# Neutralization of acyl coenzyme A binding protein for the experimental prevention and treatment of hepatocellular carcinoma

Sijing Li, Omar Motiño, Flavia Lambertucci, Jonathan Pol, Hui Chen, Long Pan, Sylvère Durand, Federica Rossin, Claudia Campani, Lucie Poupel, et al.

► To cite this version:

Sijing Li, Omar Motiño, Flavia Lambertucci, Jonathan Pol, Hui Chen, et al. Neutralization of acyl coenzyme A binding protein for the experimental prevention and treatment of hepatocellular carcinoma. Cell Reports Medicine, 2025, 6, 10.1016/j.xcrm.2025.102232. hal-05210901

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Submitted on 14 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Neutralization of acyl coenzyme A binding protein for the experimental prevention and treatment of hepatocellular carcinoma

## Graphical abstract

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## Authors

Sijing Li, Omar Motiño,
Flavia Lambertucci, ...,
Maria Chiara Maiuri,
Jessica Zucman-Rossi, Guido Kroemer

## Correspondence

maria-chiara.maiuri@inserm.fr (M.C.M.),
kroemer@orange.fr (G.K.)

## In brief

Sijing Li et al. demonstrate that ACBP/DBI expression is upregulated in human HCC. Elevated ACBP/DBI mRNA associates with poor prognosis in patients with HCC. ACBP/DBI neutralization attenuates HCC development and progression across distinct preclinical models. Anti-ACBP/DBI monoclonal antibody sensitizes malignant hepatocytes to ferroptosis and enhances HCC responses to immune checkpoint blockade.

## Highlights

* ACBP/DBI contributes to the pathogenesis of human HCC

* ACBP/DBI neutralization inhibits HCC proliferation in mice

* ACBP/DBI neutralization sensitizes hepatocytes and HCC cells to ferroptosis

* ACBP/DBI neutralization enhances cancer immunosurveillance

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<figure><img src="image_4.png" /></figure>

Article

# Neutralization of acyl coenzyme A binding protein for the experimental prevention and treatment of hepatocellular carcinoma

Sijing Li,<sup>1,2,3,4</sup> Omar Motiño,<sup>1,2,3,22</sup> Flavia Lambertucci,<sup>1,2,3,22</sup> Jonathan Pol,<sup>1,2,3</sup> Hui Chen,<sup>1,2,3</sup> Long Pan,<sup>5</sup>

Sylvère Durand,<sup>1,2,3</sup> Federica Rossin,<sup>6,7</sup> Claudia Campani,<sup>5,8</sup> Lucie Poupel,<sup>9</sup> Christophe Klein,<sup>9</sup> Léa Montégut,<sup>1,2,3</sup>

María Pérez-Lanzón,<sup>1,2,3</sup> Gerasimos Anagnostopoulos,<sup>1,2,3</sup> Uxia Nogueira-Recalde,<sup>1,2,3,10</sup> Alexandra Cerone,<sup>1,2,3</sup>

Fanny Aprahamian,<sup>1,2,3</sup> Yanbing Dong,<sup>1,2,3,4</sup> Manuela Lizarralde-Guerrero,<sup>1,2,3,4</sup> Enfu Xue,<sup>1,2,3,4</sup> Peng Liu,<sup>1,2,3</sup>

Liwei Zhao,<sup>1,2,3</sup> Hui Pan,<sup>1,2,3</sup> Vincent Carbonnier,<sup>1,2,3</sup> Sylvie Lachkar,<sup>1,2,3</sup> Ester Gloria Saavedra Díaz,<sup>1,2,3,11</sup> Li Sun,<sup>12</sup>

Chantal Desdouets,<sup>13</sup> Sabine Colnot,<sup>14</sup> Oliver Kepp,<sup>1,2,3</sup> Isabelle Martins,<sup>1,2,3</sup> Laurence Zitvogel,<sup>15,16,17,18</sup>

Mauro Piacentini,<sup>6,7</sup> Jean-Charles Nault,<sup>5,20</sup> Maria Chiara Maiuri,<sup>1,2,19,*</sup> Jessica Zucman-Rossi,<sup>5,21</sup>

and Guido Kroemer<sup>1,2,3,21,23,*</sup>

<sup>1</sup>Université Paris Cité, Sorbonne Université, Inserm U1138, Centre de Recherche des Cordeliers, F-75006 Paris, France

<sup>2</sup>Équipe Labellisée Par La Ligue Contre le Cancer, Institut Universitaire de France, Paris, France

<sup>3</sup>Université Paris-Saclay, INSERM US23 / CNRS UAR 3655, Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, 94805 Villejuif, France

<sup>4</sup>Faculté de Médecine, Université de Paris Saclay, Kremlin Bicêtre, Paris, France

<sup>5</sup>Team "Functional Genomics of Solid Tumors", Centre de Recherche des Cordeliers, Inserm U1138, Université Paris Cité, Sorbonne Université, 75006 Paris, France

<sup>6</sup>Department of Biology, University of Rome 'Tor Vergata', Rome, Italy

<sup>7</sup>National Institute for Infectious Diseases IRCCS "Lazzaro Spallanzani", 00133 Rome, Italy

<sup>8</sup>Department of Experimental and Clinical Medicine, Internal Medicine and Hepatology Unit, University of Firenze, Florence, Italy

<sup>9</sup>CHICS, Centre de Recherche des Cordeliers, Inserm U1138, Université Paris Cité, Sorbonne Université, Paris, France

<sup>10</sup>Grupo de Investigación en Reumatología (GIR), Instituto de Investigación Biomédica de A Coruña (INIBIC), Fundación Profesor Novoa Santos, A Coruña, Spain

<sup>11</sup>Departamento de Bioquímica y Biología Molecular, Fisiología, Genética e Inmunología, Instituto Universitario de Investigaciones Biomédicas y Sanitarias (IUIBS), Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

<sup>12</sup>Jiangsu Key Laboratory of Drug Screening, China Pharmaceutical University, Nanjing, China

<sup>13</sup>Team Genomic Instability, Metabolism, Immunity and Liver Tumorigenesis Laboratory, Equipe Labellisée LIGUE 2023, Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université de Paris, Paris, France

<sup>14</sup>Team "Oncogenic functions of β-catenin Signaling in the Liver (ONCOLIV)", Centre de Recherche des Cordeliers, Equipe Labellisée Par La Ligue Contre le Cancer, Inserm U1138, Université Paris Cité, Sorbonne Université, Paris, France

<sup>15</sup>Gustave Roussy Cancer Center, ClinicoBiome, 94805 Villejuif, France

<sup>16</sup>Université Paris Saclay, Faculty of Medicine, 94270 Kremlin Bicêtre, France

<sup>17</sup>Inserm U1015, Equipe Labellisée Par La Ligue Contre le Cancer, 94800 Villejuif, France

<sup>18</sup>Center of Clinical Investigations in Biotherapies of Cancer (CICBT), Gustave Roussy, 94805 Villejuif, France

<sup>19</sup>Department of Molecular Medicine and Medical Biotechnologies, University of Napoli Federico II, 80131 Naples, Italy

<sup>20</sup>Liver Unit, Avicenne Hospital, Paris-Seine-Saint-Denis University Hospitals, AP-HP, Bobigny, France

<sup>21</sup>Institut du Cancer Paris CARPEM, Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France

<sup>22</sup>These authors contributed equally

<sup>23</sup>Lead contact

*Correspondence: maria-chiara.maiuri@inserm.fr (M.C.M.), kroemer@orange.fr (G.K.)

https://doi.org/10.1016/j.xcrm.2025.102232

## SUMMARY

Acyl coenzyme A binding protein (ACBP encoded by *diazepam binding inhibitor DBI*) is involved in non-malignant liver diseases. Here, we show that *DBI* mRNA and circulating ACBP/DBI levels are increased in patients with hepatocellular carcinoma (HCC). We investigated its role in hepatocarcinogenesis in mice, inhibiting ACBP/DBI by three methods: (1) inducible whole-body or liver-specific knockout of *DBI*, (2) a point mutation of the ACBP/DBI receptor (*GABRG2*), and (3) induction of autoantibodies neutralizing ACBP/DBI. ACBP/DBI plays a major pro-carcinogenic role in HCC induced by intrahepatic transplantation of HCC cell lines, transgenic co-expression of the two oncogenes *Myc* and *Ctnnb1*, and chronic challenge with a Western-style diet together with either carbon tetrachloride (*CCl*₄) or diethylnitrosamine. ACBP/DBI inhibition normalizes HCC-associated gene expression, reducing oncogenic alterations in cell cycle-, immunomodulatory-, and ferroptosis-regulatory genes. ACBP/DBI inhibition increases HCC responses to

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PD-1 blockade and sensitizes HCC to the therapeutic induction of ferroptosis. Hence, ACBP/DBI constitutes an actionable target involved in HCC pathogenesis.

## INTRODUCTION

Hepatocellular carcinoma (HCC), the third leading cause of cancer-related deaths worldwide,¹ is linked to a series of established risk factors that include metabolic dysregulation (such as obesity and diabetes favoring the development of metabolic dysfunction-associated steatohepatitis [MASH] and progression to advanced fibrosis and cirrhosis), repeated insult by hepatotoxins (such as alcohol and aflatoxin), as well as infection by hepatotropic viruses (such as hepatitis viruses B and C).² In spite of the ever more detailed comprehension of the molecular and cellular pathways leading to HCC development,²⁻⁵ there are rather few successful treatment options. Curative attempts (resection, thermal ablation, and transplantation) are usually limited to patients with early-stage HCC (BCLC-0 and BCLC-A), while transarterial chemoembolization or radioembolization and systemic treatments (tyrosine-kinase inhibitors and immunotherapy) are usually reserved for intermediate-(BCLC-B) and advanced-stage (BCLC-C) patients.⁶,⁷

Acyl coenzyme A (CoA) binding protein (ACBP) is a small (87 amino acids for the dominant isoform) protein encoded by *diazepam binding inhibitor (DBI)* (Montégut et al., 2023). ACBP/DBI is expressed by all nucleated cell types but is particularly abundant in hepatocytes.⁷,⁸ In the cytoplasm of cells, ACBP/DBI interacts with various lipids including medium-chain acyl CoA esters and contributes to metabolic homeostasis.⁹,¹⁰ ACBP/DBI can be released from autophagic, stressed, and dying cells through a nonconventional pathway⁸,¹¹ and acts on a particular subunit of the γ-aminobutyric acid type A receptor (GABA~A~R), namely GABRG2,¹² to mediate a series of effects that include (1) inhibition of autophagy, (2) lipo-anabolic reactions favoring lipid accumulation in cells, and (3) stimulation of food intake.⁸,¹³ Hence, inhibition of ACBP/DBI by its inducible knockout, mutation of its receptor GABRG2 on a specific residue (F77I), or antibody-mediated neutralization of the extracellular ACBP/DBI pool has a triple consequence: (1) stimulation of autophagy, (2) lipo-catabolic reactions favoring lipolysis and fatty acid oxidation, and (3) inhibition of food intake.⁸,¹³

All three methods of ACBP/DBI inhibition also have marked organ-protective effects that are mechanistically linked to the induction of macroautophagy (to which we refer as “autophagy”).¹⁴ Thus, ACBP/DBI inhibition protects the heart against ischemia-reperfusion damage and anthracycline toxicity, the lung against bleomycin-induced inflammation and fibrosis, and has marked hepatoprotective effects as well.¹⁴,¹⁵ In the liver, ACBP/DBI neutralization prevents excessive cell death, inflammation, and fibrosis in response to a broad array of insults. Such hepatic insults can be mechanical (e.g., transient arterial occlusion, and bile duct ligation), chemical (e.g., acetaminophen, carbon tetrachloride [CCl~4~], concanavalin A, and rosiglitazone), and dietary (e.g., high-fat or methionine/choline-deficient diets),¹³,¹⁴ underscoring the broad contribution of ACBP/DBI to hepatic pathogenesis.

The implications of ACBP/DBI in malignant disease are poorly studied. ACBP/DBI is upregulated in breast cancer and non-small cell lung cancers (NSCLCs), associated with poor patient prognosis.¹⁶,¹⁷ *In vitro*, knockdown of ACBP/DBI mitigated NSCLC cell proliferation.¹⁶ In glioma and glioblastoma, ACBP/DBI is strongly overexpressed and stimulates fatty acid oxidation through cell-autonomous effects relying on its interaction with acyl CoA esters. Its knockdown inhibits proliferation and triggers glioblastoma cell senescence as well.¹⁸ Immunohistochemical detection of ACBP/DBI also reveals its overexpression in cholangiocarcinoma.¹⁹ However, there is no knowledge on the role of ACBP/DBI in HCC.

Prompted by these premises, we investigated the role of ACBP/DBI in hepatic carcinogenesis. Using a compendium of methods to inhibit ACBP/DBI (by its knockout, receptor mutation, or antibody-mediated neutralization), we demonstrate the critical implication of ACBP/DBI in hepatocarcinogenesis and tumor progression. Of note, high intratumoral ACBP/DBI mRNA expression and high levels of circulating ACBP/DBI protein levels are associated with features of poor prognosis in patients with HCC, supporting the translational relevance of our preclinical findings.

## RESULTS

### Increased ACBP/DBI levels in human HCC

In 9 different publicly available gene expression datasets including The Cancer Genome Atlas (TCGA), HCC tumors consistently contain higher *DBI* mRNA levels than the normal adjacent liver tissue (*Figures* 1A and S1A–S1I). According to TCGA, this *DBI* elevation occurred independently from each of the components of the tumor/node/metastasis (TNM) staging system, Child-Pugh grade, tumor differentiation, vascular invasion, as well as levels of fibrosis and inflammation (*Figures* 1B, 1C, and S1J–S1Q). However, *DBI* mRNA was particularly abundant in patients with alpha-fetoprotein (AFP) protein levels >400 ng/mL (*Figure* 1D), and *DBI* mRNA levels above the median values were associated with shorter overall survival in TCGA and in an independent HCC patient cohort (*Figures* 1E and S1R). Of note, we detected a significant positive correlation of *DBI* mRNA with the transcription of multiple HCC-relevant oncogenes across 34 publicly available datasets. This analysis also revealed a negative correlation between the tumor suppressor *TPR53* (best known as p53) and *DBI* mRNAs (*Figure* S2), in accord with prior observations.²⁰

Using ELISA, we measured ACBP/DBI protein concentrations in the plasma of 146 patients with HCC belonging to different BCLC stages and 58 plasmas of patients with chronic liver diseases without any history of HCC. We found that ELISA-detectable plasma ACBP/DBI levels (ng/mL) were higher in patients with HCC compared with the group without any current or prior HCC (*Figure* 1F), correlating with poor prognostic features such as advanced BCLC stage (*Figure* 1G), vascular invasion
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(Figure 1H), and extrahepatic metastasis (Figure 1I). In addition, DBI mRNA correlated with AFP mRNA in liver biopsies (Figure 1J), echoing a correlation between plasma protein levels of ACBP/DBI and AFP (Figure 1K). Moreover, ACBP/DBI plasma concentrations correlated with tumor size, as detected by CT scans (Figure 1L). Altogether, these results support the idea that advanced HCC is associated with high ACBP/DBI levels.

Importantly, when human HCC cells were orthotopically inoculated into the livers from immunodeficient mice (Figure 1M), human but not mouse ACBP/DBI plasma concentrations measured with species-specific ELISAs (that distinguish human and mouse ACBP/DBI)²¹ increased over time, correlating with the size of the tumors (Figures 1N, 1O, and S3). These findings indicate that raising circulating ACBP/DBI may directly stem from HCC.

Altogether, these findings suggest that human HCC progression is associated with an increase in ACBP/DBI expression by malignant tissues as well as by an increase in ACBP/DBI plasma levels.

## Intra- and extracellular ACBP/DBI contribute to the pathogenesis of transplantable, oncogene-, and carcinogen-induced HCC

Among a collection of distinct human HCC cell lines, HUH-7 cells express the highest ACBP/DBI mRNA and protein levels (Figure S4A). Knockdown of *DBI* with three different short hairpin RNAs (shRNAs) yielded HUH-7 clones that exhibited less proliferation and clonogenic potential, as well as a reduced proportion of cells in the S-phase of the cell cycle. Similar results were obtained for human hepatoblastoma HEP-G2 cells and mouse HCC Hep55.1C cells subjected to the knockdown of *DBI* and *Dbi*, respectively (Figures S4B–S4M). Of note, both HEP-G2 cells and Hep55.1C express the ACBP/DBI receptor GABRG2,²²,²³ as do freshly isolated mouse hepatocytes,²² Kupfer cells, and several immune cell types including B and T lymphocytes, as well as dendritic cells (Figure S5).

As compared to the parental cell line, three Hep55.1C clones subjected to *DBI* depletion (by three distinct shRNAs) were relatively poorly pathogenic when inoculated orthotopically into the livers of immunocompetent C57BL/6 mice (Figure 2A), as indicated by reduced mortality of recipient mice (Figure 2B), a diminished number of mice developing macroscopic HCC (Figure 2C),

as well as a decreased number and total weight of HCC nodules at endpoint (Figures 2D and 2E). Constant monitoring of tumor growth using luciferase-transduced Hep55.1C clones confirmed the failure of *Dbi*-depleted cells to develop HCC (Figures 2F–2K). Of note, neutralization of extracellular ACBP/DBI protein in mouse plasma (Figure 2M) by means of a suitable auto-vaccination protocol (in which autoantibodies against ACBP/DBI were induced by adjuvanted inoculation of ACBP/DBI conjugated to the immunogen keyhole limpet hemocyanin [KLH])²⁴ also delayed the orthotopic growth of parental (ACBP/DBI-expressing) luciferase-transduced Hep55.1C cancers as compared to control animals immunized with KLH alone (Figures 2L–2R).

We next resorted to a model of oncogene-induced hepatocarcinogenesis in which two plasmids coding for *Myc* and *Ctnnb1* were co-transfected into hepatocytes by hydrodynamic injection. Mice in which a floxed version of *DBI* (*DBI*tf/+) was ubiquitously excised by a tamoxifen-inducible Cre recombinase (UBCcre/ERT2) (genotype: UBCcre/ERT2::DBItf/+, Dbit+/+, control: DBItf/ without CRE, Dbit−/−) were rather resistant to *Myc/Ctnnb1*-induced hepatic carcinogenesis (Figures 3A–3D and S6A). Moreover, mice in which the γ2 subunit of GABAA receptor (Gabrg2) was homozygously mutated (F77I) (genotype: Gabrg2F77IF77I), causing a loss of interaction with ACBP/DBI,²⁴ were relatively resistant to *Myc/Ctnnb1*-induced HCC as well (Figures 3E–3H and S6B). Finally, immunization with KLH-ACBP/Dbi conferred partial protection against *Myc/Ctnnb1*-mediated hepatocellular carcinogenesis (Figures 3I–3L and S6C). Of note, neutralization of ACBP/DBI did not affect *Ctnnb1* expression in the mouse liver but did reduce that of *Myc* (Figures S6D–S6F).

We previously demonstrated the contribution of ACBP/DBI to diet and toxin-induced hepatosteatosis and fibrosis.¹² However, the possible implication of ACBP/DBI in diet- and toxin-induced HCC is elusive. For this reason, we examined a model of hepatic carcinogenesis induced by a Western-style diet (WD) combined with weekly intraperitoneal (i.p.) injections of the hepatotoxin CCl₄.²⁵ We observed that knockout of *Dbi* (Figures 4A–4F and S7A–S7F), homozygous *Gabrg2* mutation (Figures 4G–4L and S7G–S7L), as well as immunization with KLH-ACBP/Dbi (Figures 4M–4R and S7M–S7R) mitigated the stigmata of WD + CCl₄-induced liver damage (MASH, lobular inflammation, ballooning with Mallory-Denk bodies, and fibrosis), reduced liver

## Figure 1. Overabundance of ACBP/DBI in patients with HCC

(A-E) Analysis of data from The Cancer Genome Atlas (TCGA) dealing with liver hepatocellular carcinoma (HCC or LIHC). (A) Higher expression of *DBI* mRNA in HCC tissues versus normal tissues (n = 50-374/group). (B and C) *DBI* mRNA levels are increased in HCC tissues compared with normal tissues independent of tumor grades and TNM stages in TCGA (n = 12-276/group). (D) Elevated *DBI* mRNA levels are detected in tumor tissues in HCC patients with an AFP >400 ng/mL compared with AFP ≤400 ng/mL (n = 50-221/group). (E) High *DBI* expression is associated with poor prognosis of patients with HCC (n = 186-187/group).

(F-L) Analysis of data from the AP-HP cohort. (F) ACBP plasma levels are increased in patients with HCC compared to controls (n = 64-193/group). (G-I) Elevated ACBP plasma levels are associated with advanced BCLC stages, vascular invasion, and tumor metastasis (n = 37-159/group). (J and K) Positive correlation between ACBP/DBI and AFP at both mRNA (n = 374) and protein levels (n = 233). (L) Plasma ACBP levels are positively associated with the largest tumor size in patients with HCC (n = 188).

(M-O) Tumor origin of ACBP/DBI in mice bearing human HCC.

(M) Schematic diagram of orthotopic liver transplantation of HUH-7 cells in nude mice. HUH-7 tumors were monitored by sonography (for representative images, see Figure S2), while blood samples were simultaneously collected for ELISA-based ACBP detection.

(N) Longitudinal evolution of human ACBP levels in mouse plasma after tumor implantation by ELISA. Data from representative mice are shown in a representative line chart (n = 6).

(O) HUH-7 cell-derived human ACBP in mouse plasma correlates with tumor size (n = 18). Note that tumors were monitored by sonography at different time points post inoculation to create heterogeneity in tumor size.

p values were calculated by Welch's t test (A), Kruskal-Wallis test with Dunn's post hoc test (B, C, and G), Welch's one-way ANOVA with Games-Howell test (D), log rank test (E), Mann-Whitney U test (F, H, and I), and Spearman's rank correlation (J, K, L, and O).
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ACBP/Dbi mRNA and protein expression as well as circulating ACBP/Dbi concentrations, and inhibited the development of HCC. Thus, all protocols of ACBP/DBI inhibition reduced the size and number of WD/CCl₄-induced tumor lesions developing in the liver (Figures 4E, 4F, 4K, 4L, 4Q, and 4R). Similar anti-MASH, anti-fibrotic, and oncosuppressive effects were observed for KLH-ACBP/Dbi vaccination (as compared to KLH-only-vaccinated controls) when the disease was induced by a combination of high-fat diet (HFD) and the carcinogen diethylnitrosamine (DEN) (Figures 4S–4X and S7S–S7X). In addition, the hepatocyte-specific knockout of ACBP/DBI was sufficient to suppress liver carcinogenesis in mice receiving a carcinogenic WD plus CCl₄ (Figures S8A–S8F).

Altogether, these results indicate that hepatic carcinogenesis is favored by ACBP/DBI. This effect of ACBP/DBI is at least partially mediated by the extracellular pool of ACBP/DBI acting on GABAA receptors.

## ACBP/DBI neutralization improves immunosurveillance

To elucidate the mechanisms through which ACBP/DBI facilitates hepatic carcinogenesis, we performed liver bulk RNA sequencing (RNA-seq) on three of the aforementioned models, namely (1) WD + CCl₄-treated Dbi⁺/+ versus Dbi⁻/⁻ mice, (2) WD + CCl₄-treated KLH-only versus KLH-ACBP-vaccinated mice, and (3) HFD + DEN-treated KLH-only versus KLH-ACBP-vaccinated animals. We then compared the effects of unrestrained ACBP/DBI action to 25 publicly available datasets detailing the deregulation of molecular pathways in different human liver diseases/injury (Figure 5A). Non-supervised hierarchical clustering indicated that many molecular pathways that are downregulated by ACBP/DBI inhibition are upregulated in several liver diseases (or conversely molecular pathways upregulated by ACBP/DBI inhibition are downregulated in such liver diseases), irrespective of their etiology (alcoholic, inflammatory, metabolic, or toxic), with the notable exception of infection by hepatitis viruses B, C, and D, which do not cluster with ACBP/DBI inhibition (Figure 5A).

In the three RNA-seq datasets that reflect ACBP/DBI inhibition, several gene ontology terms suggesting immunosuppression were downregulated, as this applies to a signature specific for regulatory T cells in lymph nodes,²⁶ pro-inflammatory T helper (Th)17 cells,²⁷ and polarized Th2 cells²⁸ (Figure 5B). According

to this prediction, orthotopic Hep55.1C HCC tumors were highly responsive to combination treatment with a neutralizing monoclonal antibody (mAb) against ACBP/DBI plus a PD-1-blocking antibody. This combination was significantly more efficient in reducing tumor growth than each of the two monotherapies (anti-ACBP/DBI alone or PD-1 blockade alone), thus markedly extending survival (Figures 5C–5F). T cells infiltrating orthotopic Hep55.1C liver cancers were characterized by immunofluorescence cytometry (Figures S9A–S9C). Anti-ACBP/DBI increased the infiltration of such tumors by Th cells (CD3⁺CD4⁺Foxp3⁻) and improved the ratio of Th cells over regulatory T cells (Tregs, CD3⁺CD4⁺Foxp3⁺), as well as that of cytotoxic T cells (CTLs, CD3⁺CD8⁺) over Tregs. In addition, anti-ACBP/DBI enhanced the expression of ICOS1 on Th cells and CTLs, as it reduced the expression of PD1 on Tregs. Moreover, as compared to anti-PD-1-treated tumors, the combination of anti-PD-1 plus anti-ACBP/DBI reduced the expression of TIGIT on tumor-infiltrating Th cells (Figure 5G). These changes in the composition of the immune infiltrate indicate an immunostimulatory effect of ACBP/DBI blockade on T lymphocytes present in the tumor microenvironment. We conclude that ACBP/DBI inhibition can enhance the susceptibility of HCC to immunotherapy.

## ACBP/DBI neutralization reduces HCC proliferation

Cell cycle-associated genes were strongly downregulated in all three RNA-seq datasets reflecting ACBP/DBI inhibition (Figure 5B). A bioinformatic analysis identified commonly up-or downregulated genes in livers from (1) WD + CCl₄-treated Dbi⁺/+ versus Dbi⁻/⁻, (2) WD + CCl₄-treated, or (3) HFD + DEN-treated KLH-only versus KLH-ACBP/Dbi-vaccinated mice (Figures S10A and S10B). We then identified genes downregulated by ACBP/DBI inhibition that are also overexpressed in human HCC and are associated with poor prognosis of HCC (Figures S10C and S10D). Among these ACBP/DBI-dependent, disease-relevant genes, many were associated with cell cycle advancement (and in particular mitosis) (Figure S10E). Quantitative reverse-transcription PCR (RT-qPCR) measurements confirmed that ACBP/DBI inhibition downregulated genes required for cell cycle advancement (such as *Ccnd1*, *Ccne1*, *Cdk4*, *Cdk6*, or *Pcna*) and upregulated genes that block the cell cycle (such as *Atr*, *Gadd45a*, *Gadd45b*, *Cdkn1a*, or *Cdkn2a*) (Figure S11A). Similarly, knockdown of *DBI* in HEP-G2 cell lines

<caption>**Figure 2.** Intra- and extracellular ACBP/DBI inhibition blunt tumor development in an orthotopic transplantation model</caption>

(A) Schematic diagram of the orthotopic HCC model involving intrahepatic injection of Hep55.1C cell-derived DBI depletion clones (SH1, SH2, and SH3) or parental control clones (NC).
(B–E) Log rank test for survival curve, tumor incidence, number of HCC nodules, and tumor weight of mice at endpoint (n = 19–23/group).
(F) Schematic diagram of the orthotopic HCC model using Hep55.1C-Luc-derived cell clones (NC, SH1, SH2, and SH3) (n = 18–19/group).
(G) Survival of mice treated as shown in (F).
(H) Representative IVIS images of HCC lesions for the different experimental conditions (n = 3/group).
(I) Quantification of total intensity of tumor lesions from IVIS images (n = 18–19/group).
(J and K) Number of HCC nodules and tumor weight of mice at endpoint (n = 18–19/group).
(L) Schematic diagram of the orthotopic HCC mouse model (Hep55.1C-Luc) treated with KLH/KLH-ACBP.
(M) Plasma ACBP levels.
(N) Survival of mice treated as shown in (L).
(O and P) Representative IVIS images and quantification of tumor burden by IVIS imaging (n = 15–21/group).
(Q and R) Number of HCC nodules and tumor weight of mice at endpoint (n = 15–21/group). Scale bars: 2 cm (H, N).
ρ values were calculated by log rank test (B, G, and N), Kruskal-Wallis test with Dunn's post hoc test (D, E, J, and K), two-way ANOVA (I), and Mann-Whitney U test (M, P, Q, and R). The mean values ± SEM are shown.
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<figure><img src="image_38.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>E</figcaption></figure>

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<figure><img src="image_43.png" /><figcaption>G</figcaption></figure>

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<figure><img src="image_45.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>J</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>K</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>L</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>Figure 3. Intra- and extracellular ACBP/DBI inhibition delay hepatic oncogenesis driven by *Myc* plus *Ctnnb1*</figcaption></figure>

(A) Schematic diagram of *Myc/Ctnnb1*-induced hepatic carcinogenesis in tamoxifen-induced conditional *Dbi* knockout mice (*Dbi<sup>-/-</sup>*) and control mice (*Dbi<sup>+/+</sup>*).

(E) Schematic diagram of *Myc/Ctnnb1*-induced hepatocarcinogenesis in *Gabrg2*-mutated (*Gabrg2<sup>F77I/F77I</sup>*) and control (*Gabrg2<sup>WT</sup>*) mice.

(I) Schematic diagram of *Myc/Ctnnb1*-induced hepatic tumorigenesis in *KLH/KLH-ACBP*-immunized mice.

(B, F, and J) Survival of mice in days (n = 8-12/group).

(C, G, and K) Size of the largest tumor per mouse (n = 8-12/group).

(D, H, and L) Quantification of the number of tumors with different size (<2, 2-5, 5-10, and >10 mm) (n = 8-12/group). Error bars represent mean ± SEM.

p values were calculated by log rank test (B, F, and J) and Mann-Whitney U test (C, D, G, H, K, and L).
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<figure><img src="image_69.png" /><figcaption>Figure 4. Intra- and extracellular ACBP/DBI inhibition impaired MASH-driven hepatocarcinogenesis</figcaption></figure>

(A) Schematic diagram of hepatic carcinogenesis induced by Western-style diet (WD) plus CCl₄ in tamoxifen-induced conditional Dbi knockout mice (Dbi -/-) and control mice (Dbi +/+).

(G) Schematic diagram of WD plus CCl₄-induced hepatic carcinogenesis in Gabrg2-mutated (Gabrg2<sup>F77I/F77I</sup>) and control (Gabrg2<sup>WT</sup>) mice.

(M) Schematic diagram of WD plus CCl₄-induced hepatic carcinogenesis in KLH/KLH-ACBP-immunized mice.

(legend continued on next page)
elicited transcriptional signs of cell cycle blockade (Figure S11B), in accord with the reduced oncogenic and proliferative ability of such DBI-depleted cells (Figures S4E–S4J).

Prompted by these results, we assessed cell proliferation in livers from mice subjected to carcinogen-induced oncogenesis using immunohistochemical detection of Ki67 (Figures S12A–S12E) and PCNA (Figure S13). Of note, ACBP/DBI inhibition by Dbi knockout, Gabrg2 mutation, or KLH-ACBP/DBI vaccination resulted in reduced proliferation, both in tumor lesions (if detectable) and in the non-malignant hepatic parenchyma, irrespective of the precise oncogenic stimulus (CCl₄ or DEN) (Figures S12A–S12E and S13). Intraperitoneal injection of a neutralizing anti-ACBP/DBI mAb inhibited hepatocyte proliferation both after sham operation and after partial hepatectomy, which induces liver regeneration²⁹ (Figure S12E), suggesting a general pro-proliferative effect of ACBP/DBI in normal hepatocytes.

Finally, we isolated HCC cells from Dbi⁺/⁺ versus Dbi⁻/⁻ mice subjected to WD + CCl₄-triggered carcinogenesis (Figure S12F). Dbi⁻/⁻ HCC cells exhibited comparatively low expression of proliferation markers (Ki67 and PCNA) and malignancy-linked markers (AFP, CK19, and GPC3) (Figure S12G), attenuated clonogenic potential (Figure S12H), and reduced in vitro proliferation (Figure S12I). These findings indicate that ACBP/DBI is required for full-blown HCC proliferation. We conclude that ACBP/DBI has profuse pro-proliferative effects on normal and transformed hepatocytes.

## ACBP/DBI neutralization sensitizes to the induction of ferroptosis

The aforementioned bioinformatic analysis identified alterations in pathways of cellular self-consumption (Figure 5B). Thus, ACBP/DBI suppression entailed a reduction in transcripts relevant to apoptosis and necroptosis but an increase in the expression of genes relevant to ferroptosis (Figure 5B). In addition, ACBP/DBI inhibition enhanced the expression of autophagy-related genes (Figure 5B), commensurate with the well-documented autophagy-repressive function of ACBP/DBI,³⁰,³¹ as well as the known pro-ferroptotic effects of autophagy.³²,³³

Validation by RT-qPCR confirmed the upregulation of ferroptosis driver genes as well as the downregulation of ferroptosis suppressor genes in livers from (1) WD + CCl₄-treated Dbi⁻/⁻ versus Dbi⁺/⁺ mice, (2) WD + CCl₄-treated Gabrg2F77I/F77I versus Gabrg2WT mice, (3) WD + CCl₄-treated mice, and (4) WD + DEN-treated KLH-ACBP/Dbi versus KLH-only-vaccinated mice (Figure 6A). Moreover, immunoblots confirmed the increased protein expression of ferroptosis effectors (such as ACSL4, KEAP1, NCOA4, and POR) but decreased expression of ferroptosis suppressors (such as ACSL3, BMAL1, GPX4, and SQSTM1/p62) after inhibition of ACBP/DBI (Figures 6B and S14A). Such shifts from ferroptosis suppression toward execu-

tion were also observed for HCC clones derived from the livers of WD + CCl₄-treated Dbi⁻/⁻ versus Dbi⁺/⁺ mice, both at the mRNA (Figure S14B) and at the protein levels (Figures S14C–S14E). Spatial transcriptomics or spatially resolved mass spectrometric metabolomics coupled to hematoxylin-eosin staining allowed to distinguish non-malignant and malignant areas of the liver (Figures 6C and S15A–S15C). Of note, inhibition of ACBP/DBI by Dbi knockout or KLH-ACBP/Dbi vaccination caused a higher expression of ferroptosis drivers in apparently (still) non-malignant tissue compared to ACBP/DBI-uninhibited controls (non-tumor and tumor tissues). In ACBP/DBI-uninhibited controls, ferroptosis suppressors were upregulated in tumors as compared to the non-malignant adjacent liver tissue (Figures 6C and 6D). Hence, the alterations in ferroptosis-relevant gene expression levels found in total livers were detected both in HCC and in non-malignant tissues. In non-tumor tissues, spatial transcriptomics also revealed a downregulation of immunosuppressive genes and an upregulation of immunostimulatory genes upon ACBP/DBI inhibition (Figure 6E), supporting improved immunosurveillance.

In accord with the gene/protein expression data, HCC cells were particularly vulnerable to losing their viability in response to the combination of Dbi knockout and the addition of pharmacological ferroptosis inducers including RSL3, imidazole ketone erastin (IKE), linoleic acid, and linolenic acid (Figures 7A–7D). Moreover, orthotopic Hep55.1C cancers responded to treatment with a combination of anti-ACBP mAb plus ferroptosis induction (with RSL3 or IKE) more efficiently than to standalone therapies with either anti-ACBP mAb or ferroptosis inducers (Figures 7E–7P and S15D). This combination was more efficient in reducing tumor burden than each of the two monotherapies (anti-ACBP alone or RSL3/IKE alone), thus markedly extending survival (Figures 7E–7P and S15E).

We conclude that inhibition of DBI sensitizes HCC to therapeutic interventions with ferroptosis inducers.

## DISCUSSION

ACBP/DBI has previously been shown to contribute to the pathogenesis of a wide array of non-malignant experimental liver diseases ranging from acute hepatic necrosis to steatohepatitis and fibrosis/cirrhosis.⁸,¹³,¹⁴ Here, we demonstrate that ACBP/DBI also contributes to the development and progression of HCC induced by a variety of rather distinct methods including (1) intrahepatic inoculation of HCC cells, (2) oncogene-induced transformation, and (3) chronic challenge with the hepatotoxin CCl₄ or (4) the mutagen DEN in the context of WD. We used a compendium of genetic methods to inhibit the ACBP/DBI system (i.e., knockdown or knockout of DBI and mutation of its receptor GABRG2), as well as immunological approaches

(S) Schematic diagram of HFD plus DEN-induced hepatic carcinogenesis in KLH/KLH-ACBP-immunized mice.

(B, H, N, and T) Representative immunoblots detecting ACBP protein in the liver (n = 5–9/group). GAPDH was used as a loading control.

(C, I, O, and U) Quantification of western blots (n = 5–9/group). Densitometric ratios of ACBP/GAPDH were normalized to control groups, shown as (Nor.).

(D, J, P, and V) Plasma ACBP levels (n = 5–18/group). Values were normalized to control groups, presented as (a.u.).

(E, K, Q, and W) Quantification of the largest tumor size of each treatment group (n = 5–27/group).

(F, L, R, and X) Quantification of the number of tumors with different size (n = 5–27/group). Error bars represent mean ± SEM. *p* values were calculated by Welch's *t* test (C, D, and E), Mann-Whitney *U* test (F, Q, R, and W), *t* test (I, J, K, O, P, U, and V), as well as *t* test and Mann-Whitney *U* test (L and X).
A

Datasets
GSE120652 healthy vs acetaminophen
GSE87028 aflatoxin B1 0uM vs 25uM
GSE119600 ctrl vs primary biliary cholangitis
GSE164760 NASH VS NASH-HCC
GSE74000 healthy vs acetaminophen
GSE164760 NASH vs Cirrhosis
(3) HFD+DEN KLH-ACBP vs KLH
(2) WD+CCI4 KLH-ACBP vs KLH
(1) WD+CCI4 Db1<sup>+/+</sup> vs Db1<sup>+/+</sup>
GSE23343 ctrl vs type 2 diabetes
GSE16470 healthy vs NASH
GSE14951 donor vs transplanted-reperfused liver
GSE15239 partial hepatectomy
GSE119600 ctrl vs primary sclerosing cholangitis
GSE7706 ctrl vs posttransplantation
GSE164760 Healthy vs NASH-HCC
GSE16470 Healthy vs NASH-HCC
GSE14951 normal vs transplanted-reperfused liver
GSE16470 healthy vs cirrhosis
GSE103580 steatosis vs mild alcoholic hepatitis
GSE103580 steatosis vs alcoholic cirrhosis
GSE49541 fibrosis 0-1 vs 3-4
GSE34798 ctrl vs HCV
GSE98383 HDV-non-tumor vs HDV-HCC
GSE107170 Ctrl vs HDV-HCC
GSE45114 normal HBV neg+pos vs HCC pos
GSE47197 HBV infected non-tumor vs HCC

<figure><img src="image_70.png" /><figcaption>Liver damage

Acetaminophen
Type 2 diabetes

Aflatoxin B1
Liver diseases
NASH/NASH(Mouse)
Alcohol

Infection by HBV/HCV/HDV</figcaption></figure>

B

<table><thead><tr><th>Pathways</th><th>NES (1)</th><th>p</th><th>NES (2)</th><th>p</th><th>NES (3)</th><th>p</th></tr></thead><tbody><tr><td>Apoptosis</td><td>-1.8315</td><td>8.26e-05</td><td>-1.9109</td><td>4.78e-05</td><td>-2.1985</td><td>7.70e-07</td></tr><tr><td>Autophagy</td><td>1.8908</td><td>0.0003</td><td>1.0329</td><td>0.4209</td><td>2.9130</td><td>2.45e-14</td></tr><tr><td>Cell cycle</td><td>-2.3066</td><td>1.36e-12</td><td>-2.6089</td><td>5.82e-16</td><td>-1.8306</td><td>0.0008</td></tr><tr><td>Ferroptosis</td><td>1.8742</td><td>0.0001</td><td>1.3634</td><td>0.0374</td><td>1.5065</td><td>0.0097</td></tr><tr><td>Necroptosis</td><td>-1.3877</td><td>0.0470</td><td>-1.5147</td><td>0.0112</td><td>-1.8863</td><td>0.0002</td></tr><tr><td>Platelet activation</td><td>-1.7697</td><td>6.50e-07</td><td>-1.9646</td><td>2.33e-08</td><td>-1.8725</td><td>2.50e-05</td></tr><tr><td>Pyroptosis</td><td>0.7107</td><td>0.9191</td><td>-1.1434</td><td>0.2694</td><td>-1.9180</td><td>0.0002</td></tr><tr><td>up_Th17 vs Th1/Naive CD4 T cell</td><td>-2.4877</td><td>1.00e-16</td><td>-2.3069</td><td>2.40e-10</td><td>-1.4548</td><td>0.0336</td></tr><tr><td>up_Th2 vs CD4 T cell/DC/B cell/NK cell</td><td>-2.6947</td><td>1.00e-16</td><td>-2.6498</td><td>1.00e-16</td><td>-1.5348</td><td>0.0018</td></tr><tr><td>up_Treg_LN vs Thymus</td><td>-2.2719</td><td>3.37e-11</td><td>-1.9961</td><td>1.81e-05</td><td>-1.8529</td><td>0.0006</td></tr></tbody></table>

C

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D

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E

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F

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G

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(i.e., neutralizing antibodies against ACBP/DBI), finding that all these strategies mitigated hepatic carcinogenesis. This preclinical evidence in favor of pro-HCC effects of ACBP/DBI is backed up by clinical correlations showing that aggressive HCC is associated with the upregulation of *DBI* mRNA, as well as with an increase in circulating ACBP/DBI protein concentrations.

Based on the literature, ACBP/DBI can mediate cell-autonomous cancer cell-supportive metabolic effects, as indicated by the fact that glioblastoma growth was enhanced by transfection with the wild-type ACBP/DBI protein but not by a mutant (K32A) that lacks acyl-CoA binding capacity and fails to stimulate fatty acid oxidation,¹⁸ but still is able to bind to GABRG2 and to mediate metabolic effects when injected into mice.²¹ Here, we used several methods to inhibit intracellular ACBP/DBI, namely, its knockdown in HCC cell lines and its tamoxifen-inducible knockout in mice, as well as methods to interfere with extracellular ACBP/DBI, namely, its antibody-mediated neutralization and the mutation of its receptor GABRG2. Both the inhibition of intracellular and extracellular ACBP/DBI reduced hepatic carcinogenesis, suggesting that the two pools of ACBP/DBI are disease relevant. As a caveat, however, it can be argued that a significant amount of extracellular ACBP/DBI is produced by malignant cells (as suggested by the fact that human HCCs present in mice generate ACBP/DBI that becomes detectable in the circulation) and then could affect the tumor microenvironment and even systemic circuitries. Conversely, as shown here, inhibition of extracellular ACBP/DBI (by antibodies or GABRG2 mutation) reduced the expression of liver ACBP/DBI, in accord with the interruption of a feedback loop through which extracellular ACBP/DBI acting on GABRG2 activates the transcription factor peroxisome proliferator-activated receptor-g to stimulate the transcription of the *DBI* gene.¹³ Hence, the intra- and extracellular pools of ACBP/DBI are functionally linked, rendering it difficult to weigh their relative contribution to hepatic oncogenesis. That said, it appeared that the knockout of ACBP/DBI had more consistent oncosuppressive effects than F77I GABRG mutation and vaccination with KLH-ACBP/DBI against hepatocarcinogenesis induced by oncogenes (*Myc* plus *Ctnnb1*) or hepatic damage (by WD plus *CCl*₄), arguing in favor of the importance of both pools of ACBP/DBI.

Tumorigenesis cannot be only ascribed to cell-intrinsic alterations (such as (epi)genetic mutations generated in the context of cellular stress and excessive proliferation)³⁴ but also implies cell-extrinsic alterations including excessive inflammation³⁵ and the failure of immunosurveillance, i.e., the elimination of (pre-)malignant cells by innate or acquired immune effectors.³⁶,³⁷ It

appears probable that ACBP/DBI neutralization affects both cell-intrinsic and -extrinsic facets of liver cancer, in line with the fact that both hepatocytes and immune cells express GABRG2. Thus, beyond its cell biological effects, inhibition of ACBP/DBI consistently reduced signs of liver inflammation leading to excessive macrophage infiltration and fibrosis but also reduced the expression of genes associated with immunosuppressive T cell subtypes (such a T<sub>regs</sub>, T<sub>H2</sub>, and T<sub>H17</sub>). In addition, ACBP/DBI neutralization enhanced the expression of genes favoring immune responses in non-tumoral liver tissue, as determined by spatial transcriptomics, and improved the ratio among immunostimulatory and immunosuppressive HCC-infiltrating T lymphocytes (i.e., the Th:Treg and CTL:Treg ratios), as determined by immunofluorescence cytometry. Accordingly, inhibition of extracellular ACBP/DBI by means of a mAb sensitized HCC to immunotherapy by PD-1 blockade. In this context, it may be important to note that ACBP/DBI neutralization did not modify the hepatic expression of genes directly involved in the response to PD-1 blockade (such as PD-1 itself and that of its ligands PD-L1/CD274 and PD-L2/PDCD1LG2). Thus, the immunotherapy-enhancing effect of ACBP/DBI blockade is likely mediated by a general improvement of the immune tonus.

At the cancer cell-specific level, we observed that ACBP/DBI inhibition induced several phenomena beyond the well-studied enhancement of autophagy. Thus, any strategy for inhibiting intracellular or extracellular ACBP/DBI resulted in reduced proliferation, not only of malignant HCC cells but also of normal hepatocytes, even in mice that were subjected to partial hepatectomy (to study liver regeneration) and in control animals. This may reflect the known trophic and anabolic effects of ACBP/DBI, which stimulates the AKT/mTORC1 pathway⁸ involved in liver growth stimulation.³⁸,³⁹ In addition, we observed that ACBP/DBI neutralization reduced the expression of pro-apoptotic and pro-necrotic genes. However, regardless of the mode of inhibition (knockout, receptor mutation, or antibody-mediated neutralization), ACBP/DBI inhibition resulted in the upregulation of 13 pro-ferroptotic mRNAs and the downregulation of even more anti-ferroptotic mRNAs. This effect, which could be validated at the protein level, was accompanied by increased susceptibility of HCC cells to pharmacological induction of ferroptosis both *in vitro* and *in vivo*. It is well established that autophagy (which is induced by ACBP/DBI inhibition) can contribute to ferroptotic cell death,³²,³³ but autophagy generally inhibits apoptosis and often interferes with necroptosis.³²,⁴⁰ Further cell biological studies must explore the molecular mechanics

**Figure 5.** ACBP/DBI neutralization enhances the susceptibility of HCC to immunotherapy

(A) Cross-species comparison of dysregulated molecular pathways between three mouse models and human liver diseases. Normalized enrichment score (NES) of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway is row-normalized and presented as Z score. Euclidean distance was calculated for row and column clustering analysis.

(B) Representative KEGG pathways in three mouse datasets generated in this study. NESs are shown.

(C) Schematic diagram of anti-ACBP plus anti-PD-1 combination therapy tested in orthotopic Hep55.1C HCC model.

(D) Survival of the mice in days (n = 9/group).

(E and F) Tumor weight and number of tumor nodules per mouse in each treatment group (n = 9/group). Error bars represent means ± SEM.

(G) Analysis of the T lymphocyte infiltration in the orthotopic Hep55.1C HCC model treated with anti-ACBP plus anti-PD1 (see experimental design in Figure S9A) by immunofluorescence cytometry (n = 9-13/group). Results are shown as Z scores for each mouse.

p values are calculated by t test (B), log rank test (D), one-way ANOVA post hoc Tukey HSD test (E), Kruskal-Wallis test with Dunn's multiple comparison (F), and two-way ANOVA test (G). p values in (G) correspond to the global effects (1-3) and post hoc pairwise comparisons (4-9), respectively.
A

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underlying the crosstalk between these pathways of self-con- sumption in HCC.

In conclusion, it appears that hepatocellular carcinogenesis
involves the contribution of ACBP/DBI, meaning that the neutral-
ization of its intracellular and extracellular pools reduces or de-
lays the manifestation of HCC in various preclinical models. In
addition, we observed that inhibition of ACBP/DBI inhibits prolif-
eration and sensitizes established HCC to PD-1-targeted immu-
notherapy, as well as pharmacological induction of ferroptosis.
In patients with HCC, both intralesional and circulating ACBP/
DBI are associated with more aggressive tumors and poor prog-
nosis, arguing in favor of the possible clinical translation of these
findings.

Limitations of the study

Limitations also apply to our study, which has not explored the
detailed mechanisms linking ACBP/DBI/GABA<sub>A</sub>R interactions
to intracellular signaling cascades that explain the broad impact
of ACBP/DBI neutralization on HCC. Similarly, we have not deter-
mined whether the cell-autonomous effects of ACBP/DBI favor-
ing HCC proliferation are mediated through GABA<sub>A</sub>R or other
mechanisms that might involve the lipid metabolism-enhancing
effects of ACBP/DBI. Other limitations concern the absence of
data on ACBP/DBI protein in HCC samples, meaning that we
only confirmed the negative prognostic impact of DBI mRNA in
HCC samples (and ACBP/DBI protein in plasma), as well as the
absence of food intake measurements for each individual mouse
included in this study. We used orthogonal approaches to inhibit
the ACBP/DBI system (by knockdown, knockout, antibody-
mediated neutralization, or receptor mutation) and to induce
HCC (orthotopic inoculation of HCC cells, hydrodynamic injec-
tion of vectors expressing MYC and beta-catenin, WD plus
CCl<sub>4</sub>, and WD plus DEN) that yielded similar conclusions with
respect to the pro-HCC effects of ACBP/DBI. However, it is
well possible that these pro-HCC effects are heterogeneous or
multipronged. The likely complex links between ferroptosis and
oncogenesis<sup>41,42</sup> or between ferroptosis and immunomodula-
tion<sup>43</sup> have not been disentangled in this study. At this point,
we cannot establish a clear hierarchical relationship among the
autophagy-inducing, cell cycle-inhibitory, ferroptosis-sensi-
tizing, and immunostimulatory effects of ACBP/DBI neutraliza-
tion, although the width of these effects suggests pleiotropic ac-
tions of ACBP/DBI. We found that ACBP/DBI inhibition could be
favorably combined with ferroptosis-inducing agents of PD-1
blockade. However, we did not investigate the triple combination
of (1) ACBP/DBI inhibition, (2) ferroptosis induction, and (3) PD-1
blockade. These questions must be addressed in future studies.

RESOURCE AVAILABILITY

Lead contact

Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Guido Kroemer (kroemer@orange.fr).

Materials availability

This study did not generate new unique reagents.

Data and code availability

* The sequencing data reported in this paper have been deposited in NCBI GEO database with accession number GEO: GSE255799 and GSE296545 (URL: https://www.ncbi.nlm.nih.gov/geo/) and are publicly available as of the date of publication. The metabolomics data reported in this paper have been deposited in Metabolomics Workbench with accession number ST003957 (URL: https://www.metabolomicsworkbench.org/). The accession numbers are listed in the key resources table as well.

* This study did not report new original code.

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

ACKNOWLEDGMENTS

The authors thank the CRC core facilities. We thank the help by Olivia Bawa from the PETRA platform of Gustave Roussy Institute for technical support. G.K. is supported by the Ligue Contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR-22-CE14-0066 VIVORUSH, ANR-23-CE44-0030 COPPERMAC, ANR-23-R4HC-0006 Ener-LIGHT); Association pour la recherche sur le cancer (ARC); Cancéropôle Île-de-France; Fondation pour la Recherche Médicale (FRM); European Research Council Advanced Investigator Award (ERC-2021-ADG, grant no. 101052444; project acronym: ICD-Cancer, project title: Immunogenic cell death [ICD] in the cancer-immune dialogue); the ERA4 Health Cardinoff Grant Ener-LIGHT; European Union Horizon 2020 research and innovation programs Oncobiome (grant agreement no. 825410, project acronym: ONCOBIOME, project title: Gut OncoMicrobiome Signatures [GOMS] associated with cancer incidence, prognosis, and prediction of treatment response), Prevalung (grant agreement no. 101095604, project acronym: PREVALUNG EU, project title: Biomarkers affecting the transition from cardiovascular disease to lung cancer: toward stratified interception), and Neutrocure (grant agreement no. 861878, project acronym: Neutrocure; project title: Development of "smart" amplifiers of reactive oxygen species specific to aberrant polymorphonuclear neutrophils for treatment of inflammatory and autoimmune diseases, cancer, and myeloablation); national support managed by the Agence Nationale de la Recherche under the France 2030 programme (reference number 21-ESRE-0028, ESR/Equipex+ Onco-Pheno-Screen); Hevolution Network on Senescence in Aging (reference HF-E Einstein Network); Institut National Du Cancer (INCa); Institut Universitaire de France; PAIR-Obésité INCa_18713, the RHUs Immunolife and LUCA-pi (ANR-21-RHUS-0017 and ANR-23-RHUS-0010, both dedicated to France Relance 2030); Seerave Foundation; and SIRIC Cancer Research and Personalized Medicine (CARPEM, SIRIC CARPEM INCa-DGOS-Inserm-ITMO Cancer_18006 supported by Institut National du Cancer, Ministère des Solidarités et de la Santé, and INSERM). This study contributes to the IdEx Université de Paris Cité ANR-18-IDEX-0001. G.K., J.Z.-R., and C.D. are supported by INCa PAIR Obesity & Cancer. J.Z.-R. is supported by the Ligue Contre

<caption>**Figure 6.** ACBP/DBI inhibition enhances ferroptosis sensitivity at the transcriptional level</caption>

(A) RT-qPCR analysis showed upregulation of ferroptosis-promoting genes and downregulation of ferroptosis-inhibiting genes by ACBP/DBI inhibition. Results were displayed in heatmaps (n = 5–10/group).

(B) Quantification of western blots shown in Figure S12A. Ferroptosis and autophagy-related proteins were detected by immunoblotting. Results were quantified and displayed in heatmaps (n = 5–7/group).

(C) Spatial transcriptomic landscape of individual liver sections from WD/CCl₄-induced HCC in Dbi⁺/+/Dbi⁻/⁻ mice or KLH/KLH-ACBP-immunized mice. Black rectangles (6.5 mm × 6.5 mm) indicate regions of interest for each sample. Scale bars in spot images indicate 2 mm. Error bars represent means ± SEM. *p* values were calculated by t test, Welch's t test, or Mann-Whitney U test (A, B).

(D) Quantification of genes classified as ferroptosis drivers or suppressors in non-tumor (NT) and tumor (T) areas defined by H&E staining in (C).

(E) Immunosuppression scores and immunostimulation scores of non-tumor (NT) and tumor (T) areas.

*p* values were calculated by means of the Wilcoxon signed-rank test (D and E).
<figure><img src="image_81.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_83.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_84.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_85.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_86.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_87.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_88.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_89.png" /><figcaption>J</figcaption></figure>

<figure><img src="image_90.png" /><figcaption>K</figcaption></figure>

<figure><img src="image_91.png" /><figcaption>L</figcaption></figure>

<figure><img src="image_92.png" /><figcaption>M</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>N</figcaption></figure>

<figure><img src="image_94.png" /><figcaption>O</figcaption></figure>

<figure><img src="image_95.png" /><figcaption>P</figcaption></figure>

<figure><img src="image_96.png" /></figure>

(legend on next page)
le Cancer, SIRIC Cancer Research and Personalized Medicine (CARPEM), and THRIVE EU Horizon funding Mission Cancer (EU-HORIZON-MISS-2023-CANCER-01). This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001. O.K. is supported by INCa and ARC. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union, the European Research Council, or any other granting authority. Neither the European Union nor any other granting authority can be held responsible for them. S. Li, H.C., L. Pan, and H.P. are supported by the China Scholarship Council (CSC, file no. 201907060011, file no. 201908070134, file no. 202006320074, and file no. 201908500100, respectively). U.N.R. was supported by Axudas de apoio á etapa de formación posdoutoral da Xunta de Galicia-GAIN. M.P. and F.R. were supported by European Union NextGenerationEU through the Italian Ministry of University and Research under PNRR-MAC2-II.3 project PE6 "Heal Italia." F.R. was supported by Associazione Italiana Ricerca sul Cancro (AIRC 27116). N°Expediente: IN606B-2021/015. E.G.S.D. was supported by the University of Las Palmas de Gran Canaria (ULPGC), financed by the Ministry of Universities, granted by Order UNI/501/2021 of May 26, and by the European Union-Next Generation EU Funds. J.-C.N. was supported by Association Française pour l'Étude du Foie (AFEF) 2022 projet radio-moléculaire, Agence Nationale De La Recherche (ANR) 2022 SYSTHEC, Agence nationale de recherches sur le sida et les hépatites virales (ANRS) 2023 CSS13 HBV-LIRAGE ECTZ232901, and SIRIC CAncer Research in multiple dimensions to accelerate PrEcision Medicine (CARPEM) INCa-DGOS-Inserm-12561. Figures 1M, 2A, 2F, 2L, 3A, 3E, 3I, 4A, 4G, 4M, 4S, 5C, 6F, 7E, 7K, S6D, S8A, S9A, and S12F and the graphical abstract were created with "BioRender.com." The funders had no role in the design of the study, in the writing of the manuscript, or in the decision to publish the results.

## AUTHOR CONTRIBUTIONS

S. Li, O.M., F.L., I.M., M.C.M., and G.K. designed research. G.K. and M.C.M. supervised the study. S. Li performed most of the *in vitro* and *in vivo* experiments. O.M., F.L., J.P., H.C., F.R., C.C., G.A., U.N.R., E.G.S.D., O.K., P.L., L. Zhao, H.P., L.M., E.X., M.L.G., and S. Lachkar helped with the research. S.D., A.C., and F.A. performed the metabolomic experiments. S. Li, O.M., and F.L. contributed to setting up novel *in vivo* models. S. Li, S.D., C.C., L. Pan, L. Poupel, and V.C. analyzed the data. L. Poupel and C.K. performed the spatial transcriptomic experiments. S. Li prepared the figures. G.K. and S. Li wrote the paper. G.K., M.C.M., J.Z.-R., J.-C.N., M.P., L. Zitvogel, I.M., S.C., C.D., and L.S. provided intellectual input and edited the paper.

## DECLARATION OF INTERESTS

G.K. and O.K. have been holding research contracts with Daiichi Sankyo, Kaleido, Lytix Pharma, PharmaMar, Osasuna Therapeutics, Samsara Therapeutics, Sanofi, Sutro, Tollys, and Vascage. G.K. is on the Board of Directors of the Bristol Myers Squibb Foundation France. O.K. is a scientific co-founder of Samsara Therapeutics and holds patents covering therapeutic targeting of neurodegenerative diseases and cancer. G.K. is a scientific co-founder of EverImmune, Osasuna Therapeutics, Samsara Therapeutics, and Therafast Bio. G.K. is on the scientific advisory boards of Centenara Labs (formerly Rejuveron Life Sciences), Revolution, and Institut Servier. G.K. is the inventor of patents covering therapeutic targeting of ACBP/DBI, aging, cancer, cystic fibrosis, and metabolic disorders. G.K.'s wife, L. Zitvogel, has held research

contracts with Glaxo Smyth Kline, Incyte, Lytix, Kaleido, Innovate Pharma, Daiichi Sankyo, Pilege, Merus, Transgene, 9M, Tusk, and Roche, was on the Board of Directors of Transgene, a cofounder of EverImmune, and holds patents covering the treatment of cancer and the therapeutic manipulation of the microbiota. G.K.'s brother, Romano Kroemer, was an employee of Sanofi and now consults for Boehringer Ingelheim. I.M. is a consultant for Osasuna Therapeutics.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

* HCC patient cohort

* Animals

* METHOD DETAILS

* Generation of stable cell lines, cell culture, and cell assays

* Inhibition of ACBP/DBI *in vivo*

* MASH-driven HCC model

* Oncogene-induced HCC

* Orthotopic transplantation of HCC cells

* Partial hepatectomy

* Combination therapies

* Isolation of primary HCC cells

* Isolation and purification of primary mouse hepatocytes and immune cells

* Multi-omics analysis

* Flow cytometry analyses of the immune infiltrate

* Liver histology and Immunohistochemistry

* Immunofluorescence

* Enzyme-linked immunosorbent assay

* Quantitative real-time PCR

* Immunoblotting

* QUANTIFICATION AND STATISTICAL ANALYSIS

* Bioinformatics analyses

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102232.

Received: March 13, 2024

Revised: September 20, 2024

Accepted: June 12, 2025

Published: July 7, 2025

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### Figure 7. ACBP/DBI neutralization increased sensitivity to ferroptosis induction *in vitro* and *in vivo*

(A–D) CCK-8 viability assays of Dbi<sup>+/+</sup> and Dbi<sup>-/-</sup> HCC cells treated with ferroptosis inducers, including RSL3, IKE (imidazole ketone erastin), LA (linoleic acid), and LNA (linolenic acid) (n = 6/group). Groups receiving ferroptosis inducers were normalized to their respective Dbi<sup>+/+</sup> and Dbi<sup>-/-</sup> vehicle (Veh)-treated control groups.

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## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Antibodies</strong></td></tr><tr><td>Anti-ACBP/DBI antibody</td><td>Abcam</td><td>RRID: AB_231910</td></tr><tr><td>Anti-POR antibody</td><td>Abcam</td><td>RRID: AB_180597</td></tr><tr><td>Anti-KEAP1 antibody</td><td>Abcam</td><td>RRID: AB_119403</td></tr><tr><td>Anti-CHAC1 antibody</td><td>Abcam</td><td>RRID: AB_217808</td></tr><tr><td>Anti-NCOA4 antibody</td><td>Abcam</td><td>RRID: AB_86707</td></tr><tr><td>Anti-ACSL4 antibody</td><td>Abcam</td><td>RRID: AB_234167</td></tr><tr><td>Anti-ACSL3 antibody</td><td>Abcam</td><td>RRID: AB_151959</td></tr><tr><td>Anti-GPC3 antibody</td><td>Thermo Fisher Scientific</td><td>Cat# MA5-17083</td></tr><tr><td>Anti-BMAL1 antibody</td><td>Abcam</td><td>RRID: AB_93806</td></tr><tr><td>Anti-Ki67 antibody</td><td>Abcam</td><td>RRID: AB_119403</td></tr><tr><td>Anti-PCNA antibody</td><td>Abcam</td><td>RRID: AB_217808</td></tr><tr><td>Anti-alpha 1 Fetoprotein antibody</td><td>Abcam</td><td>RRID: AB_86707</td></tr><tr><td>Anti-Cytokeratin 19 antibody</td><td>Abcam</td><td>RRID: AB_52625</td></tr><tr><td>Anti-c-Myc antibody</td><td>Abcam</td><td>RRID: AB_32072</td></tr><tr><td>Anti-β-Catenin antibody</td><td>Abcam</td><td>RRID: AB_32572</td></tr><tr><td>Anti-GPX4 antibody</td><td>Cell Signaling Technology</td><td>Cat# 52455</td></tr><tr><td>Anti-GAPDH antibody</td><td>Cell Signaling Technology</td><td>Cat# 2118</td></tr><tr><td>Anti-MAP1LC3B antibody</td><td>Cell Signaling Technology</td><td>Cat# 2775</td></tr><tr><td>Anti-p62/SQSTM1 antibody</td><td>Abnova</td><td>Cat# H00008878-M01</td></tr><tr><td>Goat Anti-Rat IgG (H + L) secondary antibody</td><td>Southern Biotech</td><td>Cat# 3050-05</td></tr><tr><td>Goat Anti-Rabbit IgG (H + L) secondary antibody</td><td>Southern Biotech</td><td>Cat# 4050-05</td></tr><tr><td>IgG2a antibody (in vivo isotype control)</td><td>Bioxcell</td><td>Cat# BE0085</td></tr><tr><td>Monoclonal anti-ACBP/DBI (in vivo neutralization)</td><td>Fred Hutch Antibody Technology</td><td>N/A</td></tr><tr><td colspan="3"><strong>Biological samples</strong></td></tr><tr><td>Human plasma from HCC patients</td><td>Samples provided by Prof. Jessica Zucman-Rossi</td><td>Centre de Recherche des Cordeliers, Inserm U1138, Team "Functional Genomics of solid Tumors"</td></tr><tr><td colspan="3"><strong>Chemicals, peptides, and recombinant proteins</strong></td></tr><tr><td>Imidazole ketone erastin(IKE)</td><td>SelleckChem</td><td>100mg</td></tr><tr><td>Erastin</td><td>Sigma Aldrich</td><td>E7781-1M</td></tr><tr><td>1S,3R-RSL 3</td><td>Sigma Aldrich</td><td>SML2234-25MG</td></tr><tr><td>Blasticidin (solution)</td><td>Invivogen</td><td>ant-bl-05</td></tr><tr><td>Linoleic acid</td><td>Sigma Aldrich</td><td>L1012-1G</td></tr><tr><td>Linolenic acid</td><td>Sigma Aldrich</td><td>L2376-500MG</td></tr><tr><td>Arachidonic Acid</td><td>Sigma Aldrich</td><td>181198-100MG</td></tr><tr><td>Albumin Bovine FrV BSA</td><td>Euromedex</td><td>04-100-812-E</td></tr><tr><td>Poly(ethylene glycol)</td><td>Sigma Aldrich</td><td>202371-250G</td></tr><tr><td>TWEEN® 80</td><td>Sigma Aldrich</td><td>P1754-500ML</td></tr><tr><td>Imject mCKLH Subunits</td><td>Thermo Fisher Scientific</td><td>77649</td></tr><tr><td>Montanide ISA 51 VG</td><td>SEPPIC</td><td>36362/FL2R3</td></tr><tr><td>Recombinant mouse ACBP/DBI</td><td>Custom-made</td><td>N/A</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>QIAzol Lysis Reagent (200 mL)</td><td>QIAGENE</td><td>79306</td></tr><tr><td>SuperSignal West Pico chemiluminescent substrate</td><td>Thermo Fisher Scientific</td><td>34579</td></tr><tr><td>SYBR Green Master Mix</td><td>Applied Biosystems</td><td>4367659</td></tr><tr><td>Tamoxifen Free Base</td><td>Sigma Aldrich</td><td>T5648</td></tr><tr><td>Firefly luciferase lentivirus</td><td>GenTarget Inc</td><td>LVP568-PBS</td></tr><tr><td>Polybrene</td><td>Sigma Aldrich</td><td>TR-1003-G</td></tr><tr><td>Beetle Luciferin, Potassium Salt, 1g</td><td>Promega</td><td>E1605</td></tr><tr><td>PercollTM</td><td>Cytiva</td><td>17-0891-01</td></tr><tr><td>Laminine</td><td>Sigma Aldrich</td><td>11243217001</td></tr><tr><td>Collagénase/dispase®</td><td>Sigma Aldrich</td><td>10269638001</td></tr><tr><td>Red Blood Cell Lysis Buffer</td><td>Sigma Aldrich</td><td>11814389001</td></tr><tr><td>ITS+1 Liquid Media Supplement (100×)</td><td>Sigma Aldrich</td><td>I2521-5ML</td></tr><tr><td>EGFh</td><td>Sigma Aldrich</td><td>E9644-.2MG</td></tr><tr><td>IGF-II human</td><td>Sigma Aldrich</td><td>SRP3070-50UG</td></tr><tr><td>Entellan rapid mouting medium, with Xylene</td><td>Sigma Aldrich</td><td>PN 1.07961.0100</td></tr><tr><td>Mayer's hemalum solution</td><td>Sigma Aldrich</td><td>PN 1.09249.0500</td></tr><tr><td>Eosine - Y in aqueous solution 0.5%</td><td>VWR</td><td>PN 10047001</td></tr><tr><td>D(-)-Fructose</td><td>Sigma Aldrich</td><td>F0127-1KG</td></tr><tr><td>D(+)-Glucose</td><td>Sigma Aldrich</td><td>G8270-5KG</td></tr><tr><td>Carbon tetrachloride (CCl<sub>4</sub>)</td><td>Sigma Aldrich</td><td>289116-100ML</td></tr><tr><td>Corn oil</td><td>Sigma Aldrich</td><td>C8267-500ML</td></tr></tbody></table>

Critical commercial assays

<table><tbody><tr><td>Cell counting kit-8</td><td>Sigma Aldrich</td><td>96992-500TESTS-F</td></tr><tr><td>miRNeasy Mini Kit (50)</td><td>Qiagen</td><td>217004</td></tr><tr><td>SuperScript™ IV VILO™ Master Mix with ezDNase™ Enzyme</td><td>Invitrogen</td><td>11766500</td></tr><tr><td>Visium CytAssist for FFPE Spatial Gene Expression 6.5mm, Mouse, 4 rxns</td><td>10x Genomix</td><td>PN-1000251</td></tr><tr><td>Dual Index Kit TS Set A, 96 rxn</td><td>10x Genomix</td><td>PN-1000251</td></tr><tr><td>MojoSort™ Mouse CD3 T cell Isolation Kit</td><td>BioLegend</td><td>Cat# 480024</td></tr><tr><td>MojoSort™ Biotin anti-mouse CD19 Antibody</td><td>BioLegend</td><td>Cat# 152420</td></tr><tr><td>MojoSort™ Mouse Pan Dendritic Cell Isolation Kit</td><td>BioLegend</td><td>Cat# 480097</td></tr></tbody></table>

Deposited data

<table><tbody><tr><td>Mouse (MASH-driven HCC) bulk RNA sequencing</td><td>NCBI Gene expression omnibus (GEO)</td><td>GEO: GSE255799</td></tr><tr><td>Mouse (MASH-driven HCC) spatial transcriptomics</td><td>NCBI Gene expression omnibus (GEO)</td><td>GEO: GSE296545</td></tr><tr><td>Mouse (MASH-driven HCC) spatially resolved mass spectrometric imaging (MSI)-based metabolomics</td><td>Metabolomics Workbench</td><td>ST003957</td></tr></tbody></table>

Experimental models: Cell lines

<table><tbody><tr><td>HUH-7</td><td>Cells provided by Dr. Patrick Soussan</td><td>N/A</td></tr><tr><td>HEP-G2</td><td>ATCC</td><td>HB-8065</td></tr><tr><td>Hep55.1C</td><td>Cytion</td><td>400201</td></tr><tr><td>Mouse primary MASH-induced HCC cell lines</td><td>This paper</td><td>N/A</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Experimental models: Organisms/strains</strong></td></tr><tr><td>ACBP/DBI III/III mice in which loxP sites flank ACBP exon 2</td><td>Ozgene</td><td>N/A</td></tr><tr><td>Gabrg2tm1Wul/J GABAA g2<sup>+/+</sup> mice</td><td>Charles River Laboratory, Lentilly, France</td><td>N/A</td></tr><tr><td>Gabrg2tm1Wul/J GABAA g2<sup>F77I/F77I</sup> mice</td><td>Charles River Laboratory, Lentilly, France</td><td>N/A</td></tr><tr><td>Liver-specific ACBP/DBI III/III mice in which loxP sites flank ACBP exon 2</td><td>Ozgene</td><td>N/A</td></tr><tr><td colspan="3"><strong>Oligonucleotides</strong></td></tr><tr><td>Mouse qRTPCR</td><td></td><td>N/A</td></tr><tr><td>Primer: 36b4<br>F: 5'-ACTGGTCTAGGACCCGAGAAG-3'<br>R: 5'-TCCCACCTTGTCTCCAGTCT-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Abcc5<br>F: 5'-GCAAACTGGTTGGAATCTGCGG-3'<br>R: 5'-CAAAGGTCCCACTGACGGCAAT-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Acs13<br>F: 5'-GCGAGAAGGATTCCAAGACTGG-3'<br>R: 5'-GAAGAGTAGCCGATTCGGCATC-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Acs14<br>F: 5'-CCTTTGGCTCATGTGCTGGAAC-3'<br>R: 5'-GCCATAAGTGTGGGTTTCAGTAC-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Aldh3a2<br>F: 5'-GAGACCGGCTAAGAAGAACCT-3'<br>R: 5'-CGAAAGGGTAATTCCAAGCTCC-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Arntl<br>F: 5'-ACCTCGCAGAATGTCACAGGCA-3'<br>R: 5'-CTGAACCATCGACTTCGTAGCG-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Atr<br>F: 5'-GAATGGGTGAACAATACTGCTGG-3'<br>R: 5'TTTGGTAGCATACACTGGCGA-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr><tr><td>Primer: Ccnb1<br>F: 5'-CTTGCAGTGAGTGACGTAGAC-3'<br>R: 5'-CCAGTTGTCGGAGATAAGCATAG-3'</td><td>Sigma-Aldrich</td><td>N/A</td></tr></tbody></table>

The list of additional mouse and human oligonucleotides used in this study is provided in Table S2.

Software and algorithms

<table><tr><td>ImageJ</td><td>National Institutes of Health</td><td>https://imagej.nih.gov/ij/notes.html</td></tr><tr><td>GraphPad Prism 8 software</td><td>Graph Pad Software Inc</td><td>https://www.graphpad.com/features</td></tr><tr><td>QuPath 0.2.3 software</td><td>Center for Cancer Research & Cell Biology at Queen's University Belfast</td><td>https://qupath.github.io/</td></tr><tr><td>StepOne Software v2.3</td><td>Applied Biosystems Inc</td><td>https://stepone-software.software.informer.com/2.3/</td></tr><tr><td>Zen 3.2 software</td><td>ZEISS</td><td>https://www.zeiss.com/microscopy/en/home.html</td></tr><tr><td>BD FACSDiva v8.0.2</td><td>BD Biosciences</td><td>https://www.bdbiosciences.com/en-fr/products/software/instrument-software/bd-facsdiva-software</td></tr><tr><td>OMIQ</td><td>Dotmatics</td><td>https://www.omiq.ai/</td></tr><tr><td>R software (4.2.1; 4.3.3)</td><td>Posit, PBC</td><td>https://www.R-project.org/</td></tr><tr><td>MetaXpress® Image software v6.6.3.55</td><td>Molecular Devices</td><td>N/A</td></tr></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td>Other</td><td></td><td></td></tr><tr><td>42%Kcal/Fat Diet (incr.Sucrose 12,5% Chol) Teklad</td><td>Envigo</td><td>MD.120528</td></tr><tr><td>Precellys tissue homogenizing ceramic beads</td><td>Bertin Technologies</td><td>CKMix</td></tr><tr><td>Lithium heparin blood collection tubes</td><td>Sarstedt</td><td>6443</td></tr><tr><td>Poches Transparentes Sipper Sack</td><td>Avidity Science</td><td>2204-002117</td></tr><tr><td>Corning® cell strainer 100uM</td><td>Corning</td><td>CLS431752-50EA</td></tr><tr><td>Corning® cell strainer 70uM</td><td>Corning</td><td>CLS431751-50EA</td></tr><tr><td>Corning® cell strainer 40uM</td><td>Corning</td><td>CLS431750-50EA</td></tr><tr><td>Polysine adhesion microscope slides with white tab</td><td>Epredia</td><td>PN J2800AMNZ</td></tr><tr><td>Cover splits 22x50mm</td><td>Epredia</td><td>PN BB02200500A113MNZ0</td></tr><tr><td>Slide mailer 5 places with side open</td><td>VWR</td><td>PN 631-1515</td></tr><tr><td>Histosette I</td><td>Simport</td><td>PN M490-3</td></tr><tr><td>Biopsy foam pads square</td><td>Simport</td><td>PN M476-1</td></tr></tbody></table>

## EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

### HCC patient cohort

A total of 260 plasma samples were collected from 146 patients with HCC and 58 patients with chronic liver diseases without HCC at the Avicenne tertiary University Hospital (Bobigny, France) between March 2013 and May 2021. Two or more plasma samples were collected from 46 patients. Plasma samples were divided into two groups: (i) plasma collected at the time of diagnosis of HCC, the day of treatment or at the time of radiological evaluation showing active HCC (n = 195), (ii) plasma collected in patients with chronic liver disease without HCC or plasma collected after HCC treatment without any active tumor at imaging (n = 65). Whole blood (5 mL) was collected using EDTA tubes. Blood samples were centrifuged at 2000 × g for 10 min at room temperature and were immediately stored at -80°C. All patients signed an informed consent for sample collection and the ethics committee approved the study (CCPPRB Paris Saint-Louis IRB00003835).

Patient, tumor characteristics, and treatment types were obtained from medical records. Patient baseline characteristics were collected before treatment: including age, gender, etiology of liver disease, and presence of cirrhosis. Cirrhosis was defined either by histology or by the combination of clinical-biological data, ultrasonography, and liver stiffness measurement (Fibroscan). HCC was diagnosed at histology or using non-invasive criteria through imaging techniques (magnetic resonance imaging [MRI] and/or triphasic computed tomography [CT]) according to the European Association for the Study of the Liver (EASL) guidelines.⁴⁴ Tumor characteristics at imaging (tumor size and number, macrovascular invasion, and metastasis), Barcelona Clinic liver cancer staging system (BCLC), alpha-fetoprotein (AFP), and Child-Pugh (CP) score were also collected. Data about the type of treatment (ablation, embolization, and/or systemic treatment), and radiological response assessed by mRECIST criteria at 4 and 12 weeks after locoregional and systemic treatment respectively, were also recorded.

An independent HCC series from Functional Genomics of Solid Tumors, Center de recherche des Cordeliers (Paris, France) was used to evaluate the prognostic value of DBI in HCC.⁴⁵

### Animals

All mice (males and females, aged 2-7 weeks) used in this study had a C57BL/6 background. Five mice/cage were housed in a 12 h light/dark cycle under specific pathogen-free conditions. Mice were allowed at least 1 week of acclimation to the new housing facilities prior to use. All mouse experiments were performed according to protocols approved by the local Animal Experimental Ethics Committee (protocols #25000, #25355, #10862, #8530, #25010, #0447.02, #31411, #34537, #34538, and #34539; 490/2019-PR).

## METHOD DETAILS

### Generation of stable cell lines, cell culture, and cell assays

#### Generation of stable luciferase-expressing Hep55.1C cells

Hep55.1C cells (2×10⁵ per well in 6-well plates) were cultured overnight in 2 mL full medium (DMEM, 10% FBS). Then 10 µL firefly luciferase lentivirus (GenTarget Inc, LVP568-PBS) was added at an MOI of 10 and 8 µg/mL Polybrene (Sigma, TR-1003-G). After 72h of culture, the virus-containing media were replaced by fresh full medium containing 10 µg/mL blasticidin, and media were replaced every 2-3 days until cells cultures reached ~70% confluency before expansion in T25 flasks. Single cell sorting was performed in
96-well plates. Around 30 days clones were expanded, and the expression of luciferase was validated. For this, ~5 × 10³cells/well were seed in 96-well plate, 150 µg/mL D-Luciferin (Promega, E1605) was added for 5min, and bioluminescence was measured.

### Generation of stable DBI knockdown liver cancer cell lines

Liver cancer cells were grown under the following conditions: HEP-G2 (EMEM +10% FBS +1% sodium pyruvate +1% HEPES), HUH-7 (DMEM, 10% FBS), Hep55.1C (DMEM, 10% FBS) and Hep55.1C cells expressing firefly luciferase (Hep55.1C-luc, DMEM, 10% FBS, 0.1% blasticidin). These cancer cells were grown in 6-well dishes to 60-70% confluency then transfected using 25-35 µL lentiviral shRNAs targeting DBI (SH1, SH2, SH3) or negative control (NC) with 5 µg/mL polybrene (5-8 µL) mixed thoroughly in 1mL medium. The medium was replaced with fresh medium after 24-48 h, and the cells were maintained for another 24 h. Puro-mycin (10 µg/mL) was used to select the transduced cells. Single cell clones were isolated by single-cell FACS sorting in 96-well plates and gradually expanded in 24-well plates, 12-well plates and 6-well plates. All the cell clones from 6-well plates were duplicated, one stocked at -80°C for stock and one collected for qRT-PCR to detect the knockdown efficiency of DBI. Clones with the highest knockdown efficiency were chosen to perform further assays.

### Proliferation assays

CCK-8 assays were performed with a commercial kit (Cell Counting Kit-8, Sigma-Aldrich) according to the manufacturer's instructions. For colony formation assays, HEPG-2 (NC, SH1, SH2, and SH3, 1000 cells/well), HUH-7 (NC, SH1, SH2, and SH3, 1000 cells/well) and Hep55.1C (NC, SH1, SH2, and SH3, 500 cells/well) cancer cells were seeded into 6-well plates in 2 mL medium with 10% FBS. The medium was changed every 3 days until control (NC) wells approached confluency, within 7-14 days. After gently washed twice with PBS, colonies were fixed with 4% paraformaldehyde for 30 min and stained with 0.2% crystal violet for 30min at room temperature. The colony number (colonies >0.3 mm) was counted with ImageJ software. All experiments were performed in triplicates.

### Cell cycle analysis

Following the instructions of a commercial kit (Cell Cycle Analysis Kit, Sigma-Aldrich), the aforementioned transfected cells (25x10⁴ cells/well) were plated in 6-well plates for 24 h. Cell cycle was then synchronized with medium containing 0.1% FBS for 24h, followed by culture in medium with 10% FBS for 24 h. The cells were harvested and fixed using ice-cold 70% ethanol. The cell cycle distribution was then determined by flow cytometry.

### Reverse siRNA transfection

Hep55.1C cells were transfected with Gabrg2-targeting siRNA (SMARTpool, Dharmacon, Horizon Discovery, Cat# M-045579-01-0010) using lipofectamine (RNAiMAX, Invitrogen, Cat# 13778150) following the manufacturers's instructions. The culture medium was replaced 24 h post-transfection, and cells were harvested 48 h later for gene knockdown validation by qRT-PCR.

### Inhibition of ACBP/DBI in vivo

Four strategies were developed to inhibit the expression or function of ACBP/DBI. (i) Tamoxifen-inducible ACBP/DBI knockout. Mice with tamoxifen-inducible ACBP/DBI knockout were generated by crossing ACBP/DBI<sup>fl/fl</sup> mice (obtained from OZgene) with B6.Cg-Tg(UBC-Cre/ERT2)1Ejb/1J mice (obtained from Jackson laboratory, Bar Harbor, ME, USA), followed by tamoxifen injection (i.p. 75 mg/kg/day, for 5 consecutive days). Control mice were ACBP/DBI<sup>fl/fl</sup> mice without Cre. Tamoxifen (Sigma-Aldrich) was dissolved in corn oil (90%) + ethanol (10%) at a concentration of 20 mg/mL, aliquoted and stored at -20°C. (ii) Constitutive Gabrg2<sup>F77I/F77I</sup> point mutation. Gabrg2<sup>tm1Wul/J</sup> mice (obtained from Charles River Laboratory, Lentilly, France) containing the point mutation F77I were compared to control mice with wild type mice without the mutation. (iii) Induction of ACBP/DBI-specific autoantibodies. As previously described,<sup>8,24,30,46</sup> KLH was conjugated with recombinant ACBP at 1:30 molar ratio to produce KLH-ACBP. KLH alone was used as control. Montanide (Seppic, Paris, France) and KLH/KLH-ACBP were then mixed a 1:1 volume ratio. The mice were i.p. vaccinated with the aforementioned mixture on days 0 (30 µg), 7 (30 µg), 14 (30 µg), and 21 (10 µg of KLH-ACBP) to induce anti-ACBP autoantibodies. Immunoblots of recombinant ACBP were incubated with plasma from KLH/KLH-ACBP-immunized mice to detect anti-ACBP antibody. (iv) Monoclonal anti-ACBP antibody. Mice were i.p. injected with anti-ACBP mAb (a-ACBP, 5mg/kg) or its control isotype (5mg/kg, Bioxcell, NH, USA) 3-4 times/week to neutralize ACBP.

Liver tissues and plasma samples were collected for further analysis. Liver tissues were freshly frozen in liquid nitrogen and stored at -80°C, fixed in 4% paraformaldehyde, or embedded in optimum cutting temperature compound (OCT).

### MASH-driven HCC model

Three major types of HCC mouse models were tested in this study. Diet plus toxin-induced mouse models were based on a western diet (WD) or high-fat diet (HFD) plus carbon tetrachloride (CCl₄) or diethylnitrosamine (DEN), respectively. In most experiments, mice received a western diet (WD, i.e., a high-fat, high-fructose and high-cholesterol diet, MD.120528, Envigo, Paris, France), high-sugar water (23.1 g/L D-fructose plus 18.9 g/L D-glucose), and weekly i.p. injections of carbon tetrachloride (CCl₄, 1:10 diluted in corn oil, Sigma-Aldrich, Darmstadt, Germany) at a final dose of 2 µL/g of body weight.<sup>25</sup> High-sugar water was added in the sterile 450mL sipper sack bags and replaced weekly. Also, food was added weekly. Male mice were used in this HCC model. Four ACBP/DBI inhibition strategies (conditional whole body DBI knockout, Gabrg2<sup>F77I/F77I</sup> mutation, KLH-ACBP vaccination, and liver-specific DBI knockout) were used. The detailed experimental designs are depicted in Figures 4A, 4G, 4M, 4S, and S8A. Mice were sacrificed after 27-49 weeks, based on hepatic tumor detection by means of medical ultrasound (also called sonography). At necropsy, tumor number and size were determined by counting the number of visible tumors and measuring their size with a caliper. Body weights of mice
were monitored weekly. HFD-DEN induced HCC mouse model (combined with KLH-ACBP) was used to confirm data from WD plus CCl₄ mouse model. For this, male C57BL/6 mice (15 dpp) were treated with a single dose of diethylnitrosamine (DEN, N0258, Merck, Darmstadt, Germany) dissolved in saline at a dose of 25 mg/kg body weight by intraperitoneal injection.⁴⁷,⁴⁸ After 2 weeks, concomitant with the weaning, mice started the vaccination protocol and HFD composed by 60% fat calories (Bioserv F3282, Flemington, NJ, US). Fresh diet was provided every 2 to 3 days and body weights were recorded monthly. Mice were sacrificed after 36 weeks and livers explanted to measure tumor number and size. Vaccination was performed with KLH/KLH-ACBP. Specifically, for the vaccination protocol, DEN injected mice (28 dpp) were randomized in two groups.

## Oncogene-induced HCC

Hydrodynamic transfection of oncogenes (Myc+Ctnnb1). A mixture of transposase-encoding vector (SB100, 1.5 µg), pT3-EF1a-Myc plasmid (7.5 µg), and pT3-N90-Ctnnb1 plasmid (7.5 µg) were prepared fresh in 1mL 1xPBS at a ratio 1:5:5. The final solutions was sterile-filtrated through a 0.22 µm filter and placed in a 37°C water bath prior to use. A volume equivalent to 10% of mouse body weight (1mL/10g) was injected via tail veins with high pressure within 8-10s, using a 3 mL syringe with 30G x 1/2 needles in female mice (7 weeks-old). Combinations of the aforementioned four ACBP/DBI inhibition strategies and this HCC mouse models were tested to and tumor formation were monitored with ultrasound. The survival was monitored and mice were sacrificed at the humane endpoint, defined by mouse grimace scale, notable abdominal distension caused by tumor burden, >20% initial body weight loss, or largest tumor size reaching 20 mm based on ultrasound. The tumor number and size were measured and recorded at necropsy. To determine the effect of ACBP/DBI blockade on Myc and β-catenin, anti-ACBP was administered intraperitoneally at 5 mg/kg once per day for one week. At day 8, the livers were collected and protein was extracted from the whole liver to detect the expression of Myc and β-catenin by immunoblotting.

## Orthotopic transplantation of HCC cells

Murine HCC cell lines (Hep55.1C and Hep55.1C-derived cell lines such as Hep55.1C-Luc, or Hep55.1C/Hep55.1C-Luc-NC, -SH1, -SH2, and -SH3, 30x10⁴/50µL/mouse in DMEM without FBS) and human HCC cells (HUH-7, 200x10⁴/50µL/mouse in DMEM without FBS) were transplanted into mouse liver to generate orthotopic liver tumors in female C57BL/6J mice or male nude mice, respectively. Tumor growth of mouse models implanted with luciferase-labeled Hep55.1C-Luc was monitored by IVIS bioluminescence in vivo imaging system, otherwise via ultrasound unless noted. Mice were monitored for survival and symptoms (such as skin jaundice). Mice were sacrificed at the humane endpoints mentioned above.

## Partial hepatectomy

9 weeks-old male mice were submitted to a 70% partial hepatectomy (Ph). Briefly, mice were administered with buprenorphine for analgesia and anesthetized by inhalation of isoflurane (2%). Then, a midline abdominal skin and muscle incision was made to expose the peritoneal cavity, followed by the removal of the left and median hepatic lobes. First, a 4-0 silk thread was placed on the base of the left lateral lobe, the knot was tied and the tied lobe was cut just above the suture. The same procedure was done with the median lobe. Finally, the peritoneum was closed with a 5-0 suture and the skin with wound clips. Sham controls mice were submitted to the same surgical procedure without removing the liver lobes. Mice were randomized into four groups (Sham+Isotype, Sham+anti-ACBP, Ph+Isotype and Ph+anti-ACBP), 9-10 mice/group. Anti-Acbp mAb (i.p. 2.5mg/kg) or Isotype (i.p. 2.5mg/kg, Bioxcell, NH, USA) were administered 4h and 1h before the surgery and three more times before sacrifice. Seven days after the partial hepatectomy, animals were sacrificed, and the remaining liver was harvested and processed for further analysis.

## Combination therapies

### Anti-ACBP plus anti-PD-1

Hep55.1C cells (3x10⁵/50µL/mouse in DMEM without FBS) were inoculated into the liver of 7-week-old female C57BL/6J mice. Mice were randomized into four groups (isotype1+isotype2, isotype1+anti-PD1, anti-ACBP+isotype2, and anti-ACBP+anti-PD1), 9-10 mice/group, 10 days after tumor initiation. Treatment with anti-ACBP mAb (i.p. 5 mg/kg) or isotype1 (i.p. 5 mg/kg, Bioxcell, Lebanon, NH, USA) was then started and administered 3 times/week. Treatment of anti-PD1/isotype2 (i.p. 200µg/mice) started at the week after 6-time dosing of anti-ACBP/isotype1. Mice were sacrificed at the humane endpoint mentioned above. In Figures 5 and 7, Iso represents isotype, and αACBP/αPD1 indicate anti-ACBP/anti-PD1, respectively. Similarly, effect of anti-ACBP/anti-PD1 on immune infiltration was investigated in this model by means of Flow cytometry analysis. The exact experiment design referred to Figure S9A and the details for flow cytometry analyses of the immune infiltration shown below.

### Anti-ACBP plus ferroptosis inducers

Hep55.1C-Luc cells (3x10⁵/50µL/mouse in DMEM without FBS) were inoculated into the liver of 7-week-old female C57BL/6J mice. The tumors were assessed by in vivo bioluminescence imaging using the IVIS system (i.p. injection of 200 µL 15 mg/mL luciferin/mice 5 min before detection) at day 7 after tumor initiation. The mice were divided into 4 groups (isotype+vehicle, isotype+RSL3/IKE, anti-ACBP+vehicle, anti-ACBP+RSL3/IKE) based on the bioluminescence imaging. Treatment with anti-ACBP/isotype (i.p. 5 mg/kg) was then started with a regular dosing schedule, 2 days on/1 day off for 8 out of 10 days. RSL3 and IKE were administered at 50 mg/kg for 4 times in 2 continuous weeks, started at day 21/19 from tumor inoculation. Tumor growth was monitored for 4 weeks. Mice were sacrificed at endpoints mentioned above.
## Isolation of primary HCC cells

Six-well plates were coated with 2%FBS/PBS+0.1% laminin. HCC tumors were isolated from the MASH-driven mouse model (WD + D-fructose+D-glucose in water+CCl₄ administered to DBI⁺/⁺ or DBI⁻/⁻ mice) and washed twice with ice-cold PBS. The tumor tissues were transferred to 10 cm plates, minced completely with a razor blade and then transferred to 15 mL falcon tubes. Ten mL 2 mg/mL collagenase dispase solution was added, the cells were incubated rotating for 30 min at 37°C and were filtered through 100 µm meshes into 50 mL falcon tubes, before addition of 10 mL 2%FBS/PBS to wash the tissues. The samples were then filtered sequentially through 70 µm and 40 µm strainers, brought up to 15 mL volume and centrifuged again (room temperature (RT), 1000 rpm, 2 min). The cells were then resuspended in 5 mL of 1× red blood cell (RBC) lysis buffer, incubated on ice for 10 min before addition of 5 mL 2%FBS/PBS, followed by spinning (RT, 1000 rpm, 2 min). The washing step was repeated 2 times, and the cells were resuspended in culture medium (500 mL DMEM +10% FBS+1×ITS +20µL EGF (1 µg/µL) + 20 µL IGFII (0.2 µg/µL)), before seeding into 6-well plates.

## Isolation and purification of primary mouse hepatocytes and immune cells

### Isolation of primary hepatocytes and Kupffer cells

Hepatocytes were isolated from 11-week-old female C57BL/6 mice as described.⁴⁹ Simultaneously, Kupffer cells were isolated. For this, the digested liver cell suspension was filtered through a 100 µm strainer, and centrifuged at 50 × g for 2 min at 4°C. The pellet was sued for the purification of hepatocytes and the supernatant, which contains non-parenchymal cells (NPCs), for the purification of Kupffer cells. For the enrichment of NPCs, the supernatant was centrifuged at 300 × g for 5 min at 4°C, and the pellet was resuspended in 1x HBSS with 2% FBS. Percoll gradient centrifugation was then performed to collect Kupffer cells. Isotonic Percoll solutions (50% and 25%) were prepared by diluting Percoll (Cytiva, 17-0891-01) with 10× HBSS and distilled water. The 25% Percoll was carefully layered over 50% Percoll in a 15 mL conical tube, and the NPC suspension was gently added on top of the 25% Percoll layer and centrifuged at 800 × g for 20 min at 4°C without braking. After centrifugation, the interphase layer (between 25% and 50% Percoll) was collected by means of a pipette, transferred to a fresh tube, diluted with 1x HBSS containing 2% FBS, centrifuged at 300 × g for 5 min at 4°C. Finally, the pellet was resuspended in QIAzol lysis reagent for RNA extraction to proceed to qRT-PCR.

### Isolation and purification of T cells, B cells and dendritic cells

Isolation and purification of T and B lymphocytes, and dendritic cells (DCs) were performed using MojoSort mouse immune cell isolation kits (BioLegend, Cat: 480024, 152420, and 480097) from the spleen and lymph nodes of 11-week-old female C57BL/6J mice. Harvested tissues were placed in cold RPMI-1640, mechanically dissociated, and filtered through a 70 µm strainer. The suspension was adjusted to 5 mL with RPMI-1640, filtered again, and centrifuged at 400 × g for 5 min at 4°C, after which the supernatant was discarded. For spleen samples, cells were resuspended in 1x RBC Lysis Buffer, incubated at room temperature for 5 min, then washed with MACS buffer (1x PBS with 0.5% BSA and 2 mM EDTA), centrifuged at 400 × g for 5 min at 4°C, and the supernatant was discarded. The cells were resuspended in MACS buffer and divided into three aliquots per mouse for T cell, B cell and DC isolation. Each aliquot was incubated with 10 µL of the respective biotin-antibody cocktail on ice for 15 min, followed by washing with 5 mL of MACS buffer and centrifugation at 300 × g for 5 min at 4°C. After discarding the supernatant, 10 µL of streptavidin nanobeads were added per tube, mixed and incubated on ice for 15 min. The cells were then washed twice with 5 mL of MACS buffer, centrifuged at 300 × g for 5 min at 4°C and resuspended in 1 mL of MACS buffer. For magnetic separation, an LS column was placed onto a MultiMACS Cell24 Separator Plus single-column adapter, and a 40 µm MACS filter was positioned on top. The column was rinsed with 2 mL of MACS buffer before applying the resuspended negative fraction onto the LS column. The column was washed three times with 1 mL of MACS buffer, following the MultiMACS Cell24 Separator Plus user manual. Correctly labeled 5 mL tubes were placed in the MultiMACS Cell24 Separator Plus 5 mL tube rack on the elution chamber, and plugged columns were positioned to ensure a vacuum seal. The single-column adapter with the columns was placed on top of the elution chamber, and the system was operated according to on-screen instructions. To elute the cells, 1 mL of MACS buffer was added to each column, and collection tubes were retrieved. The eluted samples were centrifuged at 300 × g for 5 min, and the pellets were resuspended in QIAzol lysis reagent for RNA extraction and later qRT-PCR.

## Multi-omics analysis

### Bulk RNA-sequencing

Murine liver tissue samples were homogenized in QIAzol lysis reagent (Cat. 79306, QIAGEN, Hilden, Germany). Total RNA was extracted and purified by means of the miRNeasy Mini Kit (Cat. 217084, Qiagen), following the manufacturer's protocol. Total RNA was subjected to RNA sequencing on a NovaSeq 6000 PE150 instrument by Novogene (London, UK). The RNA sequencing data were obtained in Fasta file format. Reads were mapped to mouse genome assembly (GRCm39, mm10) using HISAT2 (Version 2.2.1), followed by read counting with HTSeq-count (Version 2.0.2). The differential expression analysis was estimated using DESeq2 R-package (1.36.0). We used variance stabilizing transformation (VST) to transform and normalize the count matrix as implemented in DESeq2. Gene set enrichment analysis (GSEA)-based KEGG pathway and gene ontology (GO), including biological process (BP), molecular function (MF), and cellular component (CC), were performed with the differential expression data using WebGestalt, a web-based tool for functional enrichment analysis. R software (4.2.1), as well as ggplot2[3.3.6] and Complex Heatmap[2.13.1] packages were used to visualize data.
Spatial transcriptomic

Tissue samples were initially screened for RNA quality, ensuring DV200 scores exceeded 50% (RNA 6000 Nano kit, Part number 5067-1511, Agilent, Santa Clara, CA USA), as recommended by the Visium Tissue Preparation Guide (10X Genomics, Pleasanton, CA, USA). The Visium spatial gene expression slides and reagent kits were employed following the manufacturer's instructions (User Guide, CG000495 Rev C, Product number 1000520). Five-micrometer thick tissue sections were cut from the FFPE tissue blocks and mounted on Superfrost Plus slide (VWR, Radnor, PA, USA). The sections underwent initial deparaffinization, imaging and permeabilization steps (User Guide, CG000520 Rev B) before being meticulously hybridized with the probes. Then, probes pairs that had successfully hybridized to RNA are ligated to facilitate the sealing of junctions between them and finally released from the tissue within the Visium CytAssist instrument for capture on the FFPE Visium Spatial Gene Expression Slide within the fiducial frame.

Each capture area comprised approximately 5000 gene expression spots, that include an Illumina TruSeq partial read 1 sequencing primer, 16 nucleotide (nt) Spatial Barcode, 12 nt unique molecular identifier (UMI), 30 nt poly(dT) sequence (captures ligation product). These spots offered a resolution of approximately 5–10 cells. Ligation products were extended by the addition of UMI, partial Read 1, and spatial barcodes, ultimately yielding spatially barcoded products for subsequent library preparation. qRT-PCR was meticulously employed to ascertain cycle numbers, and the ligated and spatially barcoded products underwent indexing via Sample Index PCR. The sequencing of libraries was executed on a Novaseq 6000 instrument with an SP flow cell (100 cycles, Illumina, CA, USA). The Space Ranger pipeline v2.1.1 (10×Genomics, Pleasanton, CA, USA) and the refdata-gex-mm10-2020-A reference were used to process FASTQ files and generate the.cloup files. tSNE and spatial plots were run and plotted using Loupe Browser (10× genomics). Then, the R package seruat (version 5.0.0) was used to conduct downstream analyses. We used sctransform to normalize the count data to remove technical variability. Ferroptosis driver and suppressor molecular scores were calculated as the average expression of genes involved in ferroptosis gene signature (referred to Figure 6A) and *p* values calculated by means of the Wilcoxon signed-rank test. Immunosuppression scores and immunostimulation scores were calculated as the average expression of genes shown in Table S1.

Spatial metabolomics

The details including sample preparation, H&E staining, mass spectrometry imaging (MSI) acquisition, and data analysis are described. (i) *Sample preparation for MSI.* Liver tissues were harvested from mice, rinsed with PBS and placed in embedding cassette with one piece of foam pads then gently frozen in steam bath of liquid nitrogen. Cassettes were stored at -80°C. Tissue sections were performed with a cryotome HM 500 O (Micro). Tissues were warmed from -80°C to -15°C in 20 min. The piece of tissue was mounted with milliQ water on a peltier platform, and the object was fast-frozen for 10 min. Sections of tissues were cut with a thickness of 20 µm and put on labeled microscope slides. Three slides were alternatively dedicated to the Mass Spectrometry Imaging (MSI) positive mode, MSI negative mode and Hematoxylin and Eosin (H&E) staining. MSI Mass spectrometry imaging (MSI) sections were frozen in slide mailers at -80°C and H&E staining was performed right after cutting. (ii) *H&E staining.* Sections on the microscope slide were successively bathed in 100%, 70% and 50% of ethanol (EtOH), each step during 2 min, then followed by 2 min in tap water. Tissues were afterward colored with hematoxylin for four 4.5 min, and rinsed with water for 4 min. Second coloration was done with eosin for 2.2 min, rinsed again with water for 1.5 min. Three last baths were applied on the sections, respectively at 50, 70 and 100% of EtOH, for 1 min of each. Finally, a quick rinse with Xylene was done before mounting cover slips with Entellan. (iii) *MSI acquisition.* Full scan (50 m/z to 1200 m/z) acquisitions were performed in sensitive mode with a synapt XS waters equipped with a DESI XS source. Three slices were acquired in positive polarity acquisition mode, and three others were used for negative polarity acquisition mode. Tissue sections acquired in negative polarity were sprayed with 2 µL/min of methanol/water buffer (96/4, with 1 mmol of ammonium acetate) with lockspray leucine-enkephaline (250 ng/mL). Capillary voltage was set at 0.70V, sampling cone at 40V, source temperature at 150°C. Regarding the negative polarity acquisition mode, the 2 µL/min of buffer was composed of methanol/water (82/18) with 0.1% of formic acid and 250 ng/mL of leucine-enkephaline. Capillary voltage was set at 0.40V, sampling cone at 40V, source temperature at 150°C. DESI source was scanning the tissue spatial resolution with a spatial resolution of 50 µm² per pixel, and a scan time of 0.153s. Acquisition parameters were set with HDI v1.6 and acquired with MassLynx v4.2. (iv) *Data analysis.* Raw data were processed by HDI v1.6. A target list was built from one replicate, based on the thousand most intense ion signals. Then we added to this data-driven built list our own targeted list of special interest. It is resulting to an identical list of m/z searched throughout pixels (corresponding to mass spectra) of the whole set of MSI files. HDI raw outputs were exported in text files and import in R software to further data treatments. Data handling with R included raw data cleaning (image cropping, mis-acquired replicate exclusion) and normalization by total ion count (TIC). A smooth function was applied on the MSI files, in order to limit the artifactual signal variations between pixels. Then, artifactual metabolites were removed: if the mean of the tissue region of interest (tissue ROI, based on Kmeans pixels clustering) was less than the mean of the glass ROI for more than the half of the MSI files, then metabolites were excluded from ulterior analysis. All pixel values corresponding to the tissue replicates were then gathered and analyzed by a centered and non-scaled principal component analysis (PCA). PCA values were then clusterized by the Kmeans method, and each pixel was assigned by a color code depending on its cluster. Images of the replicated tissue sections were then reconstructed from this color code assignation. Finally, each mean values for each ion signal were calculated for each ROI and computed in a heatmap.
Flow cytometry analyses of the immune infiltrate

Tumor infiltrating lymphocytes were isolated and characterized by immunofluorescence staining as described.²⁰ Orthotopic Hep55.1C tumors were harvested at the end of the experiment (refer to Figure S9A) and dissociated into a single-cell suspension using mechanical and enzymatic disruption, following the manufacturer's instructions (Tumor Dissociation Kit, Miltenyi Biotec, Cat# 130-096-730). For flow cytometry analysis, cells were first stained with a viability dye (Live/Dead Fixable Yellow Dye, Invitrogen, Cat# L34967). Fc receptors were blocked using an anti-mouse CD16/CD32 antibody (BD Biosciences, clone 2.4G2) before incubation with fluorophore-conjugated antibodies targeting specific surface markers: CD45-BUV661 (RRID: AB_2870247), CD3-APC (RRID: AB_10597589), CD4-APC-Vio770 (RRID: AB_2751634), CD8a-PE (RRID: AB_394570), ICOS-BV421 (RRID: AB_2738576), GITR-BV786 (RRID: AB_2740641), LAG3-BV605 (RRID: AB_2742805), PD1-BUV395 (RRID: AB_2742320), TIGIT-BV711 (RRID: AB_2742063), TIM3-PerCP-Cy5.5 (RRID: AB_2561400). For intranuclear staining, cells were fixed and permeabilized using the FoxP3/Transcription Factor Staining Buffer (eBioscience, Thermo Fisher, Cat# 00-5523-00), followed by staining with FoxP3-FITC (RRID: AB_465243). Fluorescence data were acquired using a BD LSRFortessa X20 cytometer with BD FACS Diva software. Compensation, scaling, gating and data analysis were performed using the Omiq.ai (https://www.omiq.ai/) online tool. Gating strategies were determined based on the marker expression profiles of the clusters of interest (refer to Figure S9C). Statistical comparisons were conducted using one-way ANOVA with Sidak's correction for multiple comparisons. Data was exported to R for subsequent analysis (version 4.3.3, https://www.R-project.org/). Samples with low counts in at least one of the cell populations (<5 for tumor samples) were excluded, and counts relative to the number of CD45⁺ cells as well as geometric means of fluorescence intensity of activation/inhibition markers were Z-score-transformed and plotted as heatmap with the ComplexHeatmap package (version 2.16.0). Statistical comparisons were performed for each variable by ANOVA with two factors (anti-ACBP treatment and anti-PD1 treatment) with the stats package (version 4.3.3) and post-hoc pairwise comparisons with the emmeans package (version 1.10.4).

Liver histology and Immunohistochemistry

Liver tissues in cassettes were fixed with 4% paraformaldehyde for 24h, and transferred into 70% ethanol. After dehydration steps, cassettes were embedded in paraffin. The liver sections were stained with hematoxylin and eosin (H&E) and Sirius red for histology assessment. Slides were scanned and imaged using a slide scanner (Nanozoomer 2.0-HT, Hamamatsu, Japan). NAFLD Activity Score (NAS) was examined by an expert pathologist, who was blinded to the treatments received, according to the NASH-CRN scoring system.⁵⁰ Fibrosis stage was evaluated by Sirius red staining and quantified as % Sirius red+ area by QuPath software. The proliferation score was evaluated by Ki67 and PCNA IHC staining of the liver sections. The image analysis was performed with QuPath software as % positive area.

Immunofluorescence

Primary HCC cells (DBI⁺⁺ and DBI⁻⁻) were seeded at 2000cells/well using four replicates. After 24h, the cells were fixed with 4% PFA/PBS (100 µL/well) for 20 min at room temperature (RT). Fixed plates were washed twice with PBS (200 µL/well), permeabilized and blocked with 0.1% Triton X-100 + 5% BSA +10% FBS for 60 min (100 µL/well) at RT. Primary antibodies were diluted to the appropriate concentration in 1% BSA (100 µL/well), and incubated overnight at 4°C. After two steps of PBS washing (200 µL/well), secondary antibodies (1:250) and DAPI (1:5000) in 1% BSA (50 µL/well) were subsequently added to the cells and incubated for 1 h at RT in the dark. Plates were washed twice with PBS followed by adding PBS (100 µL/well). Image acquisition and analysis were performed with the MetaXpress Image software (Molecular Devices, Sunnyvale, CA, US).

Enzyme-linked immunosorbent assay

For enzyme-linked immunosorbent assays (ELISA), whole blood was collected from mice. Plasma was isolated by centrifugation at
5000 × rpm, 4°C for 10 min. ACBP levels in plasma were assayed by ELISA. High binding ELISA plates were coated with 100 µL anti-
ACBP antibodies (human: MBS768488, mouse: ab231910) overnight at 4°C. Plates were washed twice with 100 µL washing buffer
(PBS+0.05% Tween 20) and blocked with 200 µL blocking buffer (PBS+1%BSA+0.05% Tween 20) for 2h at RT. After washing, 100 µL
standards or diluted plasma samples (human:1/50, mouse:1/20) were added to the plates and incubated for 2h at RT. Plates were
washed 3 times with washing buffer, followed by incubation with 1µg/µl anti-ACBP detection antibody (100 µL/well, human: LS-
C299614, mouse: MBS2005521) for 1h at RT. After 3 times washing buffer, 100 µL of diluted Avidin-HRP (human:1/5000,
mouse:1/1000) was added to each well and incubated at RT for 30 min. The plates were washed 3 times and 100 µL substrates
were added, followed by 30-min incubation in dark. After which 50µL stop solution was added and the absorbance at 450 nm
was read by a microplate reader.

Quantitative real-time PCR

Liver tissues (about 25mg) were homogenized in QIAzol lysis reagent. Total RNA was extracted and purified with miRNeasy Mini Kit (Cat. 217004, QIAGEN, Germany) according to the manufacturer's instructions. Total RNA (1000ng) was reversed transcribed into cDNA. Quantitative real-time PCR (qRT-PCR) detection was carried out using PowerUp SYBR Green Master Mix (Cat. A25776, Applied Biosystems, Thermo Fisher Scientific, USA). The primers used in the study were listed in the KEY RESOURCES TABLE.
<figure><img src="image_97.png" /></figure>

## Immunoblotting

Liver tissues were homogenized in protein lysis buffer (20 mM Tris buffer pH 7.4 + 150 mM NaCl+1% Triton X-100 + 10 mM EDTA +Complete protease inhibitor cocktail) by a Precellys 24 Homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France). Protein extracts were centrifuged at 12000g (4°C) for 15 min to collect supernatants. Protein concentration was determined by BCA protein assay kit (Thermo Fisher Scientific) and samples were then boiled in Laemml buffer for 10 min at 100°C. Total protein (25 µg) of each sample was loaded onto the 4–12% NuPAGE Bis-Tris gel (Thermo Fisher Scientific) and transferred to PDVF membranes (BioRad). To simultaneously detect different antigens within the same experiment, the membranes were sliced horizontally in several parts based on the molecular weight of the protein of interest. Membranes were blocked with 5% (w/v) BSA in TBST (TBS with 0.1% Tween 20) for 1h at RT and incubated with the appropriate antibodies (KEY RESOURCES TABLE) in 5% (w/v) BSA in TBST overnight at 4°C. Detection was performed with incubation of appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies for 1h at RT. Blots were visualized by ECL detection reagent (GE Healthcare). Densitometric analysis were performed using ImageJ software (NIH). GAPDH serves as a loading control. Densitometric ratios of target protein/GAPDH were normalized to control groups, respectively.

## QUANTIFICATION AND STATISTICAL ANALYSIS

Unless indicated otherwise, data are presented as means ± SEM. Normality was assessed by D'Agostino-Pearson, Shapiro-Wilk and Kolmogorov-Smirnov normality tests when use GraphPad Prism 8. Normally distributed data were analyzed by T test, one-way ANOVA or two-way ANOVA. Non-normally distributed data were analyzed using the Welch's t test (two groups), Mann-Whitney U test (two groups) or Kruskal-Wallis test (multiple groups) followed by Dunn's post-test. Body weight curves were analyzed using two-way ANOVA performed through the Tum-Growth online tool (https://kroemerlab.shinyapps.io/TumGrowth/). The log-rank test was used for Kaplan-Meier (KM) survival analyses. Correlations were assessed using Spearman's rank correlation analysis. Unless indicated otherwise, statistical analyses were performed by GraphPad Prism 8.

## Bioinformatics analyses

For *DBI* differential expression of cancer patients, RNA-seq data were downloaded from TCGA database (https://portal.gdc.cancer.gov), extracted in TPM format and presented as log2(value + 1). *DBI* differential expression (presented as log2(value)) in HCC patients were further validated by analyzing data from HCC-related GEO datasets (GSE36376, GSE76427, GSE39791, GSE54236, GSE10143, GSE98617, GSE65372, and GSE112790). For HCC patient plasma analysis, continuous variables are presented as median and interquartile range, and categorical variables as number and percentages. Correlations between continuous variables were assessed using Spearman's rank correlation analysis. Survival curves plotted using Kaplan-Meier (KM) analysis was undertaken using survival [3.3.1] survminer, ggplot2 [3.3.6] packages. Liver diseases related GEO datasets (GSE120652, GSE164760, GSE74000, GSE120652, GSE87028, GSE103580, GSE49541, GSE45114, GSE47197, GSE34798, GSE98383, GSE107170, GSE15239, GSE119600, GSE7706, GSE14951, GSE23343, and GSE59045) downloaded from the GEO database through the GEOquery package [2.64.2]. The data were normalized with the NormalizeBetweenArrays function of the Limma package [3.52.2]. Only the probe with the largest signal value was retained when encountering multiple probe values for the same gene. GSEA-based KEGG pathway analysis was performed using WebGestalt 2019 (https://www.webgestalt.org/), an online tool for functional enrichment analysis in various biological contexts. The similarity of the global transcriptome dysregulation of the aforementioned liver disease GEO datasets and three MASH-driven mouse RNA-seq datasets in this study (WD + CCl₄_ACBP/DBI knockout, WD + CCl₄_KLH-ACBP, and HFD+DEN_KLH-ACBP) were analyzed by row and column clustering analysis (Euclidean distance) of the normalized enrichment score (NES). We calculated Spearman's correlations between ACBP/DBI and oncogenes in HCC, using data from 33 HCC GEO datasets (refer to Figure S2 for GEO accession numbers) and the TCGA-LIHC dataset. The data were normalized and processed as described above. Statistical analyses and data visualization were performed with R software (4.2.1) and R packages including ggplot2 [3.3.6], stats [4.2.1], car [3.1-0], and ComplexHeatmap [2.13.1].