Standardization of Somatic Variant Classifications in Solid and Haematological Tumours by a Two-Level Approach of Biological and Clinical Classes: An Initiative of the Belgian ComPerMed Expert Panel.

NGS cancer classification guideline variant

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
16 Dec 2019
Historique:
received: 13 11 2019
revised: 03 12 2019
accepted: 05 12 2019
entrez: 1 1 2020
pubmed: 1 1 2020
medline: 1 1 2020
Statut: epublish

Résumé

In most diagnostic laboratories, targeted next-generation sequencing (NGS) is currently the default assay for the detection of somatic variants in solid as well as haematological tumours. Independent of the method, the final outcome is a list of variants that differ from the human genome reference sequence of which some may relate to the establishment of the tumour in the patient. A critical point towards a uniform patient management is the assignment of the biological contribution of each variant to the malignancy and its subsequent clinical impact in a specific malignancy. These so-called biological and clinical classifications of somatic variants are currently not standardized and are vastly dependent on the subjective analysis of each laboratory. This subjectivity can thus result in a different classification and subsequent clinical interpretation of the same variant. Therefore, the ComPerMed panel of Belgian experts in cancer diagnostics set up a working group with the goal to harmonize the biological classification and clinical interpretation of somatic variants detected by NGS. This effort resulted in the establishment of a uniform, two-level classification workflow system that should enable high consistency in diagnosis, prognosis, treatment and follow-up of cancer patients. Variants are first classified into a tumour-independent biological five class system and subsequently in a four tier ACMG clinical classification. Here, we describe the ComPerMed workflow in detail including examples for each step of the pipeline. Moreover, this workflow can be implemented in variant classification software tools enabling automatic reporting of NGS data, independent of panel, method or analysis software.

Identifiants

pubmed: 31888289
pii: cancers11122030
doi: 10.3390/cancers11122030
pmc: PMC6966529
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Guy Froyen (G)

Department of Clinical Biology, Jessa Hospital, 3500 Hasselt, Belgium.

Marie Le Mercier (M)

Department of Clinical Biology, University Hospital Antwerp, 2650 Edegem, Belgium.

Els Lierman (E)

Center for Human Genetics, University Hospital Leuven, 3000 Leuven, Belgium.

Karl Vandepoele (K)

Laboratory of Molecular Hematology, Ghent University Hospital, 9000 Ghent, Belgium.

Friedel Nollet (F)

AZ Sint-Jan Brugge-Oostende AV, Department of Laboratory Medicine, 8000 Brugge, Belgium.

Elke Boone (E)

Department of Laboratory Medicine, AZ Delta Hospital, 8800 Roeselare, Belgium.

Joni Van der Meulen (J)

Molecular Diagnostics, Ghent University Hospital, 9000 Ghent, Belgium.

Koen Jacobs (K)

Clinical Laboratory, AZ St-Lucas Hospital, 9000 Ghent, Belgium.

Suzan Lambin (S)

Department of Pathology, University Hospital Antwerp, 2650 Edegem, Belgium.

Sara Vander Borght (S)

Laboratory of Pathology, University Hospital Leuven, 3000 Leuven, Belgium.

Els Van Valckenborgh (E)

Cancer Centre, Sciensano, 1050 Brussels, Belgium.

Aline Antoniou (A)

Department of Quality Laboratories, Sciensano, 1050 Brussels, Belgium.

Aline Hébrant (A)

Cancer Centre, Sciensano, 1050 Brussels, Belgium.

Classifications MeSH