Consistency of variant interpretations among bioinformaticians and clinical geneticists in hereditary cancer panels

Abstract

Next-generation sequencing (NGS) is used increasingly in hereditary cancer patients' (HCP) management. While enabling evaluation of multiple genes simultaneously, the technology brings to light the dilemma of variant interpretation. Here, we aimed to reveal the underlying reasons for the discrepancy in the evidence titles used during variant classification according to ACMG guidelines by two different bioinformatic specialists (BIs) and two different clinical geneticists (CGs). We evaluated final reports of 1920 cancer patients and 189 different variants from 285 HCP were enrolled to the study. A total of 173 of these variants were classified as pathogenic (n = 132) and likely pathogenic (n = 41) by the BI and an additional 16 variants, that were classified as VUS by at least one interpreter and their classification would change the clinical management, were compared for their evidence titles between different specialists. The attributed evidence titles and the final classification of the variants among BIs and CGs were compared. The discrepancy between P/LP final reports was 22.5%. The discordance between CGs was 30% whereas the discordance between two BIs was almost 75%. The use of PVS1, PS3, PP3, PP5, PM1, PM2, BP1, BP4 criteria markedly varied from one expert to another. This difference was particularly noticeable in PP3, PP5, and PM1 evidence and mostly in the variants affecting splice sites like BRCA1(NM_007294.4) c.4096 + 1 G > A and CHEK2(NM_007194.4) c.592 + 3 A > T. With recent advancements in precision medicine, the importance of variant interpretations is emerging. Our study shows that variant interpretation is subjective process that is in need of concrete definitions for accurate and standard interpretation.

Fig. 1. The workflow of variant interpretation by bioinformation (BI) and clinical geneticist (CG). The BI follows a partially automated workflow during the variant interpretation.

The reference alignment (hg19), variant filtering and annotations are done by an automated pipeline. The BI adds the population data and the impact on protein level by using different tools. Variant classification is done according to ACMG/AMP guideline and ClinVar database. On the other hand, the CGs follows a multistep approach consisting pre- and post- test genetic counseling. The patients are referred to cancer clinics mostly by medical oncologists. The CGs perform a pre-test evaluation which starts by taking a detailed family history, followed by clinical phenotyping in support of laboratory tests, images and the epicrisis of the oncologist. Following the NGS panel test, the CG evaluates pre-report of the BI with the information obtained during pre-test genetic counseling in six different steps; (1) Phenotype description (Pubmed, ClinVar, OMIM) (2) Population frequency (1000 G, GnomAD, Iranome, in house database) (3) in silico prediction tools (PolyPhen, SIFT, Meta LV, HSF, mutation tasting) (4) Functional evidence (Uniprot, Pubmed) (5) Disease database search (OMIM, ClinVar, HGMD, Pubmed) (6) Segregation analysis. The variant is classified by the relevant ACMG/AMP evidence that are selected in accordance with these findings and the final report is presented to the patient during post test genetic counseling by the CGs.