دورية أكاديمية
BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD
| العنوان: | BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD |
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| المؤلفون: | Nassiri, Farshad, Chakravarthy, Ankur, Feng, Shengrui, Shen, Roxana, Nejad, Romina, Zuccato, Jeffrey, Voisin, Mathew, Patil, Vikas, Horbinski, Craig, Aldape, Kenneth, Zadeh, Gelareh, de Carvalho, Daniel |
| المصدر: | Neuro-Oncology ; volume 22, issue Supplement_2, page ii15-ii15 ; ISSN 1522-8517 1523-5866 |
| بيانات النشر: | Oxford University Press (OUP) |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Cancer Research, Neurology (clinical), Oncology |
| الوصف: | BACKGROUND The diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Non-invasive diagnostic approaches, particularly for patients with brain tumours, provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. We reasoned that DNA methylation profiles of circulating tumor DNA in blood can be used as a clinically useful biomarker for patients with brain tumors, given the specificity of DNA methylation profiles for cell-of-origin. METHODS We generated methylation profiles on the plasma of 608 patients with cancer (219 intracranial, 388 extracranial) and 60 healthy controls using a cell-free methylated DNA immunoprecipitation combined with deep sequencing (cfMeDIP-seq) approach. Using machine-learning approaches we generated and evaluated models to distinguish brain tumors from extracranial cancers that may metastasize to the brain, as well as additional models to discriminate common brain tumors included in the differential diagnosis of solitary extra-axial and intra-axial tumors. RESULTS We observed high sensitivity and discriminative capacity for our models to distinguish gliomas from other cancerous and healthy patients (AUC=0.99, 95%CI 0.96–1), with similar performance in IDH mutant and wildtype gliomas as well as in lower- and high-grade gliomas. Excluding non-malignant contributors to plasma methylation did not change model performance (AUC=0.982, 95%CI 0.93–1). Models generated to discriminate intracranial tumors from each other also demonstrated high accuracy for common extra-axial tumors (AUCmeningioma=0.89, 95%CI 0.80–0.97; AUChemangiopericytoma=0.95, 95%CI 0.73–1) as well as intra-axial tumors ranging from low-grade indolent glial-neuronal tumors (AUC 0.93, 95%CI 0.80 – 1) to diffuse intra-axial gliomas with distinct molecular composition (AUCIDH-mutant glioma = 0.82, 95%CI 0.66 -0.98; AUCIDH-wildtype-glioma = 0.71, 95%CI 0.53 – 0.9). Plasma cfMeDIP-seq signals originated from corresponding tumor tissue DNA methylation signals (r=0.37, ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1093/neuonc/noaa215.059 |
| الإتاحة: | https://doi.org/10.1093/neuonc/noaa215.059Test http://academic.oup.com/neuro-oncology/article-pdf/22/Supplement_2/ii15/34689847/noaa215.059.pdfTest |
| حقوق: | https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_modelTest |
| رقم الانضمام: | edsbas.C6EA8012 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1093/neuonc/noaa215.059 |
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