Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.



Published on

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

Published in: Healthcare
  • Be the first to comment

  • Be the first to like this


  1. 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. 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. 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. 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?
  5. 5. What have clinical trials shown, worldwide?
  6. 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. 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. 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. 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. 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
  11. 11. Model-based cost- effectiveness analysis
  12. 12. Other data sources: ● CMS Fee Schedule ● 2017 ASP drug costs ● published literature or expert opinion. Main data source: Flatiron Health Database (2011-2016)
  13. 13. Patient characteristics 30.1% 23.3%
  14. 14. Treatments received 2.7% difference 6.8% difference
  15. 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.
  16. 16. Cost Inputs
  17. 17. not a massive investment just over 3 weeks Incremental Cost-Effectiveness
  18. 18. Scenario Analysis
  19. 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. 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. 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. 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
  23. 23. To keep up with the latest news and research, subscribe to our blog. OHE’s publications may be downloaded free of charge from our website. OHE Southside 105 Victoria Street London SW1E 6QT United Kingdom Telephone +44 (0)20 77478850 FOLLOW US To enquire about additional information and analyses, please contact: Lotte Steuten, PhD Vice President and Head of Consulting