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Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning

Nature Medicine (2024)Cite this article

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Abstract

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images (‘direct model’), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification (‘indirect model’), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame.

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Fig. 1: Overview of the datasets and computational workflow.
Fig. 2: Performance of DEPLOY in predicting methylation beta values.
Fig. 3: Performance of DEPLOY in classifying brain tumor types.
Fig. 4: The diagnostic changes suggested by DEPLOY in a subcohort of diagnostically challenging cases.
Fig. 5: DEPLOY provides spatial methylation predictions, allowing assessment of tumor heterogeneity, as illustrated by analysis of a dual-genotype oligoastrocytoma.

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Data availability

DBTA data, including WSIs, tumor types and demographics, are available on EBRAINS (https://ebrains.eu; https://doi.org/10.25493/WQ48-ZGX). Registration and acceptance of terms and data use agreements are required for access. CBTN WSIs and demographic data can be accessed via a request form at the Children Brain Tumor Network (https://cbtn.org/). Requests for data collected in-house, including the NCI and NCI–Prospective cohorts, should be directed to the corresponding authors. Each request will be assessed individually within 15 business days to ensure adherence to intellectual property rights and patient privacy standards.

Code availability

The DEPLOY code is available for academic research purposes via Zenodo at https://doi.org/10.5281/zenodo.8381774 (ref. 57).

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Acknowledgements

This work was partially supported by grant DP190103402 from the Australian Research Council (D.-T.H and E.A.S.) and by the Intramural Research Program of the National Institutes of Health, NCI and Center for Cancer Research (E.D.S., R.T., Z.A., E.M.C., H.L., E.R. and K.A). E.D.S. is supported by the NCI T2I Fellowship. This work utilized the super computational resources of the Australian National Computational Infrastructure, the Australian National University Merit Allocation Scheme and National Institutes of Health High Performance Computing (Biowulf cluster).

Author information

Author notes

  1. These authors contributed equally: Danh-Tai Hoang, Eldad D. Shulman.

  2. These authors jointly supervised this work: Eytan Ruppin, Kenneth Aldape.

Authors and Affiliations

  1. Biological Data Science Institute, College of Science, Australian National University, Canberra, Australian Capital Territory, Australia

    Danh-Tai Hoang & Eric A. Stone

  2. Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Eldad D. Shulman, Emma M. Campagnolo & Eytan Ruppin

  3. Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Rust Turakulov, Zied Abdullaev, Omkar Singh, H. Lalchungnunga & Kenneth Aldape

  4. Division of Neuropathology, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    MacLean P. Nasrallah

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Contributions

K.A. and E.R. conceived and designed the study. D.-T.H. and E.D.S. developed the DEPLOY models. D.-T.H. and E.D.S. conducted data analysis and validation. M.P.N. generated data and figures for mapping tumor diagnostic class heterogeneity. K.A. and M.P.N. provided expertise in neuropathology. R.T., Z.A. and O.S. generated and collected the NCI datasets. R.T. normalized methylation data. O.S., E.M.C. and H.L. provided assistance with computing and data visualization. E.A.S. interpreted the results and provided feedback on the study. K.A. and E.R. supervised the study. D.-T.H., E.D.S., M.P.N., E.R. and K.A. wrote the paper with the assistance and feedback from all the other co-authors.

Corresponding authors

Correspondence to Eytan Ruppin or Kenneth Aldape.

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Competing interests

D.-T.H., E.D.S., E.R. and K.A. are listed as inventors on a provisional patent (application no. 63/626,277, United States, 2024) filed based on the methodology outlined in this study. E.R. is a co-founder of Medaware, Metabomed and Pangea Biomed (divested from the latter). E.R. serves as a non-paid scientific consultant to Pangea Biomed under a collaboration agreement between Pangea Biomed and the NCI. The other authors declare no competing interests.

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Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Lorenzo Righetto and Saheli Sadanand, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Pathway enrichment analysis of predicted beta values across brain tumor types not covered by the ten predefined categories.

a, This panel broadens the scope of Fig. 2f, incorporating brain tumor types that are not included in the ten predefined types. Values inside the grid denote the mean of the scaled normalized enrichment score for the specific tumor type and pathway combination. Analyses based on actual and predicted beta values from the NCI cohort are displayed on the right and left, respectively (Methods). b, This panel follows the format of (a) but focuses on gene body methylation. It extends the analysis from Fig. 2g to include 31 additional brain tumor types and two control tissue signatures not covered by the ten predefined categories. c, The bar plots display the Pearson correlation coefficients, which quantify the agreement between DEPLOY’s predicted and actual average enrichment scores across various brain cancer types in the NCI cohort (n = 31 tumor types). The upper section of the panel presents correlations for promoter methylation, whereas the lower section addresses gene body methylation. Error bars indicate the 95% confidence intervals calculated using Fisher transformation and centers correspond to the computed correlation values. This panel focuses on brain tumor types that fall outside the scope of the ten predefined tumor types. For correlations associated with these predefined tumor types, refer to Fig. 2h.

Extended Data Fig. 2 Micro-averaged precision-recall curves and the area under the precision-recall curve (AUPRC) values on each cohort.

Results are depicted for the four models: Demographic (light blue), Direct (orange), Indirect (green), and Integrated (DEPLOY, dark blue). Error bands represent the 95% confidence intervals for each model, calculated through 1,000 bootstrapping iterations and centered on each model’s computed values.

Extended Data Fig. 3 Overall (micro-averaged) accuracy and balanced accuracy for all the external datasets together.

a-b, results for all samples (n = 2,156 patients). c-d, results for samples with high prediction scores (above 0.39; n = 1,407 patients). Error bars denote the 95% confidence intervals derived from bootstrapping. For every case, the top-1 and top-2 accuracies of the integrated model are significantly higher than those of each individual component (P-values < 0.001, according to one-sided bootstrap test, see Supplementary Table 3).

Extended Data Fig. 4 Performance of the combination of the direct and demographic models, and the combination of the indirect and demographic models.

Top-1 and top-2 accuracies obtained by the four models: Indirect, combination of Direct and Demographic (Direct+Demographic), combination of Indirect and Demographic (Indirect+Demographic), and Integrated model. a, The results are shown for all samples. Results for each cohort are organized separately from left to right: NCI (n = 1,796 patients), DBTA (n = 1,522 patients), CBTN (n = 348 patients), and NCI-Prospective (n = 286). b, Results for samples with prediction score above 0.39. Results for each cohort are organized separately from left to right: NCI (n = 1,193 patients), DBTA (n = 1,026 patients), CBTN (n = 212 patients), and NCI-Prospective (n = 169 patients) (b). Error bars denote the 95% confidence intervals derived from 1,000 bootstrapping iterations and the centers correspond to the computed accuracy values for each model.

Extended Data Fig. 5 Model performance on NCI cohort for each individual cancer type.

The one-versus-rest AUPRC, Precision, Recall, and F1 score for each brain tumor type achieved by demographic (light blue), direct (orange), indirect (red), and our integrated model (dark blue). The number of patients in each cancer type is shown in parentheses. For each metric indicated on the vertical axis, the error bars represent the 95% confidence intervals, derived from 1,000 bootstrapping iterations and the centers correspond to computed values of the respective metric.

Extended Data Fig. 6 Model performance on all the three external cohorts together for each individual cancer type.

The one-versus-rest AUPRC, Precision, Recall, and F1 score for each brain tumor type achieved by demographic (light blue), direct (orange), indirect (red), and our integrated model (dark blue). The number of patients in each cancer type is shown in parentheses. For each metric indicated on the vertical axis, the error bars represent the 95% confidence intervals, derived from 1,000 bootstrapping iterations and the centers correspond to computed values of the respective metric.

Extended Data Fig. 7 Model performance on DBTA cohort for each individual cancer type.

The one-versus-rest AUPRC, Precision, Recall, and F1 score for each brain tumor type achieved by demographic (light blue), direct (orange), indirect (red), and our integrated model (dark blue). The number of patients in each cancer type is shown in parentheses. For each metric indicated on the vertical axis, the error bars represent the 95% confidence intervals, derived from 1,000 bootstrapping iterations and the centers correspond to computed values of the respective metric.

Extended Data Fig. 8 Model performance on CBTN cohort for each individual cancer type.

The one-versus-rest AUPRC, Precision, Recall, and F1 score for each brain tumor type achieved by demographic (light blue), direct (orange), indirect (red), and our integrated model (dark blue). Notably, GBM, SE and O-IDH are not present in the CBTN cohort. The number of patients in each cancer type is shown in parentheses. For each metric indicated on the vertical axis, the error bars represent the 95% confidence intervals, derived from 1,000 bootstrapping iterations and the centers correspond to computed values of the respective metric.

Extended Data Fig. 9 Model performance on NCI-Prospective cohort for each individual cancer type.

The one-versus-rest AUPRC, Precision, Recall, and F1 score for each brain tumor type achieved by demographic (light blue), direct (orange), indirect (red), and our integrated model (dark blue). The number of patients in each cancer type is shown in parentheses. For each metric indicated on the vertical axis, the error bars represent the 95% confidence intervals, derived from 1,000 bootstrapping iterations and the centers correspond to computed values of the respective metric.

Extended Data Fig. 10 Balanced accuracies on each cohort for samples with high prediction scores (above 0.39).

Results for each cohort are organized separately from left to right: NCI (first column), DBTA (second column), CBTN (third column) and NCI-Prospective (fourth column). Error bars denote the 95% confidence intervals obtained via bootstrapping (1,000 iterations) and the centers correspond to the computed accuracy values for each model.

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Hoang, DT., Shulman, E.D., Turakulov, R. et al. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning. Nat Med (2024). https://doi.org/10.1038/s41591-024-02995-8

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