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Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns
Acknowledgements: S.A.S conceived the study. I.N developed the machine learning models and carried out the data processing and analysis. M.S contributed to the initial machine learning models and analysis during a summer studentship. S.J contributed an external data set and expertise. I.N and S.A.S wrote the manuscript with input from the other authors. We acknowledge the contribution of Dr Charles Massie (In Memoriam) of the University of Cambridge who was also involved in the conception of the study and whose advice and expertise on cancer early detection and cancer-related DNA methylome analysis was invaluable to this study. We are thankful to Prof. Rebecca Fitzgerald (University of Cambridge), who contributed an oesophagus cancer data set to this study. We also thank members of the S.A.S laboratory that read and commented on the manuscript.Abstract
Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.</jats:p
Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.
Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.Cancer Research UK Clinical Doctoral Fellowship. (S.H.R.)
UK Medical Research Council doctoral training award. (I.N)
UK Medical Research Council funding (MC UU 12022/10) (S.A.S, J.H)
Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge grant (S.A.S, J.H)
CRUK Fellowship award (A26718) (C.E.M.)
The Mark Foundation for Cancer Research, the Cancer Research UK Cambridge Centre [C9685/A25177] and NIHR Cambridge Biomedical Research Centre (BRC- 1215-20014). (G.D.S., A.Y.W)
The Human Research Tissue Bank at Addenbrooke’s Hospital is supported by the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care