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.
Science advances. 2022 Sep 28 [Epub]
Sabrina H Rossi, Izzy Newsham, Sara Pita, Kevin Brennan, Gahee Park, Christopher G Smith, Radoslaw P Lach, Thomas Mitchell, Junfan Huang, Anne Babbage, Anne Y Warren, John T Leppert, Grant D Stewart, Olivier Gevaert, Charles E Massie, Shamith A Samarajiwa
Department of Oncology, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK., MRC Cancer Unit, University of Cambridge, Hutchison-MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK., Stanford Centre for Biomedical Informatics Research, Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA., Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK., Department of Surgery, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK., Department of Histopathology, University of Cambridge, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK., Department of Urology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.