University Honors

TP53 is one of the most commonly mutated genes in human cancers and is linked to major body regulation roles like cell cycle arrest, DNA repair, apoptosis, and oxidative stress responses. Because TP53 mutations display diverse functional behavior, researchers have increasingly relied on clustering analyses to identify the complexity of its behavior. Recent studies have clustered TP53 mutations into functional groups using different analyses and clustering methods. This study, however, applies exploratory data analysis (EDA), principal component analysis (PCA), and unsupervised clustering methods to analyze and comprehend functional transactivation patterns across eight TP53 scores that closely relate to the major regulation roles being studied (WAF1, H1433S, MDM2, BAX, AIP1, GADD45, p53R2, NOX). These scores are measured using a yeast transactivation assay (YTA) (Kato et al.). Clustering techniques and validation, such as k-means clustering, hierarchical clustering, and silhouette analysis, helped identify mutation groups that follow loss-of-function, intermediate, and gain-of-function activity patterns. Results revealed significant variability across mutations, while correlation analyses and PCA found coordinated relationships. Overall, this study values data-driven analysis in the comprehension of complexities in mutational behavior and the significance of utilizing clustering methods to improve mutation classification.