In order to understand whether cancer-related genomic alterations can be modeled by gene expression in scenarios with lower signal-to-noise ratio, we artificially perturbed the TCGA gene expression dataset via the addition of Gaussian noise and then proceeded to build models to predict the presence of TP53 mutations in breast cancer, the largest dataset in TCGA by number of samples. The gene discussed is TP53; the disease is breast carcinoma.