TY - JOUR
T1 - Cerebrospinal fluid methylome-based liquid biopsies for accurate malignant brain neoplasm classification
AU - Zuccato, Jeffrey A.
AU - Patil, Vikas
AU - Mansouri, Sheila
AU - Voisin, Mathew
AU - Chakravarthy, Ankur
AU - Yi Shen, Shu
AU - Nassiri, Farshad
AU - Mikolajewicz, Nicholas
AU - Trifoi, Mara
AU - Skakodub, Anna
AU - Zacharia, Brad
AU - Glantz, Michael
AU - De Carvalho, Daniel D.
AU - Mansouri, Alireza
AU - Zadeh, Gelareh
N1 - Funding Information:
This study was financed by our MacFeeters-Hamilton Grant.
Publisher Copyright:
© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - Background. Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches. Methods. Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization. Results. Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71–1.0), GBM (AUROC = 0.83, 95% CI: 0.63–1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66–1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy. Conclusions. CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.
AB - Background. Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches. Methods. Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization. Results. Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71–1.0), GBM (AUROC = 0.83, 95% CI: 0.63–1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66–1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy. Conclusions. CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.
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U2 - 10.1093/neuonc/noac264
DO - 10.1093/neuonc/noac264
M3 - Article
C2 - 36455236
AN - SCOPUS:85166442376
SN - 1522-8517
VL - 25
SP - 1452
EP - 1460
JO - Neuro-oncology
JF - Neuro-oncology
IS - 8
ER -
