Oral Presentation NSW State Cancer Conference 2023

Perspectives of health professionals and patients on implementation of a predictive model of response to immunotherapies in advanced melanoma (#54)

Rehana A Salam 1 2 , Tuba N Gide 2 3 4 , Anne E Cust 1 2 3 , Richard A Scolyer 2 3 4 5 , Georgina V Long 2 3 4 6 , Ines P Silva 2 3 4 7 , Peter Ferguson 2 3 5 , Graham J Mann 2 8 , Caroline Watts 1 9 , James S Wilmott 1 2 3 4 , Andrea L Smith 1 10
  1. The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia, Sydney
  2. Melanoma Institute Australia, The University of Sydney, Sydney, New South Wales, Australia, Sydney
  3. University of Sydney, Camperdown, NSW, Australia
  4. Charles Perkins Centre, The University of Sydney, Sydney, New south Wales, Australia, Sydney
  5. Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital & NSW Health Pathology Camperdown NSW Australia, Sydney
  6. Royal North Shore and Mater Hospitals, Sydney, NSW, Australia, Sydney
  7. Westmead and Blacktown Hospitals, Sydney, New South Wales, Australia, Sydney
  8. John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia, Canberra
  9. Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia , Sydney
  10. Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia, Sydney

Background

Immunotherapies have significantly improved the overall survival for patients with advanced melanoma. However, almost half of such patients either do not respond to the therapy or develop resistance to it, subjecting patients to ineffective treatments and unnecessary costs. Predictive biomarker testing can ensure that the patient receives the most effective therapy thereby reducing costs and toxicities. More recently, there has been an increased interest in utilising predictive biomarker tests (combining information from patients’ tumour and disease characteristics) within an artificial intelligence-based model to predict the likelihood of the patient responding to standard of care immunotherapies. This study was conducted prior to and alongside a clinical validation study of routine predictive biomarker testing for patients with advanced melanoma to gain an insight into the factors associated with successful implementation of this intervention.

Methods

We conducted semi-structured interviews (n=25) with health professionals and patients guided by the EPIS (Exploration, Preparation, Implementation, and Sustainment) framework to understand enablers and barriers of implementation. Data analysis involved inductive and deductive thematic analysis using the Consolidated Framework for Implementation Research (CFIR).

Results

Health providers and patients consistently reported ‘clinical utility of predictive biomarker test’ as a major enabler, recognising that an effective test would assist in identifying likely non-responders and consequently avoid the side effects and other costs of ineffective treatment. Trust in data scientists, adaptability of the test platform, pre-existing organisational infrastructure, and supportive organisational implementation culture were also identified as factors that would support implementation. Lack of validated predictive biomarkers, resources and costs required to implement the test, and health providers’ knowledge, beliefs and concerns around the test were the principal factors that would impede implementation.

Conclusion

This study identifies factors influencing implementation of biomarkers as predictors of treatment response to immunotherapy for melanoma and potential strategies to overcome barriers impeding their transition from discovery to the clinic.