NGS-based diagnostic testing compared to single-marker genetic testing (SMGT), has the potential to improve testing efficiency and to identify more cancer patients who could benefit from targeted therapies, but the impact on outcomes and total costs of care is uncertain. Recent studies using simulation modeling informed with data from the Flatiron Health database, representing curated electronic health record-derived clinical information from 191 oncology practices, has shown only moderate cost effectiveness of NGS vs. SGMT for patients with advanced non-small cell lung cancer (aNSCLC). The data suggests, however, that efforts to increase the proportion of patients who receive targeted therapies would improve the cost-effectiveness of NGS. To effectively inform access and reimbursement policy decisions there is a need to examine the NGS value proposition from the perspective of all stakeholders.
Author(s) and affiliation(s): Lotte Steuten (Office of Health Economics, London, UK); Bernardo Goulart (Fred Hutchinson Cancer Research Center, Seattle, WA, US & Seattle Cancer Care Alliance, Seattle, WA, US); Neal J. Meropol (Flatiron Health, New York, NY, US & Case Western Reserve University, Cleveland, OH, US); Daryl Pritchard (Personalized Medicine Coalition, Washington, DC, US); and Scott D. Ramsey (Fred Hutchinson Cancer Research Center, Seattle, WA, US)
Event: ISPOR 2019
Location: New Orleans, LA, United States
Date: 20/05/2019
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THE CLINICAL AND ECONOMIC VALUE OF GENETIC SEQUENCING IN CANCER CARE
1. ISPORNEWORLEANS
THE CLINICAL AND
ECONOMIC VALUE
OF GENETIC
SEQUENCING IN
CANCER CARE
Prof. Lotte Steuten, PhD
VP and Head of Consulting, The Office of Health Economics, London, UK
Honorary Professor at City, University of London, UK
MAY2019
2. Neal MeropolLotte Steuten
Issue panel agenda
2
Health Economics Real World Data
Sean Tunis and Daryl Pritchard
Head-to-Head
Perspectives:
● Economics: Lotte Steuten, Office of Health Economics, London, UK
● Data: Neal Meropol, Flatiron Health, New York, NY, USA
● Payer: Sean Tunis, Center for Medical Technology Policy, Baltimore, MD, USA
● Industry: Daryl Pritchard, Personalized Medicine Coalition, Washington, DC, USA
3. US coverage broad genomic testing
March 18, 2018:
Foundation Medicine Announces Final Coverage Determination from CMS, including
coverage for FoundationOne CDxTM across all solid tumors
● Patient Eligibility: Coverage for Stage III and IV, metastatic, recurrent, relapsed or refractory
cancers
● Breadth of coverage for F1CDxTM: Coverage across all solid tumors
● Repeat testing: Covered when a new primary diagnosis is made and the patient meets other
criteria
Final NCD significantly expands patient access beyond preliminary NCD from Nov 2017.
4. Will Broad Genomic Testing improve survival in:
A: >75%
B: 50-75% of all tested patients
C: 25-49%
D: <25%
Broad Genomic Testing - what’s your expectation?
6. SHIVA trial (n=195, any tumor type, France)
Lancet Oncology 2015;16:1324-1334
The use of molecularly targeted agents
outside their indications does not
improve progression-free survival
compared with treatment at physician's
choice in heavily pretreated patients with
cancer.
7. Moscato-1 trial (n=1035, advanced cancer, US)
1035 patients enrolled
● 948 (91.5%) Successfully underwent biopsy
● 843 (81.4%) Obtained a molecular analysis
● 411 (39.7%) Had an actionable mutation
● 199 (19.2%) Received targeted therapy
● 22 (2.1%) Achieved a major objective response
● 11% of the 199 who received a targeted therapy
Cancer Discovery 2017; 7(6); 1–10
This study suggests that high-throughput genomics could improve outcomes in a
subset of patients with hard-to-treat cancers. Although these results are encouraging,
only 2% of the successfully screened patients benefited from this approach.
Expanding drug access could increase the percentage of patients who benefit.
8. NCI-MATCH trial (n=6000, advanced tumors)
Target N=6000; as of March 2017:
● 4702 Patients with tumor samples
● 3516 (74.8%) Patients received test results
● 722 (15.4%) Gene abnormality matching treatment
● 495 (10.5%) Patients enrolled for treatment
Results presented at ASCO, 2018
Target Drug # pts
assigned
# pts
treated
# pts with
PR/CR
ORR
PIK3CA* Taselisib 65 0 0%
FGFR AZD4547 70 50 2 4%
HER 2 T-DM1 37 3 8%
*Excluded patients with KRAS or PTEN mutations
9. Large US retrospective cohort study n=5688, advanced NSCLC
Presley CJ, et al. Association of Broad-Based Genomic Sequencing With Survival Among Patients With
Advanced Non-Small Cell Lung Cancer in the Community Oncology Setting. JAMA. 2018;320(5):469-477.
10. Cost-effectiveness of multiplex NGS testing in cancer
Rationale:
● Targetable mutations ; Costs NGS platforms
Objectives:
1. Estimate cost effectiveness of mNGS testing vs. single gene(s) testing in
advanced NSCLC (and melanoma)
2. Explore the cost effectiveness across different scenarios
3. Inform design of future studies
Funding: Personalized Medicine Coalition (private non-profit) to Fred Hutch
Cancer Research Institute, Seattle (WA) US
12. Other data sources:
● CMS Fee Schedule
● 2017 ASP drug costs
● published literature or expert
opinion.
Main data source: Flatiron Health Database (2011-2016)
15. Large US retrospective cohort study n=5688, advanced NSCLC
Presley CJ, et al. Association of Broad-Based Genomic Sequencing With Survival Among Patients With
Advanced Non-Small Cell Lung Cancer in the Community Oncology Setting. JAMA. 2018;320(5):469-477.
19. Conclusion
●Under an extremely narrow definition of benefit, MGPS is expected to be at the
high end of what is considered cost-effective compared to SMGT
●decision uncertainty driven almost entirely by uncertainty in treatments efficacy and to a lesser
extent by costs of MGPS tests vs. SMGT tests
●Increasing access to targeted therapies in patients with actionable mutations
improves cost-effectiveness of MGPS versus SMGT
●assuming incremental costs and outcomes of targeted treatments remain unchanged
●value of implementation
●Increasing number of actionable mutations to test for will make MGPS less
costly than stacking single gene tests
20. Strengths and weaknesses
●One of the first studies to explore potential cost-effectiveness of MGPS vs SMGT
using a nationwide oncology patient database
●Broad range of sensitivity and scenario analyses to better understand the key value drivers of
MGPS testing
●Estimates of overall survival are based on retrospective cohort data
●prone to biases associated with non-randomized study results
●higher generalizability to “real world’ setting
●Health-Related Quality of Life not included, neither are critical aspects of testing
that produce value e.g.:
●Value of knowing
●Real option value
●Value of hope
21. Further research considerations
●Value in prospective studies that directly compare MGPS versus SMGT incl.
clinical and economic endpoints
●Who will/should pay for these given what’s driving uncertainty?
●Reimbursement of these tests is still largely cost-based and not indication specific
●US: imbalance between evidence requirements for pharma and Dx, considering
the opportunity for each to achieve appropriate reimbursement
●Future research should address, e.g.:
●How value breaks down by:
- net health gain and cost-offsets in responder group (pharma reward) and
- avoided health losses and associated costs of AEs in non-responders (Dx reward)
●Real option value
●Quantify value of knowing and value of hope
- Considering patient out of pocket costs
22. Acknowledgements:
Fred Hutch: Scott Ramsey, Bernardo
Goulart and HICOR team
Flatiron Health: Neal Meropol and data
team
Personalized Medicine Coalition: Daryl
Pritchard
Stakeholder Advisory Group
Payer Advisory Panel
22
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To enquire about additional information and analyses,
please contact:
Lotte Steuten, PhD
Vice President and Head of Consulting
lsteuten@ohe.org