Personalised Risk Prediction in Hereditary Breast and Ovarian Cancer: A Protocol for a Multi-Centre Randomised Controlled Trial

Stephanie Archer; Nichola Fennell; Ellen Colvin; Rozelle Laquindanum; Meredith Mills; Romy Dennis; Francisca Stutzin Donoso; Rochelle Gold; Alice Fan; Kate Downes; James Ford; Antonis C Antoniou; Allison W Kurian; D Gareth Evans; Marc Tischkowitz

doi:10.3390/cancers14112716

Personalised Risk Prediction in Hereditary Breast and Ovarian Cancer: A Protocol for a Multi-Centre Randomised Controlled Trial

Cancers (Basel). 2022 May 31;14(11):2716. doi: 10.3390/cancers14112716.

Authors

Stephanie Archer^{1

2}, Nichola Fennell³, Ellen Colvin⁴, Rozelle Laquindanum⁵, Meredith Mills⁵, Romy Dennis³, Francisca Stutzin Donoso¹, Rochelle Gold³, Alice Fan⁵, Kate Downes⁶, James Ford^{5

7}, Antonis C Antoniou⁸, Allison W Kurian^{5

9}, D Gareth Evans^{4

10}, Marc Tischkowitz³

Affiliations

¹ Primary Care Unit, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK.
² Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK.
³ Academic Department of Medical Genetics, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
⁴ Manchester Centre for Genomic Medicine, St. Marys Hospital, Oxford Road, Manchester M13 9WL, UK.
⁵ Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁶ Cambridge Genomics Laboratory, Cambridge University Hospitals Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁸ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge CB1 8RN, UK.
⁹ Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹⁰ Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK.

Abstract

Women who test positive for an inherited pathogenic/likely pathogenic gene variant in BRCA1, BRCA2, PALB2, CHEK2 and ATM are at an increased risk of developing certain types of cancer-specifically breast (all) and epithelial ovarian cancer (only BRCA1, BRCA2, PALB2). Women receive broad cancer risk figures that are not personalised (e.g., 44-63% lifetime risk of breast cancer for those with PALB2). Broad, non-personalised risk estimates may be problematic for women when they are considering how to manage their risk. Multifactorial-risk-prediction tools have the potential to deliver personalised risk estimates. These may be useful in the patient's decision-making process and impact uptake of risk-management options. This randomised control trial (registration number to follow), based in genetic centres in the UK and US, will randomise participants on a 1:1 basis to either receive conventional cancer risk estimates, as per routine clinical practice, or to receive a personalised risk estimate. This personalised risk estimate will be calculated using the CanRisk risk prediction tool, which combines the patient's genetic result, family history and polygenic risk score (PRS), along with hormonal and lifestyle factors. Women's decision-making around risk management will be monitored using questionnaires, completed at baseline (pre-appointment) and follow-up (one, three and twelve months after receiving their risk assessment). The primary outcome for this study is the type and timing of risk management options (surveillance, chemoprevention, surgery) taken up over the course of the study (i.e., 12 months). The type of risk-management options planned to be taken up in the future (i.e., beyond the end of the study) and the potential impact of personalised risk estimates on women's psychosocial health will be collected as secondary-outcome measures. This study will also assess the acceptability, feasibility and cost-effectiveness of using personalised risk estimates in clinical care.

Keywords: CanRisk; breast cancer; epithelial ovarian cancer; genetics; personalised risk prediction; polygenic risk scores.

Abstract

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