Investigating the Retained Inhibitory Effect of Cobimetinib against p.P124L Mutated MEK1: A Combined Liquid Biopsy and in Silico Approach

Simple Summary This work focuses on the peculiar contribution made by molecular dynamics simulation and in silico tools, in the choice of an effective second line therapy for a BRAF-mutated melanoma patient who developed resistance to the undergoing targeted therapy with BRAF/MEK inhibitors. Among the MEK inhibitors, we identified a drug alternative to trametinib, able to block the target even in the presence of a damaging mutation, and supported these findings, gathered by an in silico approach, with a liquid biopsy tracking of the response to treatment. The evolution of the disease, before and after the therapy change, was followed by analysis of the circulating tumor DNA and circulating melanoma cells. The systemic treatment of metastatic melanoma has radically changed, due to an improvement in the understanding of its genetic landscape and the advent of targeted therapy. However, the response to BRAF/MEK inhibitors is transitory, and big efforts were made to identify the mechanisms underlying the resistance. We exploited a combined approach, encompassing liquid biopsy analysis and molecular dynamics simulation, for tracking tumor evolution, and in parallel defining the best treatment option. The samples at different time points were collected from a BRAF-mutant melanoma patient who developed an early resistance to dabrafenib/trametinib. The analysis of the circulating tumor DNA (ctDNA) identified the MEK1 p.P124L mutation that confers resistance to trametinib. With an in silico modeling, we identified cobimetinib as an alternative MEK inhibitor, and consequently suggested a therapy switch to vemurafenib/cobimetinib. The patient response was followed by ctDNA tracking and circulating melanoma cell (CMC) count. The cobimetinib administration led to an important reduction in the BRAF p.V600E and MEK1 p.P124L allele fractions and in the CMC number, features suggestive of a putative response. In summary, this study emphasizes the usefulness of a liquid biopsy-based approach combined with in silico simulation, to track real-time tumor evolution while assessing the best treatment option.