Abstract 58P
Background
ESMO Precision Medicine Working Group recommends use of next generation sequencing (NGS) for the detection of actionable genomic biomarkers in metastatic non-small cell lung cancer (mNSCLC). This modeling study was designed to assess the potential improvement of biomarker detection in Spanish patients with mNSCLC when using NGS versus combinations of single gene tests (SGTs).
Methods
A dynamic decision tree-based model was developed using Microsoft Excel in the US and adapted for Spain to compare NGS (including comprehensive and small NGS panels) with various combinations of common SGTs. NGS included ALK, EGFR, ROS-1, BRAF, KRAS, RET, MET, and NTRK1/2/3, whereas SGTs covered ALK, EGFR, ROS-1 and BRAF. All strategies included PD-L1, and the model assumed 100% of patients being tested. Input data were obtained from the published literature, including ESMO guidelines. The likelihood of correctly diagnosing patients was based on prevalence of actionable biomarkers, testing strategy, and sensitivity and specificity of tests. Treatment was assigned per ESMO guidelines (September 15, 2020) for patients with actionable biomarkers considering treatment options available in Spain.
Results
The model predicted that, compared with SGT strategies, the use of NGS-based diagnostic testing in patients with mNSCLC can improve detection of actionable biomarkers by relative 51.6% (54.7% vs 36.1%); therefore for every 100 patients tested with NGS testing, 19 additional patients with actionable biomarkers would be detected. Patients initially receiving suboptimal first-line treatment due to incorrect biomarker test results decreased by 25.8% ( decrease from13,136 to 9,749 patients).
Conclusions
Testing strategies with NGS are more comprehensive than SGT strategies in the detection of actionable biomarkers in patients with mNSCLC. We conclude that better diagnostic accuracy of NGS vs SGTs strategies will translate into improved targeting of mNSCLC therapy and associated health outcomes.
Editorial acknowledgement
Greg Plosker and Karen Goa (Rx Communications) provided writing support on this disclosure.
Legal entity responsible for the study
Eli Lilly and Company.
Funding
Eli Lilly and Company.
Disclosure
A. Mota, A. Molero, K. Taipale, A. Gulati, M. Jen, L. Hess, B. Goel: Financial Interests, Institutional, Full or part-time Employment: Eli Lilly and Company.