Patients who failed to conceive following an in vitro fertilization cycle can be clustered into different failure causes using gene expression hierarchical analysis†.

The patient's response to an IVF stimulation protocol is highly variable and thus difficult to predict. When a cycle fails, there are often no apparent or obvious reasons to explain the failure. Having clues on what went wrong during stimulation could serve as a basis to improve and personalize the next protocol. This exploratory study aimed to investigate if it is possible to distinguish different failure causes or different follicular responses in a population of nonpregnant IVF patients. Using qRT-PCR, we analyzed a panel of genes indicative of different failure causes in patients who did not achieve pregnancy following an IVF cycle. For each patient, a pool of follicular cells from all aspirated follicles was used as a sample which gives a global picture of the patient's ovary and not a specific picture of each follicle. We performed hierarchical clustering analysis to split the patients according to the gene expression pattern. Hierarchical analysis showed that the population of nonpregnant IVF patients could be divided into three clusters. Gene expression was significantly different, and each cluster displayed a particular gene expression pattern. Follicular cells from patients in clusters 1, 2 and 3 displayed respectively a pattern of gene expression related to large incompetent follicles with a higher apoptosis (over matured), to follicles not ready to ovulate (under mature) and to an excess of inflammation with no visible symptoms. This study reinforces the idea that women often have different response to the same protocol and would benefit from more personalized treatments.

Gene expression analysis of follicular cells revealed inflammation as a potential IVF failure cause.

PURPOSE: Hormonal stimulation prior to IVF influences the ovarian environment and therefore impacts oocytes and subsequent embryo quality. Not every patient has the same response to the same treatment and many fail for unknown reasons. Knowing why a cycle has failed and how the follicles were affected would allow clinicians to adapt the treatment accordingly and improve success rate. This study examines the hypothesis that transcriptomic analysis of follicular cells from failed IVF cycles reveals potential reasons for failure and provides new information on the physiological mechanisms related to IVF failure. METHODS: Follicular cells (granulosa cells) were obtained from IVF patients of four Canadian fertility clinics. Using microarray analysis, patients that did not become pregnant following the IVF cycle were compared to those that did. Functional analysis was performed using ingenuity pathway analysis and qRT-PCR was used to validate the microarray results in a larger cohort of patients. RESULTS: The microarray showed 165 differentially expressed genes (DEGs) in the negative group compared to the pregnancy group. DEGs include many pro-inflammatory cytokines and other factors related to inflammation, suggesting that this process might be altered when IVF fails. Overexpression of several factors, some of which act upstream from vascular endothelial growth factor (VEGF), also indicates increased permeability and vasodilation. Some DEGs were related to abnormal differentiation and increased apoptosis. CONCLUSIONS: Our results suggest that failure to conceive following IVF cycles could be associated with an imbalance between pro-inflammatory and anti-inflammatory mediators. The findings of this study identify potential failure causes and pathways for further investigation. Stimulatory protocols personalized according to patient response could improve the chances of later success.