Format

Send to

Choose Destination
Int J Cancer. 2019 Apr 22. doi: 10.1002/ijc.32358. [Epub ahead of print]

Variant Classification in Precision Oncology.

Author information

1
Institute of Pathology, University Hospital Heidelberg, Germany.
2
National Center for Tumor Diseases (NCT) Heidelberg, Germany.
3
German Cancer Consortium (DKTK), Heidelberg, Germany.
4
National Center for Tumor Diseases (NCT) Dresden, Germany.
5
University Hospital Carl Gustav Carus, Dresden, Germany.
6
Thoraxklinik, Heidelberg, Germany.
7
Translational Lung Cancer Research Heidelberg (TLCR-H), member of the German Center for Lung Research (DZL), Germany.
8
German Cancer Research Center (DKFZ) Heidelberg, Germany.
9
Faculty of Biosciences, Heidelberg University, Heidelberg.
10
Institute of Pathology, Hannover Medical School, Germany.
11
Institute of Pathology, Technische Universität München, Germany.
12
Clinic of Internal Medicine I, University Hospital Ulm, Germany.
13
Institute of Pathology, University Hospital Tübingen, Germany.
14
Institute of Pathology, University Hospital Ulm, Germany.
15
Institute of Pathology, Medical Center, University of Freiburg, Germany.
16
German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Partner Site Freiburg, Institute of Molecular Medicine and Cell Research, University of Freiburg, and MIRACUM Consortium of the Medical Informatics Initiative, Freiburg, Germany.
17
Department of Hematology, Oncology and Stem Cell Transplantation, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
18
German Cancer Consortium (DKTK), partner site Freiburg and German Cancer Research Center (DKFZ), Heidelberg, Germany.
19
Medical Center, University of Schleswig Holstein, Department of Hematology and Oncology.
20
Heidelberg-Göttingen-Hannover Medizininformatik (HiGHmed) Konsortium, Heidelberg, Germany.
21
Department of Internal Medicine I, University Hospital Tübingen, Germany.
22
Institute of Pathology, Charité Universitätsmedizin Berlin, Germany.
23
DKFZ-Heidelberg Center for Personalized Oncology (HIPO), Heidelberg, Germany.

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. This article is protected by copyright. All rights reserved.

KEYWORDS:

Molecular Pathology; Molecular Tumor Board; Next Generation Sequencing; Variant Classification

PMID:
31008532
DOI:
10.1002/ijc.32358

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center