Project description:Endometriosis, an estrogen-dependent, progesterone-resistant, inflammatory disorder affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Since the uterine lining (endometrium) in women with endometriosis has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n=148 archived endometrial samples from women with endometriosis or without endometriosis (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with endometriosis or no endometriosis involved two binary decisions each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no endometriosis from endometriosis, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified endometriosis with 90-100% accuracy, were cycle phase-specific or independent, and utilized relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism and growth factor signaling in endometrium of women with endometriosis. Similar findings were observed with other disorders versus controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic endometriosis with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate-genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of endometriosis and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue. We analyzed endometrial samples from n=148 women without or with endometriosis and/or other uterine/pelvic pathologies, using whole genome microarrays.

Project description:Endometriosis, an estrogen-dependent, progesterone-resistant, inflammatory disorder affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Since the uterine lining (endometrium) in women with endometriosis has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n=148 archived endometrial samples from women with endometriosis or without endometriosis (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with endometriosis or no endometriosis involved two binary decisions each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no endometriosis from endometriosis, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified endometriosis with 90-100% accuracy, were cycle phase-specific or independent, and utilized relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism and growth factor signaling in endometrium of women with endometriosis. Similar findings were observed with other disorders versus controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic endometriosis with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate-genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of endometriosis and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue.

Project description:Eutopic endometrium in endometriosis has molecular evidence of resistance to progesterone (P4) and activation of the PKA pathway in the stromal compartment. To investigate global and temporal responses of eutopic endometrium to P4, we compared early (6-h), intermediate (48-h), and late (14-day) transcriptomes, signaling pathways, and networks of human endometrial stromal fibroblasts (hESFs) from women with endometriosis (hESFendo) to hESFs from women without endometriosis (hESFnonendo). Endometrial biopsy samples were obtained from subjects with and without mild peritoneal endometriosis (n = 4 per group), and hESFs were isolated and treated with P4 (1 μM) plus estradiol (E2) (10 nM), E2 alone (10 nM), or vehicle for up to 14 days. Total RNA was subjected to microarray analysis using a Gene 1.0 ST (Affymetrix) platform and analyzed by using bioinformatic algorithms, and data were validated by quantitative real-time PCR and ELISA. Results revealed unique kinetic expression of specific genes and unique pathways, distinct biological and molecular processes, and signaling pathways and networks during the early, intermediate, and late responses to P4 in both hESFnonendo and hESFendo, although a blunted response to P4 was observed in the latter. The normal response of hESF to P4 involves a tightly regulated kinetic cascade involving key components in the P4 receptor and MAPK signaling pathways that results in inhibition of E2-mediated proliferation and eventual differentiation to the decidual phenotype, but this was not established in the hESFendo early response to P4. The abnormal response of this cell type to P4 may contribute to compromised embryonic implantation and infertility in women with endometriosis. We compared early (6-h), intermediate (48-h), and late (14-day) in vitro whole-genome responses of hESF from women with endometriosis (hESFendo) to hESF from women without endometriosis (hESFnonendo) treated with P4 plus E2 (E2P4), E2 alone, or vehicle alone. Using this experimental paradigm, the data demonstrate unique phenotypes, gene expression processes, biochemical and signaling pathways, and networks suggestive of early, intermediate, and late responses of hESFnonendo and hESFendo to P4, giving insights into the complexity of events occurring normally in response to P4 and in the setting of endometriosis.

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Homo sapiens

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