# BET inhibition revealed varying MYC dependency mechanisms independent of gene alterations in aggressive B-cell lymphomas

Loris Delrieu, Sieme Hamaidia, Emilie Montaut, Andrea Cecilia

Garcia-Sandoval, Camille Teste, Patricia Betton-Fraisse, Thierry Bonnefoix, Sylvain Carras, Rémy Gressin, Christine Lefebvre, et al.

► To cite this version:

Loris Delrieu, Sieme Hamaidia, Emilie Montaut, Andrea Cecilia Garcia-Sandoval, Camille Teste, et al.. BET inhibition revealed varying MYC dependency mechanisms independent of gene alterations in aggressive B-cell lymphomas. Clinical Epigenetics, 2024, 16 (1), pp.185. 10.1186/s13148-024-01788-7. inserm-05011277

HAL Id: inserm-05011277

https://inserm.hal.science/inserm-05011277v1

Submitted on 29 Mar 2025

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_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH

Open Access

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

Check for
updates

# BET inhibition revealed varying MYC dependency mechanisms independent of gene alterations in aggressive B-cell lymphomas

Loris Delrieu¹, Sieme Hamaidia¹,², Emilie Montaut¹,², Andrea Cecilia Garcia-Sandoval¹, Camille Teste¹, Patricia Betton-Fraisse¹, Thierry Bonnefoix¹,², Sylvain Carras¹,³,⁴, Rémy Gressin¹,⁴, Christine Lefebvre¹,³, Jérôme Govin¹ and Anouk Emadali¹,²*

**Abstract**

**Background** MYC-driven lymphomas are a subset of B-cell lymphomas characterized by genetic alterations that dysregulate the expression of the *MYC* oncogene. When overexpressed, typically through chromosomal translocations, amplifications, or other mechanisms, MYC can drive uncontrolled cell growth and contribute to cancer development. MYC-driven lymphomas are described as aggressive entities which require intensive treatment approaches and can be associated with poor prognosis. In the absence of direct MYC-targeting therapy, epigenetic drugs called BET inhibitors (BETi) were shown to reduce MYC levels by disrupting BRD4-dependent transcription associated with the expression of *MYC*, as well as other oncogenes. Here, we used BETi as molecular tools to better understand oncogenic dependencies in a panel of cell line models of MYC-driven B-cell lymphoma selected to represent their genetic heterogeneity.

**Results** We first showed that, in these models, MYC expression level does not strictly correlate to the presence of gene alterations. Our data also demonstrated that BETi induces similar growth arrest in all lymphoma cell lines independently of *MYC* mutational status or expression level. In contrast, BETi-induced cell death was only observed in two cell lines presenting the highest level of MYC protein. This suggests that some MYC-driven lymphoma could present a stronger dependency on MYC for their survival which cannot be predicted on the sole basis on their genetics. This hypothesis was confirmed by gene invalidation experiments, which showed that MYC loss recapitulates the effect of BETi treatment on both cell proliferation and survival, confirming MYC oncogene dependency in models sensitive to BETi cytotoxicity. In contrast, the growth arrest observed in cell lines resistant to BETi-induced apoptosis is not mediated through MYC, but rather through alternative pro-proliferative or oncogenic pathways. Gene expression profiling revealed the basal activation of a specific non-canonical WNT/Hippo pathway in cell death-resistant cell lines that could be targeted in combination therapy to restore BETi cytotoxicity.

**Conclusion** This work brings new insights into the complexity of MYC-dependencies and unravels a novel targetable oncogenic pathway in aggressive B-cell lymphomas.

**Keywords** MYC, BET inhibition, B-cell lymphoma, Oncogene dependency

<footnote>*Correspondence:
Anouk Emadali
anouk.emadali@univ-grenoble-alpes.fr
Full list of author information is available at the end of the article</footnote>
## Background

B-cell lymphomas enclose a diverse group of hematological malignancies arising from B lymphocytes, with varying clinical presentations and prognoses. These highly heterogeneous pathologies are categorized based on their histological, immunophenotypic, and genetic features, covering a wide range of distinct entities [1]. The dysregulation of MYC oncogene has emerged as a critical factor in the pathogenesis of several subtypes of B-Cell lymphomas such as Diffuse Large B-Cell Lymphoma (DLBCL) and Burkitt lymphoma (BL) [2]. MYC is a central hub in the growth regulatory signaling networks and its dysregulation has been shown to severely impact growth regulatory signaling leading to uncontrolled cell growth and contributing to lymphoma development [3].

MYC alterations, including translocations, amplifications or mutations, are frequently observed in B-cell lymphomas, leading to MYC overexpression and oncogenic activity. Chromosomal translocations involving *MYC* and immunoglobulin (IG) loci, such as t(8;14) (q24;q32) /IGH::MYC, t(8;22)(q24;q11)/IGL::MYC and t(2;8):(p11;q24)/IGK::MYC, are characteristic of BL and sometimes observed in DLBCL, resulting in *MYC* activation by *IG* enhancers [4]. In addition, the amplification of the *MYC* locus or alterations in its regulatory elements contribute to MYC overexpression in various lymphoma subtypes [2].

The overexpression of MYC in DLBCL is associated with an aggressive clinical behavior and a poor prognosis, especially when co-occurring with BCL2 in subcategories defined as “double expressors” and representing 20–30% of cases. In addition, B-cell lymphomas harboring dual *MYC* and *BCL2* rearrangements have been described as particularly aggressive and treatment refractory [5, 6]. This entity, presenting features of both DLBCL and BL, previously referred as “Double Hit Lymphoma” (DHL), has been recently reassigned as “High-grade B-cell lymphoma with *MYC* and *BCL2* rearrangements” (HGBL *MYC/BCL2*) in the current WHO of hematolymphoid tumors [7]. HGBL also encompasses rare cases with a triple rearrangement “triple-hit” *MYC/BCL2/BCL6* [7]. A DHL-specific gene expression signature has been described and associated with disease aggressivity and adverse clinical outcome [8]. Noteworthy, it can be found in absence of any detectable *MYC* rearrangement suggesting that DHL signature can be activated independently of chromosomal translocations by other mechanisms of MYC dysregulation including MYC stabilizing point mutations [8].

MYC-driven lymphomas, and especially HGBL, frequently exhibit high proliferative index, resistance to conventional therapies and increased risk of relapse. Targeting MYC would clearly represent an attracting

therapeutical strategy, but the development of direct MYC inhibitors has been challenging due to its transcriptional nature and lack of druggable binding sites [9].

BET (Bromodomain and Extra-Terminal domain) inhibition emerged as a potent epigenetic therapy for MYC-driven malignancies [10]. It is based on the development of small inhibitory molecules targeting BET proteins [11, 12], a family of chromatin readers that include four members in humans: BRD2, BRD3, BRD4 and a testis-specific variant BRDT. BET proteins share the same structure with two bromodomains modules (BD1 and BD2) and an extra-terminal (ET) domain in their C-terminal moiety. BET bromodomains recognize acetylated lysines on histone and non-histone proteins, whereas the ET domain can interact with other nuclear proteins, creating a molecular platform that can modulate epigenetic signaling [13]. BRD4, the most extensively studied family member, has been shown to be involved in Polymerase II-mediated transcription by recruiting several transcriptional regulators, including the Mediator complex and activating transcription elongation factor b (P-TEFb) to gene promoters and enhancers [13].

BET inhibitors (BETi) anti-tumor activity has initially been attributed to their repressive effect on *MYC* transcription in MYC-driven malignancies, such as multiple myeloma [14], acute myeloid leukemia and Burkitt lymphoma [15]. Later on, two major studies revealed that BETi preferentially suppress BRD4-dependent transcription mediated by super-enhancers in multiple myeloma [16] and lymphoma [17] affecting oncogenic and lineage-specific transcriptional circuits. BETi therapeutic activity can therefore be attributed to specific subsets of target genes whose expression would be “hypersensitive” to BET inhibition in a cell-specific context. This has raised hope about the potential of BETi to treat DLBCL and HGBL where MYC, or other oncogene expression, is placed under the control of BRD4-dependent super-enhancers, such as *IG* regulatory regions.

The cellular response to diverse BETi chemical scaffolds has largely been studied in aggressive B-Cell lymphoma [15, 17–24] using various cellular and mice models of DLBCL, BL and/or HGBL. A large majority of lymphoma cells displayed a G1 cell cycle arrest correlated with a decrease in MYC expression upon BETi treatment. This observation is expected when chromosomal rearrangements place *MYC* under the control of BET-dependent regulatory sequences, like *IG* super-enhancers, but is also observed in cellular models with wild-type *MYC* gene locus [15, 17, 18, 24]. Moreover, a reduction in MYC expression does not always correlate with BET inhibition’s impact on cell proliferation [15]. BETi have also been reported to induce cell apoptosis or senescence in several B-cell lymphoma models [15, 17–19, 24],
but whether this cytotoxicity depends on MYC status
remains unclear.

Despite the promising results obtained mainly in blood
cancers, BETi clinical development has been impeded
by high toxicity levels, potentially caused by off-target
effects related to their lack of selectivity toward the 8
human BET BDs [25]. Indeed, this first-generation of
BETi, referred to as pan-BETi, can bind all 8 BET bromo-
domains. Recent studies have demonstrated the potential
of a new generation of inhibitors selective toward BET
BD1 or BD2 domains as anti-tumor or anti-inflamma-
tory agents [26, 27]. These compounds have not yet been
evaluated in the context of B-Cell lymphoma. Nowadays,
BETi clinical potential is mostly evaluated in combina-
tion therapy rather than single agent, but the severe side
effects, modest response rates and lack of biomarkers to
predict which patients could benefit from this therapy
compromise the future of these molecules to treat hema-
tological malignancies, as well as solid tumors [25].

Here, we postulated that BETi of various scaffold and selectivity can be used as molecular tools to modulate MYC expression in order to (i) better understand MYC-dependencies in B-cell lymphoma in relation with their gene alterations and (ii) identify biomarkers and other oncogenic pathways associated with response to BET inhibition in MYC-driven lymphoma. In this setting, we performed an extensive genetic characterization of a panel of HGBL, DLBCL and BL cell lines. We then utilized both pan- and selective BETi to downregulate MYC and examined their impact on gene expression, cell proliferation, and cell death in relation to MYC mutational and rearrangement status. This revealed strong MYC oncogene dependencies in some models that could be predicted by higher MYC protein level rather than any other parameters. Gene expression profiling was performed to find oncogenic pathways that could contribute to MYC-driven lymphoma pathogenesis, leading to the identification of a non-canonical WNT/Hippo pathway that can be targeted in a combination therapeutic strategy to restore BETi sensitivity in resistant MYC-driven lymphoma.

**Methods**

**Cell lines and culture conditions**

OCI-Ly1, OCI-Ly18, SUDHL-6, SUDHL-4, HT, HBL-1, NUDUL-1 (DLBCL), RAJI and RAMOS (BL) cell lines were purchased from DSMZ. B593 DLBCL cell line was developed in house [28]. NUDUL-1, OCI-Ly18, RAMOS cell lines were cultured in RPMI 1640 medium supplemented with 20% fetal bovine serum (FBS), 1% glutamine, 100 mg/mL penicillin/streptomycin. SUDHL-4, SUDHL-6, HT, B593 and RAJI cell lines were cultured in RPMI Glutamax supplemented with 20% FBS, 1 mM

sodium pyruvate, 1% minimum essential medium non-essential amino acids (MEM NEAA) and 100 mg/mL penicillin/streptomycin. OCI-Ly1 cell line was grown in IMDM medium with 20% FBS and 100 mg/mL penicillin/streptomycin. Cell culture reagents were purchased from Gibco. All cells were grown at 37 °C, 5% CO₂.

**Karyotype and FISH**

B593, OCI-Ly18, OCI-Ly1, SUDHL-6, SUDHL-4, HT, HBL-1 and NUDUL-1 were prepared in a supplemented cell culture medium for cytogenetic analyses including RPMI, 15% FBS, 1% L-glutamine and 1% penicillin. Karyotype was performed on cell suspension following 17 h unstimulated culture. Metaphase preparation slides for chromosome banding analysis were stained to reveal R-banding patterns. Karyotypes were described according to the current version of International System for Human Cytogenetic Nomenclature [29]. Metaphase and interphase fluorescence hybridization (FISH) studies were performed on cytogenetic pellet using Dual Color breakapart probes (BCL2, BCL6 and c-MYC, Cytocell) to detect the presence of BCL2, BCL6 and/or MYC rearrangement. The detection of IGH::MYC rearrangement was performed using a dual color dual-fusion probe (XL IGH::MYC DF, Metasystems). Slides were fixed in methanol/acetic acid 3:1 for 30 min, air dried and dehydrated through graded alcohols. Both FISH probes and target DNA were denatured simultaneously for 1 min at 73 °C, and incubated for 20 h at 37 °C. The post-hybridization washes were performed according to the manufacturer's instructions (Cytocell, Metasystems). A total of 100 nuclei were analyzed for each slide. FISH results on metaphases were interpreted with the karyotype for all cell lines tested.

**Optical genome mapping (OGM)**

OGM was performed for the following cell lines: B593, NUDUL-1, SUDHL-4, SUDHL-6, OCI-Ly1, OCI-Ly18 and HT.

**DNA extraction and direct enzymatic labeling**

Ultra-high-molecular-weight genomic DNA (UHMW gDNA) was extracted manually from 1.5 million cells using the Prep SP Frozen Cell Pellet DNA Isolation kit according to the manufacturer's instructions (Bionano Genomics). Briefly, the samples were lysed and digested with Proteinase K, RNase A and Lysis and Binding buffer. DNA was precipitated with isopropanol and bound to a nanobind magnetic disk. Subsequently, DNA was resuspended in the elution buffer and quantified with Qubit Broad Range double-stranded DNA assay kits (ThermoFisher Scientific). UHMW gDNA was incubated for 48 h at room temperature for homogenization. DNA
labeling was performed following manufacturer's protocols (Bionano Genomics). Standard Direct Label Enzyme 1 (DLE-1) reaction was carried out using 750 ng of UHMW gDNA. DNA was labeled at a sequence-specific motif (CTTAAG) with DL-green fluorophores. Thereafter, DLE-1 enzyme was digested using puregene proteinase K (Qiagen) and excess DL-green fluorophores were removed with an adsorption membrane in a microtiter plate. The labeled DNA were counterstained (blue backbone staining) and homogenized for 48 h at room temperature before quantification using Qubit High Sensitivity double-stranded DNA assay kits (ThermoFisher Scientific).

## Data collection and quality metrics

The labeled DNA molecules were loaded on a Saphyr G2.3 chip and molecules were imaged using the Saphyr instrument (Bionano GenomicsSingle linearized DNA molecules travel through nanochannels by electrophoretic migration. For each cell line, multiple cycles were run to collect 800 Gb of data per cell line and reach an average genome coverage of 180X that is sufficient for accurate genomic analysis of a cell line. Specific quality control parameters were evaluated for each cell line to achieve a valid OGM analysis, namely, a map rate ≥ 70%, a label density of 14 to 17 (labels per 100 kbp) and an appropriate labeled DNA length (N50) ≥ 230 kbp of filtered DNA (> 150 kbp) with ≥ 9 labeled sites, according to the manufacturer's instructions.

## OGM variant calling and data filtering

The capture images were converted to a barcode pattern and aligned to the human genome reference (GRCh38) for annotation. Data were analyzed using Bionano Access software. We applied the two methods provided by the supplier to identify both structural variants (SV) and copy-number variants (CNV): (i) the rare variant analysis (RVA) pipeline detects SV on the basis of the differences between aligned barcode patterns and the reference genome; this RNA pipeline enables to identify SV and CNV occurring at low frequency (about 10%) and is therefore largely recommended for hematological neoplasms, (ii) The De Novo assembly (DN) pipeline consists of a first reconstruction of the sample genome, which is subsequently compared to the reference genome; this DN pipeline is commonly used to identify germline chromosomal abnormalities but offers three advantages in a context of B-cell lymphoma cell lines: first, a high resolution, enabling detection of fine, cryptic or subtelomeric anomalies (like IGH/14q32.3 rearrangements); second, the capacity of detection of copy neutral loss of heterozygosity (cn-LOH); third, as a complementary and confirmation method together with the RNA pipeline.

## RNA extraction & RT-qPCR

Total RNA was extracted from 3 million cells with NucleoSpin RNA extraction kit (Macherey-Nagel) according to manufacturer's instructions. Reverse transcription was carried out following RT SuperscriptTM III kit (Invitrogen RT) from 1 µg extracted RNA. Target genes were amplified using PCR primers (MYCforw GGCTCCTGG CAAAAGGTCA, MYCrev CTGCGTAGTTGTGCTGAT GT, BCL2forw GTGGATGACTGAGTACCTGAAC, BCL2rev GAGACAGCCAGGAGAAATCAA, BCL6forw TCTGGAGAGAAGCCCTACAA, BCL6rev CCACAG ATTTCACAGGGATAGG, GAPDHforw CCACTCCTC CACCTTTGAC, GAPDHrev ACCCTGTTGCTGTAG CCA) and quantified by qPCR on a thermocycler CF384 Touch Real-Time PCR (Bio-Rad). Ct values (cycle threshold) were analyzed with CFX ManagerTM Software (Bio-Rad) and normalized to GAPDH.

## RNA sequencing

Cell lines were treated with 500 nM JQ1 for 24 h. Total RNA was extracted and purified according to the procedure mentioned in the RNA extraction & qPCR section. RNA paired-ended sequencing and stranded library were performed by BGI Genomics or GenomEast (IGBMC, Strasbourg, France) sequencing facilities from 500 ng total RNA. Sequencing depth was at least 30 million reads per sample. Reads were quality-checked using FastQC. Reads were then aligned to *H. sapiens* genome build hg38 using STAR 2.7.1a (Dobin et al., 2013). Raw read counts for each gene were calculated using HTSeqCount 0.11.2 using defaults parameters [30]. Read count data was normalized using RPM normalization and DESeq2 1.32. [31]. Analyses were performed with R release 4.1.0. DESeq2 normalized read counts were used to identify differentially expressed genes with an adjusted *p* value < 0.05 and a fold change below –1.5 or above 1.5. Enrichment analysis were performed using GSEA software [32] and Cluster Profiler R package [33].

## Western blot

Proteins were extracted from 5 million cells with 100 µL of RIPA buffer (150 mM NaCl, 50 mM Tris-HCl pH 8.0, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, 1% NP-40). Lysates were incubated 30 min at 4 °C under agitation. Samples were then sonicated with VibraCell sonicator (15 s ON/OFF for 1 min per sample). Then, samples were centrifuged 20 min at 16,000 g and 4 °C to clear insoluble material and quantified with Bradford protein assay. 10 µg of each sample was loaded on a gradient gel NuPAGE 4–12% Bis-Tris (Invitrogen) and run in MES buffer (Invitrogen). The proteins were transferred on an activated PVDF membrane for 1 h at 100 V at 4 °C in Tris Glycine 10% ethanol. Membranes were
blocked in either 5% milk or BSA in PBS-Tween (PBST) or TBS-Tween (TBST) for 1 h, and incubated overnight with primary antibodies under constant rotation at 4 °C. The following primary antibodies were used: anti-MYC 1/1,000 TBST 5% BSA (rabbit, #A9169, Cell Signaling), anti-BCL2 1/1,000 PBST 5% milk (mouse, #sc-7382, Santa-Cruz Biotechnology), anti-BCL6 1/2,000 PBST 5% milk (mouse, #sc-7388, Santa-Cruz Biotechnology), anti-BRD4 1/1,000 PBST 5% milk (rabbit, #A301-985A, Bethyl laboratories), anti-BRD3 1/1,000 PBST 5% milk (rabbit, #A302-368A, Bethyl laboratories), anti-BRD2 1/2,000 PBST 5% milk (rabbit, #A302-583A, Bethyl laboratories), anti-αTubulin 1/5,000 PBST 5% milk (mouse, #T5168, Merck), anti-H4 1/5,000 PBST 5% milk (rabbit, #05-858, Upstate), anti-WNT5A/B 1/1,000 TBST 5% BSA (rabbit, #2530, Cell Signaling), anti-YAP 1/1,000 TBST 5% BSA (mouse, #sc-101199, Santa-Cruz Biotechnology) and anti-pan-TEAD 1/1,000 TBST 5% BSA (rabbit, #13,295, Cell Signaling). The membranes were washed three times in PBST or TBST, incubated with the following secondary antibody: goat anti-rabbit IgG peroxidase 1/10,000 (#A9169-2ML, Merck) or polyclonal rabbit anti-mouse IgG peroxidase 1/2,000 (#P026002-2, Agilent) for 1 h at room temperature, washed three times and revealed with Luminol/peroxidase mix Clarity Western ECL (Biorad). Acquisitions were realized using Vilber imaging system. Signal quantification was performed with ImageJ.

### Establishment of inducible MYC knockdown cell lines

shRNA sequences either targeting MYC (shMYCA: GCA ATCACCTATGAACTTGTT and shMYCB: GACGAC GAGACCTTCATCAAA) [34] or non-targeting (shControl: CCTAAGGTTAAGTCGCCCTCG) were used. Oligos were annealed and ligated by T4 ligase with the pLK0 Tet-On inducible plasmid previously digested with AgeI/EcoRI [35]. Lentiviral particles were produced by the ANIRA platform (ENS Lyon, France). B593, SUDHL-4 and OCI-Ly1 cell lines were transduced with lentiviral particles at multiplicity of infection (MOI) of 10, and NUDUL-1 with a MOI of 20. Viral particles were washed away after 24 h. 1 µg/mL of puromycin was added at day 3 post-infection. Induction was performed during 72 h with 200 ng/mL doxycycline for B593, SUDHL-4 and OCI-Ly1 and with 500 ng/mL doxycycline during 72 h for NUDUL-1. Knockdown efficiency was assessed by Western Blot.

### Cell cycle analysis

1 million cells were harvested and washed with PBS. Cell pellets were resuspended in 100µL PBS and fixed with 900µL of cold ethanol (70%) added drop by drop under constant agitation of the sample collection tube. After 30 min, the samples were stored at –20 °C for some days.

The day of the analysis, samples were slowly thawed at 4 °C and then centrifuged (2000 rpm, 10 min). Cell pellets were resuspended in 1 mL of PBS in order to complete rehydration of the fixed samples during 4 h at 4 °C. After rehydration, cells were collected by centrifugation (2000 rpm, 5 min) and resuspended in 1 mL PBS, 10 µg/ mL of RNAse A (Sigma Aldrich) and 10 µg/mL propidium iodide (Sigma Aldrich). Samples were incubated at room temperature in the dark during 30 min before being analyzed by flow cytometry (LSRII, BD Biosciences). Data were analyzed using ModFit LT software.

### Assessment of cell proliferation and cell death

Cell viability and proliferation were determined by dual Annexin V-FITC (BD Biosciences) and propidium iodide (Sigma Aldrich) staining and by numeration of the absolute count of fluorescent beads (Precision count beads, Biolegends) and cells ratio. 0.1 µg/mL Annexin V was diluted in Binding buffer solution (0.1 M HEPES pH7.4, 1.4 M NaCl, 25 mM $CaCl_2$). Next, cells were harvested and washed with PBS and stained in 100 µL of Annexin V staining mix at room temperature for 10 min in the dark. Then, 100 µL of Binding Buffer 1X and 50 µL of Precision count beads were added. Finally, 5 µg/mL propidium iodide is added at the last minute before flow cytometry analysis (10,000 events analyzed per condition). Live cells, apoptotic cells, and fluorescent beads populations were gated in order to get the number of events for each population. Cell viability was determined by the ratio of the number of alive cells events (AnV - /IP -) and that of apoptotic cells events (AnV + /IP -, AnV + /IP +) comparing to the total number of events. Cell proliferation was determined based on absolute cell counts calculated as follow: absolute cell count (cells/µl) = (cell count/"precision count beads" counts) x "precision count beads" concentration (beads/µl). Data were acquired using LSRII (BD Biosciences) or Attune NxT (Thermo Fisher Scientific) flow cytometer.

### Drug treatments

Dose response experiments were performed on 9 DLBCL, HGBL and BL cell lines using 6 BETi of different scaffold and selectivity using concentrations ranging from 1 nM to 1 mM. We chose to test diazepine (JQ1, IBET-762) and tetrahydroquinoline (IBET-726) pan-BETi, as well as 3 distinct BD2-selective molecules (ABBV-744, RVX-208 and GSK-046) that have not been evaluated in the context of B-Cell lymphoma. JQ1 was synthesized as described previously [36]. IBET-726, IBET-762 and RVX-208 have been purchased from Selleckchem. GSK-046 from Chemietek and ABBV-744 from Abmole. All compounds were dissolved in DMSO, which was used as a negative control. Cell lines were
treated using 500 nM JQ1 or ABBV-744 for the indi-
cated time (between 6 and 72 h).

IC50 were calculated using GraphPad Prism. CA-3
was purchased from Euromedex (#SE-S8661-5MG) and
used in dose-response experiments alone or in com-
bination with JQ1 using concentrations ranging from
1 nM to 100 µM. Bliss synergy scores were calculated
using SynergyFinder 3.0 [37].

Statistics

Statistical analyses and graphical representations were
performed with R (release 4.1.0) or GraphPad Prism
(v10) as indicated.

Mixed nonlinear statistical modeling

The general specification of models is: Mij = b0 + b1
Treatmentij + b2 Gene Levelij + b3 Treatmentij X
Gene Levelij + uj, where Mij is the number of dead
cells of data i within cell line j, beta0 is the fixed inter-
cept term, b1 is the fixed coefficient of the covariate
Gene Level (RNA-seq corresponding to gene expres-
sion), b2 is the fixed coefficient of the covariate Treat-
ment representing the effect of JQ1 treatment versus
control DMSO, b3 represents the fixed effect associated
with the Treatment by Gene Level interaction and uj
is the random effect associated with the intercept for
cell line j. Nonlinear relationship between cell mortality
and Gene Level was explored by restricted cubic spline
(RCS) modeling. Models were fitted via restricted
maximum log likelihood (REML). All statistical analy-
ses from models were performed after non-parametric
bootstrap data resampling with replacement (2000 rep-
licates) with bootstrap samples taken independently
within each cell line considered as stratum. The overall
statistical significance of the interaction terms between
Treatment covariate and RCS covariates evaluating
the nonlinearity hypothesis was tested by a Wald test.
A REML-based likelihood ratio test was used to test
the variance of the random cell line effects. Both non-
linearity of models and random effects were retained
with p < 0.001. Statistical significance of predicted JQ1/
DMSO ratios of cell mortality number at various per-
centiles of Gene Level (percentile 10th to percentile
100th) were performed by a Wald test. Elasticity (E)
was used to compare the effect of different genes on cell
mortality M. The elasticity of M with respect to Gene_
Level is the proportional (%) change in M for a propor-
tional (1%) change in Gene Level. Data were analyzed
using Stata 16.1. Estimate margin effects (predicted
mortality) in models using transformed gene level data

with RCS was obtained by the f_able post-estimation
command.

Results

Genetic and expression profiling of B-Cell lymphoma
model cell lines

We first performed an extensive characterization of gene
alterations in a panel of 10 B-cell lymphoma cell lines,
selected to reflect disease heterogeneity. We have tested
5 HGBL (High-Grade B-Cell Lymphoma) and 3 DLBCL
(Diffuse Large B-Cell Lymphoma) cell lines, as well as
2 Burkitt lymphoma (BL) cell lines, characterized by an
IG::MYC gene rearrangement.

First, karyotyping and FISH analyses were performed using probes covering *MYC*, *BCL2* and *BCL6* loci (Figs. 1A and S1A). Involvement of *IG* gene loci together with *MYC* or *BCL2* was also specifically investigated using dual-fusion FISH probes (Figs. 1A and S1A). Concomitant *MYC* and *BCL2* gene rearrangements were detected for 4 cell lines (namely B593, OCI-Ly18, OCI-Ly1 and SUDHL-6), confirming their HGBL status. SUDHL-4 was also identified as a triple-hit with non-IG::MYC, non-IG::BCL6 and IG::BCL2 gene rearrangement. OGM was also performed to fully characterize gene rearrangements and copy-number variants for all cell lines (Figs. 1A and S1B) and further confirmed the above observations derived from conventional cytogenetic methods.

Targeted sequencing using a panel of 20 genes frequently mutated in lymphoma revealed mutations in *BCL2*, *EZH2* or *TP53* in 4 out of the 8 HGBL/DLBCL cell lines (Fig. S1C) consistently with previous reports [38, 39]. In addition, the mutational status of *MYC* second exon, presenting frequent occurrence of clustered mutations, was explored by Sanger sequencing. This analysis revealed the presence of a stabilizing T58 substitution [40] in NUDUL-1 cell line (Fig. S1C), as well as other *MYC* somatic variants in B593 and OCI-Ly18 lines.

The impact of these genetic alterations on mRNA and
protein levels has been assessed for MYC, BCL2 and
BCL6. They appear very heterogeneous among cell lines
(Fig. 1B-D). Of note, whereas mRNA and protein levels
correlate for BCL2 (Fig. 1C), this is not the case for MYC
(Fig. 1B) and BCL6 (Fig. 1D), most likely due to post-
transcriptional regulation events.

For *MYC*, the presence of *IG* or *non-IG* rearrange-
ment does not systematically drive high mRNA expres-
sion (Fig. 1E, left). Of note, *IG::MYC* gene rearrangement,
especially when co-occurring with *MYC* stabilizing muta-
tions can still translate into very high MYC protein levels
as observed for NUDUL-1 cell line (Fig. 1B). A significant
difference in BCL2 mRNA levels was observed between
normal loci and IG-rearranged cell lines, indicating
<figure><img src="image_3.png" /><figcaption>B.</figcaption></figure>

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

<figure><img src="image_5.png" /><figcaption>D.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>E.</figcaption></figure>

<figure><img src="image_7.png" /></figure>

**Fig. 1** Genetics and gene expression profiles of lymphoma model cell lines. A Summary of karyotyping, FISH (fluorescent in-situ hybridization) and optical genome mapping (OGM) cytogenetics analyses for the 10 lymphoma cell lines as indicated, CA: number of cytogenetics anomalies determined using Optical Genome Mapping (OGM), EC: extra-copies, NL: normal locus, nd: non-determined. **B-D MYC, BCL2** and **BCL6** gene (RT-qPCR data normalized to GAPDH represented as bar graphs, upper panels) and protein (representative western blot images, lower panels) expression levels for the 10 lymphoma cell lines as indicated (n = 3). Coomassie staining was used as western blot loading control. Protein quantification relative to control condition (B593) is represented below each image. Inserts in the upper right represent correlation between mRNA and protein expression (mutational status, circle: non-mutated, star: mutated). *R* and *p* value were derived from a Pearson correlation test. **E MYC, BCL2** and **BCL6** protein expression level according to their rearrangement and mutational (circle: non-mutated, star: mutated) status. *P* value are derived from a Mann-Whitney test. EC: extra-copies, NL: normal locus

a direct correlation between the rearrangement and increased mRNA expression for this gene. Of note, additional point mutations identified by targeted NGS (represented as stars) seem to be associated with even higher *BCL2* expression levels. (Fig. 1E, middle). No such correlation between gene rearrangement and expression level could be observed for *BCL6* (Fig. 1E, right).

Altogether, this systematic analysis of *MYC*, *BLC2* and *BCL6* rearrangements, mutational status and expression levels showed that *IG* and *non-IG* rearrangements are not always associated with higher protein levels in lymphoma lines. Particularly for *MYC*, additional stabilizing mutations or post-transcriptional events can also affect protein levels and mitigate the functional impact
of chromosomal rearrangements, which contribute to explain why they cannot be used at sole indicators of MYC signaling activation.

## Evaluation of lymphoma cells response to pan- and BD2-selective BETi

These 10 model cell lines were then subjected to BETi treatment. We chose to evaluate both pan-BETi (JQ1, IBET-762 and IBET-726) and BD2-selective compounds

(RVX-208, GSK-046 and ABBV-744) of different chemical scaffolds. In vitro IC50 toward BD1 and BD2 are represented in Fig. 2A. Dose-response experiments were conducted on 5 HGBL, 2 DLBCL and 2 BL cell lines to determine the impact of each of these inhibitors on cell proliferation and viability (Fig. 2B). Data could not be obtained for HBL-1 cell line due to high spontaneous mortality rate.

<figure><img src="image_8.png" /><figcaption>B.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>C.</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>D.</figcaption></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

**Fig. 2** Evaluation of BETi anti-tumor activity on lymphoma lines. **A** Plot of in vitro IC50 toward BD1 and BD2 for tested BETi compounds (data derived from supplier datasheets). **B** Schematic representation of the experimental strategy for evaluation of BETi activity toward lymphoma growth arrest and cell death **C-D**. Heatmap (left) and scatter plot (right) representation of IC50 for the 6 BETi tested across cell lines for growth arrest (C) and cell death (D) after 48 h of treatment. Cell viability and proliferation were determined by dual Annexin V-FITC and propidium iodide staining together with numeration of the absolute count of fluorescent beads as described in the Methods section. On the scatter plots each dot represent one cell line
Analysis of cell proliferation revealed a globally similar growth arrest of pan-BETi across all cell lines (Figs. 2C and S2) with IC50 in the 100 nM–1 µM range. In comparison, BD2-selective compounds exhibited a modest effect on cell proliferation except for ABBV-744 (Figs. 2C and S2). This could be expected since ABBV-744 was described to have anti-tumor activity in prostate cancer models [26], whereas RVX-208 and GSK-046 were essentially reported for their anti-inflammatory effect [12, 27, 41]. Of note, the cytostatic activity of BETi compounds on lymphoma cells seems to be related to their in vitro inhibitory activity toward BD1 bromodomains (Fig. 2A and C) independently of their molecular scaffold or activity toward BD2.

In contrast, analysis of cell viability revealed heterogeneous responses across the different cell lines, with B593 and NUDUL-1 showing a high sensitivity to BETi-induced cell death (Fig. 2D). As observed for cell proliferation, ABBV-744 was the only BD2-selective BETi impacting lymphoma cell viability (Fig. 2C), even though in a lesser extent compared to pan-BETi. Again, this is most likely due to ABBV-744 higher residual activity toward BD1 compared to other BD2-selective compounds.

Altogether these results show that pan-BETi, as well as ABBV-744, have a similar cytostatic activity in DLBCL and BL cell models independently of MYC mutational status. However, their cytotoxic activity differs between cell lines models, defining 2 classes of lymphomas lines, either sensitive or resistant to BETi-induced cell death.

We first further studied the dynamics of BETi response using one cell lines of each class to perform time-course analyses of cell proliferation and cell death in B593 and SUDHL-4 treated with 500 nM JQ1, IBET-762 or IBET-726 for 7 days (Fig. S2B). These data revealed a progressive decrease in proliferation in both cell lines and an increased apoptosis in B593, but not in SUDHL-4, consistently with data presented in Fig. 2C. MYC levels was also evaluated by RT-qPCR upon 24 h and 48 h of JQ1 treatment and wash-out. In both cell lines models (Fig. S2C, up), MYC reaches a minimal expression level 24 h post-JQ1 treatment that will be maintained upon further treatment. Upon JQ1 wash-out, MYC levels are progressively restored and reach pre-treatment levels after 48 h. Interestingly, this reversible MYC decrease perfectly correlates in both models with the G1 arrest observed by cell cycle analysis (Fig. S2C, down).

We next investigated which molecular determinants could drive these phenotypes and whether they could be related to MYC oncogenic dependency.

### BETi cytotoxicity correlates to MYC basal protein level

First, we tested whether BET protein basal levels could explain how lymphoma cells respond to BETi. Western blot analysis of BRD2-4 protein levels, revealed rather constant level of BRD4, where BRD2 and BRD3 present heterogeneous protein profiles across cell lines (Fig. S3A). However, no significant association could be observed between BRD2-4 levels and BETi-induced cell death.

We then questioned whether BETi could induce distinct BRD4 chromatin binding between cell death sensitive (B593 and NUDUL-1) and resistant cellular models (SUDHL-4 and OCI-Ly1). BRD4 levels were quantified in chromatin, as well as in nuclear and cyto-soluble fractions (Fig. S3B). These data confirmed that a 24 h treatment with 500 nM JQ1 induces a massive release of chromatin-bound BRD4, with a relocalization into soluble fractions. As expected from previously published data [26], BRD4 remains mostly associated to chromatin upon similar treatment conditions using ABBV-744 BD2-selective BETi (Fig. S3B), most likely because the BRD4 BD1 is sufficient to anchor it to chromatin. In any case, we could not highlight any association between BET release from chromatin and response to BETi in these models.

To further explore MYC, BCL2 and BCL6 involvement in BETi response in DLBCL cell lines, we treated B593 and NUDUL-1 (cell death sensitive) as well as SUDHL-4 and OCI-Ly1 (cell death-resistant) cell lines with 500 nM of JQ1 and ABBV-744 for 6 h and 24 h and evaluated MYC, BCL2 and BCL6 protein levels by western blot. We opted to use a 500 nM dose based on the IC50 determined in Fig. 2C and D and supported by previous studies indicating that this drug concentration helps minimize off-target effects [25]. The results showed that JQ1 induces a similar decrease in MYC protein level in both BETi sensitive and resistant cell lines (Fig. S3C). This indicates that MYC downregulation could not be as central as thought in the BETi anti-tumor effects. Moreover, MYC decrease is not higher in *MYC*-rearranged cells as it could be expected. Interestingly, we also noticed that the BD2-selective compound ABBV-744 induced a decrease in MYC expression only in SUDHL-4 and OCI-Ly1 cell death-resistant models. This suggests a distinct mechanism of action of BD2-selective compounds between these models. No major change in BCL2 and BCL6 could be consistently detected upon BETi treatment in sensitive or resistant cell lines, suggesting they do not play a key role in response to BETi in this setting.

Next, we investigated whether lymphoma subtype, *MYC* or *BCL2* rearrangement or mutation status could influence BETi-induced cell death, without being able to link any of these parameters to BETi cytotoxicity (Fig. 3A). Several other determinants have been previously suspected to be related to response to BETi,
<figure><img src="image_14.png" /><figcaption>B.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 3** Potential determinants of BETi cytotoxicity. A Violin plots showing JQ1-induced cell death (IC50) according to lymphoma subtype (upper left), MYC (middle) and BCL2 (left) rearrangement (upper) and mutational status (lower). p values are derived from Mann-Whitney tests. B Pearson correlation analysis between JQ1-induced cell death (IC50) and several parameters suspected to be involved in BETi response as indicated (left) and correlation plot between JQ1-induced cell death (IC50) and MYC protein levels (right). R and p value were derived from a Pearson correlation test</figcaption></figure>

including BRD4, MYC and BCL2 protein levels, MYC/BCL2 ratio and MYC/BCL2 fold decrease upon treatment (Fig. 3B, left). Correlation analyses revealed that MYC protein level is the only determinant that is strongly correlated ($R^2=0.93, p<0.001$) to BET cytotoxicity (Fig. 3B, right panel), independently of any genetic defects (Fig. 3A) or any of the parameters evaluated here (Figs. 3B, left panel and S3).

These data suggest that a high basal MYC protein level may play a central role in driving BETi-induced cell death and could potentially serve as a predictive marker for BETi response in B-cell lymphoma, possibly more reliably than MYC rearrangements or mutations. However,

as this conclusion is based on results from a limited number of cell lines, further validation across a broader range of models is needed to confirm its predictive value and general applicability.

### BETi-induced cell death is restricted to MYC-dependent lymphoma cell lines

To further characterize MYC involvement in BETi-induced lymphoma cell death, we compared the respective impact of MYC knockdown and JQ1 treatment on cell proliferation and cell cycle for B593, NUDUL-1, SUDHL-4 and OCI-Ly1 cell lines. Similar levels of MYC downregulation were achieved with both approaches
except for NUDUL-1 cell line in which knockdown effi-
cacy was limited (Fig. S4A). This might be explained by
the stabilizing T58 mutation present in this cell line.
Follow-up of cell proliferation for 72 h showed that, as
described above (Fig. 2C), JQ1 induces a growth arrest in
all four cell lines (Fig. 4A).

In contrast, MYC knockdown reduces proliferation
only in B593 and, to a lesser extent, in NUDUL-1 lines.
These data suggest that MYC is not mediating the growth
arrest observed upon BETi treatment in SUDHL-4 and
OCI-Ly1. This was further confirmed by cell cycle analy-
sis, that revealed that the G1 arrest induced by JQ1 can
only be recapitulated in B593 and, to a certain degree,
in NUDUL-1 cells (Fig. 4B). In cell viability assessment,
MYC knockdown also phenocopies JQ1-induced apop-
totic cell death (AnV+ and AnV+/PI+) in B593 (Figs. 4C
and S4B). The same tendency is observed for NUDUL-
1, even if statistical significance was not reached. In
contrast, no cell death could be detected neither for
SUDHL-4 and OCI-Ly1 upon MYC knockdown.

Taken together, these results show that B593 and
NUDUL-1 sensitivity to BETi-induced cell death could
be explained by their dependency to MYC oncogene.
Moreover, the growth arrest observed in models resistant
to BETi-induced cell death does not seem to be mediated
through MYC, but rather through alternative pro-prolif-
erative or oncogenic pathways. Although based on the
study of a limited number of cell lines, this suggests that
MYC-driven lymphomas exhibit different levels of acti-
vation and dependency on MYC signaling, even though
they present similar genetic alterations.

Resistance to BETi cytotoxicity is associated with innate
activation of non-canonical WNT pathway involving YAP/
TEAD

To get a global view of the genes and pathways affected
upon BETi treatment, we performed gene expression
profiling for the 9 HGBL, DLBCL and BL cell lines using
RNA sequencing. First, we compared changes in gene
expression profiles between control and JQ1-treated
cells. Numbers of commonly affected genes between
cell lines are detailed for downregulated (Fig. S5A, left
panel) and upregulated (Fig. S5A, right panel) genes. Few
genes were commonly affected by BETi between cell lines

regardless of whether they present similar MYC altera-
tions or sensitivity to BETi cytotoxicity. On the other
hand, a gene set enrichment analysis (GSEA) revealed
that global cellular responses were very similar for all cell
lines with alteration of pathways related to inflammation
and MYC signaling as previously reported (Fig. S5B).
Here, again no specific pathways could be related to MYC
dependency or sensitivity to BETi cytotoxicity.

We then compared for each cell line, genes affected
by copy-number variants (CNV) or chromosomal rear-
rangement and genes impacted by JQ1 treatment to
determine whether they would represent preferential
BETi-target genes (Fig. S5C). However, no such associa-
tion could be found from this set of data.

We next investigated basal gene expression profiles
between MYC-dependent, which are sensitive to BETi
cytotoxicity, and resistant DLBCL cell line models. This
revealed downregulation of 362 and up-regulation of
1741 genes in cell lines resistant to BETi-induced cell
death (Table S1). Using GSEA and gene ontology analy-
ses, we showed an enrichment in MYC targets (Fig. 5A,
up), as well as increase ribosomal and protein biosyn-
thesis activity associated with MYC signaling (Fig. 5A,
down), further confirming the link between BETi cyto-
toxicity and MYC dependency. In contrast, genes related
to WNT signaling were largely enriched in MYC-inde-
pendent cell lines resistant to BETi-induced cell death
(Fig. 5A, up). Interestingly, these same signatures were
also observed when comparing the two groups after JQ1
treatment, indicating that they still contribute to differ-
entiate them post-treatment (Fig. S5D). Consistently,
gene ontology analyses revealed enrichment in mem-
brane receptor signaling and cell fate commitment genes
(Fig. 5A, down) that could be related to WNT pathway.

We then investigated more specifically which mem-
bers of this pathway were affected. We could not detect
any difference between effectors of the canonical WNT
pathway at the transcriptional level, as exemplified by
DVL1, AXIN1 or beta-catenin (CTNNB1) (Fig. 5B,
up). However, one very specific non-canonical WNT
pathway connected with Hippo signaling described
in MEF cells [42] and breast cancer [43] seems to
be activated in cells resistant to BETi cytotoxicity as
shown by WNT5A and WNT5B increased expression

(See figure on next page.)

**Fig. 4** Response to BETi is related to MYC oncogene dependency. **A** Follow-up of proliferation for B593, NUDUL-1, SUDHL-4 and OCI-Ly1 after 500 nM JQ1 treatment or MYC doxycycline-inducible shRNA knockdown using 2 distinct MYC-targeting sequences (shMYCA and shMYCB) (n = 4). **B** Cell cycle analysis for B593, NUDUL-1, SUDHL-4 and OCI-Ly1 measured 48 h after 500 nM JQ1 treatment or MYC doxycycline-inducible shRNA knockdown using 2 distinct MYC-targeting sequences (shMYCA and shMYCB) (n = 4). **C** Apoptosis detection by Annexin V-FITC/IP staining for B593, NUDUL-1, SUDHL-4 and OCI-Ly1 measured 72 h after 500 nM JQ1 treatment or MYC doxycycline-inducible shRNA knockdown using 2 distinct MYC-targeting sequences (shMYCA and shMYCB) (n = 4)
<figure><img src="image_16.png" /><figcaption>A.</figcaption></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>B.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>C.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Fig. 4** (See legend on previous page.)</figcaption></figure>
A.

<figure><img src="image_22.png" /></figure>

B.

<figure><img src="image_23.png" /></figure>

C.

<figure><img src="image_24.png" /></figure>

D.

<figure><img src="image_25.png" /><figcaption>**Fig. 5** Gene expression profiling revealed activation of WNT/Hippo non-canonical pathway in cell lines resistant to BETi-induced cell death. A GSEA enrichment plots (up) for the most significantly enriched signatures in MYC-dependent sensitive to BETi cytotoxicity (MYC dpt, sens) compared to resistant to BETi cytotoxicity (res) cell lines and bubble plot (down) showing most significantly affected ontologies (ClusterProfiler) between the 2 groups of cell lines as indicated. B Bar graphs showing difference in gene expression (logRPM + 1 from RNA sequencing normalized counts) between MYC-dependent sensitive to BETi cytotoxicity (sens) compared to resistant to BETi cytotoxicity (res) cell lines for typical genes from canonical or non-canonical WNT pathway. p values are derived from Mann-Whitney tests. C Heatmap showing YAP and TEAD expression (logRPM + 1 from RNA sequencing normalized counts) across 7 DLBCL cell lines grouped as sensitive to BETi cytotoxicity (MYC dpt, sens) compared to resistant to BETi cytotoxicity (res). D Western blots analysis of WNT5A/B, pan-TEAD, YAP and phospho-YAP levels for 7 DLBCL cell lines (n = 3). Ponceau staining was used as western blot loading control. Signal quantification normalized to loading control and control condition (OCI-Ly18) is displayed beneath each image</figcaption></figure>

(Fig. 5B, down). Accordingly, the downstream trans-
scription factors YAP and TEAD family transcription
were enriched in MYC-independent cell lines in both
untreated (Fig. 5C) and JQ1-treated cell lines (Fig. S5E).
We confirmed by western blot increased expression of
at least one of the main effectors of this pathway in cell
lines resistant to BETi cytotoxicity (Fig. 5D). Intrinsic
activation of this signaling pathway, which has been

described as connected to stemness and drug resistance
could therefore represent a novel oncogenic pathway
in MYC-driven lymphoma that do not present a MYC
dependency. The opposite effect of MYC and WNT5B/
TEAD3-4 on BETi sensitivity was confirmed by math-
ematical modeling using mixed linear models and elas-
ticity assessment (Figs. 6A and S5F). This was further
supported by the inverse correlation between MYC and
<figure><img src="image_26.png" /><figcaption>**Fig. 6** Targeting WNT/Hippo pathway can overcome resistance to JQ1 cytotoxicity. A JQ1-to-control (DMSO) cell mortality ratio evaluated by fitting by nonlinear mixed models for *MYC* (left) and *WNT5B* and *TEAD3-4* (right) genes. Overall statistical significance of each model was based on a Wald test rejecting the hypothesis that ratios are jointly equal at *p* < 0.0001. B Pearson correlation analysis between YAP and MYC expression in the NCICCR-DLBCL TCGA dataset. *R* and *p* value were derived from a Pearson correlation test *R* = -0.44, confidence interval [-0.5084679; -0.3643806]. C Dose response curves evaluating cell viability of 5 lymphoma cell lines treated for 48 h with concentration ranging from 1 nM to 100 µM of JQ1, CA-3 or a combination of both (n = 3). Bar graph showing Bliss synergy score evaluated using Synergy Finder tool for each cell lines (upper right panel)</figcaption></figure>

YAP in the NCICCR-DLBCL DLBCL patient cohort from TCGA database (Fig. 6B).

To confirm the cooperation of MYC and WNT/Hippo pathway, we evaluated the YAP/TEAD small molecule inhibitor, CA-3. We performed dose-response experiments with JQ1 and CA-3 as single agents or in combination using concentration ranging from 1 nM to 100 µM for the two JQ1-sentitive cell lines (B593 and NUDUL-1), as well as for 3 JQ1-resistant cell lines (OCI-Ly18, OCI-Ly1 and SUDHL-4) (Fig. 6C). These data confirm that

combining JQ1 with CA-3 specifically increase cell death in cell lines resistant to BETI cytotoxicity as seen by IC50 shift. Using the same set of data, we evaluated a potential synergic effect using the Bliss independence model [44]. Bliss synergy score, also represented in Fig. 6C, revealed a synergic effect of JQ1 and CA-3 for OCI-Ly18 and SUDHL-4 (S<sub>Bliss</sub> > 10) and a strong additive effect (S<sub>Bliss</sub> = 7.7) for OCI-Ly1. S<sub>Bliss</sub> close to 0 were obtained for B593 and NUDUL-1 confirming that there is no specific interaction between the 2 drugs in these cell models.
Taken together, our data demonstrate that combining JQ1 and CA-3 can restore JQ1 cytotoxicity in MYC-independent cell lines that are resistant to BETi-induced cell death.

## Discussion

MYC-driven lymphomas represent an heterogeneous groups of B-cell lymphoma characterized by diverse alterations leading to MYC oncogene overexpression [2]. Although these pathologies are generally viewed as aggressive and treatment refractory entities, the type of MYC alterations, together with the presence of additional genetic defects, can influence lymphoma progression and patient prognosis [3, 44]. However, the actual MYC activity and dependency in each specific context remains unclear.

Chromosomal translocations involving *MYC* locus have been reported to be associated with inferior outcomes especially together with concurrent *BCL2/BCL6* gene rearrangements defining the HGBL subtype [45]. When *MYC* rearrangements involve *IG* as partner genes, MYC expression can be placed under the control of BET-dependent super-enhancers present on *IG* regulatory regions leading to particularly elevated MYC protein levels. This provides an obvious rational to use BET inhibition as a therapeutic strategy in this setting.

During the past 10 years, response to BETi has been the focus of a large effort in both pre-clinical and clinical research, showing that BETi possess significant anti-tumor activity in various cancers. Their potential to treat hematological malignancies, and particularly MYC-driven blood cancers, has been clearly demonstrated in mechanistical and pre-clinical studies in B-Cell lymphoma including DLBCL, BL and HGBL [16, 43-49] and myeloma [14-16] as well as in clinical studies [46-51]. However, molecular, cellular and clinical responses to BETi treatment are highly variable, presumably due to the high degree of genetic and biological heterogeneity found even within one single subtype of these pathologies. In addition, BETi have been shown to be associated with a high level of toxicity and induce severe side effects that hamper their clinical development [25].

These studies nevertheless revealed that BETi leads to a G1 cell cycle arrest, concomitant with a decrease in MYC expression, in a large majority of lymphoma cell lines [15, 17-24], as in several other types of hematological malignancies and solid tumors. Cell apoptosis was also described upon BETi treatment in some lymphoma models [15, 17-19, 24] but could not be associated to any specific entity or gene alteration. Interestingly, these responses are not restricted to cellular models where chromosomal rearrangements place *MYC* under the control of BET-dependent regulatory sequences, such

as super-enhancers, but were also observed in cellular models with other types of *MYC* gene alterations or even wild-type *MYC* gene locus [15, 17, 18, 24]. Moreover, reduction in *MYC* expression does not always correlate with BET inhibition's impact on cell proliferation [15]. Altogether, this suggests that *MYC* downregulation by itself may not be as central as thought mediating response to BETi. This could also imply that lymphoma cells could present elevated *MYC* activity, and subsequent high dependency on *MYC* for their survival independently of *MYC* gene alteration.

To elucidate these aspects, we examined MYC regulation, activity and signaling using various pan- and BD2-selective BETi as molecular tools in a panel of B-cell lymphoma cell lines presenting diverse MYC alterations and concurrent oncogene expression.

First, we performed a detailed genetic characterization of aggressive B-cell lymphoma cellular models, including HGBL, DLBCL and BL, to unambiguously characterize their mutational profiles, including both structural and sequence alterations. Our data first showed that *MYC*, *BCL2* or *BCL6* gene rearrangements, including those involving *IG* regulatory regions, frequent in B-cell lymphomas, do not always translate in an increased gene expression, consistently with recently published studies [45, 52]. However, it should be noted that these data have been obtained from model cell lines with very complex karyotypes that might not reflect genetic abnormalities in patient material.

We then tested BETi with different scaffold and selectivity and showed that cellular responses were very similar for pan-BETi of different scaffold and mainly dependent of their activity toward BD1. This is in line with a recent report showing the preferential efficacy of BD2-selective on induced gene expression such as immune response, rather than on oncogene expression [27]. This led us to focus the rest of our study on the prominent and largely studied JQ1 pan-BETi.

We have shown that BETi exhibit similar cytostatic profiles, but different cytotoxicity profiles across the model cell lines tested. Among all gene alterations and expression profiles evaluated, MYC protein expression was the only parameter strictly correlated to BETi-induced cell death, suggesting it could be used as a surrogate marker for MYC dependency. Of note, evaluation of MYC level by immunohistochemistry is not correlated to patient prognosis in DLBCL [53]. This apparent discrepancy could be due to either: (i) immunohistochemistry lacking the quantitative precision to distinguish very high levels of MYC expression, or (ii) MYC dependency not being indicative of treatment response.

Invalidation of *MYC* expression in models sensitive to BETi cytotoxicity confirm their dependency to
MYC expression for both cell growth and cell survival. In contrast, in models resistant to BETi cytotoxicity, the cytostatic effect observed does not seem to be mediated through MYC, even in presence of *MYC* rearrangement, which was thought be a major indicator of MYC dependency.

By analyzing JQ1 global impact on gene expression, we showed that the response to BETi is very different between cell lines, with few commonly impacted genes. None of them could be specifically associated with BETi-induced cell death in MYC-dependent cell lines. Analysis of changes in gene expression profiles does not therefore seem to be efficient to identify other drivers of BETi cytotoxicity. We could neither correlate JQ1-induced changes in gene expression with any CNV or chromosomal alterations determined by OGM. This suggest that genes affected by structural variations, including chromosomal rearrangements involving the *IG* loci, are not preferential transcriptional targets of JQ1. Of note, the sample size used here may not be sufficient to draw a definite conclusion.

However, this set of data allowed to highlight the innate activation of WNT pathway in models that are resistant to BETi cytotoxicity. BETi have been shown to decrease WNT signaling in physiological conditions: mesenchymal stem cells [54] and zebrafish liver regeneration [55] as well as colon cancer [56], gastric cancer [57] and glioma [58]. However, whether members of WNT pathway are direct BET targets remains unclear and most likely cell context-dependent. In contrast, WNT pathway has been clearly associated with innate and acquired resistance to BET inhibitors in leukemic cells as described in 2 back-to-back papers published in Nature in 2015 [59, 60] and were proposed to restore crucial pro-survival genes such as *MYC*.

Here, a detailed analysis of baseline gene expression data suggested that effectors of canonical WNT pathway were not different between the 2 groups of lymphoma cell lines. But our data revealed a specific alternative pathway involving WNT5A and WNTB ligand and downstream activation of Hippo pathway through YAP and TEAD transcription factors, that form, when co-expressed, a transcriptionally active complex controlling pro-proliferative programs. This alternative WNT/Hippo pathway was previously described in MEF cells [42] and breast cancer [43], where it has been related to altered cell migration and differentiation phenotypes, but has never been described in another context to our knowledge. Even, if our conclusions are only drawn from gene expression data, mathematical modeling confirmed the involvement of WNT5B and TEAD in driving resistance to JQ1 cytotoxicity in MYC-independent models.

Several studies have already related Hippo transcriptional regulators and co-activators YAP, TAZ and TEAD with resistance to chemotherapy, but also to several types of targeted therapies (hormones, small molecules inhibitors, inhibitors of immune checkpoint) [61, 62]. Interestingly, two recent papers report several connections between YAP/TAZ and BET proteins in lung and breast cancer [63, 64]. These papers show that (i) YAP/TAZ and TEAD are directly regulated by BRD4 and (ii) a direct interaction of BRD4 on the promoter of YAP/TAZ target genes. Accordingly, YAP signaling can therefore be inhibited by BETi. Moreover, BET inhibition was also reported to upregulate TAZ and therefore shut down canonical WNT signaling in colon cancer [65], but also to downregulate YAP1 and YAP1/TEAD transcriptional activity in esophageal cancer [66], suggesting BETi can have opposite effect on Hippo pathway, probably depending on cell type.

In the context of lymphoma cells, we showed that the baseline activation of WNT5A-B/ YAP/TEADs in MYC-independent cellular models is maintained upon JQ1 treatment and might contribute to resistance to BET inhibition. Although YAP/TEAD inhibitor surprisingly shows similar IC50 across cell lines independently of YAP/TEAD expression levels, combination therapy with JQ1 clearly demonstrates a synergic effect specifically restricted to models resistant to BETi cytotoxicity. Our data strongly suggest that WNT/Hippo crosstalk could represent a novel oncogenic pathway in B-Cell lymphoma associated with innate BETi resistance in MYC-independent models, and confirmed it represents a novel therapeutic vulnerability to design combination therapy.

In conclusion, our study provides new insight into the complexity of the molecular mechanisms underlying MYC and related oncogenic signaling in B-cell lymphoma. Our findings also pave the way for the development of rationalized combination therapies strategies that could be validated in further pre-clinical and clinical studies.

**Supplementary Information**

The online version contains supplementary material available at https://doi.org/10.1186/s13148-024-01788-7.

Additional file 1.

**Acknowledgements**

We acknowledge the contribution of SFR Biosciences (UAR3444/CNRS, US8/Inserm, ENS de Lyon, UCBL) facilities especially AniRA lentivectors production facility from the CELPHEDIA Infrastructure (Gisèle Froment, Didier Nègre and Caroline Costa). We thank the Microcell core facility of the Institute for Advanced Biosciences (UGA—Inserm U1209—CNRS 5309), especially Mylène Pezet and Solenne Dufour, for their help with flow cytometry experiments. This facility belongs to the IBISA-IsdV platform, member of the national infrastructure France-Biolmaging supported by the French National Research Agency (ANR-10-INBS-04). Sequencing was performed by
the GenomEast platform, a member of the "France Génomique" consortium
(ANR-10-INBS-0009).

Author contributions

LD, SH, ACGS and CT performed cellular experiments and analyzed data. SC and CL performed genomics (OGM and targeted NGS) experiment and analyzed data. EM, PBF and TB performed bioinformatics and biostatistics analysis. TB, SC, RG, CL, JG and AE conceived and designed the study. JG and AE coordinated the study. All authors reviewed the manuscript.

Funding

This work is supported by the French National Research Agency in the frame-
work of the "Investissements d'avenir" program (ANR-15-IDEX-02) that pro-
vided PhD studentship to LD. This work has also been supported by CBH-EUR-
GS (ANR-17-EURE-0003) and Grenoble University Hospital Innovative Research
Fund (AO Interne CHUGA). CL has been supported by ARAMIS association and
Grenoble University Hospital Innovative Research Fund to develop OGM.

Availability of data and materials

Gene expression (RNaseq) data generated and analyzed during the current study are available in the GEO repository (GSE271562). List of differentially expressed genes between cell lines sensitive and resistant to BETi-induced cell death are presented in Table S1. OGM and targeted sequencing raw and processed data are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Non-applicable.

Consent for publication

Non-applicable.

Competing interests

The authors declare no competing interests.

Author details

¹Univ. Grenoble Alpes, Inserm, CNRS, Institute for Advanced Biosciences, Grenoble, France. ²Recherche and Innovation Unit, Grenoble Alpes University Hospital, Grenoble, France. ³Hematology, Oncogenetics and Immunology Unit, Grenoble Alpes University Hospital, Grenoble, France. ⁴Department of Clinical Hematology, Grenoble Alpes University Hospital, Grenoble, France.

Received: 27 June 2024 Accepted: 19 November 2024

Published online: 19 December 2024

References

1. Swerdlow SH, Campo E, Pileri SA, Harris NL, Stein H, Siebert R, et al. The 2016 revision of the world health organization classification of lymphoid neoplasms. Blood. 2016;127(20):2375-90.

2. Bisso A, Sabò A, Amati B. MYC in Germinal Center-derived lymphomas: mechanisms and therapeutic opportunities. Immunol Rev. 2019;288(1):178-97.

3. Ott G, Rosenwald A, Campo E. Understanding MYC-driven aggressive B-cell lymphomas: pathogenesis and classification. Hematology. 2013;2013(1):575-83. https://doi.org/10.1182/asheducation-2013.1.575.

4. Xia Y, Zhang X. The spectrum of MYC alterations in diffuse large B-cell lymphoma. Acta Haematol. 2020;143(6):520-8.

5. Sarkozy C, Traverse-Glehen A, Coiffier B. Double-hit and double-protein-expression lymphomas: aggressive and refractory lymphomas. Lancet Oncol. 2015;16(15):e555-67.

6. Scott DW, King RL, Staiger AM, Ben-Neriah S, Jiang A, Horn H, et al. High-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements with diffuse large B-cell lymphoma morphology. Blood. 2018;131(18):2060-4.

7. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IB de O, Berti E, et al. (2022). The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia. 36(7):1720-48.

8. Ennishi D, Jiang A, Boyle M, Collinge B, Grande BM, Ben-Neriah S, et al. Double-hit gene expression signature defines a distinct subgroup of germinal center B-cell-like diffuse large B-cell lymphoma. J Clin Oncol. 2019;37(3):190-201.

9. Wang C, Zhang J, Yin J, Gan Y, Xu S, Gu Y, et al. Alternative approaches to target Myc for cancer treatment. Signal Transduct Target Ther. 2021;6(1):117.

10. Cochran AG, Conery AR, Sims RJ. Bromodomains: a new target class for drug development. Nat Rev Drug Discov. 2019;18(8):609-28.

11. Filippakopoulos P, Qi J, Picaud S, Shen Y, Smith WB, Fedorov O, et al. Selective inhibition of BET bromodomains. Nature. 2010;468(7327):1067-73.

12. Nicodeme E, Jeffrey KL, Schaefer U, Beinke S, Dewell S, wa Chung C, et al. Suppression of inflammation by a synthetic histone mimic. Nature. 2010;468(7327):1119-23.

13. Filippakopoulos P, Knapp S. Targeting bromodomains: epigenetic readers of lysine acetylation. Nat Rev Drug Discov. 2014;13(5):337-56.

14. Delmore JE, Issa GC, Lemieux ME, Rahl PB, Shi J, Jacobs HM, et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell. 2011;146(6):904-17.

15. Mertz JA, Conery AR, Bryant BM, Sandy P, Balasubramanian S, Mele DA, et al. Targeting MYC dependence in cancer by inhibiting BET bromodomains. Proc Natl Acad Sci USA. 2011;108(40):16669-74.

16. Lovén J, Hoke HA, Lin CY, Lau A, Orlando DA, Vakoc CR, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell. 2013;153(2):320-34.

17. Chapuy B, McKeown MR, Lin CY, Monti S, Roemer MGM, Qi J, et al. Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer Cell. 2013;24(6):777-90.

18. Boi M, Gaudio E, Bonetti P, Kwee I, Bernasconi E, Tarantelli C, et al. The BET bromodomain inhibitor OTX015 affects pathogenetic pathways in preclinical B-cell tumor models and synergizes with targeted drugs. Clin Cancer Res. 2015;21(7):1628-38.

19. Ceribelli M, Kelly PN, Shaffer AL, Wright GW, Xiao W, Yang Y, et al. Blockade of oncogenic IκB kinase activity in diffuse large B-cell lymphoma by bromodomain and extraterminal domain protein inhibitors. Proc Natl Acad Sci. 2014;111(31):11365-70.

20. Cummin TEC, Cox KL, Murray TD, Turaj AH, Dunning L, English VL, et al. BET inhibitors synergize with venetoclax to induce apoptosis in MYC-driven lymphomas with high BCL-2 expression. Blood Adv. 2020;4(14):3316-28.

21. Esteve-Arenys A, Valero JG, Chamorro-Jorganes A, Gonzalez D, Rodriguez V, Dlouhy I, et al. The BET bromodomain inhibitor CPI203 overcomes resistance to ABT-199 (venetoclax) by downregulation of BFL-1/A1 in in vitro and in vivo models of MYC+/BCL2+ double hit lymphoma. Onco-gene. 2018;37(14):1830-44.

22. Hogg SJ, Newbold A, Vervoort SJ, Cluse LA, Martin BP, Gregory GP, et al. BET inhibition induces apoptosis in aggressive B-cell lymphoma via epigenetic regulation of BCL-2 family members. Mol Cancer Ther. 2016;9(9):2030-41.

23. Li W, Gupta SK, Han W, Kundson RA, Nelson S, Knutson D, et al. Targeting MYC activity in double-hit lymphoma with MYC and BCL2 and/or BCL6 rearrangements with epigenetic bromodomain inhibitors. J Hematol Oncol J Hematol Oncol. 2019;12(1):73.

24. Trabucco SE, Gerstein RM, Evens AM, Bradner JE, Shultz LD, Greiner DL, et al. Inhibition of bromodomain proteins for the treatment of human diffuse large B-cell lymphoma. Clin Cancer Res. 2015;21(1):113-224.

25. Shorstova T, Foulkes WD, Witcher M. Achieving clinical success with BET inhibitors as anti-cancer agents. Br J Cancer. 2021;124(9):1478-90.

26. Faivre EJ, McDaniel KF, Albert DH, Mantena SR, Plotnik JP, Wilcox D, et al. Selective inhibition of the BD2 bromodomain of BET proteins in prostate cancer. Nature. 2020;578(7794):306-10.

27. Gilan O, Rioja I, Knezevic K, Bell MJ, Yeung MM, Harker NR, et al. Selective targeting of BD1 and BD2 of the BET proteins in cancer and immunoin-flammation. Science. 2020;368(6489):387-94.

28. Callanan MB, Le Bacon P, Mossuz P, Duley S, Bastard C, Hamoudi R, et al. The IgG Fc receptor, FcgammaRIIB, is a target for deregulation by
chromosomal translocation in malignant lymphoma. Proc Natl Acad Sci USA. 2000;97(1):309–14.

29. ISCN 2020 | Karger Book [Internet]. [cité 12 janv 2023]. Disponible sur: https://www.karger.com/Book/Home/279152

30. Putri GH, Anders S, Pyt PL, Pimanda JE, Zanini F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics. 2022;38(10):2943–5.

31. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):1–21.

32. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50.

33. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2(3):100141.

34. Zippo A, De Robertis A, Serafini R, Oliviero S. PIM1-dependent phosphorylation of histone H3 at serine 10 is required for MYC-dependent transcriptional activation and oncogenic transformation. Nat Cell Biol. 2007;9(8):932–44.

35. Wiederschain D, Wee S, Chen L, Loo A, Yang G, Huang A, et al. Single-vector inducible lentiviral RNAi system for oncology target validation. Cell Cycle Georgetown Tex. 2009;8(3):498–504.

36. Emadali A, Rousseaux S, Bruder-Costa J, Rome C, Duley S, Hamaidia S, et al. Identification of a novel BET bromodomain inhibitor-sensitive, gene regulatory circuit that controls Rituximab response and tumour growth in aggressive lymphoid cancers. EMBO Mol Med. 2013;5(8):1180–95.

37. lanevski A, Giri AK, Aittokallio T. SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples. Nucleic Acids Res. 2022;50(W1):W739–43. https://doi.org/10.1093/nar/gkac382.

38. Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, Redd RA, et al. Molecular subtypes of diffuse large B-cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 2018;24(5):679–90.

39. Reddy A, Zhang J, Davis NS, Moffitt AB, Love CL, Waldrop A, et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell. 2017;171(2):481–494.e15.

40. Sears RC. The life cycle of C-Myc: from synthesis to degradation. Cell Cycle. 2004;3(9):1131–5. https://doi.org/10.4161/cc.3.9.1145.

41. Picaud S, Wells C, Felletar J, Brotherton D, Martin S, Savitsky P, et al. RVX-208, an inhibitor of BET transcriptional regulators with selectivity for the second bromodomain. Proc Natl Acad Sci. 2013;110(49):19754–9.

42. Park HW, Kim YC, Yu B, Moroishi T, Mo JS, Plouffe SW, et al. Alternative Wnt signaling activates YAP/TAZ. Cell. 2015;162(4):780–94.

43. Samanta S, Guru S, Elaimy AL, Amante JJ, Ou J, Yu J, et al. IMP3 stabilization of WNT5B mRNA facilitates TAZ activation in breast cancer. Cell Rep. 2018;23(9):2559–67.

44. Bliss CI. The toxicity of poisons applied jointly. Ann Appl Biol. 1939;26(3):585–615.

45. Collinge B, Ben-Neriah S, Chong L, Boyle M, Jiang A, Miyata-Takata T, et al. The impact of MYC and BCL2 structural variants in tumors of DLBCL morphology and mechanisms of false-negative MYC IHC. Blood. 2021;137(16):2196–208.

46. Abedin SM, Boddy CS, Munshi HG. BET inhibitors in the treatment of hematologic malignancies: current insights and future prospects. Onco Target Ther. 2016;9:5943–53.

47. Amorim S, Stathis A, Gleeson M, Iyengar S, Magarotto V, Leleu X, et al. Bromodomain inhibitor OTX015 in patients with lymphoma or multiple myeloma: a dose-escalation, open-label, pharmacokinetic, phase 1 study. Lancet Haematol. 2016;3(4):e196–204.

48. Blum KA, Supko JG, Maris MB, Flinn IW, Goy A, Younes A, et al. A phase I study of pelabresib (CPI-0610), a small-molecule inhibitor of BET proteins, in patients with relapsed or refractory lymphoma. Cancer Res Commun. 2022;2(8):795–805.

49. Falchook G, Rosen S, LoRusso P, Watts J, Gupta S, Coombs CC, et al. Development of 2 bromodomain and extraterminal inhibitors with distinct pharmacokinetic and pharmacodynamic profiles for the treatment of advanced malignancies. Clin Cancer Res. 2020;26(6):1247–57.

50. Moreno V, Sepulveda JM, Vieito M, Hernández-Guerrero T, Doger B, Saavedra O, et al. Phase I study of CC-90010, a reversible, oral BET inhibitor in

patients with advanced solid tumors and relapsed/refractory non-Hodgkin's lymphoma. Ann Oncol. 2020;31(6):780–8.

51. Moreno V, Vieito M, Sepulveda JM, Galvao V, Hernández-Guerrero T, Doger B, et al. BET inhibitor trottresib in heavily pretreated patients with solid tumors and diffuse large B-cell lymphomas. Nat Commun. 2023;14(1):1359.

52. Zhang C, Stelloo E, Barrans S, Cucco F, Jiang D, Tzioni MM, et al. (2024). Non-IG-1:MYC in diffuse large B-cell lymphoma confers variable genomic configurations and MYC transactivation potential. Leukemia. 1-9.

53. Johnson NA, Slack GW, Savage KJ, Connors JM, Ben-Neriah S, Rogic S, et al. Concurrent expression of MYC and BCL2 in diffuse large B-Cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone. J Clin Oncol. 2012;30(28):3452–9.

54. Alghamdi S, Khan I, Beeravolu N, McKee C, Thibodeau B, Wilson G, et al. BET protein inhibitor JQ1 inhibits growth and modulates WNT signaling in mesenchymal stem cells. Stem Cell Res Ther. 2016;7(1):1–16.

55. Russell JO, Ko S, Saggi HS, Singh S, Poddar M, Shin D, et al. Bromodomain and extraterminal (BET) proteins regulate hepatocyte proliferation in hepatocyte-driven liver regeneration. Am J Pathol. 2018;188(6):1389–405.

56. Zhang Y, Tian S, Xiong J, Zhou Y, Song H, Liu C. JQ-1 inhibits colon cancer proliferation via suppressing Wnt/β-catenin signaling and miR-21. Chem Res Toxicol. 2018;31(5):302–7.

57. Song H, Shi L, Xu Y, Xu T, Fan R, Cao M, et al. BRD4 promotes the stemness of gastric cancer cells via attenuating miR-216a-3p-mediated inhibition of Wnt/β-catenin signaling. Eur J Pharmacol. 2019;852:189–97.

58. Tian T, Guo T, Zhen W, Zou J, Li F. BET degrader inhibits tumor progression and stem-like cell growth via Wnt/β-catenin signaling repression in glioma cells. Cell Death Dis. 2020;11(10):900.

59. Rathert P, Roth M, Neumann T, Muerdter F, Roe JS, Muhar M, et al. Transcriptional plasticity promotes primary and acquired resistance to BET inhibition. Nature. 2015;525(7570):543–7.

60. Fong CY, Gilan O, Lam EYN, Rubin AF, Ftouni S, Tyler D, et al. BET inhibitor resistance emerges from leukaemia stem cells. Nature. 2015;525(7570):538–42.

61. Keren-Paz A, Emmanuel R, Samuels Y. YAP and the drug resistance highway. Nat Genet. 2015;47(3):193–4.

62. Reggiani F, Gobbi G, Ciarrocchi A, Ambrosetti DC, Sancisi V. Multiple roles and context-specific mechanisms underlying YAP and TAZ-mediated resistance to anti-cancer therapy. Biochim Biophys Acta BBA—Rev Cancer. 2020;1873:188341.

63. Gobbi G, Donati B, Do Valle IF, Reggiani F, Torricelli F, Remondini D, et al. The Hippo pathway modulates resistance to BET proteins inhibitors in lung cancer cells. Oncogene. 2019;38(42):6801–17.

64. Zanconato F, Battilana G, Forcato M, Filippi L, Azzolin L, Manfrin A, et al. Transcriptional addiction in cancer cells is mediated by YAP/TAZ through BRD4. Nat Med. 2018;24(10):1599–610.

65. Duan Q, Xiao Y, Zhu L, Liu Z, Mao X, Zhou Z, et al. BET bromodomain is a novel regulator of TAZ and its activity. Biochim Biophys Acta. 2016;1859(12):1527–37.

66. Song S, Li Y, Xu Y, Ma L, Pool Pizzi M, Jin J, et al. Targeting Hippo coactivator YAP1 through BET bromodomain inhibition in esophageal adenocarcinoma. Mol Oncol. 2020;14(6):1410–26.

## Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.