A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Hugo Pinto-Marques; Joana Cardoso; Sílvia Silva; João L Neto; Maria Gonçalves-Reis; Daniela Proença; Marta Mesquita; André Manso; Sara Carapeta; Mafalda Sobral; Antonio Figueiredo; Clara Rodrigues; Adelaide Milheiro; Ana Carvalho; Rui Perdigoto; Eduardo Barroso; José B Pereira-Leal

doi:10.1097/SLA.0000000000005637

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Hugo Pinto-Marques, Joana Cardoso, Sílvia Silva, João L Neto, Maria Gonçalves-Reis, Daniela Proença, Marta Mesquita, André Manso, Sara Carapeta, Mafalda Sobral, Antonio Figueiredo, Clara Rodrigues, Adelaide Milheiro, Ana Carvalho, Rui Perdigoto, Eduardo Barroso, José B Pereira-Leal

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

46 Downloads (Pure)

Abstract

OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).

SUMMARY BACKGROUND DATA: 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. Additionally, 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 y follow up, 32% beyond Milan criteria). The resulting four 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 y) 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.

Original language	English
Pages (from-to)	868 - 874
Journal	Annals of surgery
Volume	276
Issue number	5
Early online date	1 Aug 2022
DOIs	https://doi.org/10.1097/SLA.0000000000005637
Publication status	Published - 1 Nov 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1097/SLA.0000000000005637

A_Gene_Expression_Signature_to_Select.18Final published version, 273 KBLicence: CC BY-NC-ND

Cite this

Pinto-Marques, H., Cardoso, J., Silva, S., Neto, J. L., Gonçalves-Reis, M., Proença, D., Mesquita, M., Manso, A., Carapeta, S., Sobral, M., Figueiredo, A., Rodrigues, C., Milheiro, A., Carvalho, A., Perdigoto, R., Barroso, E., & Pereira-Leal, J. B. (2022). A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation. Annals of surgery, 276(5), 868 - 874. Advance online publication. https://doi.org/10.1097/SLA.0000000000005637

@article{5fd71a0eca05430bbedc482673874573,

title = "A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation",

abstract = "OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).SUMMARY BACKGROUND DATA: 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. Additionally, 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 y follow up, 32% beyond Milan criteria). The resulting four 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 y) 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.",

author = "Hugo Pinto-Marques and Joana Cardoso and S{\'i}lvia Silva and Neto, {Jo{\~a}o L} and Maria Gon{\c c}alves-Reis and Daniela Proen{\c c}a and Marta Mesquita and Andr{\'e} Manso and Sara Carapeta and Mafalda Sobral and Antonio Figueiredo and Clara Rodrigues and Adelaide Milheiro and Ana Carvalho and Rui Perdigoto and Eduardo Barroso and Pereira-Leal, {Jos{\'e} B}",

note = "Copyright {\textcopyright} 2022 The Author(s). Published by Wolters Kluwer Health, Inc.",

year = "2022",

month = nov,

day = "1",

doi = "10.1097/SLA.0000000000005637",

language = "English",

volume = "276",

pages = "868 -- 874",

journal = "Annals of surgery",

publisher = "Lippincott, Williams & Wilkins",

number = "5",

}

Pinto-Marques, H, Cardoso, J, Silva, S, Neto, JL, Gonçalves-Reis, M, Proença, D, Mesquita, M, Manso, A, Carapeta, S, Sobral, M, Figueiredo, A, Rodrigues, C, Milheiro, A, Carvalho, A, Perdigoto, R , Barroso, E & Pereira-Leal, JB 2022, 'A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation', Annals of surgery, vol. 276, no. 5, pp. 868 - 874. https://doi.org/10.1097/SLA.0000000000005637

TY - JOUR

T1 - A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

AU - Pinto-Marques, Hugo

AU - Cardoso, Joana

AU - Silva, Sílvia

AU - Neto, João L

AU - Gonçalves-Reis, Maria

AU - Proença, Daniela

AU - Mesquita, Marta

AU - Manso, André

AU - Carapeta, Sara

AU - Sobral, Mafalda

AU - Figueiredo, Antonio

AU - Rodrigues, Clara

AU - Milheiro, Adelaide

AU - Carvalho, Ana

AU - Perdigoto, Rui

AU - Barroso, Eduardo

AU - Pereira-Leal, José B

PY - 2022/11/1

Y1 - 2022/11/1

N2 - OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).SUMMARY BACKGROUND DATA: 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. Additionally, 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 y follow up, 32% beyond Milan criteria). The resulting four 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 y) 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.

AB - OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT).SUMMARY BACKGROUND DATA: 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. Additionally, 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 y follow up, 32% beyond Milan criteria). The resulting four 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 y) 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.

U2 - 10.1097/SLA.0000000000005637

DO - 10.1097/SLA.0000000000005637

M3 - Article

C2 - 35916378

VL - 276

SP - 868

EP - 874

JO - Annals of surgery

JF - Annals of surgery

IS - 5

ER -

A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation

Abstract

UN SDGs

Access to Document

Fingerprint

Cite this