Genomic, epigenomic, and transcriptomic profiling towards identifying omics-features and specific biomarkers that distinguish uterine leiomyosarcoma and leiomyoma at molecular levels
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE68312 | GEO | 2017/02/09
SECONDARY ACCESSION(S): PRJNA282411
REPOSITORIES: GEO
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Project description:Uterine leiomyosarcoma (LMS) is the worst malignancy among the gynecologic cancers. Uterine leiomyoma (LM), a benign tumor of myometrial origin, is the most common among women of childbearing age. Because of their similar symptoms, it is difficult to preoperatively distinguish the two conditions only by ultrasound and pelvic MRI. While histopathological diagnosis is currently the main approach used to distinguish them postoperatively, unusual histologic variants of LM tend to be misdiagnosed as LMS. Therefore, development of molecular diagnosis as an alternative or confirmatory means will help to diagnose 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 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, hypomethylation spreading through large genomic regions, and frequent hypermethylation at the polycomb group target genes and protocadherin genes. We also identified candidate expression and DNA methylation markers, which will facilitate establishing postoperative molecular diagnostic tests based on conventional quantitative assays. Our results demonstrate the feasibility of establishing such tests and the possibility of developing preoperative and noninvasive methods.
| S-EPMC4707342 | biostudies-literature
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.
2017-02-09 | GSE68304 | GEO
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.
2017-02-09 | GSE68295 | GEO
Project description:PURPOSE:To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). METHODS:This retrospective study included 41 women (ALM?=?22, LMS?=?19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM?=?14, LMS?=?10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. RESULTS:Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p???0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ?3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). CONCLUSIONS:Combination of ?3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. KEY POINTS:• Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ?3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.
| S-EPMC5459669 | biostudies-literature
Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.
2023-12-13 | GSE222045 | GEO
Project description:OBJECTIVE:To retrospectively assess conventional magnetic resonance imaging (MRI) features that differentiate malignant pure mesenchymal uterine tumors (MPMUT); endometrial stromal sarcoma (ESS) and leiomyosarcoma (LMS) from uterine leiomyoma with cystic degeneration (ULCD). METHODS:We retrospectively reviewed magnetic resonance (MR) images of 30 patients with ULCD, 18 with ESS, and 15 with LMS, to assess tumor location, margin, T2 signal intensity (SI), speckled appearance, and peripheral band using univariate and multivariate analyses. RESULTS:ULCD more frequently showed subserosal location (53.3%), well-defined margin (96.7%), and speckled appearance (90.0%) compared with ESS (0%, 33.3%, and 33.3%, respectively) or LMS (20.0%, 33.3%, and 60.0%, respectively). In quantitative T2 SI comparisons, the T2 SI ratio of the main solid tumor portion to gluteus maximus muscle differed significantly among the three groups, with ULCD showing a lower SI ratio (0.62) compared with ESS (2.44) and LMS (1.13). On multivariate analysis, an ill-defined margin (odds ratio [OR]=44.885; p=0.003) and high T2 SI (OR=4.396; p=0.046) were the significant MR differentiators. CONCLUSION:An ill-defined tumor margin and high T2 SI ratio of the main solid tumor-to-gluteus maximus muscle were useful MRI features in the differentiation of MPMUT from ULCD.
| S-EPMC5920213 | biostudies-literature
Project description:Uterine leiomyosarcoma (ULMS) is an aggressive, rapidly progressive tumor lacking clinical and molecular predictors of outcome.ULMS patients (n = 349) were classified by disease status at presentation to MDACC as having intra-abdominal (n = 157) or distant metastatic disease (n = 192). Patient, tumor, treatment, and outcome variables were retrospectively retrieved. Formalin-fixed, paraffin-embedded tumor and control tissues from these patients (n = 109) were assembled in a tissue microarray and evaluated for hormone receptors and markers of angiogenesis, cell-cycle progression and survival. Patient, tumor, and treatment variables were correlatively analyzed.The 5- and 10-year disease-specific survival (DSS) for the cohort was 42 and 27 %, respectively. Patients with primary intra-abdominal tumors had better outcomes than those with recurrent intraperitoneal tumors. Whites had a more favorable prognosis. In patients with intra-abdominal tumors, only mitotic count >10M/10HPF portended poorer prognosis. Patients with pulmonary metastasis had improved outcomes with "curative" metastasectomy. ULMS samples exhibited loss of ER and PR expression, overexpressed Ki-67, and altered p53, Rb, p16, cytoplasmic ?-catenin, EGFR, PDGFR-?, PDGFR-?, and AXL levels. Metastatic tumors had increased VEGF, Ki-67, and survivin expression versus localized disease. Survivin and ?-catenin expression were associated with intraperitoneal recurrence; high bcl-2 expression predicted longer DSS.Analysis of both clinicopathologic factors and immunohistochemical biomarkers in ULMS identified several prognostic clinical and molecular factors, suggesting that further study may lead to improved ULMS understanding and treatment.
| S-EPMC3913478 | biostudies-literature
Project description:Uterine leiomyomas are common benign smooth muscle tumors that impose a major burden on women's health. Recent sequencing studies have revealed recurrent and mutually exclusive mutations in leiomyomas, suggesting the involvement of molecularly distinct pathways. In this study, we explored transcriptional differences among leiomyomas harboring different genetic drivers, including high mobility group AT-hook 2 (HMGA2) rearrangements, mediator complex subunit 12 (MED12) mutations, biallelic inactivation of fumarate hydratase (FH), and collagen, type IV, alpha 5 and collagen, type IV, alpha 6 (COL4A5-COL4A6) deletions. We also explored the transcriptional consequences of 7q22, 22q, and 1p deletions, aiming to identify possible target genes. We investigated 94 leiomyomas and 60 corresponding myometrial tissues using exon arrays, whole genome sequencing, and SNP arrays. This integrative approach revealed subtype-specific expression changes in key driver pathways, including Wnt/?-catenin, Prolactin, and insulin-like growth factor (IGF)1 signaling. Leiomyomas with HMGA2 aberrations displayed highly significant up-regulation of the proto-oncogene pleomorphic adenoma gene 1 (PLAG1), suggesting that HMGA2 promotes tumorigenesis through PLAG1 activation. This was supported by the identification of genetic PLAG1 alterations resulting in expression signatures as seen in leiomyomas with HMGA2 aberrations. RAD51 paralog B (RAD51B), the preferential translocation partner of HMGA2, was up-regulated in MED12 mutant lesions, suggesting a role for this gene in the genesis of leiomyomas. FH-deficient leiomyomas were uniquely characterized by activation of nuclear factor erythroid 2-related factor 2 (NRF2) target genes, supporting the hypothesis that accumulation of fumarate leads to activation of the oncogenic transcription factor NRF2. This study emphasizes the need for molecular stratification in leiomyoma research and possibly in clinical practice as well. Further research is needed to determine whether the candidate biomarkers presented herein can provide guidance for managing the millions of patients affected by these lesions.
| S-EPMC4747776 | biostudies-literature
Project description:A 50 year old woman underwent laparoscopic supracervical hysterectomy because of symptomatic fibroids. Histologic examination of samples obtained after morcellation revealed typical uterine leiomyomas in all samples investigated. 28 and 47 months later, respectively, the patient presented with peritoneal spreading of nodules that were surgically removed and histologically classified as leiomyosarcoma. In 3/4 of samples obtained after morcellation copy number/SNP-array hybridization showed complex genomic alterations widely identical to the pattern characterizing the sarcoma. Therefore, we conclude that the leiomyosarcoma had unambiguously developed from one of the leiomyomas as a result of secondary genetic alterations i.e. a rearrangement of ALK and a del(14q). The case is challenging the current risk estimates for spreading of unexpected malignant uterine tumors due to power morcellation and highlights the relevance of certain genetic alterations for rare malignant transformation of uterine benign smooth muscle tumors.
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Project description:The purpose of this work was to engineer polymeric nanoparticles to encapsulate and deliver 2-methoxyestradiol, a potential antitumor drug for treatment of uterine leiomyoma (fibroids), the most common hormone-dependent pathology affecting women of reproductive age.Encapsulation efficiency and drug release from the nanoparticles were monitored by HPLC. Cell morphology and in vitro cytotoxicity experiments were carried out in a human leiomyoma cell line. The nanoparticles displayed high encapsulation efficiency (>86%), which was verified by differential scanning calorimetry and x-ray diffraction. Excellent long-term stability of the nanoparticles and gradual drug release without burst were also observed. Cellular uptake of fluorescent nanoparticles was confirmed by confocal imaging. The drug-loaded poly(lactic acid) and poly(lactic-co-glycolic acid) nanoparticles induced cytotoxicity in human leiomyoma cells to a significantly greater extent than the free drug at 0.35 µM.This novel approach represents a potential fertility-preserving alternative to hysterectomy.
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