Florence Koeppel1, Etienne Muller2, Alexandre Harlé3, Céline Guien4, Pierre Sujobert5, Olfa Trabelsi Grati6, Olivier Kosmider7, Laurent Miguet8, Laurent Mauvieux8, Anne Cayre9, David Salgado4, Claude Preudhomme10, Lucie Karayan-Tapon11, Gaëlle Tachon11, Florence Coulet12, Alexandra Lespagnol13, Christophe Beroud14, Karen Leroy15, Etienne Rouleau16, Isabelle Soubeyran17. 1. Gustave Roussy, Direction de la Recherche, Villejuif, F-94805, France. 2. Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, 14000, France; Inserm U1245, Normandie Univ, UNIROUEN, Normandy Centre for Genomic and Personalized Medicine, Rouen, 76031, France. 3. Université de Lorraine CNRS UMR 7039 CRAN, Service de Biopathologie, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, F-54519, France. 4. Aix Marseille Univ, INSERM, MMG, Bioinformatics & Genetics, Marseille, France. 5. Hospices Civils de Lyon, Groupement Hospitalier Sud, Service d'hématologie biologique, Pierre-Bénite, France; Cancer Research Center of Lyon, INSERM U1052 UMR CNRS 5286, Equipe labellisée Ligue Contre le Cancer, Université de Lyon, Lyon, France. 6. Unité de pharmacogénomique, Service de Génétique, Institut Curie, 26 rue d'Ulm, Paris, 75005, France. 7. AP-HP Centre, Hôpital Cochin, Service d'hématologie Biologique et Université de Paris, Paris-Descartes, France. 8. Laboratoire d'hématologie, CHRU Strasbourg, INSERM U1113, Avenue Molière, Strasbourg, 67100, France. 9. LBM OncoGenAuvergne, UF de Pathologie, Centre Jean Perrin, 58 Rue Montalembert, BP392, Clermont-Ferrand, 63011, France. 10. Center of Pathology, Laboratory of Hematology, University Hospital of Lille, Lille, France. 11. Université de Poitiers, INSERMU1084 et CHU de Poitiers, Laboratoire de Cancérologie Biologique, Poitiers, France. 12. Genetics Department, Assistance publique - Hôpitaux de Paris, Pitié Salpêtrière Hôpital, Paris, France. 13. CHU Pontchaillou - Laboratoire de Génétique Somatique des Cancers, Rennes, France. 14. Aix Marseille Univ, INSERM, MMG, Bioinformatics & Genetics, Marseille, France; AP-HM, Hôpital d'Enfants de la Timone, Département de Génétique Médicale et de Biologie Cellulaire, Marseille, France. 15. AP-HP Centre, Hôpital Européen Georges Pompidou, Service de Biochimie et Université de Paris, France. 16. Gustave Roussy, Département de biologie et pathologie médicales, Villejuif, F-94805, France. Electronic address: etienne.rouleau@gustaveroussy.fr. 17. Unité de Pathologie Moléculaire et Inserm U1218, Institut Bergonié, 229 cours de l'Argonne, Bordeaux, 33076, France.
Abstract
BACKGROUND: The difficulty in interpreting somatic alterations is correlated with the increase in sequencing panel size. To correctly guide the clinical management of patients with cancer, there needs to be accurate classification of pathogenicity followed by actionability assessment. Here, we describe a specific detailed workflow for the classification of the pathogenicity of somatic variants in cancer into five categories: benign, likely benign, unknown significance, likely pathogenic and pathogenic. METHODS: Classification is obtained by combining a set of eight relevant criteria in favour of either a pathogenic or a benign effect (pathogenic stand-alone, pathogenic very strong, pathogenic strong, pathogenic moderate, pathogenic supporting, benign supporting, benign strong and benign stand-alone). RESULTS: Our guide is concordant with the ACMG/AMP 2015 guidelines for germline variants. Interpretation of somatic variants requires considering specific criteria, such as the disease and therapeutic context, co-occurring genomic events in the tumour when available and the use of cancer-specific variant databases. In addition, the gene role in tumorigenesis (oncogene or tumour suppressor gene) also needs to be taken into consideration. CONCLUSION: Our classification could contribute to homogenize best practices on somatic variant pathogenicity interpretation and improve interpretation consistency both within and between laboratories.
BACKGROUND: The difficulty in interpreting somatic alterations is correlated with the increase in sequencing panel size. To correctly guide the clinical management of patients with cancer, there needs to be accurate classification of pathogenicity followed by actionability assessment. Here, we describe a specific detailed workflow for the classification of the pathogenicity of somatic variants in cancer into five categories: benign, likely benign, unknown significance, likely pathogenic and pathogenic. METHODS: Classification is obtained by combining a set of eight relevant criteria in favour of either a pathogenic or a benign effect (pathogenic stand-alone, pathogenic very strong, pathogenic strong, pathogenic moderate, pathogenic supporting, benign supporting, benign strong and benign stand-alone). RESULTS: Our guide is concordant with the ACMG/AMP 2015 guidelines for germline variants. Interpretation of somatic variants requires considering specific criteria, such as the disease and therapeutic context, co-occurring genomic events in the tumour when available and the use of cancer-specific variant databases. In addition, the gene role in tumorigenesis (oncogene or tumour suppressor gene) also needs to be taken into consideration. CONCLUSION: Our classification could contribute to homogenize best practices on somatic variant pathogenicity interpretation and improve interpretation consistency both within and between laboratories.
Authors: Kenneth D Doig; Christopher G Love; Thomas Conway; Andrei Seleznev; David Ma; Andrew Fellowes; Piers Blombery; Stephen B Fox Journal: BMC Med Genomics Date: 2022-03-26 Impact factor: 3.063