According to findings released ahead of the 2018 ASCO Annual Meeting, the use of next-generation sequencing (NGS) for patients with metastatic non–small cell lung cancer (NSCLC) can save Center for Medicare and Medicaid Services (CMS) payers $1.4 million to $2.1 million. The findings additionally showed that NGS saved commercial insurance providers more than $250,000.<br />
Nathan A. Pennell, MD, PhD
According to findings released ahead of the 2018 ASCO Annual Meeting, the use of next-generation sequencing (NGS) for patients with metastatic nonsmall cell lung cancer (NSCLC) can save Center for Medicare and Medicaid Services (CMS) payers $1.4 million to $2.1 million. The findings additionally showed that NGS saved commercial insurance providers more than $250,000.
This study investigated the economic impact of NGS compared with sequential single-gene testing modalities to detect genomic alterations in patients with metastatic NSCLC using a decision analytic model. Lead author Nathan A. Pennell, MD, PhD, presented these findings during a presscast ahead of the conference.
One million hypothetical newly diagnosed patients with Medicare and commercial health plans were included in the model. The inputs into the model included estimates of eligible patients, CMS and commercial unit costs for tests, time to test results, and rebiopsy rates.
There are 4 genomic alterations in NSCLC that have FDA-approved therapies associated with themEGFR,ALK,ROS1, andBRAFmutations. There are multiple other genomic alterations includingMET,HER2,RET, andNTRK, which have each shown responses to targeted therapies in clinical trials.
The four genetic testing strategies included in the study were NGS, sequential, exclusionary, and panel. If a patient was positive for an alteration after undergoing one of these tests, they moved onto targeted therapy. If negative, the patient received standard therapy.
"Our results showed that there were substantial cost savings associated with upfront NGS testing compared to all other strategies," said Pennell, an associate professor of medicine at Cleveland Clinic. "NGS had a faster turnaround time than either sequential or exclusionary testing, which is critically important for sick lung cancer patients to make sure they get their treatment as quickly as possible. Waiting a month or longer is simply no longer viable for patients because these treatments work very well and they get sick very quickly."
NGS is a single test that can concurrently test forEGFR,ALK,ROS1,BRAF,KRAS,MET,HER2,RET, andNTRK1gene alterations, among others.
Sequential testing first identified EGFR mutations, and once those results were received, patients would either be treated or move onto the next test, with a certain number of patients dropping off because of exhausted tissue, which required a rebiopsy. Exclusionary testing was similar to sequential, but it started with KRAS mutations. Since that is the most common mutation, 25% of patients who were positive could be excluded from needing further testing.
The third testing modality was panel, where single-gene tests were all done at the same time, rather than sequentially.
Notably, the single-gene testing focused only on the alterations that have led to FDA-approved therapiesEGFR,ALK,ROS1, andBRAF. After those initial tests were done, 50% of patients went on to broader testing for potential alterations that might lead to a clinical trial, Pennell said.
For NGS and panel, estimated time to receive results was 2 weeks, which is 2.7 and 2.8 weeks faster than exclusionary and sequential, respectively. Investigators also considered that after each individual test, a certain percentage of patients would have exhausted their tissue and need to go onto another biopsy. Although, Pennell said that many patients would not be able to undergo a rebiopsy.
In addition to sequential single-gene tests being deemed time consuming, they have the potential to use up tissue and DNA, often requiring a repeat biopsy. NGS testing can be done all at once, resulting in the shortest turnaround time.
NGS identified a higher percentage of patients with targetable alterations, as each of the single-gene test strategies included patients dropping off and never having their alteration identified due to exhausted tissue.
"One of the things that is a real challenge is going back to resequence, or if a new [alteration] emerges," said ASCO president Bruce E. Johnson, MD, FASCO, and co-moderator of the presscast.
A budget impact model was included in the study, which showed that increasing the percentage of patients tested by NGS versus other strategies lead to consistent decreases in cost to payers overall.
For 1 million-member health plans, investigators suggested that 2066 tests would be paid for by CMS and 156 would be paid for by commercial insurers. This is based on the age and number of people in the United States with metastatic NSCLC. Using CMS reimbursement, NGS represented savings of $1,393,678 versus exclusionary, $1,530,869 versus sequential, and $2,140,795 versus panel, investigators noted.
Moreover, NGS was the least expensive testing modality by $3,809 versus exclusionary, $127,402 versus sequential, and $250,842 versus panel.
Johnson, who is also professor of medicine at Harvard Medical School, and of adult oncology at Dana-Farber Cancer Institute, added that new data coming out for RET alterations may make it a target that needs to be tested for in patients with NSCLC.
"Within most NGS panels, there is somewhere between 50 and 400 genesso you get a lot more information at a cost that is competitive or less. This will be welcome news to people who are taking care of ordering these gene panels," added Johnson.
"The bottom line is ultimately using [NGS] upfront resulted in the fastest turnaround time, the highest percentage of patients with targetable alterations identified, and overall, the lowest cost to payers," Pennell concluded.
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.J Clin Oncol. 2018;36 (suppl; abstr 9031).