification of PSC biomarkers is warranted, but PSC molecular profile remains to be defined. Herein, a targeted whole transcriptome analysis was performed on 14 PSC samples, evaluated also for the presence of the main oncogene mutations and rearrangements. PSC expression data were compared with transcriptome data of lung adenocarcinomas (LUAD) and squamous cell carcinomas (LUSC) from The Cancer Genome Atlas. Deregulated genes were used for pathway enrichment analysis; the most representative genes were tested by immunohistochemistry (IHC) in an independent cohort (30 PSC, 31 LUAD, 31 LUSC). All PSC cases were investigated for PD-L1 expression. Thirty-eight genes deregulated in PSC were identified, among these IGJ and SLMAP were confirmed by IHC. Moreover, Forkhead box signaling and Fanconi anemia pathways were specifically enriched in PSC. Finally, some PSC harboured alterations in genes targetable by tyrosine kinase inhibitors, as EGFR and MET. We provide a deep molecular characterization of PSC; the identification of specific molecular profiles, besides increasing our knowledge on PSC biology, might suggest new strategies to improve patients management.
Keyphrases
- genome wide
- squamous cell
- bioinformatics analysis
- single cell
- chronic kidney disease
- end stage renal disease
- healthcare
- pulmonary hypertension
- squamous cell carcinoma
- gene expression
- rna seq
- newly diagnosed
- transcription factor
- young adults
- prognostic factors
- electronic health record
- copy number
- deep learning
- machine learning
- big data
- binding protein
- single molecule
- atomic force microscopy
- mass spectrometry
- long non coding rna