The tumor suppressor p53, encoded by the TP53 gene, is mutated or nullified in nearly 50% of human cancers. It has long been debated whether TP53 mutations can be utilized as a biomarker to predict clinical outcomes of cancer patients. In this study, we applied computational methods to calculate p53 deficiency scores (PDSs) that reflect the inactivation of the p53 pathway, instead of TP53 mutation status. Compared to TP53 mutation status, the p53 deficiency gene signature is a powerful predictor of overall survival and drug sensitivity in a variety of cancer types and treatments. Interestingly, the PDSs predicted clinical outcomes more accurately than drug sensitivity in cell lines, suggesting that tumor heterogeneity and/or tumor microenvironment may play an important role in predicting clinical outcomes using p53 deficiency gene signatures.
© 2022. The Author(s).