32PApplication of variant interpretation software to decipher pathogenicity of mutations for a molecular tumour board (MTB). (7th November 2019) - Southam, S; Ayub, M; Krebs, M; Rothwell, D; Graham, D; Stevenson, J
Abstract: Background: Comprehensive genomic profiling (CGP) of tumours using next generation sequencing (NGS) is increasingly used to guide management. Broad panel sequencing may increase treatment options by identifying more potential targets but generates large quantities of data. Therefore, determining actionable alterations is a challenge. To address this the MTB for the Tumour chARacterisation to Guide Experimental Targeted therapy (TARGET) trial at the Manchester Cancer Research Centre has evaluated a variant interpretation software package. Methods: CGP of circulating tumour DNA (ctDNA) was undertaken using a somatic NGS 641 gene panel that included 24 cancer related genes and further exploratory genes. The variant allele frequency threshold was 2.5 and functional annotation of somatic variants used ANNOVAR. For cases with ≥2 mutations the vcf file was analysed using the Qiagen Clinical Insight platform (QCI) variant interpretation software. Results: Over 18 months, 122 cases with a total of 1313 mutations were analysed by QCI. The variant interpretation provided information on the pathogenicity of the mutations and actionability, which was based on clinical trials identified by QCI. 11% of the total mutations were from the NGS 24 genes, of which 88% were pathogenic and of these 63% were actionable. The NGS exploratory genes accounted for the majority of mutations (89%). In the NGS exploratory genes a smaller percentage of mutations were pathogenic (17%) and of these 9% were actionable. The benefit for the MTB was the ability to focus on the relevant pathogenic mutations. Results were discussed in MTB meetings and pathogenic mutations captured in a digital tool, eTARGET. Conclusions: Utilising variant interpretation software to identify pathogenic mutations from a broader CGP panel enabled more streamlined discussion and decision-making. Whilst a broader panel reveals numerically more mutations and the actionability rates are lower this is important to understand the biological relevance of single mutations and patterns of co-mutations. Applying a pathogenicity filter for ctDNA mutations using variant analysis software has scientific and potential clinical utility. Legal entity responsible for the study: Cancer Research UK Manchester Institute, The University of Manchester. Funding: Partly funded by AstraZeneca iDecide Programme (grant no 119106, C.D.). Disclosure: S. Southam: Shareholder / Stockholder / Stock options: AstraZeneca; Shareholder / Stockholder / Stock options: GSK; Research grant / Funding (institution): AstraZeneca. All other authors have declared no conflicts of interest.