Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136829
Title: Predictors of next-generation sequencing panel selection using a shared decision-making approach
Authors: Courtney, Eliza
Li, Shao-Tzu
Shaw, Tarryn
Chen, Yanni
Allen, John Carson
Ngeow, Joanne
Keywords: Science::Medicine
Issue Date: 2018
Source: Courtney, E., Li, S.-T., Shaw, T., Chen, Y., Allen, J. C., & Ngeow, J. (2018). Predictors of next-generation sequencing panel selection using a shared decision-making approach. npj Genomic Medicine, 3, 11-. doi:10.1038/s41525-018-0050-y
Journal: npj Genomic Medicine
Abstract: 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.
URI: https://hdl.handle.net/10356/136829
ISSN: 2056-7944
DOI: 10.1038/s41525-018-0050-y
Rights: © 2018 The Author(s). (published in partnership with the Center of Excellence in Genomic Medicine Research). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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