Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

Abstract

Objective: By focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments.

Design: A case-control study.

Setting: University based facilities for clinical services and research.

Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.

Methods: MGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.

Results: We defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.

Conclusion: We have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.

Fig 1. ROC curve of the training set.

Receiver operating characteristic (ROC) curve for the binary classifier built to distinguish between live birth (LB) and no pregnancy (NP). The curve shows the true positive rate versus false positive rate, i.e. the tradeoff between sensitivity and specificity. The area under the curve (AUC), which captures the ability of the classifier to correctly group the patients with follicular adenoma and those with follicular carcinoma, is equal to 0.86. A perfect classifier will have an AUC of 1.0, whereas an AUC value of 0.5 indicates that the classification is random.

Fig 2. Predictive probability of cumulus training set samples and validation set samples.

The predictive probability of the 30-gene signature is shown for the training set and after translation to the validation data set, part 1, 2 and 3, respectively. Each dot represents a sample and the color indicates the true (blinded) class. If a sample has a predictive value above 0.5 (p(LB) > 0.5), it is classified as predictive of leading to Live birth (LB) in the training set or reaching blastocyst (B) stage in the validation sub sets, validation-part1, validation-part2 and validation-part3, respectively. Samples with p(LB) below 0.5 are classified as predictive of no pregnancy (NP) in the training set and embryos of poor quality (EP) in the validation set. Samples which received an erroneously prediction according to their true class are indicated with a black circle and sample name.

Fig 3. Predicted activation states.

Ingenuity downstream effect analysis to predict the effect of directional gene expression resulted in two biological functions with significant activation z-scores indicative of predicted activation of apoptosis and decreased activity of cell migration. The mechanistic network of the implicated genes is shown along with the predicted relationship indicated by the color of the edges.

Fig 4. Mechanistic network.

Mechanistic network based on the 30 probe set signature used to predict live birth in cumulus cells. (A) Top predicted functions represented by the network are Cellular Movement, Nervous System Development and Function, Cellular Growth and Proliferation. (B). Top predicted functions represented by the network are Cell Death and Survival, Cell-To-Cell Signaling and Interaction, Hematological System Development and Function.