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.

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.

Fig. 1 |. Tumor-specific plasma methylomes can distinguish gliomas from extracranial cancers and healthy controls.

a, Ensemble of ROC curves for 50 iterations of trained glioma versus other classifiers. b,c, Boxplots showing distribution of AUROC statistics for glioma-versus-other classifiers stratified by IDH mutation status (n = 50 iterations) (b) and World Health Organization grade (n = 50 iterations) (c). Central bars indicate medians, the box defines the upper and lower quartiles of the distribution, and the whiskers define the 1.5× interquartile range (IQR). d, Scatterplot showing difference in plasma cfDNA methylation signals of gliomas versus healthy controls (n = 186,437 windows) against difference in methylation levels of glioma tumors versus healthy control plasma, with associated density contours. Pearson’s correlation coefficient (r = 0.37) and two-tailed P values (P < 2.2 × 10⁻¹⁶) are shown. e, Ensemble of ROC curves for 50 iterations of trained glioma versus other classifiers that are trained with windows restricted to unmethylated regions in healthy plasma (a). f,g, Heatmap (f) and multidimensional scaling plot (g) from the subset of features in e that intersect with signatures derived from glioma cell lines in gliomas (n = 59), as well as other cancers and healthy control samples (n = 388). h, Boxplots showing the distribution of per-sample summed cfMeDIP-seq signals (c.p.m.) in gliomas (n = 59) as well as other cancers and healthy control samples (n = 388), using windows that are unmethylated in healthy plasma and hypermethylated in glioma cell lines in comparison to 33 other cancer types, which also intersect with plasma-derived DMRs. Central bars indicate medians, the box defines the upper and lower quartiles of the distribution, and whiskers define the 1.5× IQR. The two-tailed P value (P < 2.2 × 10⁻¹⁶) from Wilcoxon’s rank-sum test is shown. AML, acute myeloid leukemia; BLCA, bladder cancer; BRCA, breast cancer; CRC, colorectal cancer; LUC, lung cancer; PDAC, pancreatic ductal adenocarcinoma; RCC, renal cell carcinoma.

Fig. 2 |. Plasma cfDNA methylomes can discriminate common intracranial tumors with similar cells of origin.

a, Ensemble of ROC curves from 50 iterations of trained one-class-versus-other models. b, A t-distributed stochastic neighbor embedding (t-SNE) plot, generated using the top 100 DMRs for each class-versus-other model in a with associated density contours. c, Distribution of median class probabilities for two extra-axial tumors and three intra-axial tumors that cannot be reliably distinguished by routine MRI. The results of the different one-class-versus-other models are displayed in a column for a single case. Dark-purple boxes indicate accurate prediction of tumor diagnosis with the highest median probability.