A rationale for the poor response to alectinib in a patient with adenocarcinoma of the lung harbouring a STRN-ALK fusion by artificial intelligence and molecular modelling: a case report.

Abstract

Background: Non-small cell lung cancers (NSCLCs) with ALK fusions are effectively treated with ALK tyrosine kinase inhibitors (TKIs). The widespread use of next-generation sequencing (NGS) assays to study the molecular profile of NSCLCs, can identify rare fusion partners of ALK. Therapy decisions are made without considering which fusion partner is present and its potential oncogenic properties. However clinical and experimental studies have shown that the 5' partner of kinase fusion variants could have a biological role in the response to targeted therapies. The objective of this report was to study the impact of a rare fusion partner of ALK on the specific TKI treatment with an in silico molecular modelling evaluating the efficiency of the protein-ligand site.

Case description: Here we describe a case of a stage IV lung adenocarcinoma with a rare striatin STRN-ALK fusion with a Partial Response of short duration to alectinib and no response to lorlatinib at progression. We investigated a computational molecular model of the protein translated from the translocated gene to suggest a mechanistic explanation for the clinical findings.

Conclusions: Our model calculations suggested that the effect of the translocation was to induce the dimerization of ALK into a complex that distorted the binding pocket, which is the same for alectinib, lorlatinib and crizotinib. The distortion of the binding pocket observed in the simulations also provides a rationale to explain the different variations of efficacy of alectinib, lorlatinib and crizotinib caused by the translocation. Our observations suggest that molecular modelling based on artificial intelligence (AI) tools may offer potential predictive information in fusions with rare partner genes. Further retrospective and prospective studies are warranted to demonstrate the predictive robustness of these tools.