{Reference Type}: Journal Article
{Title}: Optimizing the detection of hereditary predisposition in women with epithelial ovarian cancer: nationwide implementation of the Tumor-First workflow.
{Author}: Witjes VM;Hermkens DMA;Swillens JEM;Smolders YHCM;Mourits MJE;Ausems MGEM;de Hullu JA;Ligtenberg MJL;Hoogerbrugge N;
{Journal}: Fam Cancer
{Volume}: 0
{Issue}: 0
{Year}: 2024 May 29
{Factor}: 2.446
{DOI}: 10.1007/s10689-024-00398-9
{Abstract}: Genetic testing in patients with ovarian carcinoma (OC) is crucial, as around 10-15% of these women have a genetic predisposition to OC. Although guidelines have recommended universal germline testing for all patients with OC for a decade, implementation has proved challenging, thus resulting in low germline-testing rates (around 30-50%). Many new initiatives to improve genetic-testing rates have emerged, but most have been carried out at the local level, leading to differences in workflows within and between countries. We present an example of a nationwide implementation project that has successfully led to a uniform, high-quality genetic-testing workflow for women with OC. Nationwide multidisciplinary meetings generated consensus on the preferred workflow for OC genetic testing: the "Tumor-First" workflow. This workflow means starting by testing the tumor DNA for the presence of pathogenic variants in OC-risk genes, thus providing a prescreen to germline testing while yielding information on the effectiveness of treatment with PARP inhibitors. This new workflow efficiently stratifies genetic counseling and germline testing and reduces healthcare costs. Although challenging, the nationwide implementation of this workflow was successful, resulting in tumor-DNA testing rates exceeding 80%. In this article, we present our structured implementation approach, illustrate our implementation strategies-which were tailored to identified factors important to implementation-and share the lessons learned from the Tumor-First implementation project. This knowledge could facilitate the future implementation of workflows aimed at optimizing the recognition of hereditary cancers.