Serial Plasma Comprehensive Genomic Profiling Captures Therapy Resistance and Guides Management of Non-Small Cell Lung Cancer.

UNLABELLED: Plasma comprehensive genomic profiling (pCGP) is implemented in the clinical care of non-small cell lung cancer (NSCLC). Although less well documented, serial pCGP may also guide the management of oncogene-driven NSCLC after first progression. In this study, we assessed the clinical value of serial pCGP, focusing on EGFR-mutant NSCLC. We conducted a retrospective study of 718 patients with NSCLC who underwent pCGP between 2015 and 2022. Clinical genomic data were programmatically extracted from the data workflows of the Johns Hopkins Lung Cancer Precision Medicine Center of Excellence. After variant annotation and actionability characterization, we examined the prevalence and evolving comutation patterns across serial pCGP, focusing on genomic mechanisms of tyrosine kinase inhibitor (TKI)-acquired resistance in EGFR-mutant NSCLC. A total of 718 patients had 818 instances of pCGP, with 79 patients having longitudinal pCGP (range, 2-5). pCGP uniquely informed management in 13% of patients (n = 92), both at initial diagnosis and on serial genotyping. This occurred predominantly through the identification of actionable mutations when tissue testing was unavailable. Among 214 patients with EGFR-mutant NSCLC, pCGP identified PI3K pathway alterations in 11% after first-line therapy. BRAF V600E (3%) and MET exon 14 skipping mutations (3%) emerged after third-generation TKI therapy. After TKI progression, 31 patients (22%) with EGFR-mutant disease had actionable pCGP findings, of whom 18 (58%) were matched to targeted therapy. Serial pCGP can inform treatment decisions in patients with NSCLC. In those with EGFR-mutant disease, pCGP at progression identifies actionable drivers of therapy resistance, enabling therapeutic intervention.

SIGNIFICANCE: Our study provides critical insights into the routine implementation of serial pCGP within a thoracic oncology program, supported by a precision oncology informatics framework, in a tertiary healthcare institution. We show that pCGP enables genotyping when tissue testing is not feasible and identifies actionable mutations at resistance. The clinical implementation of pCGP can drive improved clinical outcomes by matching patients with effective interventions in a timely and minimally invasive manner.