STUDY QUESTION What are the key considerations for developing an enhanced transcriptomic method for secretory endometrial tissue dating? SUMMARY ANSWER Multiple gene expression signature combinations can serve as biomarkers for endometrial dating, but their predictive performance is variable and depends on the number and identity of the genes included in the prediction model, the dataset characteristics and the technology employed for measuring gene expression. WHAT IS KNOWN ALREADY Among the new generation of transcriptomic endometrial dating (TED) tools developed in the last decade, there exists variation in the technology used for measuring gene expression, the gene makeup and the prediction model design. A detailed study, comparing prediction performance across signatures for understanding signature behaviour and discrepancies in gene content between them, is lacking. STUDY DESIGN, SIZE, DURATION A multicentre prospective study was performed between July 2018 and October 2020 at five different centres from the same group of clinics (Spain). This study recruited 281 patients and finally included in the gene expression analysis 225 Caucasian patients who underwent IVF treatment. After preprocessing and batch effect filtering, gene expression measurements from 217 patients were combined with artificial intelligence algorithms (support vector machine, random forest and k-nearest neighbours) allowing evaluation of different prediction models. In addition, secretory-phase endometrial transcriptomes from gene expression omnibus (GEO) datasets were analysed for 137 women, to study the endometrial dating capacity of genes independently and grouped by signatures. This provided data on the consistency of prediction across different gene expression technologies and datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS Endometrial biopsies were analysed using a targeted TruSeq (Illumina) custom RNA expression panel called the endometrial dating panel (ED panel). This panel included 301 genes previously considered relevant for endometrial dating as well as new genes selected for their anticipated value in detecting the secretory phase. Final samples (n = 217) were divided into a training set for signature discovery and an independent testing set for evaluation of predictive performance of the new signature. In addition, secretory-phase endometrial transcriptomes from GEO were analysed for 137 women to study endometrial dating capacity of genes independently and grouped by signatures. Predictive performance among these signatures was compared according to signature gene set size. MAIN RESULTS AND THE ROLE OF CHANCE Testing of the ED panel allowed development of a model based on a new signature of 73 genes, which we termed ‘TED’ and delivers an enhanced tool for the consistent dating of the secretory phase progression, especially during the mid-secretory endometrium (3–8 days after progesterone (P) administration (P + 3–P + 8) in a hormone replacement therapy cycle). This new model showed the best predictive capacity in an independent test set for staging the endometrial tissue in the secretory phase, especially in the expected window of implantation (average of 114.5 ± 7.2 h of progesterone administered; range in our patient population of 82–172 h). Published sets of genes, in current use for endometrial dating and the new TED genes, were evaluated in parallel in whole-transcriptome datasets and in the ED panel dataset. TED signature performance was consistently excellent for all datasets assessed, frequently outperforming previously published sets of genes with a smaller number of genes for dating the endometrium in the secretory phase. Thus, this optimized set exhibited prediction consistency across datasets. LARGE SCALE DATA The data used in this study is partially available at GEO database. GEO identifiers GSE4888, GSE29981, GSE58144, GSE98386. LIMITATIONS, REASONS FOR CAUTION Although dating the endometrial biopsy is crucial for investigating endometrial progression and the receptivity process, further studies are needed to confirm whether or not endometrial dating methods in general are clinically useful and to guide the specific use of TED in the clinical setting. WIDER IMPLICATIONS OF THE FINDINGS Multiple gene signature combinations provide adequate endometrial dating, but their predictive performance depends on the identity of the genes included, the gene expression platform, the algorithms used and dataset characteristics. TED is a next-generation endometrial assessment tool based on gene expression for accurate endometrial progression dating especially during the mid-secretory. STUDY FUNDING/COMPETING INTEREST(S) Research funded by IVI Foundation (1810-FIVI-066-PD). P.D.-G. visiting scientist fellowship at Oxford University (BEFPI/2010/032) and Josefa Maria Sanchez-Reyes’ predoctoral fellowship (ACIF/2018/072) were supported by a program from the Generalitat Valenciana funded by the Spanish government. A.D.-P. is supported by the FPU/15/01398 predoctoral fellowship from the Ministry of Science, Innovation and Universities (Spanish Government). D.W. received support from the NIHR Oxford Biomedical Research Centre. The authors do not have any competing interests to declare.

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Study question It is possible to identify a characteristic pattern of endometrial gene expression indicative of implantation failure, which is independent of implantation window displacements? Summary answer An endometrial transcriptomic signature was able to identify patients with a > 3-fold increased risk of implantation/pregnancy failure with 95% accuracy. What is known already Implantation failure of endometrial origin is a complex and multifactorial symptom with diverse causes, diagnosed in IVF patients after repeated failures with good quality embryos. A generation of gene expression tools have assumed that Window of implantation (WOI) displacement is the principal cause of this condition, but strategies seeking to counteract this problem by adjusting the day of embryo transfer have not yielded convincing improvements in outcomes. However, it is conceivable that other forms of endometrial disruptions, relevant to embryo implantation, could exist. New endometrial diagnostic strategies are needed to understand, diagnose and potentially treat patients affected with such problems. Study design, size, duration A prospective multicenter study between January 2018 and December 2021 recruited 281 Caucasian IVF patients (mean age of 39.4±4.8 years and BMI of 22.9±3.5 kg/m2) undergoing hormone replacement therapy and encompassing 114.5±7.2 h of progesterone administration at time of endometrial biopsy. Following experimental quality controls and clinical follow up, 186 patients who had a good quality embryo transferred in the cycle after endometrial biopsy collection were included for gene discovery and prediction performance. Participants/materials, setting, methods The expression of 404 genes selected for their potential to distinguish endometrial timing and/or endometrial disruption was measured. Transcriptomic variation related to progression of the menstrual cycle was removed using transcriptomic endometrial dating (TED) and linear models. Study groups were established according to clinical and gene expression parameters through a semi-supervised artificial intelligence procedure. Gene signature discovery and a cross-validation processes were undertaken to define predictive expression patterns. Reproductive outcomes were compared between prognosis profiles. Main results and the role of chance We developed a procedure called Clinically Acute Transcriptomic Stratification (CATranS), combining clinical parameters and deep transcriptomic molecular characterisation to stratify patients according to endometrial prognosis: ‘poor’ (n = 137) or ‘good’ (n = 49). These two transcriptomic profiles were associated with differing reproductive outcomes in the single embryo transfer following biopsy: pregnancy rate (45.1% vs 79.6%, poor vs good prognosis, respectively, p = 3.8E-5); live birth rate (56.4% vs 97.5%, p = 3.0E-06); clinical miscarriage rate (27.9% vs 2.6%, p = 0.0020); biochemical pregnancy rate (20.4% vs 0%, p = 0.0023). Patients with a poor prognosis profile had 3.3-times higher relative risk of a transferred embryo failing to implant or a pregnancy not being sustained to term, compared with good prognosis patients. Initially, a reference dataset was used to build a prototype for diagnosing endometrial failure, revealing that a gene expression signature consisting of 135 genes was the most predictive. Prediction performance was estimated using a 5-fold 100-times cross-validation process, resulting in a median accuracy of 0.92, median sensitivity of 0.96, and median specificity of 0.84. From these 135 predictive genes, 122 were differentially expressed (FDR<0.05) in endometrial poor prognosis, 59 up- and 63 down-regulated, most involved in functional processes such as regulation (17%), metabolism (8.4%), immune response and inflammation (7.8%). Limitations, reasons for caution We describe a potential new strategy for evaluating endometrial competence. However, to confirm predictive value, validation using additional samples from patients independent of signature discovery set is required. Further research to identify potential treatments for patients classified as poor prognosis is needed, providing a tailored clinical pathway for these patients. Wider implications of the findings The prototype described is a novel concept, potentially leading to development of a new generation of tools for diagnosis of fertility problems related to endometrial factors. The ‘poor’ prognosis profile is not caused by asynchronies in menstrual cycle progression, opening the possibility of finding new treatment pathways for these patients. Trial registration number NA