Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes

Abstract

Definitive diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Noninvasive diagnostic approaches provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. In the present study, we show that DNA-methylation profiles from plasma reveal highly specific signatures to detect and accurately discriminate common primary intracranial tumors that share cell-of-origin lineages and can be challenging to distinguish using standard-of-care imaging.

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Fig. 1: Tumor-specific plasma methylomes can distinguish gliomas from extracranial cancers and healthy controls.
Fig. 2: Plasma cfDNA methylomes can discriminate common intracranial tumors with similar cells of origin.

Data availability

The data used to deconvolute healthy plasma cell type were previously published and available in the Gene Expression Omnibus (GEO) repository under accession code GSE122126.

All the cell line datasets analyzed during the present study were previously published and available in the GEO repository under accession code GSE68379.

Processed cfMeDIP-seq data and intermediate data objects are available in a Zenodo archive at https://doi.org/10.5281/zenodo.3715312. Preprocessed cfMeDIP-seq data generated in this manuscript are available on request from the corresponding authors (D.D.C. and G.Z.) to comply with the Princess Margaret Cancer Center Institute ethics regulations to protect patient privacy. All requests will be promptly reviewed by the Technology Development and Commercialization team to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data and materials that can be shared will be released subject to a data transfer agreement.

Code availability

R markdowns of the code used to generate the results in this paper are available in a Zenodo archive at https://doi.org/10.5281/zenodo.3715312

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Acknowledgements

We thank N. Pirouzmand for her technical assistance with experimental protocols. F.N. is supported by the Canadian Institute of Health Research (CIHR) Vanier Scholarship, AANS/CNS Section on Tumors & NREF Research Fellowship Grant, and Hold’em for Life Oncology Fellowship. A.C. is supported by a CIHR Banting Fellowship. The Northwestern Nervous System Tumor Bank (C.H.) is supported by the P50CA221747 SPORE for Translational Approaches to Brain Cancer. C.H. is funded by the National Institutes of Health (grant no. R01NS102669). G.Z. is funded by a CIHR project grant award (grant no. 159452), and the Brain Tumor Charity UK Quest for Cures grant (grant no. GN-000430). D.D.C. is funded by the CIHR New Investigator salary award (201512MSH360794-228629), Helen M. Cooke professorship and the Gattuso-Slaight Personalized Cancer Medicine Fund from Princess Margaret Cancer Foundation, Canada Research Chair, CIHR Foundation grant (grant no. FDN 148430), CIHR Project grant (grant no. PJT 165986), NSERC (grant no. 489073) and Ontario Institute for Cancer Research with funds from the province of Ontario.

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Authors

Contributions

F.N., A.C., S.F., G.Z. and D.D.D.C. conceived and designed the study. F.N., R.N., G.Z., K.A. and C.H. were responsible for clinical care and collected all the biomaterials. F.N., S.F., S.Y.S., J.A.Z. and M.R.V. carried out the laboratory testing. F.N. and R.N. collected and collated the clinical data. F.N., A.C., S.Y.S. and V.P. contributed to the data processing. G.Z. and D.D.D.C. supervised all aspects of the study. F.N., A.C., G.Z. and D.D.D.C. contributed to initial data interpretation and wrote the first draft. All authors contributed to final data interpretation and critical revision of the manuscript, and approved the final version of the manuscript.

Corresponding authors

Correspondence to Gelareh Zadeh or Daniel D. De Carvalho.

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Competing interests

D.D.D.C., S.Y.S. and A.C. are listed as inventors on patents filed that are related to this method. D.D.D.C. received research funding from Pfizer and Nektar therapeutics not related to this project.

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Peer review information Saheli Sadanand was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 cfMeDIP-seq signals of gliomas compared to extracranial cancers and healthy controls.

a, Bar-chart showing the distribution of samples in the 447 sample cfMeDIP cohort. b, Flowchart of machine learning algorithm used to train and evaluate cfMeDIP-seq in glioma detection and classification c, heatmap showing cfMeDIP-seq signals (log2 counts per million) of all DMRs (rows) derived from training sets for patients (columns) in the machine learning analyses detailed in (b). d, MDS plot of the features depicted in the heatmap in (c) in gliomas (n = 59) as well as other cancers and healthy controls samples (n = 388). e, scatterplot showing difference in plasma cfDNA methylation signals of gliomas vs healthy controls after restricting to windows typically unmethylated in healthy plasma (n = 138,328 windows) against differences in methylation levels of glioma tumors vs healthy control plasma, with associated density contours. Pearson correlation coefficient (r = 0.42) and two-tailed p values (p < 2.2 × 10−16) are shown. f, Boxplots showing the distribution of per-sample median signal (counts per million) in gliomas (n = 59) as well as other cancers and healthy controls samples (n = 388) of windows unmethylated in healthy plasma and hypermethylated in glioma cell lines compared to cell lines from 33 other cancer types (delta-Beta > 0.3, FDR < 0.01). Central bars indicate medians, the box defines the upper and lower quartiles of the distribution, and whiskers define the 1.5x interquartile range. Two-tailed p-value (p = 1.767×10-12) from Wilcoxon’s Rank Sum Test is shown.

Extended Data Fig. 2 Algorithm for machine-learning analysis of plasma-based brain tumor classifier.

a, Bar-chart showing the distribution of samples in brain cfMeDIP cohort. Hemangiopericytoma (n = 9), Meningioma (n = 60), low-grade glioneuronal (n = 14), IDH mutant glioma (n = 41), IDH-wildtype glioma (n = 22), brain metastases (n = 15). b, Flowchart of machine learning algorithm used to train and evaluate cfMeDIP-seq in brain tumor detection and classification.

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Nassiri, F., Chakravarthy, A., Feng, S. et al. Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes. Nat Med (2020). https://doi.org/10.1038/s41591-020-0932-2

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