Project description: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.

Project description:Purpose: To investigate the role of DNA methylation in human uterine leiomyoma tumorigenesis, we profiled genome-wide DNA methylation patterns of leiomyoma cells (LM) and adjacent normal myometrial cells (MM) treated with or without DNA methylation inhibitor 5-Aza. Methods: Primary cells were isolated from fresh uterine myometrium or leiomyoma tissue. Cells were treated with vehicle (DMSO) or 5 µM 5’-aza for 96h with medium refreshed every 24h. Genomic DNA was extracted from LM and MM cells using DNeasy Blood and Tissue Kit (Qiagen, 69504) and fragmented to 300-500bp using Covaris M220. Methylated DNA fragments were captured using the MethylCap Kit (Diagenode, C02020010). Next-generation sequencing libraries were prepared using the KAPA Hyper Prep Kit (KAPA Biosystems, KK8502) and KAPA Single-Indexed Adapter Kit (KAPA Biosystems, KK8710). Libraries were sequenced under 75bp paired-end sequencing format. Sequences were aligned to the hg19 reference genome using Bowtie2. Histone style peak calling, differential binding analysis (getDifferentialPeaks) and motif analysis were performed using Homer under default settings. Differentily methylated regions (DMRs) were detected with a fold enrichment over background tag count ≥4 and poisson enrichment p-value over background tag count <0.0001. Differential DNA methylation between MM and LM (with or without treatment) at select regions were validated through qPCR. Results: More than 100,000 methyalted regions were detected in each sample. We discovered 21,086 DMRs between MM and LM cells under basal (vehicle treated) condition. In cells treated with 5-aza, we detected 8811 DMRs between MM and LM. We compared the genomic context of DMRs identified by MethylCap-Seq with the probe distribution of the HM450 array and detected large differences between these two methods. For example, 44.23% of DMR were located in intergenic regions by MethylCap-Seq, which was drastically higher than the percentage of HM450 array probes located in intergenic regions Downstream network enrichment analysis and motif analysis indicated that DMR-associated genes and regions are crucial for the tumor progression and hormone responsiveness of LM. Conclusions: Our study represents the detailed analysis of differential DNA methyaltion profiles between MM and LM using MethylCap-seq. Our results showed a more comprehensive evaluation of DNA methyaltion landscapes of MM and LM compared with previous array based analysis. We conclude that DNA methylation plays a crucial role in hormone-dependent LM tumorigenesis.

Project description:Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We employed integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering demonstrated 3 reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and demonstrated that the molecular subtypes defined by gene-expression showed distinct genomic changes. Tumors from the "muscle-enriched" cluster showed significantly increased copy number changes (p=0.04). Most muscle-enriched cases showed loss at 16q24 which contains FANCA, known to play an important role in DNA repair, and loss at 1p36 which contains PRDM16, whose loss promotes muscle differentiation. Immunohistochemistry was performed on LMS tissue microarrays (n= 377) for five markers with high levels of mRNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP,CFL2, MYLK) and demonstrated significantly correlated expression of the 5 proteins (all pairwise p < 0.005). Expression of the 5 markers was associated with improved disease-specific survival (DSS) in a multivariate Cox regression analysis (p < 0.04). In this analysis that combined gene expression profiling, aCGH and immunohistochemistry, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers. This set contains microarray slides from expression studies. VARIABLES: Phenotype: Tumour morphology Age: age at the time of the primary lesion diagnosis (*:Age of diagnosis at the time of tumor resection) Individual Compound Based Treatment: Patient received treatment (CT: exposure to chemotherapy, RT: exposure to radiation therapy) Organism Part: Primary tumour site (UT: uterine origin, ST: soft tissue origin; **:clinically most likely anatomic origin of the tumor) Disease State: Tumour status (P: primary tumor, M: metastatic tumor, LR: local recurrence, N/A: information not available.) Sex/Mating type: M(ale)/F(emale) Experiment type: aCGH or Expression type II A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied.

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Genomic and Histologic Analysis of Uterine Leiomyosarcoma from Leiomyoma with Bizarre Nuclei

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