Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer.

Abstract

Simple SummaryIt is already known that DNA alterations do not fully recapitulate the complex nature of a tumor or its potential interaction with specific treatments. Therefore, in order to establish more precise and effective therapeutic approaches for non-small cell lung cancer, tumors will have to be characterized in a more accurate and comprehensive way. In this regard, transcription profiling has already demonstrated its utility in further stratifying patients in a much more refined way than genomic alterations. Examples of this include the definition of intrinsic subtypes in colorectal cancer, breast, or non-small cell lung cancer tumors based on their expression patterns. Moreover, the characterization of the activity levels of the pathways involved in tumor progression and development is bound to better predict the specific response to a certain therapy than isolated biomarkers such as specific DNA alterations or the expression of single genes. This is especially relevant in the context of patients not harboring targetable alterations or those developing resistance after treatment. 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.