Identification and development of epigenetic tumor biomarkers through computational biology approaches

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. In this study, we discuss the development and testing of a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7,200 samples demonstrate the capability of the framework to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To evaluate its applicability in the clinical context, we designed an informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients’ circulation. This study provides evidence that an extremely parsimonious approach can be used to develop cost-effective and highly-scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.