Predictors of next-generation sequencing panel selection using a shared decision-making approach

• Main Authors: Courtney, Eliza, Li, Shao-Tzu, Shaw, Tarryn, Chen, Yanni, Allen, John Carson, Ngeow, Joanne
• Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
• Format: Article
• Language: English
• Published: 2020
• Subjects: Science::Medicine; Genetic Counselling; Genetic Testing
• Online Access: https://hdl.handle.net/10356/136829
• Institution: Nanyang Technological University

The introduction of next-generation sequencing panels has transformed the approach for genetic testing in cancer patients, however, established guidelines for their use are lacking. A shared decision-making approach has been adopted by our service, where patients play an active role in panel selection and we sought to identify factors associated with panel selection and report testing outcomes. Demographic and clinical data were gathered for female breast and/or ovarian cancer patients aged 21 and over who underwent panel testing. Panel type was classified as 'breast cancer panel' (BCP) or 'multi-cancer panel' (MCP). Stepwise multiple logistic regression analysis was used to identify clinical factors most predictive of panel selection. Of the 265 included subjects, the vast majority selected a broader MCP (81.5%). Subjects who chose MCPs were significantly more likely to be ≥50 years of age (49 vs. 31%; p < 0.05), Chinese (76 vs. 47%; p < 0.001) and have a personal history of ovarian cancer (41 vs. 8%; p < 0.001) with the latter two identified as the best predictors of panel selection. Family history of cancer was not significantly associated with panel selection. There were no statistically significant differences in result outcomes between the two groups. In summary, our findings demonstrate that the majority of patients have a preference for interrogating a larger number of genes beyond those with established testing guidelines, despite the additional likelihood of uncertainty. Individual factors, including cancer history and ethnicity, are the best predictors of panel selection.