Background: Tyrosine kinase inhibitors (TKIs) significantly improve clinical outcomes in patients with non-small cell lung cancer due to anaplastic lymphoma kinase (ALK) gene rearrangement. However, the rate of relapse with TKIs is high owing to the development of resistance mutations during treatment. Repeated biopsies during disease progression are crucial for elucidating the molecular mechanisms underlying the development of resistance to ALK inhibitors. Analysis of cell-free DNA (cfDNA) obtained from plasma is a novel approach for tumor genotyping.
Methods: In this mixed prospective and retrospective observational cohort study, we investigated the clinical feasibility of continuous quantitative monitoring of ALK-acquired mutations in plasma obtained from patients with ALK+ non-small cell lung cancer by using a highly sensitive and specific droplet digital polymerase chain reaction (ddPCR) assay. We enrolled nine patients, including three treatment-naïve patients recently diagnosed with ALK+ non-small cell lung cancer via tissue biopsy and expected to receive ALK TKIs and six patients already receiving ALK TKIs. Plasma samples were collected from these patients every 3 months. cfDNA was extracted from 66 samples during the study period, and 10 ALK mutations were simultaneously evaluated.
Results: The numbers of samples showing the G1202R, C1156Y, G1269A, F1174L, T1151ins, and I1171T mutations were 32, 16, 5, 4, 1, and 1, respectively. The L1196M, L1152R, V1180L, and S1206Y mutations were not detected. Correlation analyses between progression-free survival and the time from treatment initiation (or treatment modification) to the detection of resistance mutations revealed that although resistance mutations may occur before a drug change becomes necessary, there is a duration during which the disease does not progress.
Conclusions: Our findings suggest that real-time quantitative monitoring of ALK resistance mutations during the response period could provide a time course of changes while acquiring resistance mutations. This information would be beneficial for designing an appropriate treatment strategy.
Keywords: ALK mutations; Tumor genotyping; digital polymerase chain reaction (digital PCR); lung cancer; resistance mutations.
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