Variant classification in precision oncology. Issue 11 (21st May 2019) - Leichsenring, Jonas; Horak, Peter; Kreutzfeldt, Simon; Heining, Christoph; Christopoulos, Petros; Volckmar, Anna‐Lena; Neumann, Olaf; Kirchner, Martina; Ploeger, Carolin; Budczies, Jan; Heilig, Christoph E.; Hutter, Barbara; et al.
Abstract : Next‐generation sequencing has become a cornerstone of therapy guidance in cancer precision medicine and an indispensable research tool in translational oncology. Its rapidly increasing use during the last decade has expanded the options for targeted tumor therapies, and molecular tumor boards have grown accordingly. However, with increasing detection of genetic alterations, their interpretation has become more complex and error‐prone, potentially introducing biases and reducing benefits in clinical practice. To facilitate interdisciplinary discussions of genetic alterations for treatment stratification between pathologists, oncologists, bioinformaticians, genetic counselors and medical scientists in specialized molecular tumor boards, several systems for the classification of variants detected by large‐scale sequencing have been proposed. We review three recent and commonly applied classifications and discuss their individual strengths and weaknesses. Comparison of the classifications underlines the need for a clinically useful and universally applicable variant reporting system, which will be instrumental for efficient decision making based on sequencing analysis in oncology. Integrating these data, we propose a generalizable classification concept featuring a conservative and a more progressive scheme, which can be readily applied in a clinical setting. Abstract : What's new? With increasingly comprehensive molecular profiling of cancers, the clinical interpretation of genetic alterations is becoming more and more challenging. Here the authors review several classifications for gene variant interpretation that were recently introduced to guide clinical management. They highlight shared features and differences and point out major influencing factors and unresolved issues that still need to be addressed. Based on this analysis, they propose a unified classification concept that may become broadly implemented when remaining issues are solved.