Deciphering the Molecular Profile of Lung Cancer: New Strategies for the Early Detection and Prognostic Stratification

Recent advances in radiological imaging and genomic analysis are profoundly changing the way to manage lung cancer patients. Screening programs which couple lung cancer risk prediction models and low-dose computed tomography (LDCT) recently showed their effectiveness in the early diagnosis of lung tumors. In addition, the emerging field of radiomics is revolutionizing the approach to handle medical images, i.e., from a “simple” visual inspection to a high-throughput analysis of hundreds of quantitative features of images which can predict prognosis and therapy response. Yet, with the advent of next-generation sequencing (NGS) and the establishment of large genomic consortia, the whole mutational and transcriptomic profile of lung cancer has been unveiled and made publicly available via web services interfaces. This has tremendously accelerated the discovery of actionable mutations, as well as the identification of cancer biomarkers, which are pivotal for development of personalized targeted therapies. In this review, we will describe recent advances in cancer biomarkers discovery for early diagnosis, prognosis, and prediction of chemotherapy response.