New WHO Classification of Endometrial Hyperplasias.
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ABSTRACT:
SUBMITTER: Emons G
PROVIDER: S-EPMC4361167 | biostudies-literature | 2015 Feb
REPOSITORIES: biostudies-literature
Publications
New WHO Classification of Endometrial Hyperplasias.
Emons G G Beckmann M W MW Schmidt D D Mallmann P P
Geburtshilfe und Frauenheilkunde 20150201 2
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Project description:The Illumina Infinium 450k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 480,000 CpGs in a total of 96 samples, including 8 hyperplasias, 33 endometrial cancers and 53 metastases, as well as 2 cell-lines.
2015-09-01 | GSE67116 | GEO
Project description:Over the course of the last 10 years, we have studied the genetic and molecular mechanisms leading to disorders that affect the adrenal cortex, with emphasis on those that are developmental, hereditary and associated with adrenal hypoplasia or hyperplasia, multiple tumors and abnormalities in other endocrine glands. On the basis of this work, we propose an hypothesis on how adrenocortical tumors form and the importance of the cyclic AMP-dependent signaling pathway in this process. The regulatory subunit type 1-alpha (RIalpha) of protein kinase A (PKA) (the PRKAR1A gene) is mutated in most patients with Carney complex and primary pigmented nodular adrenocortical disease (PPNAD). Phosphodiesterase-11A (the PDE11A gene) and -8B (the PDE8B gene) mutations were found in patients with isolated adrenal hyperplasia and Cushing syndrome, as well in patients with PPNAD. PKA effects on tumor suppression and/or development and the cell cycle are becoming clear: PKA and/or cAMP act as a coordinator of growth and proliferation in the adrenal cortex. Mouse models in which the respective genes have been knocked out see m to support this notion. Genome-wide searches for other genes responsible for adrenal tumors and related diseases are ongoing; recent evidece of the involvement of the mitochondrial oxidation pathway in adrenocortical tumorigenesis is derived from our study of rare associations such as those of disorders predisposing to adrenomedullary and related tumors (Carney triad, the dyad of paragangliomas and gastric stromal sarcomas or Carney-Stratakis syndrome, hereditary leiomyomatosis and renal cancer syndrome) which appear to be associated with adrenocortical lesions.
| S-EPMC2668239 | biostudies-literature
Project description:PurposeDistinguishing complex atypical hyperplasia (CAH) from grade 1 endometrioid endometrial cancer (EECG1) preoperatively may be valuable in order to prevent surgical overtreatment, particularly in patients wishing preserved fertility or in patients carrying increased risk of perioperative complications.Material and methodsPreoperative histological diagnosis and radiological findings were compared to final histological diagnosis in patients diagnosed with CAH and EECG1. Imaging characteristics at preoperative magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography/computer tomography (FDG-PET/CT) were compared with tumor DNA oligonucleotide microarray data, immunohistochemistry findings and clinicopathological annotations.ResultsMRI assessed tumor volume was higher in EECG1 than in CAH (p=0.004) whereas tumor apparent diffusion coefficient value was lower in EECG1 (p=0.005). EECG1 exhibited increased metabolism with higher maximum and mean standard uptake values (SUV) than CAH (p≤0.002). Unsupervised clustering of EECG1 and CAH revealed differentially expressed genes within the clusters, and identified PDZ-binding kinase (PBK) as a potential marker for selecting endometrial lesions with less aggressive biological behavior.ConclusionBoth PBK expression and preoperative imaging yield promising biomarkers that may aid in the differentiation between EECG1 and CAH preoperatively, and these markers should be further explored in larger patient series.
| S-EPMC5620275 | biostudies-literature
Project description:Pediatric adrenocortical hyperplasias are rare; they usually present with Cushing syndrome (CS); of them, isolated micronodular adrenal disease and its variant, primary pigmented adrenocortical disease are the most commonly encountered. Most cases are due to defects in the cyclic AMP/protein kinase A (cAMP/PKA) pathway, although a few cases remain without an identified genetic defect. Another cause of adrenal hyperplasia in childhood is congenital adrenal hyperplasia, a group of autosomal recessive disorders that affect steroidogenic enzymes in the adrenal cortex. Clinical presentation varies and depends on the extent of the underlying enzymatic defect. The most common form is due to 21-hydroxylase deficiency; it accounts for more than 90% of the cases. In this article, we discuss the genetic etiology of adrenal hyperplasias in childhood.
| S-EPMC9382287 | biostudies-literature
Project description:The Illumina Infinium 450k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 480,000 CpGs in a total of 96 samples, including 8 hyperplasias, 33 endometrial cancers and 53 metastases, as well as 2 cell-lines. Bisulphite converted DNA from the 96 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip
2015-09-01 | E-GEOD-67116 | biostudies-arrayexpress
Project description:The treatment for endometrial cancer is rapidly evolving with the development of molecular analysis and novel strategies. Surgical resection, cytotoxic chemotherapy, endocrine or hormonal treatment, and radiation have been the staples of treatment for decades. However, precision based approaches for tumours are rapidly becoming a part of these strategies. Biomarker driven treatments are now a part of primary and recurrent treatment algorithms. This review aims to describe the current state of molecular analysis and treatment for endometrial cancer as well as to elucidate potential approaches for the near future.
| S-EPMC9978763 | biostudies-literature
Project description:Benign normal (NL), premalignant (endometrial intraepithelial neoplasia, EIN) and malignant (cancer, EMCA) endometria must be precisely distinguished for optimal management. EIN was objectively defined previously as a regression model incorporating manually traced histologic variables to predict clonal growth and cancer outcomes. Results from this early computational study were used to revise subjective endometrial precancer diagnostic criteria currently in use. We here use automated feature segmentation and updated machine learning algorithms to develop a new classification algorithm. Endometrial tissue from 148 patients was randomly separated into 72-patient training and 76-patient validation cohorts encompassing all 3 diagnostic classes. We applied image analysis software to keratin stained endometrial tissues to automatically segment whole-slide digital images into epithelium, cells, and nuclei and extract corresponding variables. A total of 1413 variables were culled to 75 based on random forest classification performance in a 3-group (NL, EIN, EMCA) model. This algorithm correctly classifies cases with 3-class error rates of 0.04 (training set) and 0.058 (validation set); and 2-class (NL vs. EIN+EMCA) error rate of 0.016 (training set) and 0 (validation set). The 4 most heavily weighted variables are surrogates of those previously identified in manual-segmentation machine learning studies (stromal and epithelial area percentages, and normalized epithelial surface lengths). Lesser weighted predictors include gland and lumen axis lengths and ratios, and individual cell measures. Automated image analysis and random forest classification algorithms can classify normal, premalignant, and malignant endometrial tissues. Highest predictive variables overlap with those discovered independently in early models based on manual segmentation.
| S-EPMC6884662 | biostudies-literature
Project description:BackgroundThe aim of this study was to evaluate whether molecular classification prognosticates treatment response in women with endometrial cancers and endometrial intraepithelial neoplasia (EIN) treated with levonorgestrel intrauterine system (LNG-IUS).MethodsPatients treated with LNG-IUS for endometrial cancer or EIN from 2013 to 2018 were evaluated. Using immunohistochemistry and single gene sequencing of POLE, patients were classified into four groups as per the Proactive Molecular Risk Classifier for Endometrial cancer (ProMisE): POLE-mutated, mismatch repair-deficient (MMRd), p53 wild type (p53wt), and p53-abnormal (p53abn). Groups were assessed relative to the primary outcome of progression or receipt of definitive treatment.ResultsFifty-eight subjects with endometrioid endometrial cancer or EIN treated with LNG-IUS were included. Of these, 22 subjects (37.9%) had endometrial cancer and 36 subjects (62.1%) had EIN. Per the ProMisE algorithm, 44 patients (75.9%) were classified as p53wt, 6 (10.3%) as MMRd, 4 (6.9%) as p53abn, and 4 (6.9%) as POLE-mutated. Of the 58 patients, 11 (19.0%) progressed or opted for definitive therapy. Median time to progression or definitive therapy was 7.5 months, with p53abn tumors having the shortest time to progression or definitive therapy.ConclusionsMolecular classification of endometrial cancer and EIN prior to management with LNG-IUS is feasible and may predict patients at risk of progression.
| S-EPMC8201008 | biostudies-literature
Project description:BackgroundMultiplatform molecular subtyping has been put into clinical practice as an alternative for The Cancer Genome Atlas (TCGA)-based classification for endometrial cancer (EC), which proved a tool for predicting prognosis and guiding treatment. The traditional methods for the molecular classification of EC only based on pathological indicators are not accurate. The present study aimed to classify EC on a molecular level and explored the possibility of a one-time solution to guide clinical treatment and prognosis determination by utilizing data from a next-generation sequencing (NGS) panel. The ultimate aim was to utilize multiplatform testing to overcome disadvantages of long detection periods and limitations in the information regarding genetic variation.MethodsAn NGS-panel was produced using FFPE samples isolated from 86 patients pathologically diagnosed with EC, and molecular subtyping was performed according to the recommended criteria. In addition, 45 matched samples from 86 patients were randomly selected for immunohistochemical (IHC) staining of P53, MLH1, MSH2, PMS2, and MSH6. Another 41 samples were not analyzed due to incomplete IHC staining results. SPSS (V26.0; IBM Corp., Armonk, NY, USA) was used for receiver operating characteristic (ROC) curve analysis.ResultsThe molecular typing ratio of the 86 cases of endometrial carcinoma was calculated to be 16.28% for POLE type, 17.44% for MSI-H type, 47.67% for CN-L type, 12.79% for CN-H type, 5.81% for unclassified case. A comparison between IHC ProMisE-based subtyping and NGS-based subtyping of the 45 cases revealed that 3 cases were classified as MSI-H by IHC but as MSS by NGS. Among these cases, 1 case was deficient in MLH1 expression and PMS2 protein expression but had wild-type P53 protein, and the P53 sequencing data of this sample showed a missense mutation. Good overall consistency between the 2 determination methods was shown. Receiver operating characteristic (ROC) analysis showed that NGS had particularly high specificity and sensitivity for detecting the MSI and CN subtypes [area under the curve (AUC) =0.893>0.5, P=0.000029<0.01].ConclusionsThe present study suggested that NGS-based subtyping could serve as an effective approach for the molecular typing of EC. Both NGS and IHC bear their own unique advantages and challenges in clinical practice.
| S-EPMC9469165 | biostudies-literature
Project description:BackgroundClassification of endometrial carcinomas (ECs) by morphologic features is inconsistent, and yields limited prognostic and predictive information. A new system for classification based on the molecular categories identified in The Cancer Genome Atlas is proposed.MethodsGenomic data from the Cancer Genome Atlas (TCGA) support classification of endometrial carcinomas into four prognostically significant subgroups; we used the TCGA data set to develop surrogate assays that could replicate the TCGA classification, but without the need for the labor-intensive and cost-prohibitive genomic methodology. Combinations of the most relevant assays were carried forward and tested on a new independent cohort of 152 endometrial carcinoma cases, and molecular vs clinical risk group stratification was compared.ResultsReplication of TCGA survival curves was achieved with statistical significance using multiple different molecular classification models (16 total tested). Internal validation supported carrying forward a classifier based on the following components: mismatch repair protein immunohistochemistry, POLE mutational analysis and p53 immunohistochemistry as a surrogate for 'copy-number' status. The proposed molecular classifier was associated with clinical outcomes, as was stage, grade, lymph-vascular space invasion, nodal involvement and adjuvant treatment. In multivariable analysis both molecular classification and clinical risk groups were associated with outcomes, but differed greatly in composition of cases within each category, with half of POLE and mismatch repair loss subgroups residing within the clinically defined 'high-risk' group. Combining the molecular classifier with clinicopathologic features or risk groups provided the highest C-index for discrimination of outcome survival curves.ConclusionsMolecular classification of ECs can be achieved using clinically applicable methods on formalin-fixed paraffin-embedded samples, and provides independent prognostic information beyond established risk factors. This pragmatic molecular classification tool has potential to be used routinely in guiding treatment for individuals with endometrial carcinoma and in stratifying cases in future clinical trials.
| S-EPMC4506381 | biostudies-literature