4.7 Article

GenomeMixer and TRUST: Novel bioinformatics tools to improve reliability of Non-Invasive Prenatal Testing (NIPT) for fetal aneuploidies

Journal

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 20, Issue -, Pages 1028-1035

Publisher

ELSEVIER
DOI: 10.1016/j.csbj.2022.02.014

Keywords

Non-invasive prenatal testing; Fetal fraction; Aneuploidy detection; Confidence intervals; Semi-supervised method

Funding

  1. French government [ANR-15-IDEX-01]

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Two tools have been developed to improve the reliability of non-invasive prenatal testing (NIPT) and facilitate standardization of the test results. The interrelationships between different parameters have been analyzed, revealing their profound connection.
Non-invasive prenatal testing (NIPT) screens for common fetal chromosomal abnormalities through analysis of circulating cell-free DNA in maternal blood by massive parallel sequencing. NIPT reliability relies on both the estimation of the fetal fraction (ff) and on the sequencing depth (sd) but how these parameters are linked is unknown. Several bioinformatics tools have been developed to determine the ff but there is no universal ff threshold applicable across diagnostics laboratories. Thus, we developed two tools allowing the implementation of a strategy for NIPT results validation in clinical practice: GenomeMixer, a semi-supervised approach to create synthetic sequences and to estimate confidence intervals for NIPT validation and TRUST to estimate the reliability of NIPT results based on confidence intervals found in this study. We retrospectively validated these new tools on 2 cohorts for a total of 1439 samples with 31 confirmed aneuploidies. Through the analysis of the interrelationship between ff, sd and chromosomal aberration detection, we demonstrate that these parameters are profoundly connected and cannot be considered independently. Our tools take in account this critical relationship to improve NIPT reliability and facilitate cross laboratory standardization of this screening test. (c) 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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