Integrating Gene and Clinical Data to Overcome Melanoma Cancer with Pembrolizuamb

Abstract

Melanoma accounts for only about 1% of all skin cancer cases, but it causes the majority of skin cancer deaths. It is especially dangerous for teens and young adults, with about 1 in 50 Americans diagnosed in their lifetime. This project used machine learning to explore whether gene expression data could help predict how melanoma patients respond to pembrolizumab, an immune-based cancer treatment that targets the PD-1 receptor. Public datasets including GSE91061 were used, which include samples grouped by treatment outcomes such as complete or partial response, stable disease, or disease progression. The data was carefully processed through background correction, normalization, and filtering, followed by differential gene expression analysis using the limma linear modeling method. Ten-fold cross-validation was also used to prepare the dataset for predictive modeling. While the results revealed patterns in gene activity linked to treatment outcomes, further research with expanded datasets is needed to improve the reliability of future predictions.