BIOM-10. PREDICTION OF BRAIN METASTASIS DEVELOPMENT FROM LUNG ADENOCARCINOMA USING DNA METHYLOMES AND THEIR NON-INVASIVE DETECTION IN CSF AND PLASMA

Abstract INTRODUCTION Brain metastases (BM) are the most common intracranial tumor, with a poor prognosis of 10-16 months, and lung adenocarcinoma (LUAD) is the most common source. Currently, we cannot reliably predict which patients will develop BMs clinically, so most are detected after they develop and growing to cause deficits. Here, we use tumor DNA methylomes to predict which patients are likely to develop BM through the course of disease and we use both cerebrospinal fluid (CSF) and plasma methylomes for BM liquid biopsy, enabling their early detection and treatment. METHODS DNA methylomes were acquired from 419 tumor and plasma samples from 363 LUAD and BM patients. We built DNA methylation-based models that predict BM development and that also identify BMs non-invasively from CSF and plasma in discovery data subsets. A nomogram was built that predicts BM risk using methylation scores and clinical stage together. Models were evaluated in independent validation sets. RESULTS Our model predicting BM development was accurate (Univariable Cox: HR=5.7, 95% CI=1.9-17.2), p=0.0023), independent of clinical factors (Multivariable Cox: HR=8.9, 95% CI=2.0-40.5, p=0.0046), and more predictive than cancer staging used currently clinically (AUC=0.81 versus 0.65). Our combinatorial nomogram using methylation and staging data together showed enhanced BM prediction (Univariable Cox: HR=17.2, 95% CI=4.1-71.3, p<0.0001; AUROC=0.82). Our models identifying BM through biofluid methylomes accurately distinguished BM from differential diagnoses of gliomas and lymphomas in plasma (AUROC=0.80, 95% CI=0.68-0.93) and with greater accuracy in CSF (AUROC=0.93, 95% CI=0.71-1.0). CONCLUSION Overall, we show the first approaches for robust molecular prediction of BM development and for reliable liquid biopsy of BM. This work is expected to transform cancer management by identifying high-risk lung cancer patients after initial treatment for inclusion in screening protocols using neuroimaging and liquid biopsy, to identify BM development early with less advanced disease and more optimal therapeutic outcomes.

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Journal
Neuro-Oncology
Published
2025-11-01
DOI
https://doi.org/10.1093/neuonc/noaf201.0098
Primary Topic
Brain Metastases and Treatment
Type
article
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article

BIOM-10. PREDICTION OF BRAIN METASTASIS DEVELOPMENT FROM LUNG ADENOCARCINOMA USING DNA METHYLOMES AND THEIR NON-INVASIVE DETECTION IN CSF AND PLASMA

Farshad Nassiri, Kenneth Aldape, Jeffrey Zuccato, Mathew Ryan Voisin et al.
Neuro-Oncology
Brain Metastases and Treatment
article

BIOM-10. PREDICTION OF BRAIN METASTASIS DEVELOPMENT FROM LUNG ADENOCARCINOMA USING DNA METHYLOMES AND THEIR NON-INVASIVE DETECTION IN CSF AND PLASMA

Farshad Nassiri, Kenneth Aldape, Jeffrey Zuccato, Mathew Ryan Voisin, Jeffrey Liu, Gelareh Zadeh, Alireza Mansouri, Andrew Ajisebutu, Yasin Mamatjan, Suganth Suppiah, Ming‐Sound Tsao, Vikas Patil, Sheila Mansouri, Daniel D. De Carvalho
article en

Abstract

Abstract INTRODUCTION Brain metastases (BM) are the most common intracranial tumor, with a poor prognosis of 10-16 months, and lung adenocarcinoma (LUAD) is the most common source. Currently, we cannot reliably predict which patients will develop BMs clinically, so most are detected after they develop and growing to cause deficits. Here, we use tumor DNA methylomes to predict which patients are likely to develop BM through the course of disease and we use both cerebrospinal fluid (CSF) and plasma methylomes for BM liquid biopsy, enabling their early detection and treatment. METHODS DNA methylomes were acquired from 419 tumor and plasma samples from 363 LUAD and BM patients. We built DNA methylation-based models that predict BM development and that also identify BMs non-invasively from CSF and plasma in discovery data subsets. A nomogram was built that predicts BM risk using methylation scores and clinical stage together. Models were evaluated in independent validation sets. RESULTS Our model predicting BM development was accurate (Univariable Cox: HR=5.7, 95% CI=1.9-17.2), p=0.0023), independent of clinical factors (Multivariable Cox: HR=8.9, 95% CI=2.0-40.5, p=0.0046), and more predictive than cancer staging used currently clinically (AUC=0.81 versus 0.65). Our combinatorial nomogram using methylation and staging data together showed enhanced BM prediction (Univariable Cox: HR=17.2, 95% CI=4.1-71.3, p<0.0001; AUROC=0.82). Our models identifying BM through biofluid methylomes accurately distinguished BM from differential diagnoses of gliomas and lymphomas in plasma (AUROC=0.80, 95% CI=0.68-0.93) and with greater accuracy in CSF (AUROC=0.93, 95% CI=0.71-1.0). CONCLUSION Overall, we show the first approaches for robust molecular prediction of BM development and for reliable liquid biopsy of BM. This work is expected to transform cancer management by identifying high-risk lung cancer patients after initial treatment for inclusion in screening protocols using neuroimaging and liquid biopsy, to identify BM development early with less advanced disease and more optimal therapeutic outcomes.

Neuro-OncologyVol. 27(Supplement_5)
University Health Network (CA), Mayo Clinic (US), Thompson Rivers University (CA), University of Toronto (CA), Cancer Institute (WIA) (IN), National Cancer Institute (MY), National Cancer Institute (US), Mayo Clinic in Florida (US), University of Oklahoma Health Sciences Center (US), Penn State Milton S. Hershey Medical Center (US)
Openalex Percentile: Top 51%
Brain Metastases and Treatment
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