Receptor Status Prediction in Breast Cancer Patients from DNA Methylation Data

Abstract

Breast cancer continues to be a major worldwide health burden, and therapy and prognosis are greatly influenced by hormone receptor status. Immunohistochemistry (IHC) is currently the gold standard for evaluating the status of the estrogen receptor (ER), the progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2). However, this approach has drawbacks, such as the potential for labelling errors and inconsistency with intrinsic subtypes. To increase the precision of predicting hormone receptor status, this thesis introduces a unique predictive modelling approach employing DNA methylation and gene expression data. We created machine learning models that use data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to include DNA methylation profiles and gene expression data to forecast ER, PR, and HER2 status. Using MSigDB and STRINGDB, we discovered genes that had varying levels of methylation in relation to each receptor status and looked into the functional significance of these findings. Additionally, we used machine learning models based on noisy label training to address the issue of noisy labels that may be present as a result of potential mislabeling by IHC-based approaches. The results of the study revealed that gene expression information and DNA methylation profiles are reliable indicators of hormone receptor status. Our models performed well as compared to traditional IHC techniques, indicating potential clinical value. Additionally, our method might predict brand-new biomarkers and offer deeper perceptions into the epigenetic pathways underlying breast cancer. However, there are still certain drawbacks, such as class imbalance problems and the high-dimensionality of DNA methylation data, which may be resolved with the development of machine learning techniques and larger, more representative datasets. This thesis emphasises the potential of DNA methylation-based prediction models to increase the precision of determining hormone receptor status, provide useful information for individualised therapy approaches, and enhance patient prognosis