P-246 A novel non-invasive tool for oocyte selection using gene expression and artificial intelligence
Authors: Link C.A , Von Mengden L , De Bastiani M.A , Faller M , Dorneles L , Pedo R , Arruda L , Link R , Klamt F 
Published: June 2022

Study question

Is it possible to predict top quality embryos through gene expression analysis of cumulus cells and artificial intelligence before fertilization?

Summary answer

The artificial inteligence based tool OsteraTest is able to predict the ability of the oocyte to develop into a top quality blastocyst with 86% accuracy.

What is known already

Proper oocyte selection is an important bottleneck for In Vitro Fertilization (IVF) success. Nowadays, oocyte selection relies mainly in morphological analyses, which is not an unbiased method and may fail to reveal the real competence status of gametes. Cumulus oophorus cells (CC) are somatic cells that surround the oocyte at the antral follicle. It is directly involved in oocyte maturation and development, and thus is a valuable non-invasive source of biological information regarding the oocyte’s health. Artificial intelligence can be used to identify key biological processes and markers of interest through machine learning methods and could thus be applied.

Study design, size, duration

This is a prospective study that included data from 80 CC samples retrieved from publicly available microarray data (GSE27377) in the algorithm construction phase and 65 CC samples from each oocyte of 26 patients submitted to Intracytoplasmic Sperm Injection (ICSI) in validation phase. Samples were divided in two groups: CCs from oocytes that developed into top quality blastocysts in day 5 after ICSI and CCs from oocytes that presented arrested development.

Participants/materials, setting, methods

Samples were submitted to real time quantitative PCR with 25 target genes. Afterwards, gene expression levels for each gene and sample were submitted to the final algorithm, that was computed into a software, the OsteraTest, in a double-blind approach. The software indicated the development potential of each oocyte and this ranking was compared to the embryologist’s day 5 blastocyst classification according to Gardner.

Main results and the role of chance

The bioinformatic approach implemented resulted in the OsteraTest, composed of 8 machine learning models using a 25-gene network that altogether can predict oocyte quality, thus representing a very complex assembly. The software presented more than 86% accuracy in predicting the oocytes developmental capacity into a top-quality day 5 blastocyst. Top quality blastocysts present over 80% chance of resulting in a healthy pregnancy and live birth, and so this approach could be further used as a pregnancy potential predictor after a prospective study is conducted, analyzing CCs from oocytes that were further fertilized, developed into blastocysts and transferred in single embryo transfers. This tool can contribute greatly to improve success rates in IVF procedures and to assess egg quality in egg freezing procedures, providing information about the gametes potential even years before its use.

Limitations, reasons for caution

A large-scale, prospective, randomized study is necessary for further validation of these findings and to confirm the validity of the OsteraTest in the clinical environment. Such study is now being conducted in our lab.

Wider implications of the findings

The OsteraTest proved to be a valuable non-invasive tool to predict embryo formation and oocyte capacity even before fertilization.It can enable the clinics to anticipate successful treatments and provide a predictive report for oocyte freezing patients.

Trial registration number

#68081017.2.0000.5347