Nathan A. Pennell, MD, PhD
AN ECONOMIC model comparing different types of genetic testing in metastatic non–small cell lung cancer (NSCLC) found using next-generation sequencing to test for all known lung cancer–related gene changes at the time of diagnosis was less costly and faster than sequentially testing one or a limited number of genes at a time. According to the model, next-generation sequencing saved as much as $2.1 million for Medicare and more than $250,000 for commercial insurance providers. The study was presented at the 2018 ASCO Annual Meeting and was reported in a poster session by Nathan A. Pennell, MD, PhD, Co-Director of the Cleveland Clinic Lung Cancer Program, and colleagues.1
“The field of lung cancer treatment is moving at a rapid pace, and we need to fully characterize genomic changes to determine the best available treatment for patients shortly after they are diagnosed,” stated Dr. Pennell. “Today, many treatment decisions are guided by the presence or absence of certain genetic changes in a patient’s tumor, and I expect that several more genes that can be effectively targeted will be identified in the near future. Therefore, it becomes even more imperative to find a cost-efficient gene test that can quickly identify a large number of gene mutations that can be targeted with treatments.”
Study Design
GENETIC TESTING of the tumor is crucial to guide optimal treatment for NSCLC. Many different tests are available today, but there is no accepted standard for when and how the testing should be performed. The authors designed their model to determine which gene-testing approach is most cost-and time-efficient. The model used data from the Centers for Medicare and Medicaid Services (CMS) and U.S. commercial health plans to estimate costs for each modality.
The known genes altered in NSCLC include EGFR, ALK, ROS1, BRAF, MET, HER2, RET, and NTRK1. Of them, EGFR, ALK, ROS1, and BRAF can be targeted with approved treatments. The other genetic changes can be successfully targeted with investigational agents that are being tested in clinical trials. Newer tests also look at programmed cell death ligand 1 (PD-L1) expression to predict whether a tumor is likely to respond to immunotherapy.
In the model, patients with newly diagnosed metastatic NSCLC received PD-L1 testing and testing for the known lung cancer–related genes using one of four different approaches:
- Upfront next-generation sequencing (all eight NSCLC-related genes and KRAS were tested at once)
- Sequential tests (one gene at a time was tested)
- Exclusionary KRAS test followed by sequential tests for changes in other genes if KRAS was not mutated (if KRAS mutations were found, the tumor was not tested for other mutations because it is rare to have more than one of these genes mutated in an individual lung cancer)
- Panel test (combined testing for EGFR, ALK, ROS1, and BRAF), followed by either a single-gene or next-generation sequencing test for changes in other genes.
The model assumed that some participants who did not receive upfront next-generation sequencing might need to have another biopsy to test for additional genes (due to tissue from the first biopsy being insufficient) and that the need for rebiopsy would be reduced with upfront, comprehensive next-generation sequencing. During a press briefing where the study was presented in advance of the ASCO Annual Meeting, the authors noted that approximately 8% of patients treated with a single-gene test require a repeat biopsy.
RELATED ABSTRACTS AT ASCO 2018
- Cost-effectiveness of multigene panel sequencing for advanced non–small cell lung cancer patients (Abstract 6513)
- Effects of comprehensive genomic testing in a large NSCLC cohort: Racial and survival impacts (Abstract 8526)
- Routine use of a modest next-generation sequencing panel provides additional clinically useful data beyond single-gene testing in NSCLC and is fit for purpose as a clinical assay (Abstract 8540)
- Upfront next generation sequencing in NSCLC: A publicly funded perspective (Abstract 12062)
In addition, the model accounted for the time it took to get test results back after biopsy samples were sent to the lab, costs for each type of gene testing, and the estimated number of people with metastatic NSCLC in the United States who could be tested.
Key Findings
BASED ON the number and age of people diagnosed with metastatic NSCLC in the United States annually, the researchers estimated that for 1 million–member health plans, 2,066 tests would be paid for by CMS and 156 would be paid for by commercial insurers. The model also estimated that it would take 2 weeks for the next-generation sequencing and panel results to be processed, whereas it would take almost 5 weeks to process the exclusionary and sequential tests, respectively.
Applying economic factors to CMS payments, next-generation sequencing for 2,222 patients will save about $1.4 million compared to exclusionary testing, over $1.5 million compared to sequential testing, and about $2.1 million compared to panel testing. For commercial health plans, next-generation sequencing would save $3,809 compared to exclusionary testing and $250,842 compared to panel testing.
With next-generation sequencing, patients initiated appropriate therapy 2.8 and 2.7 weeks faster than sequential or exclusionary testing, respectively.
Dr. Pennell noted a limitation of this study is that the model is based on several assumptions. The researchers’ next step is to look at actual health systems and evaluate these differences, testing cost-efficiency in a real-world setting. ■
DISCLOSURE: This study received funding from Novartis. Dr. Pennell has had a consulting or advisory role with AstraZeneca, Lilly, and Regeneron and has received (institutional) research funding from Astex Pharmaceuticals, AstraZeneca, Celgene, Clovis Oncology, Genentech, Merck, NewLink Genetics, and Pfizer. For full disclosures of all study authors, see the abstract at meetinglibrary.asco.org.
REFERENCE
1. Pennell NA, Mutebi A, Zhou Z, et al: Economic impact of next generation sequencing vs sequential single-gene testing modalities to detect genomic alterations in metastatic non-small cell lung cancer using a decision analytic model. 2018 ASCO Annual Meeting. Abstract 9031. Presented June 3, 2018.