Molecular Classification of Endometriosis and Disease Stage Using High-Dimensional Genomic Data

John S Tamaresis, John Tamaresis, Juan C Irwin, Gabriel A Goldfien, Gabriel Goldfien, Joseph T Rabban, Richard O Burney, Camran Nezhat, Louis V DePaolo, Linda C Giudice
Endocrinology · 2014 · vol. 155(12) , pp. 4986–4999 · doi:10.1210/en.2014-1490 · PMID:25243856 · W2007347214
article OA: bronze CC0 ⤵ 127 in-corpus citations
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Genomic classification of endometrial tissue accurately detects and stages endometriosis, identifying immune activation and altered hormone signaling pathways.

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Abstract

Endometriosis (E), 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. Because the uterine lining (endometrium) in women with E 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 E or without E (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 E or no E involved 2 binary decisions, each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no E from E, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified E with 90%-100% accuracy, were cycle phase-specific or independent, and used 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 E. Similar findings were observed with other disorders vs controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic E 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 E and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue.

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Condition tags

mesh:D004715endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometrium Adult Artificial Intelligence Case-Control Studies Decision Trees Endometriosis Endometrium Female Gene Expression Profiling Genomics Humans Menstrual Cycle Middle Aged Molecular Diagnostic Techniques Severity of Illness Index Transcriptome

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