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Adjunctive markers for classification and diagnosis of central nervous system tumors: results of a multi-center neuropathological survey in Korea.

ABSTRACT: BACKGROUND:The revised 4th 2016 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) classification has adopted integrated diagnosis encompassing the histology and molecular features of CNS tumors. We aimed to investigate the immunohistochemistry, molecular testing, and testing methods for diagnosis of CNS tumors in pathological labs of tertiary centers in Korea, and evaluate the adequacy of tests for proper diagnosis in daily practice. METHODS:A survey, composed of eight questions concerning molecular testing for diagnosis of CNS tumors, was sent to 10 neuropathologists working in tertiary centers in Korea. RESULTS:For diagnosis of astrocytic and oligodendroglial tumors, all 10 centers performed isocitrate dehydrogenase mutations testing and 1p/19q loss of heterozygosity. For glioneuronal tumors, immunohistochemistry (IHC) assays for synaptophysin (n = 9), CD34 (n = 7), BRAF(VE1) (n = 5) were used. For embryonal tumors, particularly in medulloblastoma, four respondents used IHC panel (growth factor receptor bound protein 2-associated protein 1, filamin A, and yes-associated protein 1) for molecular subclassification. Regarding meningioma, all respondents performed Ki-67 IHC and five performed telomerase reverse transcriptase promoter mutation. CONCLUSIONS:Most tertiary centers made proper diagnosis in line with 2016 WHO classification. As classification of CNS tumors has evolved to be more complex and more ancillary tests are required, these should be performed considering the effect of necessity and justification.

SUBMITTER: Cha YJ

PROVIDER: S-EPMC7093289 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Adjunctive markers for classification and diagnosis of central nervous system tumors: results of a multi-center neuropathological survey in Korea.

Cha Yoon Jin YJ Kim Se Hoon SH Kim Na Rae NR

Journal of pathology and translational medicine 20200220 2

<h4>Background</h4>The revised 4th 2016 World Health Organization (WHO) classification of tumors of the central nervous system (CNS) classification has adopted integrated diagnosis encompassing the histology and molecular features of CNS tumors. We aimed to investigate the immunohistochemistry, molecular testing, and testing methods for diagnosis of CNS tumors in pathological labs of tertiary centers in Korea, and evaluate the adequacy of tests for proper diagnosis in daily practice.<h4>Methods< ...[more]

PMID: 32070090

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Project description:Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Project description:BackgroundCNS tumors, particularly gliomas, are associated with a high rate of disability and lethality, and are typically diagnosed with histopathology and immunohistochemistry. Our research aims to develop a minimally invasive method for diagnosing, grading and molecular typing glioma.MethodsWe collected patients who underwent surgery for glioma, Trigeminal neuralgia/Hemifacial spasm, schwannoma, pituitary adenomas and meningioma at our hospital from June 2019 to June 2021. Preoperative WBCs, neutrophils, lymphocytes, monocytes, platelet counts and albumin levels were collected. Preoperative NLR, dNLR, PLR, LMR and PNI were calculated, and the correlation between them and glioma diagnosis as well as grading was analyzed. We also evaluated the diagnostic significance of NLR, dNLR, PLR, LMR, PNI and their combinations for gliomas, particularly GBM, as well as the diagnostic significance of IDH molecular typing of gliomas.ResultsThere were 182 healthy samples and 3101 diseased samples in our study. Compared with other groups, glioma patients had significantly higher preoperative NLR, dNLR and PLR values, but lower LMR and PNI values. Further analysis showed that NLR, dNLR, and PLR were positively correlated with glioma grading, while LMR and PNI were negatively correlated with glioma grading. For the diagnosis of glioma, NLR showed a maximum AUC value of 0.8099 (0.7823-0.8374). For GBM, NLR showed a maximum AUC value of 0.9585 (0.9467-0.9703). In the combination, NLR+dNLR showed the highest AUC value of 0.8070(0.7849-0.8291). NLR showed significant statistical significance in all grades of glioma IDH molecular typing, while PLR did not show statistical significance.ConclusionsNLR has the greatest value for the diagnosis, differential diagnosis, grading and molecular typing of gliomas. The NLR+dNLR combination also showed high sensitivity and specificity. We believe that inflammatory parameters may serve as economical and specific markers for glioma diagnosis, grading, molecular typing, and progression.

Project description:BackgroundPrecision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification.ResultsThis paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls.ConclusionsThe proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.

Dataset Information

Adjunctive markers for classification and diagnosis of central nervous system tumors: results of a multi-center neuropathological survey in Korea.

Publications

Adjunctive markers for classification and diagnosis of central nervous system tumors: results of a multi-center neuropathological survey in Korea.

Similar Datasets

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