# Transcriptomic evidence for tumor-specific beneficial or adverse effects of TGFβ pathway inhibition on the prognosis of patients with liver cancer

Matthis Desoteux, Betty Maillot, Kevin Bévant, Tanguy Ferlier, Raffaele Leroux, Gaëlle Angenard, Corentin Louis, Laurent Sulpice, Karim Boudjema,

Cédric Coulouarn

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Matthis Desoteux, Betty Maillot, Kevin Bévant, Tanguy Ferlier, Raffaele Leroux, et al. Transcriptomic evidence for tumor-specific beneficial or adverse effects of TGFβ pathway inhibition on the prognosis of patients with liver cancer. FEBS Open Bio, 2023, 13 (7), pp.1278-1290. 10.1002/2211-5463.13647. hal-04117428

HAL Id: hal-04117428

https://hal.science/hal-04117428

Submitted on 26 Jul 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

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

# Transcriptomic evidence for tumor-specific beneficial or adverse effects of TGFβ pathway inhibition on the prognosis of patients with liver cancer

Matthis Desoteux<fnref n="1" />, Betty Maillot<fnref n="2" />, Kevin Bévant<fnref n="1" />, Tanguy Ferlier<fnref n="1" />, Raffaële Leroux<fnref n="1" />, Gaëlle Angenard<fnref n="3" />, Coretin Louis<fnref n="1" />, Laurent Sulpice<fnref n="1" />,<fnref n="2" />,<fnref n="4" />, Karim Boudjema<fnref n="2" />,<fnref n="4" /> and Cédric Coulouarn<fnref n="1" /> ID

**Keywords**

galunisertib; gene signature; hepatocellular
carcinoma; survival; TGFβ

**Correspondence**

C. Coulouarn, Inserm U1242 "Oncogenesis, Stress, Signaling" (OSS), CLCC Eugène Marquis, Rue de la Bataille Flandres Dunkerque, Bat D, 1er étage, 35042 Rennes, France

Tel: +33 2 2323 3881
E-mail: cedric.coulouarn@inserm.fr

(Received 2 March 2023, revised 3 May 2023, accepted 16 May 2023)

doi:10.1002/2211-5463.13647

Therapeutic targeting of the transforming growth factor beta (TGFβ) pathway in cancer represents a clinical challenge since TGFβ exhibits either tumor suppressive or tumor promoting properties, depending on the tumor stage. Thus, treatment with galunisertib, a small molecule inhibitor of TGFβ receptor type 1, demonstrated clinical benefits only in subsets of patients. Due to the functional duality of TGFβ in cancer, one can hypothesize that inhibiting this pathway could result in beneficial or adverse effects depending on tumor subtypes. Here, we report distinct gene expression signatures in response to galunisertib in PLC/PRF/5 and SNU-449, two cell lines that recapitulate human hepatocellular carcinoma (HCC) with good and poor prognosis, respectively. More importantly, integrative transcriptomics using independent cohorts of patients with HCC demonstrates that galunisertib-induced transcriptional reprogramming in SNU-449 is associated with human HCC with a better clinical outcome (i.e., increased overall survival), while galunisertib-induced transcriptional reprogramming in PLC/PRF/5 is associated with human HCC with a worse clinical outcome (i.e., reduced overall survival), demonstrating that galunisertib could indeed be beneficial or detrimental depending on HCC subtypes. Collectively, our study highlights the importance of patient selection to demonstrate a clinical benefit of TGFβ pathway inhibition and identifies Serpin Family F Member 2 (SERPINF2) as a putative companion biomarker for galunisertib in HCC.

Hepatocellular carcinoma (HCC) is a deadly cancer
worldwide due to limited therapeutic options [1]. Sur-
gery remains the best strategy for curative intent when
performed at an early stage [2]. However, most HCC
are diagnosed at an advanced stage and patients are
then evaluated for systemic treatments [2,3]. Although

phase III clinical trials reported positive results for
antiangiogenic drugs, alone or combined with immune
checkpoint inhibitors (ICI) [2,4], extending the thera-
peutic options to reduce vascular invasion and meta-
static progression still represents an important need.
Targeting the transforming growth factor beta (TGFβ)

**Abbreviations**

AFP, alpha-fetoprotein; ATCC, American Type Culture Collection; BCLC, Barcelona Clinic Liver Cancer; CDH1, E-cadherin; EMT, epithelial-to-mesenchymal transition; GSEA, gene set enrichment analysis; HCC, hepatocellular carcinoma; ICI, immune checkpoint inhibitors; MSigDB, molecular signatures database; qRT-PCR, quantitative real-time polymerase chain reaction; SERPINF2, serpin family F member 2; TCGA, The Cancer Genome Atlas; TGFβ, transforming growth factor beta; TNM, tumor node metastasis; VIM, vimentin.

<footnote n="1">1 Inserm, UMR_S 1242, OSS (Oncogenesis Stress Signaling), Centre de Lutte contre le Cancer Eugène Marquis, Univ Rennes, France</footnote>

<footnote n="2">2 Department of Hepatobiliary and Digestive Surgery, Rennes University Hospital, France</footnote>

<footnote n="3">3 Inserm, Inrae, UMR_S 1317, NuMeCan (Nutrition, Metabolisms and Cancer), Univ Rennes, France</footnote>

<footnote n="4">4 Clinical Investigation Center 1414, CHU de Rennes, France</footnote>
deviation. Statistical analyses were performed using R-3.5.1 and GRAPHPAD PRISM 7.0, Boston, MA, USA. For microarray data, differentially expressed genes were identified by a two-sample univariate *t*-test and a random variance model [24]. For group comparison of quantitative variables, *t*-testing was applied. Categorical data were analyzed by chi-squared testing. The cumulative survival rate was estimated by the Kaplan-Meier method, and the survival curves were compared with the log-rank test. *P*-values below 0.05 were considered as significant statistical differences.

# Results and Discussion

## PLC/PRF/5 and SNU-449 reproduce human HCC with different prognosis

Hepatocellular carcinoma cell lines constitute relevant models to investigate signaling pathways altered in liver cancer and to test new drug candidates [14,25–28]. However, to what extent these cell lines recapitulate the biology of human HCC tumors has been partly addressed [29]. Here, we evaluated the clinical relevance of PLC/PRF/5 and SNU-449 cell lines, which were representative of two clusters differing notably in the expression of genes associated with metastasis in a panel of 19 HCC cell lines [16]. In addition, from this panel, we previously demonstrated by integrative transcriptomics that PLC/PRF/5 and SNU-449 cell lines recapitulated human HCC with tumor-suppressive and tumor-promoting features of the TGFβ pathway, respectively [16]. Accordingly, PLC/PRF/5 and SNU-449 differed in the expression of epithelial and mesenchymal markers, SNU-449 exhibiting a mesenchymal-like phenotype and a higher secretion of TGFβ (Fig. S1), in agreement with previously published data [15,30]. It was also shown that a mesenchymal-like phenotype in HCC cell lines correlated with autocrine stimulation of the TGFβ pathway and resistance to TGFβ-induced suppressor effects [30]. Not surprisingly, numerous genes were differentially expressed between PLC/PRF/5 and SNU-449 cells (Fig. 1A; Table S1). A significant enrichment of signatures associated with a previously described molecular classification of human HCC [31] was highlighted. Thus, the HOSHIDA_LIVER_CANCER SUBCLASS_S1 and SUBCLASS_S3 signatures were enriched in the gene expression profiles of SNU-449 and PLC/PRF/5 cells, respectively (Fig. 1B). The S1 subtype reflected an aberrant activation of the WNT pathway through TGFβ, while the S3 subtype was associated with a preserved hepatocyte differentiation [31]. Supporting this observation, the HALLMARK_EMT [32] and the HSIAO_LIVER_SPECIFIC_GENES [33] signatures were enriched in the gene expression profiles of

SNU-449 and PLC/PRF/5 cells, respectively (Fig. 1B). In agreement with a more differentiated phenotype, key genes associated with liver metabolism (e.g., APOB) were more expressed in PLC/PRF/5 than in SNU-449 cell line. Conversely, genes associated with cancer stem cells (e.g., CD44), TGFβ pathway (e.g., SERPINE1), and EMT (e.g., SNAI2) were expressed at a higher level in SNU-449 than in PLC/PRF/5 cell line (Fig. 1C), recapitulating previous reports made at transcriptomic and proteomic levels [27,29]. Supporting a higher proliferative potential of SNU-449 cells, cyclin-dependent kinase inhibitor 2A (CDKN2A) and BIRC3, encoding an inhibitor of apoptosis, were underexpressed while MYC was overexpressed (Fig. 1C). Next, we integrated the signature made of genes differentially expressed between SNU-449 and PLC/PRF/5 cell lines with the gene expression profiles of 139 HCC cases with clinical annotations [16,21,22]. Clustering analysis of the integrated datasets identified two groups of human HCC associated with either SNU-449 (cluster 1) or PLC/PRF/5 (cluster 2) cell line (Fig. 1D). Interestingly, patients with HCC that recapitulate the gene expression signature of the PLC/PRF/5 cell line (cluster 2) had a better survival (Fig. 1E). Altogether, gene expression profiling at basal level and integrative transcriptomics demonstrated that PLC/PRF/5 and SNU-449 cell lines are relevant models to study human HCC with different prognosis.

## Specific galunisertib signatures in PLC/PRF/5 and SNU-449 cells

Transforming growth factor beta plays a key role in liver carcinogenesis but targeting this pathway is challenging due to its functional duality [34]. Supporting this hypothesis, we previously reported signatures associated with the tumor-suppressive and tumor-promoting properties of TGFβ in human HCC with good and bad prognosis, respectively [16]. As we previously reported that PLC/PRF/5 and SNU-449 cell lines were associated with this functional duality [16], we hypothesized that they may constitute relevant models to infer the impact of inhibiting the TGFβ pathway in specific human HCC. Further supporting this hypothesis, the data above demonstrated that the SNU-449 cell line is also associated with human HCC from the S1 subtype in which the tumor promoting arm of TGFβ is activated (Fig. 1B). Thus, we established gene expression signatures of TGFβ pathway inhibition by treating PLC/PRF/5 and SNU-449 cell lines with galunisertib [35]. Galunisertib treatment resulted in the modulation of 386 nonredundant and well-annotated genes in SNU-449 cell line (139 upregulated and 247 downregulated versus control) and 124
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** PLC/PRF/5 and SNU-449 cells mimic two groups of human HCC with a variable prognosis. (A) Hierarchical clustering and volcano plot of genes differentially expressed between PLC/PRF/5 and SNU-449 (dashed lines: fold-change FC > 2; *P* < 0.001; *n* = 3 independent experiments). The complete list of genes is provided in Table S1. (B) GSEA using the gene expression profiles of SNU-449 and PLC/PRF/5 cells and signatures associated with liver cancer (NES, normalized enrichment score provided by GSEA). (C) Expression of key genes representative of TGFβ-related functions. Data are presented as bar plots with mean ± SD and were compared using the Student's t-test, \* *P* < 0.05, \*\* *P* < 0.01, and \*\*\* *P* < 0.001 (*n* = 3 independent experiments). (D) Dendrogram overview of PLC/PRF/5 and SNU-449 gene expression profiles integrated with those of 139 human HCC cases (GSE1898 and GSE4024). Clustering analysis identified two major HCC clusters associated with SNU-449 (cluster 1) and PLC/PRF/5 (cluster 2) profiles. (E) Kaplan-Meier plot and log-rank statistics of HCC included in clusters 1 and 2.</figcaption></figure>

genes in PLC/PRF/5 cell line (26 upregulated and 78 downregulated versus control) (Fig. 2A; Tables S2 and S3). Only five genes were commonly deregulated in the two cell lines, demonstrating that galunisertib induces cell-specific changes, similar to TGFβ [36]. Importantly, GSEA suggested that galunisertib could switch the gene expression profile of SNU-449 cell line toward a more favorable prognosis. Indeed, a positive enrichment of gene signatures associated with a better HCC differentiation (e.g., HOSHIDA_LIVER_CANCER_SUBCLASS_S3 and HSIAO_LIVER_SPECIFIC GENES), as described above (Fig. 1B), was observed in the gene expression profile of SNU-449

cells treated with galunisertib, as compared to untreated control cells (Fig. 2B). Conversely, a negative enrichment of the HALLMARK_EMT signature was observed, suggesting that galunisertib may switch the EMT signature toward a MET signature (Fig. 2B). Accordingly, an increased expression of the epithelial marker E-cadherin (CDH1) was observed in SNU-449 cells treated with galunisertib (Table S2). These results are in agreement with previous data reporting that galunisertib reduces the stemness-gene profile of invasive HCC cells (including the SNU-449 cell line) and induces the acquisition of a more epithelial-like morphology [8,30]. Galunisertib was also shown to reduce
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Galunisertib signatures in PLC/PRF/5 and SNU-449 cells. (A) Hierarchical clustering of genes up- and down-regulated in SNU-449 (left panel) and PLC/PRF/5 (right panel) after a galunisertib treatment (10 µM, 16 h) (FC > 2; *P* < 0.001). (B) GSEA using the gene expression profiles of SNU-449 (upper panel) and PLC/PRF/5 (lower panel) after a treatment with galunisertib vs control. GSEA highlighted a cell-specific enrichment of signatures associated with the molecular classification of human HCC (e.g., HOSHIDA_LIVER_CANCER_SUBCLASS_S1 and SUBCLASS_S3 [31]), hepatocyte differentiation (HSIAO_LIVER_SPECIFIC_GENES [33]) and EMT (HALLMARK_EMT [32]).</figcaption></figure>

the migration, clonogenicity and the spheroid forma-
tion ability of HCC invasive cells [8,30,37]. Interest-
ingly, an opposite picture was highlighted for the
PLC/PRF/5 cell line since a positive enrichment of
unfavorable signatures was observed in cells treated
with galunisertib, including the HOSHIDA_LIVER_
CANCER_SUBCLASS_S1 and the HALLMAR-
K_EMT signatures (Fig. 2B). In addition, a negative
enrichment of the HSIAO_LIVER_SPECIFIC_
GENES signature was observed, suggesting that galu-
nisertib may switch the PLC/PRF/5 cell line toward a
less differentiated phenotype (Fig. 2B). Mechanisti-
cally, we hypothesize that liver-enriched transcription
factors involved in maintaining a well-differentiated
hepatocyte phenotype are repressed by galunisertib in
the PLC/PRF/5 cell line and induced in the SNU-449
cell line. Supporting this hypothesis, galunisertib was
shown to reduce the expression of HNF1B, HNF4A,
and ONECUT1 (aka HNF6A) involved in maintaining
a well-differentiated hepatocyte-like phenotype in the
PLC/PRF/5, while it induced the expression of

HNF1A, FOXA2 (aka HNF3B), and ONECUT1 in SNU-449 (Fig. S2). Collectively, the results highlighted a differential galunisertib-induced transcriptional reprogramming in PLC/PRF/5 and SNU-449 cells possibly associated with different prognosis in human HCC. It is important to note that galunisertib is a small selective kinase inhibitor for TGFβ, activin and nodal type I serine/threonine kinase receptors. Thus, we cannot exclude that part of the observed signatures is also connected to the inhibition of activin and nodal pathways, which have been also associated with HCC. In addition, TGFβ can signal via SMAD and non-SMAD signaling pathways. Such non-SMAD signaling pathways like TGFβ/PI3K/AKT or TGFβ/NFKB are thought to play a role in aggressive cancers possibly in a type I receptor kinase-independent manner (and thus are not blocked by galunisertib). Accordingly, enrichments of PI3K and NFKB signatures were observed upon TGFβ exposure in PLC/PRF/5 and SNU-449 cell lines. However, upon galunisertib exposure, these enrichments were not statistically significant anymore
suggesting that they were dependent on type I receptor
kinase (Fig. S3). Thus, one can assume that in our
experimental conditions, most of the observed effects
on gene expression upon galunisertib exposure are
related to the SMAD canonical signaling.

Galunisertib- and TGFβ-associated signatures in PLC/PRF/5 and SNU-449 cells identified human HCC with different prognosis

Gene set enrichment analysis suggested that whereas
possibly beneficial in SNU-449-like HCC,
galunisertib-induced cell reprogramming could be det-
rimental in PLC/PRF/5-like HCC, as regards limiting
tumor progression and improving patient survival. To
test this hypothesis from a clinical perspective, we
integrated cell-specific galunisertib and TGFβ signa-
tures with the gene expression profiles of 139 cases of
human HCC, as performed above. Accordingly, we
first established and then integrated the gene expres-
sion profiles of PLC/PRF/5 and SNU-449 cells trea-
ted with TGFβ in presence/absence of galunisertib
(Fig. 3). For both cell lines, clustering analysis identi-
fied two clusters, one including the samples corre-
sponding to untreated control cells or cells treated
with TGFβ (thereafter referred to control) and
another one including the samples corresponding to
cells treated with galunisertib, alone or in combina-
tion with TGFβ (thereafter referred to galunisertib)
(Fig. 3A,D). Interestingly, human HCC were not ran-
domly distributed between the two clusters and the
distribution appeared to be highly dependent on the
cell line. Thus, in SNU-449, the galunisertib cluster
was associated with human HCC previously associ-
ated with a better prognosis (Fig. 3A), while the con-
trol cluster was associated with a poor prognosis
(Fig. 3A) [21,22]. Statistical analysis of HCC sub-
groups confirmed these observations since the galuni-
sertib cluster included significantly more HCC
associated with a good survival, no sign of vascular
invasion and previously reported signatures of better
prognosis, that is, the so-called hepatocyte [22], c-
MET/HGF-negative [20], and early TGFβ signatures
[16] (Fig. 3B). Accordingly, human HCC from the
galunisertib cluster exhibited a longer overall survival
as compared to HCC from the control cluster
(Fig. 3C). Suggesting that galunisertib could be effec-
tive in Hoshida's S1 HCC subtype, a phase 2 clinical
trial demonstrated efficacy in TGFβ activated tumors
with low alpha-fetoprotein (AFP) level [13]. Similar
to previous reports, galunisertib exerted limited anti-
proliferative effects in HCC cell lines [9,37]. However,
it was suggested that galunisertib exerted anti-invasive

properties in cell lines defined by the “late-TGFβ sig-
nature” (similar to SNU-449), in agreement with our
results showing that the galunisertib signature derived
from SNU-449 cells was associated with better clini-
cal outcomes. Very interestingly, performing the same
analysis using the signatures established in PLC/PRF/
5 cells resulted in completely opposite observations
(Fig. 3D,E). Indeed, while the control and TGFβ-
associated clusters were recapitulating human HCC
with a better prognosis, the galunisertib cluster reca-
pitulated human HCC with poor prognosis signatures
and a reduced overall survival (Fig. 3E,F). GSEA
also highlighted that a statistically significant relation-
ship between the HOSHIDA_LIVER_CANCER
SUBCLASS_S1 subtype and human HCC with poor
prognosis associated with the SNU-449 signature.
Conversely, a significant relationship between the
HOSHIDA_LIVER_CANCER SUBCLASS_S3 sub-
type and human HCC with better prognosis associ-
ated with the PLC/PRF/5 signature was observed
(Fig. S4A). More interestingly and fully supporting
our results, the same approach using human HCC
tumors classified according to the galunisertib signa-
tures established in PLC/PRF/5 and SNU-449 cell
lines resulted in opposite observations (Fig. S4B,C).
We further validated these results using an indepen-
dent cohort of 225 human HCC (GSE14520) (Fig. 4).
For the SNU-449 cell line, data integration similarly
identified two clusters, the galunisertib cluster being
representative of HCC with a better prognosis as
regard to the Barcelona Clinic Liver Cancer (BCLC)
classification, Tumor Node Metastasis (TNM) stag-
ing, serum AFP level and metastasis propensity
(Fig. 4A,B). Kaplan–Meier plot and log-rank statis-
tics confirmed that HCC in the galunisertib cluster
exhibited a significantly better overall survival
(Fig. 4C). On the contrary, for the PLC/PRF/5 cell
line, the galunisertib cluster showed a significant
enrichment in HCC associated with advanced BCLC
and TNM stages (BCLC C and TNM stage III), a
high serum AFP level (³ 300 ng·mL⁻¹), and a higher
risk of metastasis (Fig. 4D,E). Accordingly, Kaplan–
Meier plot and log-rank statistics highlighted a signif-
icant decrease in overall survival for patients included
in the galunisertib cluster (Fig. 4F). Collectively, these
results demonstrated that galunisertib- and TGFβ-
induced gene expression profiles in PLC/PRF/5 and
SNU-449 cell lines recapitulated human HCC with
different prognosis. More importantly, the results
strongly suggest that galunisertib-driven transcrip-
tional reprogramming in PLC/PRF/5-like HCC (i.e.,
possibly well-differentiated early-stage HCC) could be
detrimental. However, a recent quantitative proteomic
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Validation of the clinical relevance of galunisertib- and TGFβ-associated signatures derived from SNU-449 and PLC/PRF/5 cells in an independent cohort of patients with HCC. (A) Dendrogram overview of *in vitro* experiments in SNU-449 cells integrated with 225 cases of human HCC (NCI validating set, GSE14520). Clustering analysis was based on the expression of genes differentially expressed between the four conditions (i.e., SNU-449 cells treated with TGFβ and galunisertib, alone or in combination, versus untreated control cells). Two main clusters were identified. Cluster 1 included cells treated with galunisertib (in presence and absence of TGFβ co-treatment) and cluster 2 included untreated control cells and cells treated with TGFβ. (B) Statistical analysis (chi-squared test) of HCC distribution between clusters 1 and 2 based on previous gene expression signatures and clinical parameters. Cluster 2 shows a significant enrichment in human HCC with the following features: advanced stage (BCLC B/C), TNM stage II, higher serum AFP level and a high risk of metastasis. (C) Kaplan-Meier plot and log-rank statistics analysis revealed a significant decrease in overall survival for patients included in cluster 2 (*P* < 0.001). (D) Integrative transcriptomic analysis using galunisertib- and TGFβ-associated signatures established in PLC/PRF/5 cells as performed in (A). Contrary to the SNU-449 cell line, cluster 1 included untreated control PLC/PRF/5 cells and cells treated with TGFβ while cluster 2 included cells treated with galunisertib (in presence and absence of TGFβ). (E) Statistical analysis (chi-squared test) of HCC distribution between clusters 1 and 2 based on previous gene expression signatures and clinical parameters. Cluster 2 shows a significant enrichment in human HCC with the following features: advanced stage (BCLC B/C), TNM stage II, higher serum AFP level and a high risk of metastasis. (F) Kaplan-Meier plot and log-rank statistics analysis revealed a significant decrease in overall survival for patients included in cluster 2 (*P* < 0.001).</figcaption></figure>

analysis highlighted a molecular heterogeneity of early-stage HCC (BCLC 0 and A) and demonstrated an activation of the TGFβ pathway in HCC with microscopic vascular invasion, elevated AFP, shorter survival, and tumor recurrence [38]. Poor prognosis signatures were enriched in this early HCC subtype, including the late TGFβ signature [16]. Altogether, these data suggest that some early-stage HCC could also be eligible for therapeutic inhibition of the TGFβ pathway.

## SERPINF2 as a putative biomarker for galunisertib in HCC

Identifying the patients who may benefit from galunisertib treatment represents a critical issue in the design of clinical trials given that inhibiting the TGFβ pathway in HCC in which TGFβ still limits tumor growth may contribute to tumor progression. However, very few biomarkers have been identified to distinguish those patients that may benefit from galunisertib from those
that may not [30,39]. A recent study suggests that SKIL, PMEPA1, and TGFB1 could be important biomarkers for selecting patients more likely to respond to galunisertib in HCC, although the mRNA levels of these biomarkers do not correlate with the levels of circulating proteins in patients [39]. Thus, from the galunisertib- and TGFβ-associated signatures established in SNU-449 and PLC/PRF/5 cell lines, we selected the genes significantly associated with patient survival (GSE1898 and GSE4024). From the 58 and 12 genes predictive of

survival in SNU-449 and PLC/PRF/5 signatures, respectively, only three genes were in common: *BDH1*, *RGS9*, and *SERPINF2* (Fig. 5A). We further validated in an independent HCC dataset (TCGA-LIHC) that a high expression of *SERPINF2* or *BDH1* was significantly correlated with a better survival (Fig. 5B). Interestingly, the expression of *SERPINF2* was repressed by galunisertib in PLC/PRF/5 and induced in SNU-449 cell line (Fig. 5C). At a basal level, a higher expression of *SERPINF2* was observed in PLC/PRF/5 cells (Fig. 5D).

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Identification of SERPINF2 as a putative marker of galunisertib efficacy in HCC. (A) Genes associated with the overall survival in human HCC from the galunisertib- and TGFβ-associated signatures established in SNU-449 and PLC/PRF/5 cells, as described in Fig. 3. Venn diagram unraveled three common genes negatively correlated with patient outcome. (B) Analysis of overall survival by Kaplan-Meier plot and log-rank statistics in HCC from TCGA (LIHC dataset) based on the expression of candidate genes (blue curve: low expression; red curve: high expression). (C) Gene expression analysis of *RGS9*, *SERPINF2* and *BDH1* in SNU-449 (upper panels) and PLC/PRF/5 (lower panels) cells treated with TGFβ and galunisertib, alone or in combination. (D) Gene expression analysis of *RGS9*, *SERPINF2* and *BDH1*, differentially expressed between SNU-449 and PLC-PRF/5 cells. In (C) and (D) data are presented as bar plots with mean ± SD and were compared using the Student's *t*-test, \*P < 0.05, \*\*P < 0.01, and \*\*\*P < 0.001 (n = 3 independent experiments).</figcaption></figure>
In addition, the expression of SERPINF2 was able to define human HCC with variable prognosis, a high expression being associated with a better survival, no sign of vascular invasion, a well-differentiated hepatocyte signature, a lower AFP level, and an early TGFβ signature (Fig. S5). Because SERPINF2 encodes a secreted plasma protein, we suggest that it may serve as a clinically relevant companion biomarker for galunisertib in HCC. SERPINF2 was previously identified among 41 hub driver-genes predictive of differentiation, being expressed in well-differentiated cell lines [29]. Up-regulation of SERPINF2 in exosomes of good responders was also reported in rectal cancer patients with different responses to neoadjuvant radiotherapy [40]. In addition, SERPINF2 has been proposed as a serum marker of fibrosis progression for the follow-up of patients hepatitis C [41] as well as an independent prognostic marker highly expressed in HCC with a better survival [42].

In conclusion, our study reports specific gene expression signatures suggestive of beneficial or adverse effects of galunisertib in HCC cell lines with clinical relevance in human HCC. How galunisertib drives detrimental effects in specific subsets of HCC remains to be elucidated. However, gene expression profiling presented in the study highlighted opposite pictures of galunisertib treatment in PLC/PRF/5 and SNU-449 cell lines and suggested that galunisertib may favor an EMT-like phenotype in well-differentiated tumors by possibly repressing liver-enriched transcription factors. It is also important to emphasize that our study only focused on the impact of the TGFβ pathway and its inhibition on the phenotype of tumor cells. However, TGFβ exerts critical impact on cells from the tumor microenvironment, including fibroblast-like cells contributing to ECM remodeling and immune cells [6,43,44]. TGFβ contributes to the bidirectional cross-talk between tumor cells and their microenvironment and thus to the regulation of tumor progression and response to therapies. Thus, it would be important to take into account this parameter in future studies [6].

## Acknowledgments

The authors are supported by Inserm, Université de Rennes 1, Région Bretagne, Ministère de l'Enseignement Supérieur de la Recherche et de l'Innovation, Ligue Nationale Contre le Cancer, Ligue Contre le Cancer (CD22, CD35, CD44, CD85) (R22026NN), Fondation ARC (R21043NN), INCa and ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé) dans le cadre du Plan cancer (Noncoding RNA in cancerology: fundamental to translational) (C20014NS).

This work was supported by a grant from the French Ministry of Health and the French National Cancer Institute, PRT-K20-136, CHU Rennes, CLCC Eugène Marquis, Rennes (R21095NN). BM was supported from a fellowship from Association de Chirurgie Hépatobilio-Pancréatique et Transplantation Hépatique (ACHBT) and CHU Rennes.

## Conflict of interest

The authors declare no conflict of interest.

## Peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/2211-5463.13647.

## Data accessibility

The datasets used and produced in this study are available from the gene expression omnibus database with the following accession numbers: GSE211806, GSE1898, GSE4024, and GSE14520 (e.g., https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211806).

## Author contributions

CC conceived and designed the project; MD, BM, KBe, GA, CL, and CC acquired the data; MD and CC analyzed and interpreted the data; MD and CC wrote the paper; LS, KBo, and CC acquired funding and supervised the study; CC, TF, and RL revised the manuscript; all authors reviewed and edited the manuscript.

## References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. *CA Cancer J Clin* **71**, 209–249.

2. Reig M, Forner A, Rimola J, Ferrer-Fabrega J, Burrel M, Garcia-Criado A, Kelley RK, Galle PR, Mazzaferro V, Salem R *et al*. (2022) BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. *J Hepatol* **76**, 681–693.

3. Bruix J, Chan SL, Galle PR, Rimassa L and Sangro B (2021) Systemic treatment of hepatocellular carcinoma: an EASL position paper. *J Hepatol* **75**, 960–974.

4. Sidali S, Trepo E, Sutter O and Nault JC (2022) New concepts in the treatment of hepatocellular carcinoma. *United European Gastroenterol J* **10**, 765–774.
5 Giannelli G, Villa E and Lahn M (2014) Transforming growth factor-beta as a therapeutic target in hepatocellular carcinoma. *Cancer Res* **74**, 1890–1894.

6 Dituri F, Mancarella S, Cigliano A, Chieti A and Giannelli G (2019) TGF-beta as multifaceted orchestrator in HCC progression: signaling, EMT, immune microenvironment, and novel therapeutic perspectives. *Semin Liver Dis* **39**, 53–69.

7 Larson C, Oronsky B, Carter CA, Oronsky A, Knox SJ, Sher D and Reid TR (2020) TGF-beta: a master immune regulator. *Expert Opin Ther Targets* **24**, 427–438.

8 Rani B, Malfettone A, Dituri F, Soukupova J, Lupo L, Mancarella S, Fabregat I and Giannelli G (2018) Galunisertib suppresses the staminal phenotype in hepatocellular carcinoma by modulating CD44 expression. *Cell Death Dis* **9**, 373.

9 Serova M, Tijeras-Raballand A, Dos Santos C, Albuquerque M, Paradis V, Neuzillet C, Benhadji KA, Raymond E, Faivre S and de Gramont A (2015) Effects of TGF-beta signalling inhibition with galunisertib (LY2157299) in hepatocellular carcinoma models and in ex vivo whole tumor tissue samples from patients. *Oncotarget* **6**, 21614–21627.

10 Ungerleider N, Han C, Zhang J, Yao L and Wu T (2017) TGFbeta signaling confers sorafenib resistance via induction of multiple RTKs in hepatocellular carcinoma cells. *Mol Carcinog* **56**, 1302–1311.

11 Harding JJ, Do RK, Yaqubie A, Cleverly A, Zhao Y, Gueorguieva I, Lahn M, Benhadji KA, Kelley RK and Abou-Alfa GK (2021) Phase 1b study of galunisertib and ramucirumab in patients with advanced hepatocellular carcinoma. *Cancer Med* **10**, 3059–3067.

12 Kelley RK, Gane E, Assenat E, Siebler J, Galle PR, Merle P, Hourmand IO, Cleverly A, Zhao Y, Gueorguieva I et al. (2019) A phase 2 study of galunisertib (TGF-beta1 receptor type I inhibitor) and sorafenib in patients with advanced hepatocellular carcinoma. *Clin Transl Gastroenterol* **10**, e00056.

13 Faivre S, Santoro A, Kelley RK, Gane E, Costentin CE, Gueorguieva I, Smith C, Cleverly A, Lahn MM, Raymond E et al. (2019) Novel transforming growth factor beta receptor I kinase inhibitor galunisertib (LY2157299) in advanced hepatocellular carcinoma. *Liver Int* **39**, 1468–1477.

14 Allain C, Angenard G, Clement B and Coulouarn C (2016) Integrative genomic analysis identifies the core transcriptional hallmarks of human hepatocellular carcinoma. *Cancer Res* **76**, 6374–6381.

15 Bevant K, Desoteux M, Angenard G, Pineau R, Caruso S, Louis C, Papoutsoglou P, Sulpice L, Gilot D, Zucman-Rossi J et al. (2022) TGFbeta-induced FOXS1 controls epithelial-mesenchymal transition and predicts a poor prognosis in liver cancer. *Hepatol Commun* **6**, 1157–1171.

16 Coulouarn C, Factor VM and Thorgeirsson SS (2008) Transforming growth factor-beta gene expression signature in mouse hepatocytes predicts clinical outcome in human cancer. *Hepatology* **47**, 2059–2067.

17 Berriz GF, Beaver JE, Cenik C, Tasan M and Roth FP (2009) Next generation software for functional trend analysis. *Bioinformatics* **25**, 3043–3044.

18 Coulouarn C, Factor VM, Andersen JB, Durkin ME and Thorgeirsson SS (2009) Loss of miR-122 expression in liver cancer correlates with suppression of the hepatic phenotype and gain of metastatic properties. *Oncogene* **28**, 3526–3536.

19 Desoteux M, Louis C, Bevant K, Glaise D and Coulouarn C (2021) A minimal subset of seven genes associated with tumor hepatocyte differentiation predicts a poor prognosis in human hepatocellular carcinoma. *Cancers (Basel)* **13**, 5624.

20 Kaposi-Novak P, Lee JS, Gomez-Quiroz L, Coulouarn C, Factor VM and Thorgeirsson SS (2006) Met-regulated expression signature defines a subset of human hepatocellular carcinomas with poor prognosis and aggressive phenotype. *J Clin Invest* **116**, 1582–1595.

21 Lee JS, Chu IS, Heo J, Calvisi DF, Sun Z, Roskams T, Durnez A, Demetris AJ and Thorgeirsson SS (2004) Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. *Hepatology* **40**, 667–676.

22 Lee JS, Heo J, Libbrecht L, Chu IS, Kaposi-Novak P, Calvisi DF, Mikaelyan A, Roberts LR, Demetris AJ, Sun Z et al. (2006) A novel prognostic subtype of human hepatocellular carcinoma derived from hepatic progenitor cells. *Nat Med* **12**, 410–416.

23 Roessler S, Jia HL, Budhu A, Forgues M, Ye QH, Lee JS, Thorgeirsson SS, Sun Z, Tang ZY, Qin LX et al. (2010) A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. *Cancer Res* **70**, 10202–10212.

24 Coulouarn C, Corlu A, Glaise D, Guenon I, Thorgeirsson SS and Clement B (2012) Hepatocyte-stellate cell cross-talk in the liver engenders a permissive inflammatory microenvironment that drives progression in hepatocellular carcinoma. *Cancer Res* **72**, 2533–2542.

25 Lee JS and Thorgeirsson SS (2002) Functional and genomic implications of global gene expression profiles in cell lines from human hepatocellular cancer. *Hepatology* **35**, 1134–1143.

26 Staib F, Krupp M, Maass T, Itzel T, Weinmann A, Lee JS, Schmidt B, Muller M, Thorgeirsson SS, Galle PR et al. (2014) CellMinerHCC: a microarray-based expression database for hepatocellular carcinoma cell lines. *Liver Int* **34**, 621–631.

27 Caruso S, Calatayud AL, Pilet J, La Bella T, Rekik S, Imbeaud S, Letouze E, Meunier L, Bayard Q, Rohr-Udilova N et al. (2019) Analysis of liver cancer cell lines identifies agents with likely efficacy against
hepatocellular carcinoma and markers of response.
*Gastroenterology* **157**, 760–776.

28 Cui JF, Liu YK, Zhang LJ, Shen HL, Song HY, Dai Z, Yu YL, Zhang Y, Sun RX, Chen J *et al.* (2006) Identification of metastasis candidate proteins among HCC cell lines by comparative proteome and biological function analysis of S100A4 in metastasis in vitro. *Proteomics* **6**, 5953–5961.

29 Agioutantis PC, Loutrari H and Kolisis FN (2020) Computational analysis of transcriptomic and proteomic data for deciphering molecular heterogeneity and drug responsiveness in model human hepatocellular carcinoma cell lines. *Genes (Basel)* **11**, 623.

30 Bertran E, Crosas-Molist E, Sancho P, Caja L, Lopez-Luque J, Navarro E, Egea G, Lastra R, Serrano T, Ramos E *et al.* (2013) Overactivation of the TGF-beta pathway confers a mesenchymal-like phenotype and CXCR4-dependent migratory properties to liver tumor cells. *Hepatology* **58**, 2032–2044.

31 Yoshida Y, Nijman SM, Kobayashi M, Chan JA, Brunet JP, Chiang DY, Villanueva A, Newell P, Ikeda K, Hashimoto M *et al.* (2009) Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. *Cancer Res* **69**, 7385–7392.

32 Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP and Tamayo P (2015) The molecular signatures database (MSigDB) hallmark gene set collection. *Cell Syst* **1**, 417–425.

33 Hsiao LL, Dangond F, Yoshida T, Hong R, Jensen RV, Misra J, Dillon W, Lee KF, Clark KE, Haverty P *et al.* (2001) A compendium of gene expression in normal human tissues. *Physiol Genomics* **7**, 97–104.

34 Giannelli G, Mikulits W, Dooley S, Fabregat I, Moustakas A, ten Dijke P, Portincasa P, Winter P, Janssen R, Leporatti S *et al.* (2016) The rationale for targeting TGF-beta in chronic liver diseases. *Eur J Clin Invest* **46**, 349–361.

35 Herbertz S, Sawyer JS, Stauber AJ, Gueorguieva I, Driscoll KE, Estrem ST, Cleverly AL, Desaiah D, Guba SC, Benhadji KA *et al.* (2015) Clinical development of galunisertib (LY2157299 monohydrate), a small molecule inhibitor of transforming growth factor-beta signaling pathway. *Drug Des Devel Ther* **9**, 4479–4499.

36 Massague J (2012) TGFbeta signalling in context. *Nat Rev Mol Cell Biol* **13**, 616–630.

37 Dituri F, Mazzocca A, Fernando J, Peidro FJ, Papappicco P, Fabregat I, De Santis F, Paradiso A, Sabba C and Giannelli G (2013) Differential inhibition of the TGF-beta signaling pathway in HCC cells using the small molecule inhibitor LY2157299 and the D10 monoclonal antibody against TGF-beta receptor type II. *PLoS One* **8**, e67109.

38 Jiang Y, Sun A, Zhao Y, Ying W, Sun H, Yang X, Xing B, Sun W, Ren L, Hu B *et al.* (2019) Proteomics

identifies new therapeutic targets of early-stage hepatocellular carcinoma. *Nature* **567**, 257–261.

39 Cao Y, Agarwal R, Dituri F, Lupo L, Trerotoli P, Mancarella S, Winter P and Giannelli G (2017) NGS-based transcriptome profiling reveals biomarkers for companion diagnostics of the TGF-beta receptor blocker galunisertib in HCC. *Cell Death Dis* **8**, e2634.

40 Strybel U, Marczak L, Zeman M, Polanski K, Mielanczyk L, Klymenko O, Samelak-Czajka A, Jackowiak P, Smolarz M, Chekan M *et al.* (2022) Molecular composition of serum exosomes could discriminate rectal cancer patients with different responses to neoadjuvant radiotherapy. *Cancers (Basel)* **14**, 993.

41 Caillot F, Hiron M, Goria O, Gueudin M, Francois A, Scotte M, Daveau M and Salier JP (2009) Novel serum markers of fibrosis progression for the follow-up of hepatitis C virus-infected patients. *Am J Pathol* **175**, 46–53.

42 Hou M (2022) Exploring novel independent prognostic biomarkers for hepatocellular carcinoma based on TCGA and GEO databases. *Medicine (Baltimore)* **101**, e31376.

43 Mancarella S, Krol S, Crovace A, Leporatti S, Dituri F, Frusciante M and Giannelli G (2019) Validation of hepatocellular carcinoma experimental models for TGF-beta promoting tumor progression. *Cancers (Basel)* **11**, 1510.

44 Caja L, Dituri F, Mancarella S, Caballero-Diaz D, Moustakas A, Giannelli G and Fabregat I (2018) TGF-beta and the tissue microenvironment: relevance in fibrosis and cancer. *Int J Mol Sci* **19**, 1294.

**Supporting information**

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Fig. S1.** SNU-449 and PLC/PRF/5 cell lines exhibit a mesenchymal- and epithelial-like phenotype, respectively. (A) Immunofluorescence micrographs of epithelial (CDH1) and mesenchymal (Vimentin, VIM) markers in SNU-449 and PLC/PRF/5 cells at basal level. Scale bar: 50µm. (B) Quantitative PCR analysis of CDH1 and VIM expression. ***P < 0.001; n=3. (C) Quantitative analysis by ELISA of TGFβ protein expression in the supernatant of SNU-449 and PLC-PRF-5 cells after 48 hours of culture. In (B) and (C) data are presented as bar plots with mean ± SD and were compared using the Student's t test, ***P < 0.001 (n = 3 independent experiments).

**Fig. S2.** Relative expression of genes encoding liver-enriched transcription factors in PLC/PRF/5 (upper panels) and SNU-449 (lower panels) HCC cell lines at
basal level (CTRL) as well as upon TGFβ (TGFb) and galunisertib (LY) exposure, alone (LY) or in combination (TGFb+LY). Data are presented as bar plots with mean ± SD and were compared using the Student's t test, \*P < 0.05 (n = 3 independent experiments).

**Fig. S3.** Gene set enrichment analysis (GSEA) of non-SMAD PI3K (upper 4 panels) and NFKB (lower 4 panels) signaling pathway signatures in the gene expression profiles of PLC/PRF/5 (A) and SNU-449 (B) HCC cell lines upon TGFβ exposure, alone (TGFβ) or in combination with galunisertib (TGFβ+galunisertib). The following curated signatures have been used: REACTOME_CONSTITUTIVE_SIGNALING_BY_ABERRANT_PI3K_IN_CANCER.v2023.1.Hs.grp and NFKAPPAB_01.v2023.1.Hs.grp (https://www.gsea-msigdb.org/gsea/msigdb cards/). NES, normalized enrichment score, as determined by GSEA.

**Fig. S4.** Gene set enrichment analysis (GSEA) of human HCC tumors (139 cases from the GSE1898 and GSE4024 datasets) defined by the signature of genes differentially expressed between SNU-449 and PLC/PRF/5 cell lines at basal level (A), as described in

Fig. 1E, or upon galunisertib exposure in SNU-449 cell line (B), as described in Fig. 3C or in PLC/PRF/5 cell line (C), as described in Fig. 3F. GSEA was focused on HOSHIDA_LIVER_CANCER SUBCLASS_S1 (left panels) or S3 (right panels) signatures.

**Fig. S5.** Clinical relevance of human HCC (n=139 cases, GSE1898 and GSE4024) defined by a high (n=70 cases) versus low (n=69 cases) expression of SERPINF2 (median SERPINF2 expression was used to define high versus low expressing groups). Statistical analysis (chi-squared test) of HCC distribution between high versus low SERPINF2 expressing groups was based on previous gene expression signatures and clinical parameters.

**Table S1.** List of genes differentially expressed between the PLC/PRF/5 and SNU-449 cell lines (fold-change FC > 2; P < 0.001).

**Table S2.** List of genes differentially expressed by galunisertib in SNU-449 cells (fold-change FC > 2; P < 0.01).

**Table S3.** List of genes differentially expressed by galunisertib in PLC/PRF/5 cells (fold-change FC > 2; P < 0.01).