The expression of ovulation related genes in CC is associated with patient and treatment characteristics, oocyte developmental potential and differs with the type of gonadotrophin used.

Multivariable models based on CC gene expression can be used to predict embryo development and pregnancy.

No abstract

Purpose Gene expression in human ART cumulus cell (CC) has been related to oocyte maturity and competence but requires further validation. Expression dynamics were investigated in CC of oocytes at different maturational stages and with different developmental competence in a standard in vivo mouse superovulation model. Methods Quantitative PCR analysis of Has2, Vcan, Sdc4, Alcam, Grem1, Ptgs1 and Ptgs2 in CC collected at regular time intervals from 0 to 24 h post hCG injection. Results Three expression patterns were observed each with strong regulation (4-230× differences). Immediately prior to ovulation CC of GVBD oocytes have 5× less Sdc4 and Ptgs1 and 5× more Ptgs2 when compared to the CC of freshly ovulated PB oocytes. When compared to the latter, the post-ovulatory aged PB oocytes had a 2× reduced blastocyst forming capacity and their CC expressed 2× more Sdc4 and 6× less Alcam. Conclusions Morphologically identical cumulus oocyte complexes with different developmental competence can be differentiated by CC gene expression.

Cumulus cell (CC) gene expression is being explored as an additional method to morphological scoring to choose the embryo with the highest chance to pregnancy. In 47 ICSI patients with single embryo transfer (SET), from which individual CC samples had been stored, 12 genes using QPCR were retrospectively analyzed. The CC samples were at the same occasion also used to validate a previously obtained pregnancy prediction model comprising three genes (ephrin-B2 (EFNB2), calcium/calmodulin-dependent protein kinase ID, stanniocalcin 1). Latter validation yielded a correct pregnant/non-pregnant classification in 72% of the samples. Subsequently, 9 new genes were analyzed on the same samples and new prediction models were built. Out of the 12 genes analyzed a combination of the best predictive genes was obtained by stepwise multiple regression. One model retained EFNB2 in combination with glutathione S-transferase alpha 3 and 4, progesterone receptor and glutathione peroxidase 3, resulting in 93% correct predictions when 3 patient and treatment cycle characteristics were included into the model. This large patient group allowed to do an intra-patient analysis for 7 patients, an analysis mimicking the methodology that would ultimately be used in clinical routine. CC related to a SET that did not give pregnancy and CC related to their subsequent frozen/thawed embryos which ended in pregnancy were analyzed. The models obtained in the between-patient analysis were used to rank the oocytes within-patients for their chance to pregnancy and resulted in 86% of correct predictions. In conclusion, prediction models built on selected quantified transcripts in CC might help in the decision making process which is currently only based on subjective embryo morphology scoring. The validity of our current models for routine application still need prospective assessment in a larger and more diverse patient population allowing intra-patient analysis.