Utilisation of Transcriptome-Based Biomarkers for Single Embryo Transfer


Study

Methodological approach

Samples

Observed outcome

No. of patients included

Proposed biomarkers

McKenzie et al. [38]

QPCR

Whole cumulus complex

Oocyte, embryo

8 patients

GREM1, HAS2, PTGS2, PTX3, TNFAIP6

Zhang et al. [41]

Microarray and QPCR

Cumulus cells

Embryo, pregnancy

20 patients for array and 16 patients for QPCR

PTX3

Cillo et al. [42]

semi-QPCR

Cumulus cells

Oocyte, embryo

45 patients

GREM1, HAS2, PTX3

Feuerstein et al. [84]

QPCR

Cumulus cells

Oocyte, embryo

47 patients

STAR, COX2, AREG, SCD1, SCD5, Cx43

Hamel et al. [15]

Microarray and QPCR

Granulosa cells and cumulus cells

Embryo, pregnancy

40 patients

CYP19A1, CDC42, PYSL3, HSD3B1, EREG, SERPINE2, SERPINA3, TNFAIP6, SCARB1, INHA, SPRY2, FDX1, RGS2, NRP1, EGR1, PGK1, BACH2, IL6ST

Van Montfoort et al. [85]

Microarray and QPCR

Cumulus cells

Oocyte

6 patients for array and 12 patients for QPCR

CBL, CCND2, CTNND1, CXCR4, DHCR7, DVL3, GPC4, GPX3, GUK1, HSPB1, HTRA1, ITPR1, RAB6IP2, TRIM28, VEGFC

Assou et al. [14]

Microarray and QPCR

Cumulus cells

Pregnancy

30 patients

BCL2L11, PCK1, NFIB

Anderson et al. [86]

QPCR

Cumulus cells

Pregnancy

75 patients

PTGS2, BDNF, GREM1

Gebhardt et al. [43])

QPCR

Cumulus cells

Embryo, clinical outcome

38 patients

VCAN, PTGS2, GREM1, PFKP

Wathlet et al. [44]

QPCR

Cumulus cells

Embryo, clinical outcome

33 patients

TRPM7, ITPKA, STC2, CYP11A1, HSD3B1, EFNB2, CAMK1D, STC1, STC2

Fragouli et al. [53]

Microarray and QPCR

Cumulus cells

Oocyte, clinical outcome

28 patients

SPSB2

Iager et al. [45]

Microarray and QPCR

Cumulus cells

Clinical outcome

58 patients

SCL2A9, NR2F6, ARID1B, FAM36A, GPR137B, ZNF132, DNAJC15, RHBDL2, MTUS1, NUP133, ZNF93



One of the first studies where biomarkers of successful embryo implantation were sought by using CC gene expression analysis was that of Assou et al. [14]. The results of this study revealed higher expression of BCL2L11 (involved in apoptotic pathways) and PCK1 (involved in regulation of gluconeogenesis), but lower expression of transcription factor NFIB in CC whose embryos resulted in pregnancy after transfer. However, these gene expression profiles need further validation as they were discovered in a study where double embryo transfer was performed. Elective SET was performed in a study where real-time PCR was used to analyse the expression of 13 genes in CC [43]. They have analysed the expression of genes involved in the regulation of metabolism (ALDOA, LDHA, PFKP, PKM2), extracellular matrix formation (HAS2, PTX3, TNFAIP6, VCAN), and signalling (AHR, GREM1, PTGS2, STS) in order to find genes whose expression differentiated between embryos that led or did not lead to pregnancy. The expression of VCAN and PTGS2 was significantly higher (p < 0.02) and the expression of PTX3 tended to be higher (p = 0.066) in CC whose oocytes led to pregnancy. An interesting finding of this study was that no genes correlated with clinical embryo morphology scores. This observation implies that there is no relationship between the CC gene expression profile and the embryo morphological assessment.

In 2012, Wathlet et al. [44] analysed the expression of 11 genes in CC in relation to day 3 and 5 embryo morphology and pregnancy by using qPCR. The selection of genes was based on their unpublished microarray data and they were involved in key cellular processes (TRPM7, ITPKA, VCAN, SDC4, CAMK1D, STC1, STC2, EFNB2, PTHLH, CYP11A1, HSD3B1). For embryo morphology prediction, TRPM7, ITPKA, STC2, CYP11A1, and HSD3B1 were the most informative genes. Expressions of ITPKA and EFNB2 were statistically higher in the CC of oocytes giving pregnancy, and CAMK1D showed the same trend. This investigation emphasised that gene expression-based analysis of embryo quality is independent of morphology. Another study tried to identify biomarkers for pregnancy prediction by using microarrays followed by qPCR validation on CC derived from patients from three different clinics [45]. They reported on a novel set of 12 genes that were included in a prediction model which had a 78 % accuracy. Seven genes (FGF12, GPR137B, SLC2A9, ARID1B, NR2F6, ZNF132, FAM36A) were upregulated in pregnancy samples compared with non-pregnancy samples, and five genes (ZNF93, RHBDL2, DNAJC15, MTUS1, NUP133) were downregulated in pregnancy samples compared with non-pregnancy samples.

Besides biomarker(s) search, transcriptomic analyses of GC and CC have been performed to better understand folliculogenesis [13, 46] and the impact of controlled ovarian hyperstimulation (COH) and patient characteristics on CC gene expression [4752] and to examine the follicular environment of aneuploid oocytes [53].

It has been established that global gene expression profile of human GC and CC significantly differs [13, 46]. Gene ontology analysis revealed that differentially expressed genes belong to pathways of immune response, organism injury, protein degradation [13] and steroidogenesis, cell-to-cell communication, and extracellular matrix formation [46]. These studies have helped towards better understanding of fundamental aspects of folliculogenesis; better understanding of folliculogenesis could help improve protocols for oocyte in vitro maturation procedures and improve COH protocols.

It has been speculated that COH used during IVF procedures affects oocyte and consequently embryo quality [54]. The influence of COH on CC gene expression was assessed in a study where gene expression in CC surrounding mature oocytes derived from unstimulated and stimulated IVF cycles was compared [47]. There were 66 genes significantly differentially expressed; a gene ontology analysis revealed oxidation–reduction processes were significantly enriched in CC derived from unstimulated IVF cycles, implying pronounced reactive-oxygen species production might be one of the reasons for lower success rates of unstimulated IVF cycles. In a study by Devjak et al. [48], gene expression patterns were assessed in CC after ovarian stimulation protocols incorporating GnRH agonist or GnRH antagonist and transcriptomic analysis revealed no differences. This finding supports clinical data considering pregnancy and delivery rates, where slight (and statistically non-significant) differences have been reported which favour GnRH agonists in IVF [55].

On the other hand, comparison of transcriptomic profiles of GC after COH with recombinant follicle-stimulating hormone (rFSH) or urinary human menopausal gonadotropin (hMG) showed significant differences in gene expression [49, 50]. Differentially expressed genes were involved in signal transduction and transcriptional regulation, signalling pathways, oocyte maturation, and metabolic pathways [49]. Also, expression levels of luteinising hormone/human chorionic gonadotropin (LH/hCG) receptor gene and genes involved in biosynthesis of cholesterol and steroids were lower and anti-apoptosis genes were expressed at higher levels in hMG protocols than in rFSH [50]. Differential gene expression in GC implies that gonadotropin stimulation protocols for IVF could have an impact on oocyte’s functional status and quality. Another study compared transcriptomic profiles of CC between rFSH and highly purified hMG (hMG) and found 94 genes were significantly differentially expressed [51]. In CC after treatment with HP-hMG, there was overexpression of genes involved in lipid metabolism and intercellular signalling, whereas in CC following rFSH treatment overexpressed genes were involved in cellular assembly and organisation—crucial functions in oocyte maturation and competence acquirement [56]. Interestingly, STC2 and PTX3 were related to in vitro embryo quality in both gonadotropin treatments, and it was postulated that these may serve as informative biomarkers regarding embryo quality.

Regarding patient characteristics, it has been shown that age, BMI, and FSH concentration at the end of COH correlate to CC gene expression [52]. Comparison of CC gene expression associated with chromosomally normal and abnormal oocytes revealed that aneuploid oocytes have reduced mRNA levels, indicative of impaired transcriptional activity [53]. Furthermore, signalling, metabolism, apoptosis, and transport pathways were all adversely affected in CC from aneuploid oocytes. This finding implies that aneuploid oocytes tend to be surrounded by dysfunctional or damaged CC. qPCR validation of microarray data confirmed statistically significant overexpression of SPSB2 and TP53I3 in CC of euploid oocytes. The CC expression of SPSB2 and TP53I3 was further quantified using qPCR in 38 IVF cycles; embryos were transferred according to the morphological assessment and gene expression were analysed retrospectively. Both genes tended to be overexpressed in CC whose oocytes led to live birth, indicating that SPSB2 and TP53I3 could serve as potential non-invasive biomarkers of pregnancy in IVF procedures.



Transcriptomic Analysis of the Endometrium


The endometrium is a dynamic tissue that changes under the influence of hormones in order to create optimal conditions for embryo implantation. To better understand the molecular mechanisms of endometrial receptivity, several research groups have performed transcriptomic analysis of the endometrium of mice [57, 58], rats [59], and rhesus monkeys [60]. The human endometrium has been studied in pathological conditions (such as endometrial cancer) to better characterise the molecular pathways involved in pathogenesis [61] and throughout the normal menstrual cycle [62]. The latter study showed that the endometrium may be ‘dated’ to specific phase of menstrual cycle based on its transcriptional profile. Moreover, specific gene clusters characteristic of the different phases of the menstrual cycle have been described [62]. In IVF procedures, one of the major challenges has been to identify the endometrial window of receptivity and several groups have tried to find it by using transcriptomic analysis [6366]. These investigators have generated extensive lists of genes proposed as markers of endometrial receptivity; however, only one gene—osteopontin—appears on all rosters. Osteopontin is involved in cell adhesion, but its role in human embryo implantation remains poorly understood [67].

Comparison of endometrial gene expression in unstimulated IVF cycles, stimulated IVF cycles, and immediately after removal of IUD showed there were 25 genes expressed during the window of implantation (WOI) in common for all three conditions [68]. Interestingly, these genes seemed to be regulated in one way in unstimulated cycles but in the opposite way in both stimulated and IUD cycles. In other words, if a gene was overexpressed in unstimulated cycles, it was downregulated in other two conditions and vice versa. Recently, the group of Simón [16] introduced an endometrial receptivity array (ERA) containing 238 genes, related to endometrial receptivity. By using ERA, we could determine an individual WOI for women with repeated implantation failure and thus perform the embryo transfer on an optimal day of IVF cycle.


Transcriptomic Analysis of Embryos


To better understand the molecular mechanisms during preimplantation development, several studies have analysed global gene expression profile of embryos in humans [6971] and mice [72, 73]. Precise control of gene expression during the preimplantation embryonic development is of particular significance. The first cellular differentiation occurs at this time and the embryo transfers from a reliance on maternal RNA derived from the oocyte to expression of its own genome. Wells et al. [70] examined the expression of nine known genes implicated in important cellular processes such as cell cycle regulation, DNA repair, apoptosis, maintenance of accurate chromosomal segregation, and construction of the cytoskeleton throughout the preimplantation phase of embryo development by using qPCR. The genes tested were BRCA1, BRCA2, ATM, TP53, RB1, MAD2, BUB1, APC, and β-actin. They established that the expression levels of all nine genes decreased dramatically after fertilisation and then recovered between the 4- and 8-cell embryo stages. Further increase of gene expression (or in some cases a slight reduction) was seen at the morula stage before gene expression jumped significantly at the blastocyst stage. Of note, global transcriptomic analysis of mouse embryos has revealed a requirement for maternal RNA depletion before embryonic genome activation. This process happens in two stages: zygotic genome activation and mid-preimplantation gene activation [72]. After zygotic genome activation de novo gene transcription begins, it is needed for morula to undergo morphological and functional changes and develop to blastocyst.

Many morphologically normal embryos do not achieve pregnancy after embryo transfer. It has been postulated that many failed IVF cycles occur because of chromosomally abnormal oocytes or embryos. For this reason, several groups have tried to screen oocytes and embryos for aneuploidies by using transcriptomics to identify euploid and viable oocytes and embryos with greatest chances for implantation [6, 53, 74]. Wells and Delhanty [75] introduced a molecular cytogenetic method allowing the simultaneous enumeration of all of the chromosomes in a single cell called comparative genomic hybridisation (CGH). They report an improvement in embryo implantation and pregnancy rates with the proportion of CGH screened embryos resulting in live birth was 80 % as compared to 60 % for patients without CGH screening [76, 77]. The major pitfalls of using CGH were the long time required for the method (approximately 4 days), which was incompatible with a fresh transfer timeframe during IVF.


Translation of Discovered Biomarkers into Clinical Practice


With developing technology of transcriptomics, proteomics, and metabolomics, new biomarker identification has greatly accelerated. With that has come an intense discussion on how best to measure newly discovered biomarkers. Understandably, there is great interest of implementing discovered biomarkers into clinical practice. For example, the American Society of Clinical Oncology has presented a paper where it is estimated that routinely testing people with colon cancer would save at least US $600 million a year [78].

In the past decade, we have witnessed increased numbers of biomarker publications, but most of them do not have sufficient sensitivity and/or specificity to be clinically useful. This weakness is reflected in the relatively low number of patent applications and the even lower number of successful market applications [79] for such discoveries. The major pitfalls in the translation from biomarker discovery to clinical utility are:



  • Lack of making different selections before initiating discovery phase


  • Lack in biomarker characterisation/validation strategies


  • Robustness of analysis techniques used in clinical trials [79]

In order to overcome these limitations, certain authorities [e.g. American Society of Clinical Oncology, U.S. Food and Drug Administration (FDA), European Medicines agency (EMA), European Association for Predictive, Preventive and Personalized Medicine (EPMA), National Institutes of Health (NIH)] have developed guidelines on validation process for studies of biomarker discovery. For the purpose of this chapter, these recommendations can be extracted by terms analytical validity, clinical validity, and clinical utility [80].

Biomarkers can be classified into the following categories: pharmacodynamic, prognostic, or predictive [81].

1.

Pharmacodynamic biomarkers indicate the outcome of the interaction between a drug and a target, including both therapeutic and adverse affects.

 

2.

Prognostic biomarkers were originally defined as markers that indicate the likely course of a disease in a person who is not treated, although they also include markers that suggest the likely outcome of a disease irrespective of treatment.

 

3.

Predictive biomarkers suggest the population of patients who are likely to respond to a particular treatment.

 

As a rule it can be considered that ideal biomarkers for use in diagnostics and prognostics, as well as for drug developing and targeting, should be highly specific and sensitive [82]. But in reality, only rare biomarkers have high sensitivity and specificity. According to Issaq et al. [82], the following factors attribute to this:

1.

Small number of samples are analysed

 

2.

Lack of information on the history of the samples

 

3.

Case–control and control specimens are not matched with age and sex

 

4.

Limited metabolomic and proteomic coverage

 

5.

The need to follow clear standard operating procedures for sample selection, collection, storage, handling, analysis, and data interpretation.

 


Status on Validation Process of Transcriptomic Biomarkers for SET


As described previously, many biomarkers have been proposed for various endpoints in IVF cycle (oocyte maturity, oocyte fertilisation, embryo quality, pregnancy). Biomarkers for pregnancy seem to be most appropriate for SET in clinical practice. But a major drawback of all biomarkers CC and GC thus far discovered remains the lack of validation. Only a few of all proposed biomarkers have been validated by any statistical method.

In the study of McKenzie et al., HAS2, PTGS2, and GREM1 were validated by a logistic regression model for oocyte maturity, oocyte fertilisation, and embryo quality. Regression models for embryo quality yielded an AUC 0.76, 0.76, and 0.81 for HAS2, PTGS2, and GREM1, respectively. Combining PTGS2 and GREM1 improved the predictive power only slightly (AUC 0.82 vs. 0.81) [38].

Whatlet et al. investigated PTGS2, SDC4, VCAN, GREM1, ITPKA, CALM2, and TRPM7 and used a multivariate regression model for embryo quality and pregnancy. Better cleavage-stage embryo prediction relied on TRPM7 and ITPKA expression, and the pregnancy prediction relied on SDC4 and VCAN expression. The developed multivariate regression models for prediction of pregnancy had a sensitivity of 0.70 and a specificity of 0.90 in the analysed dataset [44].

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Oct 18, 2016 | Posted by in EMBRYOLOGY | Comments Off on Utilisation of Transcriptome-Based Biomarkers for Single Embryo Transfer

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