Feasibility of precision oncology virtual tumour boards to optimise direct point-of-care management and clinical trial enrolment of advanced cancer patients: New models for personalised oncology

Abstract

Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimised cancer care. Research suggests that multidisciplinary tumour boards (TB) can informcancer patient management, however their role in the era of telemedicine, value-based care and precision oncology are not well defined, including TB effects in utilisation and interpretation of genomic test results, directing targeted therapy, optimising clinical trial enrolment and the overall cost-benefit utility of this approach. We aimed to evaluate the feasibility and clinical utility of a precision oncology virtual tumour boards (VTB) program, and its clinical impact on community-dwelling patients with advanced solid tumours to facilitate point-of-care management and clinical trial enrolment, as well as the financial impact and potential outcomes of the intervention. Methods: We report the results on the initial 10 VTB-evaluated patients of an ongoing prospective qualitative case study screened between October 2016 and March 2017. Eligibility required patients with advanced solid tumours that were either rare or refractory to standard therapy and written informed consent. Cases were evaluated by a patient-activated multidisciplinary VTB that included pathologists, genetic and molecular diagnostic specialists, oncologists, radiation-oncologists, radiologists, and clinical research coordinators. Prioritisation of recommendations by VTB was done based on a proprietary algorithm and knowledge-base, including data such as survival, response rates, primary oncologist and patient preferences, comorbidities, and anticipated adverse events as per CTCAE. Tumour biomarkers and next-generation-sequencing by CLIA-compliant settings were evaluated to provide cost-effective predictive therapeutic prioritisation, estimated time-to-results, and clinical trial allocation. A Markov model by incorporating clinical, utility and cost data was developed to evaluate the economic outcome of the intervention regarding survival and cost-of-care. Using a proprietary knowledge-base, parametric survival analyses of patient-level progression-free (PFS) and overall survival (OS) data from the clinical trial reported data in known sources were performed. Average sales prices public data sources were used to estimate unit costs associated with treatment and duration of subsequent active therapies. Oncologymodelled patient pathways, expert opinion and Delphi panel methods were used for some assumptions. Results: The mean patient age was 51.6 years-old, male (70%) and female (30%). The diagnoses included non-small cell lung cancer, breast cancer, colon, ovarian, cutaneous squamous cell carcinoma, astrocytoma, cutaneous melanoma, ewing's sarcoma, small cell lung cancer and pancreatic cancer. The VTB identified clinical trials for 80% of these heavily treated patients, and 50% of patients decided to pursue a clinical trial. VTB resulted in data that impacted clinical decisions in 100% of cases. VTB achieved 88% cost reduction compared to standard therapies due to clinical trial enrolment (517,000USD vs 61,000USD). Treatment options as prioritised by VTB also provided an estimated reported PFS advantage (6.3 months) compared to standard therapy (3.6 months). Patient and oncologist satisfaction was high. Conclusions: These results demonstrate the feasibility and benefits of incorporating precision oncology VTB into clinical practice, including its value as clinical trial recruitment engine and as a cost-effective, value-based measure for innovative care delivery models.