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BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES
BACKGROUND: Accurate diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Although the cerebrospinal fluid (CSF) is the ideal...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354165/ http://dx.doi.org/10.1093/noajnl/vdac078.002 |
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author | Mansouri, Alireza Mikolajewicz, Nicholas Khan, Shahbaz Glantz, Michael Moffat, Jason Zadeh, Gelareh Kislinger, Thomas |
author_facet | Mansouri, Alireza Mikolajewicz, Nicholas Khan, Shahbaz Glantz, Michael Moffat, Jason Zadeh, Gelareh Kislinger, Thomas |
author_sort | Mansouri, Alireza |
collection | PubMed |
description | BACKGROUND: Accurate diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Although the cerebrospinal fluid (CSF) is the ideal liquid biopsy source, the traditionally high volumes required for impactful analyses have deterred progress. The objective of this study was to identify diagnostic and prognostic CSF proteomic signatures in glioblastoma (GBM), brain metastases (BM), and central nervous system lymphoma (CNSL). METHODS: CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics with low sample volumes. Proteomic signatures were identified using machine learning classifiers and survival analyses. RESULTS: Using 30µL CSF volumes, we recovered 800 unique peptides across 73 samples [20 normal pressure hydrocephalus (NPH, non-tumor control), 22 GBM, 17 BM, and 14 CNSL]. Externally-validated proteomic-based classifiers identified malignancy with AUROC of 0.94 and distinguished individual tumor entities from others with AUROC ≥0.96. More clinically relevant triplex classifiers, comprised of just 3 peptides, distinguished individual tumor entities with AUROC ≥0.90. Novel biomarkers were identified among the top classifiers, including TFF3 and CACNA2D2, and characterized using single-cell RNA sequencing data. Survival analyses validated previously implicated prognostic signatures, including blood brain barrier disruption. DISCUSSION: Reliable classification of intra-axial malignancies using low CSF volumes is feasible, which has ramifications for longitudinal tumor surveillance. Novel biomarkers identified here necessitate future validation. Based on emerging evidence, upfront implantation of CSF reservoirs in brain tumor patients warrants consideration. |
format | Online Article Text |
id | pubmed-9354165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93541652022-08-09 BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES Mansouri, Alireza Mikolajewicz, Nicholas Khan, Shahbaz Glantz, Michael Moffat, Jason Zadeh, Gelareh Kislinger, Thomas Neurooncol Adv Supplement Abstracts BACKGROUND: Accurate diagnosis and prognostication of intra-axial brain tumors hinges on invasive brain sampling, which carries risk of morbidity. Minimally invasive sampling of proximal fluids, also known as liquid biopsy, can mitigate this risk. Although the cerebrospinal fluid (CSF) is the ideal liquid biopsy source, the traditionally high volumes required for impactful analyses have deterred progress. The objective of this study was to identify diagnostic and prognostic CSF proteomic signatures in glioblastoma (GBM), brain metastases (BM), and central nervous system lymphoma (CNSL). METHODS: CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics with low sample volumes. Proteomic signatures were identified using machine learning classifiers and survival analyses. RESULTS: Using 30µL CSF volumes, we recovered 800 unique peptides across 73 samples [20 normal pressure hydrocephalus (NPH, non-tumor control), 22 GBM, 17 BM, and 14 CNSL]. Externally-validated proteomic-based classifiers identified malignancy with AUROC of 0.94 and distinguished individual tumor entities from others with AUROC ≥0.96. More clinically relevant triplex classifiers, comprised of just 3 peptides, distinguished individual tumor entities with AUROC ≥0.90. Novel biomarkers were identified among the top classifiers, including TFF3 and CACNA2D2, and characterized using single-cell RNA sequencing data. Survival analyses validated previously implicated prognostic signatures, including blood brain barrier disruption. DISCUSSION: Reliable classification of intra-axial malignancies using low CSF volumes is feasible, which has ramifications for longitudinal tumor surveillance. Novel biomarkers identified here necessitate future validation. Based on emerging evidence, upfront implantation of CSF reservoirs in brain tumor patients warrants consideration. Oxford University Press 2022-08-05 /pmc/articles/PMC9354165/ http://dx.doi.org/10.1093/noajnl/vdac078.002 Text en © The Author(s) 2022. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Supplement Abstracts Mansouri, Alireza Mikolajewicz, Nicholas Khan, Shahbaz Glantz, Michael Moffat, Jason Zadeh, Gelareh Kislinger, Thomas BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title | BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title_full | BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title_fullStr | BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title_full_unstemmed | BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title_short | BSCI-02 CSF PROTEOMICS AS A MINIMALLY-INVASIVE STRATEGY FOR DISTINGUISHING BRAIN METASTASES FROM OTHER PRIMARY BRAIN MALIGNANCIES |
title_sort | bsci-02 csf proteomics as a minimally-invasive strategy for distinguishing brain metastases from other primary brain malignancies |
topic | Supplement Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354165/ http://dx.doi.org/10.1093/noajnl/vdac078.002 |
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