Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology

TP53 mutation is one of the most common genetic changes in hepatocellular carcinoma (HCC). It is of great clinical significance to tailor specialized prognostication approach and to explore more therapeutic options for TP53-mutant HCCs. In this study, a total of 1135 HCC patients were retrospectively analyzed. We developed a random forest-based prediction model to estimate TP53 mutational status, tackling the problem of limited sample size in TP53-mutant HCCs. A multi-step process was performed to develop robust poor prognosis-associated signature (PPS). Compared with previous established population-based signatures, PPS manifested superior ability to predict survival in TP53-mutant patients. After in silico screening of 2249 drug targets and 1770 compounds, we found that three targets (CANT1, CBFB and PKM) and two agents (irinotecan and YM-155) might have potential therapeutic implications in high-PPS patients. The results of drug targets prediction and compounds prediction complemented each other, presenting a comprehensive view of potential treatment strategy. Overall, our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy.

Automated summary

This study retrospectively analyzed 1135 HCC patients and developed a random forest-based prediction model to estimate TP53 mutational status, as well as a robust poor prognosis-associated signature which showed superior ability to predict survival in TP53-mutant patients. In silico screening revealed three targets and two agents that might have potential therapeutic implications in high-PPS patients, presenting a comprehensive view of potential treatment strategy.