Cancer prevention in cancer predisposition syndromes: A protocol for testing the feasibility of building a hereditary cancer research registry and nurse navigator follow up model.
Holly EtchegaryApril PikeRebecca PuddesterKathy WatkinsMike WarrenVanessa FrancisMichael WoodsJane GreenSevtap SavasMelanie SealZhiwei GaoSusan AveryFiona CurtisJerry McGrathDonald MacDonaldT Nadine BurryLesa DawsonPublished in: PloS one (2022)
Monogenic, high penetrance syndromes, conferring an increased risk of malignancies in multiple organs, are important contributors to the hereditary burden of cancer. Early detection and risk reduction strategies in patients with a cancer predisposition syndrome can save their lives. However, despite evidence supporting the benefits of early detection and risk reduction strategies, most Canadian jurisdictions have not implemented programmatic follow up of these patients. In our study site in the province of Newfoundland and Labrador (NL), Canada, there is no centralized, provincial registry of high-risk individuals. There is no continuity or coordination of care providing cancer genetics expertise and no process to ensure that patients are referred to the appropriate specialists or risk management interventions. This paper describes a study protocol to test the feasibility of obtaining and analyzing patient risk management data, specifically patients affected by hereditary breast ovarian cancer syndrome (HBOC; BRCA 1 and BRCA 2 genes) and Lynch syndrome (LS; MLH1, MSH2, MSH6, and PMS2 genes). Through a retrospective cohort study, we will describe these patients' adherence to risk management guidelines and test its relationship to health outcomes, including cancer incidence and stage. Through a qualitative interviews, we will determine the priorities and preferences of patients with any inherited cancer mutation for a follow up navigation model of risk management. Study data will inform a subsequent funding application focused on creating and evaluating a research registry and follow up nurse navigation model. It is not currently known what proportion of cancer mutation carriers are receiving care according to guidelines. Data collected in this study will provide clinical uptake and health outcome information so gaps in care can be identified. Data will also provide patient preference information to inform ongoing and planned research with cancer mutation carriers.
Keyphrases
- papillary thyroid
- end stage renal disease
- squamous cell
- healthcare
- chronic kidney disease
- ejection fraction
- randomized controlled trial
- study protocol
- lymph node metastasis
- squamous cell carcinoma
- newly diagnosed
- machine learning
- clinical trial
- gene expression
- palliative care
- peritoneal dialysis
- type diabetes
- dna methylation
- big data
- patient reported outcomes
- artificial intelligence
- climate change
- chronic pain
- prognostic factors
- social media
- risk assessment
- double blind
- deep learning