Cargando…

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

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 selecti...

Descripción completa

Detalles Bibliográficos
Autores principales: Courtney, Eliza, Li, Shao-Tzu, Shaw, Tarryn, Chen, Yanni, Allen, John Carson, Ngeow, Joanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923203/
https://www.ncbi.nlm.nih.gov/pubmed/29736259
http://dx.doi.org/10.1038/s41525-018-0050-y
_version_ 1783318285524140032
author Courtney, Eliza
Li, Shao-Tzu
Shaw, Tarryn
Chen, Yanni
Allen, John Carson
Ngeow, Joanne
author_facet Courtney, Eliza
Li, Shao-Tzu
Shaw, Tarryn
Chen, Yanni
Allen, John Carson
Ngeow, Joanne
author_sort Courtney, Eliza
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5923203
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-59232032018-05-07 Predictors of next-generation sequencing panel selection using a shared decision-making approach Courtney, Eliza Li, Shao-Tzu Shaw, Tarryn Chen, Yanni Allen, John Carson Ngeow, Joanne NPJ Genom Med Article 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. Nature Publishing Group UK 2018-04-27 /pmc/articles/PMC5923203/ /pubmed/29736259 http://dx.doi.org/10.1038/s41525-018-0050-y Text en © The Author(s) 2018 Open Access 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/.
spellingShingle Article
Courtney, Eliza
Li, Shao-Tzu
Shaw, Tarryn
Chen, Yanni
Allen, John Carson
Ngeow, Joanne
Predictors of next-generation sequencing panel selection using a shared decision-making approach
title Predictors of next-generation sequencing panel selection using a shared decision-making approach
title_full Predictors of next-generation sequencing panel selection using a shared decision-making approach
title_fullStr Predictors of next-generation sequencing panel selection using a shared decision-making approach
title_full_unstemmed Predictors of next-generation sequencing panel selection using a shared decision-making approach
title_short Predictors of next-generation sequencing panel selection using a shared decision-making approach
title_sort predictors of next-generation sequencing panel selection using a shared decision-making approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923203/
https://www.ncbi.nlm.nih.gov/pubmed/29736259
http://dx.doi.org/10.1038/s41525-018-0050-y
work_keys_str_mv AT courtneyeliza predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach
AT lishaotzu predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach
AT shawtarryn predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach
AT chenyanni predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach
AT allenjohncarson predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach
AT ngeowjoanne predictorsofnextgenerationsequencingpanelselectionusingashareddecisionmakingapproach