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Identification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer.

ABSTRACT: Despite the clinical success of the third-generation EGFR inhibitor osimertinib as a first-line treatment of EGFR-mutant non-small cell lung cancer (NSCLC), resistance arises due to the acquisition of EGFR second-site mutations and other mechanisms, which necessitates alternative therapies. Dacomitinib, a pan-HER inhibitor, is approved for first-line treatment and results in different acquired EGFR mutations than osimertinib that mediate on-target resistance. A combination of osimertinib and dacomitinib could therefore induce more durable responses by preventing the emergence of resistance. Here we present an integrated computational modeling and experimental approach to identify an optimal dosing schedule for osimertinib and dacomitinib combination therapy. We developed a predictive model that encompasses tumor heterogeneity and inter-subject pharmacokinetic variability to predict tumor evolution under different dosing schedules, parameterized using in vitro dose-response data. This model was validated using cell line data and used to identify an optimal combination dosing schedule. Our schedule was subsequently confirmed tolerable in an ongoing dose-escalation phase I clinical trial (NCT03810807), with some dose modifications, demonstrating that our rational modeling approach can be used to identify appropriate dosing for combination therapy in the clinical setting.

SUBMITTER: Poels KE

PROVIDER: S-EPMC8211846 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStudies of targeted therapy resistance in lung cancer have primarily focused on single-gene alterations. Based on prior work implicating apolipoprotein b mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) mutagenesis in histological transformation of epidermal growth factor receptor (EGFR)-mutant lung cancers, we hypothesized that mutational signature analysis may help elucidate acquired resistance to targeted therapies.Patients and methodsAPOBEC mutational signatures derived from an Food and Drug Administration-cleared multigene panel [Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT)] using the Signature Multivariate Analysis (SigMA) algorithm were validated against the gold standard of mutational signatures derived from whole-exome sequencing. Mutational signatures were decomposed in 3276 unique lung adenocarcinomas (LUADs), including 93 paired osimertinib-naïve and -resistant EGFR-mutant tumors. Associations between APOBEC and mechanisms of resistance to osimertinib were investigated. Whole-genome sequencing was carried out on available EGFR-mutant lung cancer samples (10 paired, 17 unpaired) to investigate large-scale genomic alterations potentially contributing to osimertinib resistance.ResultsAPOBEC mutational signatures were more frequent in receptor tyrosine kinase (RTK)-driven lung cancers (EGFR, ALK, RET, and ROS1; 25%) compared to LUADs at large (20%, P < 0.001); across all subtypes, APOBEC mutational signatures were enriched in subclonal mutations (P < 0.001). In EGFR-mutant lung cancers, osimertinib-resistant samples more frequently displayed an APOBEC-dominant mutational signature compared to osimertinib-naïve samples (28% versus 14%, P = 0.03). Specifically, mutations detected in osimertinib-resistant tumors but not in pre-treatment samples significantly more frequently displayed an APOBEC-dominant mutational signature (44% versus 23%, P < 0.001). EGFR-mutant samples with APOBEC-dominant signatures had enrichment of large-scale genomic rearrangements (P = 0.01) and kataegis (P = 0.03) in areas of APOBEC mutagenesis.ConclusionsAPOBEC mutational signatures are frequent in RTK-driven LUADs and increase under the selective pressure of osimertinib in EGFR-mutant lung cancer. APOBEC mutational signature enrichment in subclonal mutations, private mutations acquired after osimertinib treatment, and areas of large-scale genomic rearrangements highlights a potentially fundamental role for APOBEC mutagenesis in the development of resistance to targeted therapies, which may be potentially exploited to overcome such resistance.

Dataset Information

Identification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer.

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