Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients
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...
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Published in | Hepatology communications Vol. 6; no. 4; pp. 710 - 727 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins
01.04.2022
John Wiley and Sons Inc Wolters Kluwer Health/LWW |
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Abstract | 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.
We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways. |
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AbstractList | 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. 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. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways. 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. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways. 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. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC-containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways. |
Author | Nalesnik, Michael A. Tseng, George C. Luo, Jian‐Hua Michalopoulos, George Randhawa, Parmjeet Liu, Silvia Wood‐Trageser, Michelle A. Ren, Bao‐Guo Humar, Abhinav Liu, Peng Yu, Yan‐Ping Singhi, Aatur |
AuthorAffiliation | 2 Department of Surgery University of Pittsburgh School of Medicine Pittsburgh PA USA 3 Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USA 1 Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USA |
AuthorAffiliation_xml | – name: 2 Department of Surgery University of Pittsburgh School of Medicine Pittsburgh PA USA – name: 3 Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USA – name: 1 Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USA |
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Copyright | 2021 The Authors. published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases. 2021 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases. 2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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SubjectTerms | Biomarkers Carcinoma, Hepatocellular - diagnosis Cell division Discriminant analysis Exome - genetics Genomes Hepatitis B Hepatitis C Humans Hyperplasia Liver cancer Liver Neoplasms - diagnosis Liver Transplantation Liver transplants Mutation Neoplasm Recurrence, Local - diagnosis Original Retrospective Studies Transcriptome - genetics Tumors Whole Exome Sequencing |
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Title | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhep4.1846 https://www.ncbi.nlm.nih.gov/pubmed/34725972 https://www.proquest.com/docview/2642688762 https://search.proquest.com/docview/2592309894 https://pubmed.ncbi.nlm.nih.gov/PMC8948579 https://doaj.org/article/05468f8377744145b22223508e1e9d89 |
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