Open AccessTranscriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients
Author(s) -
Liu Silvia,
Nalesnik Michael A.,
Singhi Aatur,
WoodTrageser Michelle A.,
Randhawa Parmjeet,
Ren BaoGuo,
Humar Abhinav,
Liu Peng,
Yu YanPing,
Tseng George C.,
Michalopoulos George,
Luo JianHua
Publication year - 2022
Publication title -
hepatology communications
Language(s) - English
Resource type - Journals
ISSN - 2471-254X
DOI - 10.1002/hep4.1846
Subject(s) - hepatocellular carcinoma , transcriptome , liver transplantation , cohort , exome , medicine , liver cancer , exome sequencing , oncology , cancer , sorafenib , transplantation , mutation , biology , gene , genetics , gene expression
Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k ‐top scoring pairs ( k ‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.