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Hered Cancer Clin Pract. 2016 Apr 30;14:10. doi: 10.1186/s13053-016-0050-9. eCollection 2016.

Assessing biases of information contained in pedigrees for the classification of BRCA-genetic variants: a study arising from the ENIGMA analytical working group.

Author information

1
Department of Clinical Genetics, Maastricht University Medical Centre, Maastricht, The Netherlands.
2
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
3
Huntsman Cancer Institute and Department of Dermatology, University of Utah School of Medicine, Salt Lake City, USA.

Abstract

PURPOSE:

One way of evaluating family history (FH) for classifying BRCA1/2 variants of uncertain clinical significance (VUS) is to assess the "BRCA-ness" of a pedigree by comparing it to reference populations. The aim of this study was to assess if prediction of BRCA pathogenic variant (mutation) status based on pedigree information differed due to changes in FH since intake, both in families with a pathogenic variant (BRCAm) and in families with wild-type (BRCAwt).

PATIENTS AND METHODS:

We compared the BRCA1/2 pathogenic variant detection probabilities between intake and most recent pedigree for BRCAm families (n = 64) and BRCAwt (n = 118) using the BRCAPRO software program.

RESULTS:

Follow-up time between intake and most recent pedigree was significantly longer (p < 0.001) in the BRCAm compared to the BRCAwt families. Among BRCAwt families, the probability to detect a pathogenic variant did not change over time. Conversely, among the BRCAm, this probability was significantly higher for most recent vs. intake pedigree (p = 0.006).

CONCLUSION:

Clinical scores change significantly over time for BRCAm families. This may be due to differences in follow-up, but also to differences in cancer risks from carrying a pathogenic variant in a highly penetrant gene. To reduce bias, models for VUS classification should incorporate FH collected at intake.

KEYWORDS:

BRCA1/2; Family history; Intake pedigrees; Variant classification models; Variants of uncertain clinical significance

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