BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD. (9th November 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Neuro-oncology. Volume 22(2020)Supplement 2
• Page Start: ii15
• Page End: ii15
• Publication Date: 2020-11-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa215.059
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: 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, p< 2.2e-16). CONCLUSIONS: These results demonstrate the potential for cfMeDIP-seq profiles to not only detect circulating tumor DNA, but to accurately discriminate common intracranial tumors that share cell-of-origin lineages.