Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.
Sara Hijazo-PecheroAnia AlayRaúl MarínNoelia VilariñoCristina Muñoz-PinedoAlberto VillanuevaDavid SantamaríaErnest NadalXavier SoléPublished in: Cancers (2021)
Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.
Keyphrases
- gene expression
- single cell
- end stage renal disease
- high throughput
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- case report
- squamous cell carcinoma
- machine learning
- rna seq
- dna methylation
- copy number
- deep learning
- genome wide
- palliative care
- radiation therapy
- patient reported outcomes
- oxidative stress
- papillary thyroid
- heat shock
- big data
- quantum dots
- single molecule
- risk factors
- smoking cessation
- replacement therapy
- mass spectrometry
- real time pcr