A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.
Journal
Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354
Informations de publication
Date de publication:
01 11 2022
01 11 2022
Historique:
pubmed:
3
8
2022
medline:
12
10
2022
entrez:
2
8
2022
Statut:
ppublish
Résumé
To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. HepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.
Sections du résumé
OBJECTIVE
To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).
BACKGROUND
Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation.
METHODS
A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict.
RESULTS
HepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term.
CONCLUSIONS
HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.
Identifiants
pubmed: 35916378
doi: 10.1097/SLA.0000000000005637
pii: 00000658-202211000-00018
pmc: PMC9534058
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Systematic Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
868-874Informations de copyright
Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
The authors report no conflicts of interest.
Références
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.
Saito R, Amemiya H, Hosomura N, et al. Prognostic factors for post-recurrent survival in hepatocellular carcinoma after curative resection. Anticancer Res. 2019;39:3033–3038.
Golabi P, Fazel S, Otgonsuren M, et al. Mortality assessment of patients with hepatocellular carcinoma according to underlying disease and treatment modalities. Medicine. 2017;96:e5904.
Bismuth H, Chiche L, Adam R, et al. Liver resection versus transplantation for hepatocellular carcinoma in cirrhotic patients. Ann Surg. 1993;218:145–151.
Mazzaferro V, Regalia E, Doci R, et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. N Engl J Med. 1996;334:693–700.
Silva MF, Sherman M. Criteria for liver transplantation for HCC: what should the limits be? J Hepatol. 2011;55:1137–1147.
Dhir M, Melin AA, Douaiher J, et al. A review and update of treatment options and controversies in the management of hepatocellular carcinoma. Ann Surg. 2016;263:1112–1125.
Llovet J, Schwartz M, Fuster J, et al. Expanded criteria for hepatocellular carcinoma through down-staging prior to liver transplantation: not yet there. Semin Liver Dis. 2006;26:248–253.
Yao F. Expanded criteria for hepatocellular carcinoma: down-staging with a view to liver transplantation-yes. Semin Liver Dis. 2006;26:239–247.
Zaydfudim VM, Vachharajani N, Klintmalm GB, et al. Liver resection and transplantation for patients with hepatocellular carcinoma beyond milan criteria. Ann Surg. 2016;264:650–658.
Kim Y, Stahl CC, Makramalla A, et al. Downstaging therapy followed by liver transplantation for hepatocellular carcinoma beyond Milan criteria. Surgery. 2017;162:1250–1258.
Rudnick SR, Russo MW. Liver transplantation beyond or downstaging within the Milan criteria for hepatocellular carcinoma. Expert Rev Gastroenterol Hepatol. 2018;12:265–275.
Pavel M-C, Fuster J. Expansion of the hepatocellular carcinoma Milan criteria in liver transplantation: future directions. WJG. 2018;24:3626–3636.
Halazun KJ, Sapisochin G, von Ahrens D, et al. Predictors of outcome after liver transplantation for hepatocellular carcinoma (HCC) beyond Milan criteria. Int J Surg. 2020;82:61–69.
Kardashian A, Florman SS, Haydel B, et al. Liver transplantation outcomes in a US. multicenter cohort of 789 patients with hepatocellular carcinoma presenting beyond Milan Criteria. Hepatology. 2020;72:2014–2028.
Marques HP, Ribeiro V, Almeida T, et al. Long-term results of domino liver transplantation for hepatocellular carcinoma using the “double piggy-back” technique: a 13-year experience. Ann Surg. 2015;262:749–756.
Yao F. Liver transplantation for hepatocellular carcinoma: expansion of the tumor size limits does not adversely impact survival. Hepatology. 2001;33:1394–1403.
Mazzaferro V, Llovet JM, Miceli R, et al. Predicting survival after liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a retrospective, exploratory analysis. Lancet Oncol. 2009;10:9.
Prasad KR, Young RS, Burra P, et al. Summary of candidate selection and expanded criteria for liver transplantation for hepatocellular carcinoma: a review and consensus statement: Candidate Selection and Expanded Criteria. Liver Transpl. 2011;17:S81–S89.
Kaido T, Ogawa K, Mori A, et al. Usefulness of the Kyoto criteria as expanded selection criteria for liver transplantation for hepatocellular carcinoma. Surgery. 2013;154:1053–1060.
Duvoux C, Roudot–Thoraval F, Decaens T, et al. Liver transplantation for hepatocellular carcinoma: a model including α-fetoprotein improves the performance of Milan criteria. Gastroenterology. 2012;143:986–994.e3.
Mazzaferro V, Sposito C, Zhou J, et al. Metroticket 2.0 model for analysis of competing risks of death after liver transplantation for hepatocellular carcinoma. Gastroenterology. 2018;154:128–139.
Brusset B, Dumortier J, Cherqui D, et al. Liver transplantation for hepatocellular carcinoma: a real-life comparison of Milan criteria and AFP model. Cancers. 2021;13:2480.
Halazun KJ, Najjar M, Abdelmessih RM, et al. Recurrence after liver transplantation for hepatocellular carcinoma: a new MORAL to the story. Ann Surg. 2017;265:557–564.
Asman Y, Evenson AR, Even-Sapir E, et al. [ 18 F]fludeoxyglucose positron emission tomography and computed tomography as a prognostic tool before liver transplantation, resection, and loco-ablative therapies for hepatocellular carcinoma: PET for Prognosis of HCC Invasive Therapies. Liver Transpl. 2015;21:572–580.
Toniutto P, Fumolo E, Fornasiere E, et al. Liver transplantation in patients with hepatocellular carcinoma beyond the Milan criteria: a comprehensive review. J Clin Med. 2021;10:3932.
Mahmud N, John B, Taddei TH, et al. Pre‐transplant alpha‐fetoprotein is associated with post‐transplant hepatocellular carcinoma recurrence mortality. Clin Transplant. 2019;33:e13634.
Llovet JM, De Baere T, Kulik L, et al. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2021;18:293–313.
Mazzaferro V, Citterio D, Bhoori S, et al. Liver transplantation in hepatocellular carcinoma after tumour downstaging (XXL): a randomised, controlled, phase 2b/3 trial. The Lancet Oncology. 2020;21:947–956.
Parikh ND, Waljee AK, Singal AG. Downstaging hepatocellular carcinoma: a systematic review and pooled analysis: down-staging hepatocellular carcinoma. Liver Transpl. 2015;21:1142–1152.
Patel LR, Nykter M, Chen K, et al. Cancer genome sequencing: understanding malignancy as a disease of the genome, its conformation, and its evolution. Cancer Lett. 2013;340:152–160.
Chakravarty D, Solit DB. Clinical cancer genomic profiling. Nat Rev Genet. 2021;22:483–501.
Varnier R, Sajous C, de Talhouet S, et al. Using breast cancer gene expression signatures in clinical practice: unsolved issues, ongoing trials and future perspectives. Cancers. 2021;13:4840.
Choudhury A, West CML. Translating prognostic prostate cancer gene signatures into the clinic. Transl Cancer Res. 2017;6:S405–S408.
Hoshida Y, Villanueva A, Kobayashi M, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med. 2008;359:1995–2004.
Lee J-S, Chu I-S, Heo J, et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology. 2004;40:667–676.
Singhal A, Jayaraman M, Dhanasekaran DN, et al. Molecular and serum markers in hepatocellular carcinoma: predictive tools for prognosis and recurrence. Critical Reviews in Oncology/Hematology. 2012;82:116–140.
Boyault S, Rickman DS, de Reyniès A, et al. Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets. Hepatology. 2007;45:42–52.
Ke K, Chen G, Cai Z, et al. Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification. CMAR. 2018;10:5291–5302.
Zhai X, Xue Q, Liu Q, et al. Classifier of cross talk genes predicts the prognosis of hepatocellular carcinoma. Mol Med Rep. 2017;16:3253–3261.
Liu G, Xie W, Zhang C, et al. Identification of a four‐gene metabolic signature predicting overall survival for hepatocellular carcinoma. J Cell Physiol. 2020;235:1624–1636.
Zhao Q-J, Zhang J, Xu L, et al. Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma. WJG. 2018;24:3426–3439.
Qiao G, Chen L, Wu J, et al. Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis. Peer J. 2019;7:e6548.
Gu J-X, Zhang X, Miao R-C, et al. Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma. World J Gastroenterol. 2019;25:220–232.
Zheng Y, Liu Y, Zhao S, et al. Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma. Cancer Manag Res. 2018;10:6079–6096.
Vandesompele J, Preter KD, Roy NV, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:research0034.1-0034.11.
Hughes CB, Humar A. Liver transplantation: current and future. Abdom Radiol. 2021;46:2–8.
Zhang Q, Lou Y, Bai X-L, et al. Intratumoral heterogeneity of hepatocellular carcinoma: From single-cell to population-based studies. World J Gastroenterol. 2020;26:3720–3736.
Losic B, Craig AJ, Villacorta-Martin C, et al. Intratumoral heterogeneity and clonal evolution in liver cancer. Nat Commun. 2020;11:291.
Villanueva A, Hoshida Y, Battiston C, et al. Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology. 2011;140:1501–1512.e2.
Dvorchik I, Schwartz M, Fiel MI, et al. Fractional allelic imbalance could allow for the development of an equitable transplant selection policy for patients with hepatocellular carcinoma. Liver Transpl. 2008;14:443–450.
Clavien P-A, Lesurtel M, Bossuyt PM, et al. Recommendations for liver transplantation for hepatocellular carcinoma: an international consensus conference report. Lancet Oncol. 2012;13:e11–e22.
Agopian VG, Harlander-Locke MP, Markovic D, et al. Evaluation of patients with hepatocellular carcinomas that do not produce α-fetoprotein. JAMA Surg. 2017;152:55.
Carr BI, Akkiz H, Üsküdar O, et al. HCC with low- and normal-serum alpha-fetoprotein levels. Clin Pract (Lond). 2018;15:453–464.
Lerut J, Iesari S, Foguenne M, et al. Hepatocellular cancer and liver transplantation: necessity to go from chaos to order. Al'm Klin Med. 2018;46:552–559.
Tommaso LD, Spadaccini M, Donadon M, et al. Role of liver biopsy in hepatocellular carcinoma. World J Gastroenterol. 2019;25:6041–6052.
Rastogi A. Changing role of histopathology in the diagnosis and management of hepatocellular carcinoma. World J Gastroenterol. 2018;24:4000–4013.
Neuberger J, Patel J, Caldwell H, et al. Guidelines on the use of liver biopsy in clinical practice from the British Society of Gastroenterology, the Royal College of Radiologists and the Royal College of Pathology. Gut. 2020;69:1382–1403.
Eisenberg E, Konopniki M, Veitsman E, et al. Prevalence and characteristics of pain induced by percutaneous liver biopsy. Anesth Analg. 2003;96:1392–1396.
McCarty TR, Bazarbashi AN, Njei B, et al. Endoscopic ultrasound-guided, percutaneous, and transjugular liver biopsy: a comparative systematic review and meta-analysis. Clin Endosc. 2020;53:583–593.
Itzel T, Spang R, Maass T, et al. Random gene sets in predicting survival of patients with hepatocellular carcinoma. J Mol Med. 2019;97:879–888.