Unmet Need
Chromosomal aberrations like aneuploidies affect one in 150 pregnancies worldwide and are responsible for half of early pregnancy terminations (1). Preimplantation genetic testing (PGT-A) can screen for embryos with de novo aneuploidies such as subchromosomal deletions/additions (2). PGT-A can only be performed on embryos generated via in vitro fertilization (IVF) and can be used to select for embryos with intact chromosomes, improving the odds of viable conception and pregnancy. Other forms of pre-implantation genetic testing involve invasive procedures such as blastocyst biopsies (3). Other types of prenatal tests for aneuploidy include karyotyping (4), fluorescence in situ hybridization (FISH) (5), quantitative PCR of short tandem repeats (6), and comparative genomic hybridization (7). These prenatal tests may be done after implantation (like karyotyping) or in in situ fertilization patients (FISH).
PGT-A falls short in terms of distinguishing between aneuploidies of mitotic and meiotic origin. Meiotic aneuploidies affect all of the progeny cells, with the most common type of trisomies representing about 35% of spontaneous abortions (8) . Mitotic aneuploidies result in chromosomal mosaicism, which may lead to serious birth defects (9), depending on certain factors such as the types of cells and tissues in which the aneuploidies are expressed. However, some mitotic aneuploidies can be compatible with healthy live births (10). Therefore, there is a strong need for improvements in PGT-A screens to better distinguish the cellular origins of aneuploidies and aid in selection of embryos for transfer in IVF.
Technology Overview
Johns Hopkins researchers have developed a new method of assessing PGT-A data from IVF patients to distinguish the meiotic versus mitotic origins of aneuploidies. Comparative analyses of chromosomal copies and abnormalities are employed to search for certain signatures and motifs that are associated with mitotic vs meiotic origins of aneuploidies. Statistical models are also used to better distinguish results that could have arisen by chance, further boosting the confidence and fidelity of the test.
Stage of Development
A statistical algorithm has been developed and tested. Experimental data is available.
Publications
Ariad D, et al. Haplotype-aware inference of human chromosome abnormalities. PNAS. Nov 16, 2021.
