Uterine leiomyosarcoma (LMS) is the worst malignancy among the gynecologic cancers. Uterine leiomyoma (LM) is a benign tumor of myometrial origin and is the most common among women of childbearing age. Because the symptoms of women with LMS such as abnormal vaginal bleeding, palpable pelvic mass, and pelvic pain resemble with those of women with LM, it is difficult to preoperatively distinguish LMS and LM only by ultrasound and pelvic MRI. While histopathological diagnosis after hysterectomy is the current major means to distinguish them postoperatively, unusual histologic variants of LM tend to be misdiagnosed as LMS. Furthermore, the low incidence of LMS has been preventing elucidating its causal mechanisms and developing effective diagnoses and treatments. Therefore, development of molecular diagnosis as an alternative or confirmatory means helps diagnosing LMS more accurately. We adopted omics-based technologies to identify genome-wide features to distinguish LMS from LM, and revealed that copy-number, gene expression, and DNA methylation profiles successfully distinguished between these tumors. LMS was found to possess features typically observed in malignant solid tumors, such as extensive chromosomal abnormalities, overexpression of cell cycle –related genes, and hypomethylation spreading through large genomic regions as well as frequent hypermethylation at polycomb group target gene and cadherin gene loci. We also identified candidate expression and DNA methylation markers, which will help establishing diagnostic tests using conventional quantitative assays. While the identified omics-signatures and certain markers specific to LMS need to be further validated in larger numbers of LM and LMS samples, our results demonstrate the practical feasibility of establishing a postoperative diagnostic test to distinguish LMS from LM with high accuracy, and also suggest the future possibility of developing preoperative and non-invasive diagnostic methods.
Overall design: Gene expression data of human normal uterine myometrium (3 samples), human uterine leiomyoma (3 samples), human uterine leiomyosarcoma (3 samples), and three cell lines of human uterine leiomyosarcoma (SKN, SK-UT-1, SK-UT-1B) were generated with Whole Human Genome Microarray Kit, 4x44K (Agilent).
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