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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Health/LWW
2022-04-01
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| Series: | Hepatology Communications |
| Online Access: | https://doi.org/10.1002/hep4.1846 |
| _version_ | 1797936936755658752 |
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| author | Silvia Liu Michael A. Nalesnik Aatur Singhi Michelle A. Wood‐Trageser Parmjeet Randhawa Bao‐Guo Ren Abhinav Humar Peng Liu Yan‐Ping Yu George C. Tseng George Michalopoulos Jian‐Hua Luo |
| author_facet | Silvia Liu Michael A. Nalesnik Aatur Singhi Michelle A. Wood‐Trageser Parmjeet Randhawa Bao‐Guo Ren Abhinav Humar Peng Liu Yan‐Ping Yu George C. Tseng George Michalopoulos Jian‐Hua Luo |
| author_sort | Silvia Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T18:38:06Z |
| format | Article |
| id | doaj.art-05468f8377744145b22223508e1e9d89 |
| institution | Directory Open Access Journal |
| issn | 2471-254X |
| language | English |
| last_indexed | 2024-04-10T18:38:06Z |
| publishDate | 2022-04-01 |
| publisher | Wolters Kluwer Health/LWW |
| record_format | Article |
| series | Hepatology Communications |
| spelling | doaj.art-05468f8377744145b22223508e1e9d892023-02-02T00:07:30ZengWolters Kluwer Health/LWWHepatology Communications2471-254X2022-04-016471072710.1002/hep4.1846Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant PatientsSilvia Liu0Michael A. Nalesnik1Aatur Singhi2Michelle A. Wood‐Trageser3Parmjeet Randhawa4Bao‐Guo Ren5Abhinav Humar6Peng Liu7Yan‐Ping Yu8George C. Tseng9George Michalopoulos10Jian‐Hua Luo11Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Surgery University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USADepartment of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USAHepatocellular 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.https://doi.org/10.1002/hep4.1846 |
| spellingShingle | Silvia Liu Michael A. Nalesnik Aatur Singhi Michelle A. Wood‐Trageser Parmjeet Randhawa Bao‐Guo Ren Abhinav Humar Peng Liu Yan‐Ping Yu George C. Tseng George Michalopoulos Jian‐Hua Luo Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients Hepatology Communications |
| title | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
| title_full | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
| title_fullStr | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
| title_full_unstemmed | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
| title_short | Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients |
| title_sort | transcriptome and exome analyses of hepatocellular carcinoma reveal patterns to predict cancer recurrence in liver transplant patients |
| url | https://doi.org/10.1002/hep4.1846 |
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