Current and Future Molecular Testing in NSCLC, What Can We Expect from New Sequencing Technologies?

Abstract

Recent changes in lung cancer care, including new approvals in first line and the introduction of high-throughput molecular technologies in routine testing led us to question ourselves on how deeper molecular testing may be helpful for the optimal use of targeted drugs. In this article, we review recent results in the scope of personalized medicine in lung cancer. We discuss biomarkers that have a therapeutic predictive value in lung cancer with a focus on recent changes and on the clinical value of large scale sequencing strategies. We review the use of second- and third-generation EGFR and ALK inhibitors with a focus on secondary resistance alterations. We discuss anti-BRAF and anti-MEK combo, emerging biomarkers as NRG1 and NTRKs fusions and immunotherapy. Finally, we discuss the different technical issues of comprehensive molecular profiling and show how large screenings might refine the prediction value of individual markers. Based on a review of recent publications (2012⁻2018), we address promising approaches for the treatment of patients with lung cancers and the technical challenges associated with the identification of new predictive markers.

Keywords: NGS; lung cancer; molecular analysis; oncogene drivers.

Figure 1

Resistance mutations in EGFR, ALK, and ROS1 drivers. (A) Description and gene location of EGFR resistance mutations to first-second and to third EGFR-TKIs; (B) description and gene location of ALK Tyrosine kinase resistance mutations to ALK inhibitors described for ALK fusions; (C) description and gene location of ROS1 Tyrosine kinase resistance mutations to ROS1 inhibitors described for ROS1 fusions.

Figure 2

Lung cancer molecular screening options. Figure 2 shows the different technical options developed to identify oncogene drivers in lung cancer from single gene tests to WES including methods’ specificities, mutation cut-off, genomic coverage/panel size, and sample requirements.

Figure 3

Lung cancer testing algorithm, an example in clinics. Figure 3 shows the different levels of molecular testing from single gene to WES, the expected findings, and potential clinical impacts.