Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

Claude Gérard; Mickaël Di-Luoffo; Léolo Gonay; Stefano Caruso; Gabrielle Couchy; Axelle Loriot; Darko Castven; Junyan Tao; Katarzyna Konobrocka; Sabine Cordi; Satdarshan P. Monga; Emmanuel Hanert; Jens U. Marquardt; Jessica Zucman-Rossi; Frédéric P. Lemaigre

doi:10.1016/j.jhep.2019.03.024

Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

Claude Gérard, Mickaël Di-Luoffo, Léolo Gonay, Stefano Caruso, Gabrielle Couchy, Axelle Loriot, Darko Castven, Junyan Tao, Katarzyna Konobrocka, Sabine Cordi, Satdarshan P. Monga, Emmanuel Hanert, Jens U. Marquardt, Jessica Zucman-Rossi, Frédéric P. Lemaigre^*

^*Corresponding author for this work

Department of Internal Medicine I

11 Citations (Scopus)

Abstract

Background & Aims: Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs). Methods: Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer – RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of β-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data. Results: We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN. Conclusions: We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development. Lay summary: Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.

Original language	English
Journal	Journal of Hepatology
Volume	71
Issue number	2
Pages (from-to)	323-332
Number of pages	10
ISSN	0168-8278
DOIs	https://doi.org/10.1016/j.jhep.2019.03.024
Publication status	Published - 08.2019

Funding

The work of FPL was supported by the Interuniversity Attraction Pole Programme (Belgian Science Policy, PVII-47), the D.G. Higher Education and Scientific Research of the French Community of Belgium (ARC 15/20-065), the F.R.S.-FNRS (Belgium: Grants T.007214 and J.0058.15), and the Belgian Foundation Against Cancer (grant 2014-125). J.Z-R's group is supported by INSERM (France), the Ligue Nationale contre le Cancer (France, Equipe Labellis?e), Labex OncoImmunology (France, investissement d'avenir), Coup d'Elan de la Fondation Bettencourt-Shueller (France), the SIRIC CARPEM (France) and Fondation M?rieux (France). J.U.M. is supported by grants from the German Research Foundation (MA 4443/2-2) and the Volkswagen Foundation (Germany, Lichtenberg program). The authors thank P. Jacquemin, C. Pierreux, L. Desmet and the Lemaigre laboratory members for help, and L. Nguyen and H. Zhu for sharing information. The work of FPL was supported by the Interuniversity Attraction Pole Programme (Belgian Science Policy, PVII-47), the D.G. Higher Education and Scientific Research of the French Community of Belgium (ARC 15/20-065), the F.R.S.- FNRS (Belgium: Grants T.007214 and J.0058.15 ), and the Belgian Foundation Against Cancer (grant 2014-125 ). J.Z-R's group is supported by INSERM (France), the Ligue Nationale contre le Cancer (France, Equipe Labellisée), Labex OncoImmunology (France, investissement d’avenir), Coup d’Elan de la Fondation Bettencourt-Shueller (France), the SIRIC CARPEM (France) and Fondation Mérieux (France). J.U.M. is supported by grants from the German Research Foundation ( MA 4443/2-2 ) and the Volkswagen Foundation (Germany, Lichtenberg program). The work of FPL was supported by the Interuniversity Attraction Pole Programme (Belgian Science Policy, PVII-47), the D.G. Higher Education and Scientific Research of the French Community of Belgium (ARC 15/20-065), the F.R.S.-FNRS (Belgium: Grants T.007214 and J.0058.15), and the Belgian Foundation Against Cancer (grant 2014-125). J.Z-R's group is supported by INSERM (France), the Ligue Nationale contre le Cancer (France, Equipe Labellisée), Labex OncoImmunology (France, investissement d'avenir), Coup d'Elan de la Fondation Bettencourt-Shueller (France), the SIRIC CARPEM (France) and Fondation Mérieux (France). J.U.M. is supported by grants from the German Research Foundation (MA 4443/2-2) and the Volkswagen Foundation (Germany, Lichtenberg program). The authors thank P. Jacquemin, C. Pierreux, L. Desmet and the Lemaigre laboratory members for help, and L. Nguyen and H. Zhu for sharing information.

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1016/j.jhep.2019.03.024

Cite this

Gérard, C., Di-Luoffo, M., Gonay, L., Caruso, S., Couchy, G., Loriot, A., Castven, D., Tao, J., Konobrocka, K., Cordi, S., Monga, S. P., Hanert, E., Marquardt, J. U., Zucman-Rossi, J., & Lemaigre, F. P. (2019). Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma. Journal of Hepatology, 71(2), 323-332. https://doi.org/10.1016/j.jhep.2019.03.024

@article{2f25e744fd024c9aaa4a4be0003d1cb0,

title = "Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma",

abstract = "Background \& Aims: Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs). Methods: Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer – RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of β-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data. Results: We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN. Conclusions: We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development. Lay summary: Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.",

author = "Claude G{\'e}rard and Micka{\"e}l Di-Luoffo and L{\'e}olo Gonay and Stefano Caruso and Gabrielle Couchy and Axelle Loriot and Darko Castven and Junyan Tao and Katarzyna Konobrocka and Sabine Cordi and Monga, \{Satdarshan P.\} and Emmanuel Hanert and Marquardt, \{Jens U.\} and Jessica Zucman-Rossi and Lemaigre, \{Fr{\'e}d{\'e}ric P.\}",

note = "Publisher Copyright: {\textcopyright} 2019 European Association for the Study of the Liver",

year = "2019",

month = aug,

doi = "10.1016/j.jhep.2019.03.024",

language = "English",

volume = "71",

pages = "323--332",

journal = "Journal of Hepatology",

issn = "0168-8278",

publisher = "Elsevier B.V.",

number = "2",

}

Gérard, C, Di-Luoffo, M, Gonay, L, Caruso, S, Couchy, G, Loriot, A, Castven, D, Tao, J, Konobrocka, K, Cordi, S, Monga, SP, Hanert, E, Marquardt, JU, Zucman-Rossi, J & Lemaigre, FP 2019, 'Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma', Journal of Hepatology, vol. 71, no. 2, pp. 323-332. https://doi.org/10.1016/j.jhep.2019.03.024

TY - JOUR

T1 - Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

AU - Gérard, Claude

AU - Di-Luoffo, Mickaël

AU - Gonay, Léolo

AU - Caruso, Stefano

AU - Couchy, Gabrielle

AU - Loriot, Axelle

AU - Castven, Darko

AU - Tao, Junyan

AU - Konobrocka, Katarzyna

AU - Cordi, Sabine

AU - Monga, Satdarshan P.

AU - Hanert, Emmanuel

AU - Marquardt, Jens U.

AU - Zucman-Rossi, Jessica

AU - Lemaigre, Frédéric P.

PY - 2019/8

Y1 - 2019/8

N2 - Background & Aims: Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs). Methods: Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer – RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of β-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data. Results: We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN. Conclusions: We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development. Lay summary: Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.

AB - Background & Aims: Alterations of individual genes variably affect the development of hepatocellular carcinoma (HCC). Thus, we aimed to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRNs). Methods: Using data from The Cancer Genome Atlas, from the LIRI-JP (Liver Cancer – RIKEN, JP project), and from our transcriptomic, transfection and mouse transgenic experiments, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of β-catenin (CTNNB1). We further generated and validated a quantitative mathematical model of the GRN using human cell lines and in vivo expression data. Results: We found that LIN28B and CTNNB1 form a GRN with SMARCA4, Let-7b (MIRLET7B), SOX9, TP53 and MYC. GRN functionality is detected in HCC and gastrointestinal cancers, but not in other cancer types. GRN status negatively correlates with HCC prognosis, and positively correlates with hyperproliferation, dedifferentiation and HGF/MET pathway activation, suggesting that it contributes to a transcriptomic profile typical of the proliferative class of HCC. The mathematical model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal conditions, and irreversibly in HCC. The mathematical model is available via a web-based tool which can evaluate the GRN status of HCC samples and predict the impact of therapeutic agents on the GRN. Conclusions: We conclude that identification and modelling of the GRN provide insights into the prognosis of HCC and the mechanisms by which tumor-promoting genes impact on HCC development. Lay summary: Hepatocellular carcinoma (HCC) is a heterogeneous disease driven by the concomitant deregulation of several genes functionally organized as networks. Here, we identified a gene regulatory network involved in a subset of HCCs. This subset is characterized by increased proliferation and poor prognosis. We developed a mathematical model which uncovers the dynamics of the network and allows us to predict the impact of a therapeutic agent, not only on its specific target but on all the genes belonging to the network.

UR - https://www.scopus.com/pages/publications/85065831101

U2 - 10.1016/j.jhep.2019.03.024

DO - 10.1016/j.jhep.2019.03.024

M3 - Journal articles

C2 - 30953666

AN - SCOPUS:85065831101

SN - 0168-8278

VL - 71

SP - 323

EP - 332

JO - Journal of Hepatology

JF - Journal of Hepatology

IS - 2

ER -

Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

Abstract

Funding

UN SDGs

Access to Document

Other files and links

Fingerprint

Dissecting the heterogeneity of tumor-initiating cells in hepatobiliary cancers: molecular characterization and targeting of stemness features

Cite this

Dynamics and predicted drug response of a gene network linking dedifferentiation with beta-catenin dysfunction in hepatocellular carcinoma

Abstract

Funding

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Dissecting the heterogeneity of tumor-initiating cells in hepatobiliary cancers: molecular characterization and targeting of stemness features

Cite this