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 retrospectivel...
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| Published in | Briefings in bioinformatics Vol. 22; no. 3 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
20.05.2021
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| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Huang, Xiaowen Dai, Shixue Yang, Chen Lv, Yuanyuan Li, Yan Chen, Junfei |
| Author_xml | – sequence: 1 givenname: Chen surname: Yang fullname: Yang, Chen organization: Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, China – sequence: 2 givenname: Xiaowen surname: Huang fullname: Huang, Xiaowen organization: Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, China – sequence: 3 givenname: Yan surname: Li fullname: Li, Yan organization: Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, China – sequence: 4 givenname: Junfei surname: Chen fullname: Chen, Junfei organization: Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, China – sequence: 5 givenname: Yuanyuan surname: Lv fullname: Lv, Yuanyuan organization: Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, China – sequence: 6 givenname: Shixue orcidid: 0000-0001-6428-3634 surname: Dai fullname: Dai, Shixue organization: Department of Gastroenterology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, South China University of Technology, China |
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