CHICAGO—Upfront, comprehensive genetic testing in advanced lung cancer is cost-effective, according to a new study.
A model estimated that upfront next-generation sequencing (NGS) leads to the same or shorter wait time for results and the lowest payer cost to establish genomic alterations for newly diagnosed metastatic non-small cell lung cancer (NSCLC), which can inform treatment decisions. In the model, NGS saved as much as $2.1 million for Medicare and more than $250,000 for commercial insurance providers.
“The field of lung cancer treatment is moving at a rapid pace, and we need to fully characterize genomic changes to determine the best treatment for patients shortly after they are diagnosed. 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 will be identified in the near future. Therefore, it becomes even more imperative to find a cost-effective gene test that can quickly identify a large number of gene mutations that can be targeted with treatments,” said lead author Nathan A. Pennell, MD, PhD, Co-Director of the Cleveland Clinic Lung Cancer Program.
Pennell presented the results of the study at a press briefing ahead of the 2018 ASCO Annual Meeting (Abstract 9031).
Genomic testing in NSCLC is now standard of care to detect known oncogenic drivers to inform treatment decisions. “Trials have shown significant benefit in metastatic NSCLC patients with targetable genomic alterations, with FDA-approved treatments for EGFR, ALK, ROS1, and BRAF and drugs in clinical trials for the others (MET, HER2, RET, and NTRK1),” noted Pennell.
“Testing strategies include next-generation sequencing or the use of single-gene tests. Sequential single-gene tests are time-consuming and use up tissue, often requiring repeat biopsy. NGS testing identifies multiple alterations in a single timely test. Eight percent of patients need re-biopsy, but only 30 percent get one, after every single-gene test for additional testing as tissue is exhausted.”
Pennell and colleagues designed a model to assess the economic impact of NGS versus sequential single-gene testing to detect genomic alterations in a hypothetical cohort of newly diagnosed metastatic NSCLC patients. The study modeled four genomic testing strategies in NSCLC patients. Eligible patients for testing were estimated from a hypothetical health plan of 1 million enrollees, including both U.S. Centers for Medicare and Medicaid services (CMS) or commercial payer perspectives.
After single-gene testing of alterations with FDA-approved therapies, 50 percent of remaining patients with sufficient tissue underwent further testing for alterations without FDA-approved therapies for potential clinical trial enrollment.
The known genes that are altered in NSCLC include EGFR, ALK, ROS1, BRAF, MET, HER2, RET, and NTRK1. Of those, EGFR, ALK, ROS1, and BRAF can be targeted with approved treatments. The other genetic changes can be targeted with investigational agents that are being tested in clinical trials. Newer tests also look at PD-L1 expression to predict if 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 NGS (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 that combined testing for EGFR, ALK, ROS1, and BRAF, followed by either single-gene or NGS testing for changes in other genes.
The model assumed that some participants who did not receive upfront NGS might need to have another biopsy to test for additional genes due to insufficient amount of tissue from the first biopsy and that the need for re-biopsy would be lessened with upfront, comprehensive NGS testing. It also accounted for the time it took to get test results back after biopsy samples were sent to the laboratory, costs for each type of gene testing, and the estimated number of people with metastatic NSCLC in the U.S. that could be tested.
Based on the number and age of people with metastatic NSCLC in the U.S. 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 NGS and panel results to be processed, while it would take 4.7 or 4.8 weeks to process the exclusionary and sequential tests, respectively.
Applying economic factors to CMS payments, NGS testing would save about $1.4 million compared to exclusionary testing, more than $1.5 million compared to sequential testing, and about $2.1 million compared to panel testing. For commercial health plans, NGS would save $3,809 compared to exclusionary testing and $250,842 compared to panel testing.
“The bottom line is, ultimately, using the best single test upfront results in the fastest turnaround time, the highest percentage of patients with targetable alterations identified, and overall the lowest cost to payers,” said Pennell.
The next step is to look at actual health systems and evaluate these differences, testing cost efficiency in a real-world setting, he said.
“This study really shows that by doing all the testing at the same time, you can both get results back more quickly as well as get information,” noted ASCO President Bruce E. Johnson, MD, Professor of Medicine at Harvard Medical School, Boston. “This study looked at an NGS panel of eight genes, but most of the NGS panels contain somewhere between 50 and 400 genes, so you get a lot more information with this at a cost that's competitive or less. So this will be welcome news to people who are ordering these gene panels.”
Mark L. Fuerst is a contributing writer.