This phase II trial studies how well osimertinib works in treating patients with non-small cell lung cancer with EGFR exon 20 insertion mutation that is stage IIIB-IV or has come back after a period of improvement (recurrent). Osimertinib may stop the growth of tumor cells by blocking some of the enzymes needed for cell growth.
Participants must have a pathologically-confirmed diagnosis of non-small cell lung cancer (NSCLC)
Participants must have advanced disease - either stage IV disease, stage IIIB disease not amenable to definitive multi-modality therapy, or recurrent disease after a prior diagnosis of stage I-III disease. All staging is via the American Joint Committee on Cancer (AJCC)/International Association for the Study of Lung Cancer (IASLC) 7th edition staging criteria
An EGFR exon 20 insertion mutation must be detected in the tumor tissue. Patients may be enrolled in the study based on an exon 20 insertion EGFR mutation detected by any Clinical Laboratory Improvement Act (CLIA)-certified tissue assay
NOTE: Testing results are to be submitted via Medidata Rave and the study chair or delegate will review the reports
None
Single group
Biospecimen Collection
Procedure/SurgeryUndergo collection of blood samples
Computed Tomography with Contrast
Procedure/SurgeryUndergo CT with contrast
Echocardiography Test
Procedure/SurgeryUndergo ECHO
Magnetic Resonance Imaging
Procedure/SurgeryUndergo MRI
Multigated Acquisition Scan
Procedure/SurgeryUndergo MUGA
Osimertinib
DrugGiven PO
Best objective response
Will be evaluated via Response Evaluation Criteria in Solid Tumors 1.1 criteria. Categorical data, such as response rates (complete response + partial response), will be compared using Fisher's exact tests with a one-sided type I error rate of 10%; multivariable logistic regression modeling will be used to adjust for the effect of any covariates that are associated with these categorical outcomes. Any continuous outcomes will be analyzed using Wilcoxon rank sum test, and multivariable linear regression models may be used to adjust for multiple associations with outcome. Point estimates of all endpoints will be accompanied by the corresponding two-sided 80% confidence intervals. The method of Atkinson and Brown will be used for the estimation of the confidence interval for response.
Progression-free survival (PFS)
Will be estimated using the Kaplan-Meier method, and Cox proportional hazards models will be used to estimate hazard ratios. Comparisons of groups will be made using the logrank test and Cox modeling. Point estimates of all endpoints will be accompanied by the corresponding two-sided 80% confidence intervals. The method of Atkinson and Brown will be used for the estimation of the confidence interval for response.
Overall survival (OS)
Will be estimated using the Kaplan-Meier method, and Cox proportional hazards models will be used to estimate hazard ratios. Comparisons of groups will be made using the logrank test and Cox modeling. Point estimates of all endpoints will be accompanied by the corresponding two-sided 80% confidence intervals. The method of Atkinson and Brown will be used for the estimation of the confidence interval for response.
Incidence of adverse events
Will be graded according to Common Terminology Criteria for Adverse Events version 5.0. Categorical data, such as toxicity, will be compared using Fisher's exact tests with a one-sided type I error rate of 10%; multivariable logistic regression modeling will be used to adjust for the effect of any covariates that are associated with these categorical outcomes. Any continuous outcomes will be analyzed using Wilcoxon rank sum test, and multivariable linear regression models may be used to adjust for multiple associations with outcome. Point estimates of all endpoints will be accompanied by the corresponding two-sided 80% confidence intervals. The method of Atkinson and Brown will be used for the estimation of the confidence interval for response.
Characterization of molecular markers of response to treatment in circulating tumor deoxyribonucleic acid (ctDNA)
Initial analyses of data will utilize tests for association comparing the frequency of a particular genomic aberration at baseline and at progression (for example, using McNemar's test) and association between alterations and baseline characteristics (for example, using Fisher's exact test). More sophisticated analyses may include multivariable logistic regression modeling and/or competing risks analysis, however it is expected that analyzable results will not be obtained from 100% of patients (either due to things like assay failure, inability to biopsy at progression due to poor patient health, etc).
Biomarkers of response to treatment through retrospective analyses of pre-treatment tumor tissue
The rate of change in allelic fraction at a particular time point may be compared to baseline measures of ctDNA and that measure will be analyzed for association with patient demographics and/or disease characteristics using the Kruskal Wallis test. Landmark analyses of PFS and OS in which the landmark time is defined by the ctDNA measurements at a particular time point, may be used as well.
Identification of resistance mechanisms to osimertinib through post-progression tumor biopsies and ctDNA
Presence or absence of mutations in plasma will be analyzed for association with other variables using Fisher's exact test. To account for the repeated measures of plasma over time, we may potentially use these data as time varying covariates in multivariable Cox models to study their impact on outcomes like PFS and OS.
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