Identifying Epigenetic Signature of Breast Cancer with Machine Learning
This work addresses early diagnostics and treatment development for breast cancer by pinpointing specific methylation sites, though it is incremental as it applies existing methods to a new dataset.
The researchers tackled the problem of identifying key epigenetic biomarkers for breast cancer by using a machine learning model to classify tissue samples based on methylation patterns, achieving over 94% accuracy with a reduced model focused on 25 CpG sites.
The research reported in this paper identifies the epigenetic biomarker (methylation beta pattern) of breast cancer. Many cancers are triggered by abnormal gene expression levels caused by aberrant methylation of CpG sites in the DNA. In order to develop early diagnostics of cancer-causing methylations and to develop a treatment, it is necessary to identify a few dozen key cancer-related CpG methylation sites out of the millions of locations in the DNA. This research used public TCGA dataset to train a TensorFlow machine learning model to classify breast cancer versus non-breast-cancer tissue samples, based on over 300,000 methylation beta values in each sample. L1 regularization was applied to identify the CpG methylation sites most important for accurate classification. It was hypothesized that CpG sites with the highest learned model weights correspond to DNA locations most relevant to breast cancer. A reduced model trained on methylation betas of just the 25 CpG sites having the highest weights in the full model (trained on methylation betas at over 300,000 CpG sites) has achieved over 94% accuracy on evaluation data, confirming that the identified 25 CpG sites are indeed a biomarker of breast cancer.