Skye McKay

BACKGROUND: The return of research results (RoR) remains a complex and well-debated issue. Despite the debate, actual data related to the experience of giving individual results back, and the impact these results may have on clinical care and health outcomes, is sorely lacking. Through the work of the Australian Pancreatic Cancer Genome Initiative (APGI) we: (1) delineate the pathway back to the patient where actionable research data were identified; and (2) report the clinical utilisation of individual results returned. Using this experience, we discuss barriers and opportunities associated with a comprehensive process of RoR in large-scale genomic research that may be useful for others developing their own policies. METHODS: We performed whole-genome (n = 184) and exome (n = 208) sequencing of matched tumour-normal DNA pairs from 392 patients with sporadic pancreatic cancer (PC) as part of the APGI. We identified pathogenic germline mutations in candidate genes (n = 130) with established predisposition to PC or medium-high penetrance genes with well-defined cancer associated syndromes or phenotypes. Variants from candidate genes were annotated and classified according to international guidelines. Variants were considered actionable if clinical utility was established, with regard to prevention, diagnosis, prognostication and/or therapy. RESULTS: A total of 48,904 germline variants were identified, with 2356 unique variants undergoing annotation and in silico classification. Twenty cases were deemed actionable and were returned via previously described RoR framework, representing an actionable finding rate of 5.1%. Overall, 1.78% of our cohort experienced clinical benefit from RoR. CONCLUSION: Returning research results within the context of large-scale genomics research is a labour-intensive, highly variable, complex operation. Results that warrant action are not infrequent, but the prevalence of those who experience a clinical difference as a result of returning individual results is currently low.

BACKGROUND: Disentangling the interplay between experience-based intuition and theory-informed implementation is crucial for identifying the direct contribution theory can make for generating behaviour changes needed for successful evidence translation. In the context of 'clinicogenomics', a complex and rapidly evolving field demanding swift practice change, we aimed to (a) describe a combined clinician intuition- and theory-driven method for identifying determinants of and strategies for implementing clinicogenomics, and (b) articulate a structured approach to standardise hypothesised behavioural pathways and make potential underlying theory explicit. METHODS: Interview data from 16 non-genetic medical specialists using genomics in practice identified three target behaviour areas across the testing process: (1) identifying patients, (2) test ordering and reporting, (3) communicating results. The Theoretical Domains Framework (TDF) was used to group barriers and facilitators to performing these actions. Barriers were grouped by distinct TDF domains, with 'overarching' TDF themes identified for overlapping barriers. Clinician intuitively-derived implementation strategies were matched with corresponding barriers, and retrospectively coded against behaviour change techniques (BCTs). Where no intuitive strategies were provided, theory-driven strategies were generated. An algorithm was developed and applied to articulate how implementation strategies address barriers to influence behaviour change. RESULTS: Across all target behaviour areas, 32 identified barriers were coded across seven distinct TDF domains and eight overarching TDF themes. Within the 29 intuitive strategies, 21 BCTs were represented and used on 49 occasions to address 23 barriers. On 10 (20%) of these occasions, existing empirical links were found between BCTs and corresponding distinct TDF-coded barriers. Twenty additional theory-driven implementation strategies (using 19 BCTs on 31 occasions) were developed to address nine remaining barriers. CONCLUSION: Clinicians naturally generate their own solutions when implementing clinical interventions, and in this clinicogenomics example these intuitive strategies aligned with theoretical recommendations 20% of the time. We have matched intuitive strategies with theory-driven BCTs to make potential underlying theory explicit through proposed structured hypothesised causal pathways. Transparency and efficiency are enhanced, providing a novel method to identify determinants of implementation. Operationalising this approach to support the design of implementation strategies may optimise practice change in response to rapidly evolving scientific advances requiring swift translation into healthcare.