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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.

Predicting clinical outcomes of cancer patients with a p53 deficiency gene signature