An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

Yvonne E M Koot, Sander R van Hooff, Carolien M Boomsma, Dik van Leenen, Marian J A Groot Koerkamp, Mariëtte Goddijn, Marinus J C Eijkemans, Bart C J M Fauser, Frank C P Holstege, Nick S Macklon, Yvonne E M Koot, Sander R van Hooff, Carolien M Boomsma, Dik van Leenen, Marian J A Groot Koerkamp, Mariëtte Goddijn, Marinus J C Eijkemans, Bart C J M Fauser, Frank C P Holstege, Nick S Macklon

Abstract

The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention.

Conflict of interest statement

BCJM Fauser has received fees and grant support from the following companies: Organon, Schering Plough, Merck Serono, Ferring, Wyeth, Ardana, Andromed, Pantharei Bioscience and PregLem. NS Macklon has received fees and grant support from the following companies: Organon, Schering Plough, MSD, Anecova, IBSA, Merck Serono and Ferring. The other authors declare no competing financial interests.

Figures

Figure 1. Signature discovery and validation.
Figure 1. Signature discovery and validation.
This figure shows SVM classifier results on the signature discovery (a) and validation (b) sets. RIF signature genes were first determined on the signature discovery sample set by 100 rounds of cross-validation (Supplementary Fig. 3, Methods). Using these 303 genes, (panel a) shows SVM classifier scores for the signature discovery set (patients: red, controls: blue) using leave one out cross validation. Samples with a score below 0.5 are predicted to be controls, those with a score of 0.5 or higher are predicted to be RIF patients (the threshold is shown as a dotted line). (Panel b) shows all samples from the validation set scored based on the SVM classifier trained on all samples in the signature discovery set. (Panel c) shows the ROC curves for the results shown in A (blue line) and B (green line). The Area Under the Curve (AUC) with the 95% CI is shown next to the curves. The dots indicate the point of the ROC curve that corresponds with the threshold used for classification (0.5)
Figure 2. Gene set enrichment analysis.
Figure 2. Gene set enrichment analysis.
(Panels a to d) show the gene expression of RIF patients compared against controls (log2 fold change or M) and the average expression across all samples (log2 sample intensity). (Panels a to c) each focus on an example of a Gene Ontology term which was found to be significant in GSEA. Genes in the GO term up-regulated in RIF patients are shown in yellow, genes down-regulated in blue. Shown in red are the genes which are also part of the 303-gene signature. Genes that are not part of the GO term are shown as a blue density map where darker blue indicates higher gene density. (Panel d) shows all the genes of the gene signature. (Panel e) shows a selection of 55 genes from the 303-gene signature and 26 GO terms in which they are involved. The genes selection was based on the number of GO terms associated to the gene (>6), the GO terms were selected based on their statistical significance in the GSEA and the number of genes associated (>6). The selection was performed for visual clarity. The bars below the gene names and to the right of the GO terms indicates whether a gene/GO term is up-regulated in RIF patients (red) or down-regulated (green). All other colours are solely for visualization purposes and do not indicate strength of association. The rows and columns are clustered based on Euclidean distance. For an unfiltered version see Supplementary Fig. 7.
Figure 3. Patient stratification.
Figure 3. Patient stratification.
(Panel a) shows the distribution of the RIF patient classification error rates (n = 43). The error rate is the ratio of misclassifications to number of classification attempts (see Methods for details). The coloured rectangles denote patient groups with similar error rates (low < 0.1, medium ≥ 0.1 & ≤ 0.9, high > 0.9). (Panel b) shows the IVF implantation rate for the three aforementioned patient groups. Implantation rate is defined as implantations per embryo transfer (prior to the biopsy) and includes all outcomes: i.e. biochemical pregnancy, miscarriage, live birth. P was calculated using a two-sided Fisher’s exact test comparing combined outcomes of all IVF cycles.

References

    1. Polanski L. T. et al. What exactly do we mean by ‘recurrent implantation failure’? A systematic review and opinion. Reprod. Biomed. Online 28, 409–423 (2014).
    1. Thornhill A. R. et al. ESHRE PGD Consortium ‘Best practice guidelines for clinical preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS)’. Hum. Reprod. Oxf. Engl. 20, 35–48 (2005).
    1. Simon A. & Laufer N. Repeated implantation failure: clinical approach. Fertil. Steril. 97, 1039–1043 (2012).
    1. Koot Y. E. M., Teklenburg G., Salker M. S., Brosens J. J. & Macklon N. S. Molecular aspects of implantation failure. Biochim. Biophys. Acta 1822, 1943–1950 (2012).
    1. Das M. & Holzer H. E. G. Recurrent implantation failure: gamete and embryo factors. Fertil. Steril. 97, 1021–1027 (2012).
    1. Simon A. & Laufer N. Assessment and treatment of repeated implantation failure (RIF). J. Assist. Reprod. Genet. 29, 1227–1239 (2012).
    1. Penzias A. S. Recurrent IVF failure: other factors. Fertil. Steril. 97, 1033–1038 (2012).
    1. Koot Y. E. M. & Macklon N. S. Embryo implantation: biology, evaluation, and enhancement. Curr. Opin. Obstet. Gynecol. 25, 274–279 (2013).
    1. Urman B., Yakin K. & Balaban B. Recurrent implantation failure in assisted reproduction: how to counsel and manage. B. Treatment options that have not been proven to benefit the couple. Reprod. Biomed. Online 11, 382–391 (2005).
    1. Vanneste E. et al. Chromosome instability is common in human cleavage-stage embryos. Nat. Med. 15, 577–583 (2009).
    1. Revel A. Defective endometrial receptivity. Fertil. Steril. 97, 1028–1032 (2012).
    1. Macklon N. S. & Boomsma C. M. in Early Pregnancy (Cambridge University Press, 2010) (Date of access:23/10/2015). at < >
    1. Koler M. et al. Disrupted gene pattern in patients with repeated in vitro fertilization (IVF) failure. Hum. Reprod. 24, 2541–2548 (2009).
    1. Ruiz-Alonso M., Galindo N., Pellicer A. & Simón C. What a difference two days make: ‘personalized’ embryo transfer (pET) paradigm: a case report and pilot study. Hum. Reprod. Oxf. Engl. 29, 1244–1247 (2014).
    1. Brosens J. J. et al. Uterine selection of human embryos at implantation. Sci. Rep. 4, 3894 (2014).
    1. Ruiz-Alonso M., Blesa D. & Simón C. The genomics of the human endometrium. Biochim. Biophys. Acta 1822, 1931–1942 (2012).
    1. von Grothusen C., Lalitkumar S., Boggavarapu N. R., Gemzell-Danielsson K. & Lalitkumar P. G. Recent advances in understanding endometrial receptivity: molecular basis and clinical applications. Am. J. Reprod. Immunol. N. Y. N 1989 72, 148–157 (2014).
    1. Haouzi D., Dechaud H., Assou S., De Vos J. & Hamamah S. Insights into human endometrial receptivity from transcriptomic and proteomic data. Reprod. Biomed. Online 24, 23–34 (2012).
    1. Kao L. C. et al. Global gene profiling in human endometrium during the window of implantation. Endocrinology 143, 2119–2138 (2002).
    1. Mirkin S. et al. In search of candidate genes critically expressed in the human endometrium during the window of implantation. Hum. Reprod. Oxf. Engl. 20, 2104–2117 (2005).
    1. Riesewijk A. et al. Gene expression profiling of human endometrial receptivity on days LH+2 versus LH+7 by microarray technology. Mol. Hum. Reprod. 9, 253–264 (2003).
    1. Díaz-Gimeno P. et al. A genomic diagnostic tool for human endometrial receptivity based on the transcriptomic signature. Fertil. Steril. 95, 50–60, 60.e1–15 (2011).
    1. Haouzi D. et al. Gene expression profile of human endometrial receptivity: comparison between natural and stimulated cycles for the same patients. Hum. Reprod. Oxf. Engl. 24, 1436–1445 (2009).
    1. Macklon N. S., van der Gaast M. H., Hamilton A., Fauser B. C. J. M. & Giudice L. C. The impact of ovarian stimulation with recombinant FSH in combination with GnRH antagonist on the endometrial transcriptome in the window of implantation. Reprod. Sci. Thousand Oaks Calif 15, 357–365 (2008).
    1. Horcajadas J. A. et al. Controlled ovarian stimulation induces a functional genomic delay of the endometrium with potential clinical implications. J. Clin. Endocrinol. Metab. 93, 4500–4510 (2008).
    1. Hauzman E. E., Garcia-Velasco J. A. & Pellicer A. Oocyte donation and endometriosis: What are the lessons? Semin. Reprod. Med. 31, 173–177 (2013).
    1. Kao L. C. et al. Expression profiling of endometrium from women with endometriosis reveals candidate genes for disease-based implantation failure and infertility. Endocrinology 144, 2870–2881 (2003).
    1. Horcajadas J. A. et al. Effect of an intrauterine device on the gene expression profile of the endometrium. J. Clin. Endocrinol. Metab. 91, 3199–3207 (2006).
    1. Talbi S. et al. Molecular phenotyping of human endometrium distinguishes menstrual cycle phases and underlying biological processes in normo-ovulatory women. Endocrinology 147, 1097–1121 (2006).
    1. Garrido-Gómez T. et al. Profiling the gene signature of endometrial receptivity: clinical results. Fertil. Steril. 99, 1078–1085 (2013).
    1. Bhagwat S. R. et al. Endometrial receptivity: a revisit to functional genomics studies on human endometrium and creation of HGEx-ERdb. PloS One 8, e58419 (2013).
    1. Ruiz-Alonso M. et al. The endometrial receptivity array for diagnosis and personalized embryo transfer as a treatment for patients with repeated implantation failure. Fertil. Steril. 100, 818–824 (2013).
    1. Díaz-Gimeno P. et al. The accuracy and reproducibility of the endometrial receptivity array is superior to histology as a diagnostic method for endometrial receptivity. Fertil. Steril. 99, 508–517 (2013).
    1. Bartosch C., Lopes J. M., Beires J. & Sousa M. Human endometrium ultrastructure during the implantation window: a new perspective of the epithelium cell types. Reprod. Sci. Thousand Oaks Calif 18, 525–539 (2011).
    1. Bourdeau V. et al. Mechanisms of primary and secondary estrogen target gene regulation in breast cancer cells. Nucleic Acids Res. 36, 76–93 (2008).
    1. Georges A. et al. FOXL2: a central transcription factor of the ovary. J. Mol. Endocrinol. 52, R17–33 (2014).
    1. Richards J. S. & Pangas S. A. The ovary: basic biology and clinical implications. J. Clin. Invest. 120, 963–972 (2010).
    1. Landelijke netwerkrichtlijn subfertiliteit. (2010) (Date of access:23/10/2015). at < >
    1. van Wageningen S. et al. Functional overlap and regulatory links shape genetic interactions between signaling pathways. Cell 143, 991–1004 (2010).
    1. Bolstad B. M., Irizarry R. A., Astrand M. & Speed T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinforma. Oxf. Engl. 19, 185–193 (2003).
    1. Leek J. T., Johnson W. E., Parker H. S., Jaffe A. E. & Storey J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinforma. Oxf. Engl. 28, 882–883 (2012).
    1. Smyth G. K. in Bioinformatics and Computational Biology Solutions using R and Bioconductor 397–420 (Springer, 2005).
    1. Golub T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).
    1. Meyer D., Dimitriadou E., Hornik K., Weingessel A. & Leisch F. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. (2014) (Date of access:23/10/2015). at < >
    1. Robin X. et al. pROC: an open-source package for R and S+to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).
    1. Subramanian A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
    1. Ashburner M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
    1. Irizarry R. A., Wang C., Zhou Y. & Speed T. P. Gene set enrichment analysis made simple. Stat. Methods Med. Res. 18, 565–575 (2009).

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