Prospective analysis of 895 patients on a UK Genomics Review Board. Issue 2 (21st March 2019)

• Record Type: Journal Article
• Authors: Moore, David Allan; Kushnir, Marina; Mak, Gabriel; Winter, Helen; Curiel, Teresa; Voskoboynik, Mark; Moschetta, Michele; Rozumna-Martynyuk, Nataliya; Balbi, Kevin; Bennett, Philip; Forster, Martin; Kulkarni, Anjana; Haynes, Debra; Swanton, Charles; Arkenau, Hendrik-Tobias
• Is Part Of: ESMO open. Volume 4:Issue 2(2019)
• Publication Date: 2019-03-21
• Subjects: molecular oncology -- molecular tumour board -- genomic medicine -- clinical genetics; Cancer -- Periodicals; 616.994005
• DOI: 10.1136/esmoopen-2018-000469
• Languages: English
• ISSNs: 2059-7029

Abstract

Abstract : Background: The increasing frequency and complexity of cancer genomic profiling represents a challenge for the oncology community. Results from next-generation sequencing–based clinical tests require expert review to determine their clinical relevance and to ensure patients are stratified appropriately to established therapies or clinical trials. Methods: The Sarah Cannon Research Institute UK/UCL Genomics Review Board (GRB) was established in 2014 and represents a multidisciplinary team with expertise in molecular oncology, clinical trials, clinical cancer genetics and molecular pathology. Prospective data from this board were collated. Results: To date, 895 patients have been reviewed by the GRB, of whom 180 (20%) were referred for clinical trial screening and 62 (7%) received trial therapy. For a further 106, a clinical trial recommendation was given. Conclusions: Numerous challenges are faced in implementing a GRB, including the identification of potential germline variants, the interpretation of variants of uncertain significance and consideration of the technical limitations of pathology material when interpreting results. These challenges are likely to be encountered with increasing frequency in routine practice. This GRB experience provides a model for the multidisciplinary review of molecular profiling data and for the linking of molecular analysis to clinical trial networks.