
==== Front
Mol Ther
Mol Ther
Molecular Therapy
1525-0016
1525-0024
American Society of Gene & Cell Therapy

S1525-0016(24)00388-5
10.1016/j.ymthe.2024.06.006
Original Article
Targeting PD-L1 in cholangiocarcinoma using nanovesicle-based immunotherapy
Gondaliya Piyush 12
Sayyed Adil Ali 12
Yan Irene K. 1
Driscoll Julia 1
Ziemer Abbye 1
Patel Tushar patel.tushar@mayo.edu
1∗
1 Departments of Transplantation and Cancer Biology, Mayo Clinic, Jacksonville, FL 32224, USA
∗ Corresponding author: Tushar Patel, Departments of Transplantation and Cancer Biology, Mayo Clinic, Jacksonville, FL 32224, USA. patel.tushar@mayo.edu
2 These authors contributed equally

10 6 2024
07 8 2024
10 6 2024
32 8 27622777
4 12 2023
7 6 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study demonstrates the potential of using biological nanoparticles to deliver RNA therapeutics targeting programmed death-ligand 1 (PD-L1) as a treatment strategy for cholangiocarcinoma (CCA). RNA therapeutics offer prospects for intracellular immune modulation, but effective clinical translation requires appropriate delivery strategies. Milk-derived nanovesicles were decorated with epithelial cellular adhesion molecule (EpCAM) aptamers and used to deliver PD-L1 small interfering RNA (siRNA) or Cas9 ribonucleoproteins directly to CCA cells. In vitro, nanovesicle treatments reduced PD-L1 expression in CCA cells while increasing degranulation, cytokine release, and tumor cell cytotoxicity when tumor cells were co-cultured with T cells or natural killer cells. Similarly, immunomodulation was observed in multicellular spheroids that mimicked the tumor microenvironment. Combining targeted therapeutic vesicles loaded with siRNA to PD-L1 with gemcitabine effectively reduced tumor burden in an immunocompetent mouse CCA model compared with controls. This proof-of-concept study demonstrates the potential of engineered targeted nanovesicle platforms for delivering therapeutic RNA cargoes to tumors, as well as their use in generating effective targeted immunomodulatory therapies for difficult-to-treat cancers such as CCA.

Graphical abstract

Patel and colleagues have demonstrated the use of cell-targeted nanovesicle-based therapies for delivery of RNA therapeutics such as siRNA and RNP to modulate PD-L1 in tumor cells. The use of this nanotherapy to modulate anticancer immune responses has the potential to circumvent limitations of conventional immunotherapies for the treatment of cholangiocarcinoma.

Keywords

milk-derived nanovesicles
targeted delivery
RNA therapeutics
immunotherapy
biological nanoparticles
gene silencing
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pmcIntroduction

Immunotherapy, particularly immune checkpoint blockade (ICB) therapies, has revolutionized cancer treatment, offering the promise of harnessing the patient’s own immune system to combat malignancies.1 Among the most pivotal targets of ICB therapies is programmed death-ligand 1 (PD-L1), a cell surface protein involved in immune evasion by interacting with programmed cell death protein 1 (PD-1).2 The advent of PD-L1/PD-1 inhibitors has led to remarkable clinical responses and has improved survival rates in a variety of malignancies, offering new therapeutic options for patients who had exhausted conventional treatment options.3

Cholangiocarcinoma (CCA), a malignancy arising from the bile ducts, presents a formidable challenge for immunotherapies. Despite successes of ICB in many other types of cancers, their use for CCA has had limited efficacy.4 This can be attributed, in part, to their immunosuppressive tumor microenvironment, genetic heterogeneity, and a paucity of neoantigens.5 CCAs are associated with a pauci-immune tumor microenvironment with an abundance of immunosuppressive immune cells. Additionally, the local environment is uniquely associated with both an active innate immune system due to exposure to intestinal microbiome, and an immunotolerant hepatic tissue microenvironment with abundance of natural killer cells and Kupffer cells. PD-L1 expression in CCA tumor cells variably ranges from 9.1% to 72.2%, hinting at the potential for therapies targeting PD1 and PD-L1 for some tumors.6 While single-agent PD-1 inhibitors have had mixed results, the TOPAZ-1 study demonstrated improvement in survival with addition of the PD-L1 inhibitor durvalumab to gemcitabine/cisplatin and leading to subsequent Food and Drug Administration (FDA) approval of this combination for advanced biliary tract cancers. However, subgroup analyses did not reveal a difference in outcome based on PD-L1 expression. Moreover, the use of antibodies targeting PD-1/PD-L1 is hampered by limited penetration into CCA due to the presence of a dense fibroblast-induced stroma in these tumors7 as well as by cost, adverse immunogenic effects, and toxicity.8,9

RNA-based therapeutics are a promising class of drugs for cancer immunotherapy.10 Unlike antibodies, RNA therapeutics can precisely regulate targeted cells by modulating specific genes to varying degrees. Moreover, they offer advantages in design versatility over protein-based drugs. Many recent strides have been made toward translation of RNA-based therapies to the clinic. To date, several RNA therapeutic drugs have been approved by the FDA, and many more are being tested in phase 2/3 clinical trials. At present no drugs have been approved for oncology indications, but these make up approximately 22% of ongoing trials.11,12,13 Despite this robust pipeline, the clinical utility of RNAs is hampered by development challenges in part related to their intrinsic instability and physiological barriers that impede their delivery and transfection into cancer cells.13,14

Biological nanoparticles such as extracellular vesicles (EVs) have emerged as a promising delivery vehicle for RNA-based therapeutics.15 Despite their potential, translating these into clinical use poses substantial challenges, including yield, payload content, loading efficiency, and surface engineering for targeted delivery. Addressing these limitations is crucial to fully exploit the therapeutic potential of EVs. In this study, we present a novel nanotherapeutic strategy designed to target PD-L1 in CCA, utilizing milk-derived nanovesicles (MNVs) as advanced RNA delivery platforms. Herein, we generated therapeutic MNVs loaded with small interfering RNA (siRNA) (siRNA-tMNVs) or ribonucleoprotein (RNP-tMNVs) targeting PD-L1. This study demonstrated the potential of siRNA-tMNVs and RNP-tMNVs in PD-L1-based immunomodulation in CCA in vitro and in vivo. This nanovesicle-based therapeutic approach effectively silenced PD-L1 expression in CCA cells and underscores the capability of nanovesicle-based therapies for modulating anticancer immune responses while providing opportunities to circumvent the limitations of conventional ICB therapies.

Results

Generation of therapeutic nanovesicles

RNA nanotherapeutics were generated from MNV by loading with therapeutic RNA for targeting PD-L1 and decorating their surface with three-way junction RNA with an RNA aptamer that can bind to epithelial cellular adhesion molecule (EpCAM) (Figure 1A). The safety of MNV, capability of targeting to cell surface molecules such as EpCAM, and their utility as a platform for the delivery of therapeutic cargo has been demonstrated.16,17 EpCAM is highly expressed on the cell surface of epithelial cancers including CCA and furthermore, EpCAM targeted delivery has been shown to be effective in liver cancers.17 A high cell surface expression of EpCAM was observed in both HuCCT1 and SNU1079 human CCA cells, and efficient cellular uptake of plain EpCAM-targeting MNVs occurred following incubation, thus validating the basis for their use in these studies (Figures 1B and 1C).Figure 1 Generation and characterization of targeted MNVs (tMNVs) for delivery of RNA therapeutics

(A) Schematic representation of nanotherapeutic generation. MNVs were loaded with either PD-L1-targeted siRNA, or ribonucleoprotein (RNP) using CRISPR Max, and decorated with EpCAM binding RNA aptamer to generate siRNA-tMNVs or RNP-tMNVs respectively. (B) EpCAM profiling within CCA cells by flow cytometry. (C) Cellular uptake of tMNVs in CCA cells demonstrated using Alexa 647 labeled RNA 3WJ aptamers on tMNV surface. (D and E) PD-L1 expression in CCA cells assessed using (D) immunoblotting with densitometric quantitation relative to β-actin, and (E) flow cytometry. (F and G) RNA loading efficiency of siRNA or RNP into siRNA-tMNV or RNP-tMNV. (H and I) Tumor cells were treated with non-targeting control siRNA-loaded tMNVs (CON-tMNV) or PD-L1 targeted siRNA-loaded tMNVs (siRNA-tMNV) for 48 h and PD-L1 silencing was assessed in HuCCT1 and SNU1079 cells. The means ± standard deviations from three replicates are shown. ns, p > 0.05; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001.

PD-L1 is upregulated in many different cancers, including CCA, and enables cancer cells to evade detection and elimination by the immune system.18 PD-L1 expression profiling in human CCA cells revealed high surface expression, most notably on HuCCT1 and SNU1079 cells (Figures 1D and 1E). EpCAM-targeting therapeutic nanovesicles (tMNVs) were loaded with either siRNA or RNP to PD-L1 to generate siRNA-tMNVs or RNP-tMNVs, respectively. Loading efficiency was ∼50% when either siRNA or RNP were encapsulated within tMNV (Figures 1F and 1G). Notably, loading of therapeutic RNA (siRNA or RNP) into tMNV did not alter their size or zeta potential compared with their respective control tMNVs or unloaded MNVs (Figure S1). Compared with incubation with control CON-tMNVs, incubation with siRNA-tMNVs reduced PD-L1 expression by ∼ 50% in both HuCCT1 and SNU1079 cells in vitro (Figure 1H).

SiRNA-tMNVs-mediated T cell modulation

Altered expression of tumor cell PD-L1 may be expected to alter immune responses that are mediated through PD-L1-PD1 interactions. We evaluated the effect of siRNA-tMNV-mediated silencing of PD-L1 expression on T cell immunomodulation using co-cultures of tumor cells and T cells. HuCCT1 or SNU1079 cells were pretreated with either CON-tMNVs or siRNA-tMNVs and then cultured with T cells at an effector:target (E:T) ratio of 5:1 or 10:1. Notably, siRNA-tMNV pretreatment enhanced T cell degranulation at both E:T ratios (Figure 2A), as indicated by an increase in CD107a expression. Granzyme B release from T cells serves as a crucial indicator of their cytotoxic activity. We observed an increase in granzyme B secretion from T cells during co-culture with siRNA-tMNV-pretreated CCA cells at either 5:1 or 10:1 E:T ratios (Figure 2B). Next, we assessed the effects of modulation of tumor cell PD-L1 by siRNA-tMNVs on T cell proliferation in co-culture studies of CON-tMNVs or siRNA-tMNVs pretreated CCA cells. Compared with CON-tMNVs as a negative control, treatment with siRNA-tMNVs for 48 h increased proliferation of T cells during co-culture with SNU1079 cells. While T cell proliferation was not observed during co-culture with HuCCT1 cells after 48 h, increased proliferation was present after an extended duration of treatment with siRNA-tMNVs for 7 days (Figure 2C).Figure 2 siRNA-tMNV-mediated T cell modulation

Tumor cells were treated with non-targeting control siRNA-loaded tMNVs (CON-tMNV) or PD-L1 targeted siRNA-loaded tMNVs (siRNA-tMNV) for 48 h, then co-cultured with T cells in an effector to target (E:T) ratio of 5:1 or 10:1. (A) CD107a expression was used to evaluate T cell degranulation. (B) T cell degranulation was evaluated as the percentage of CD8+CD107A + double-positive events (Q2). (C) The level of secreted granzyme B was also measured in T cell-CCA cell co-cultures. SNU1079 cells were treated for 48 h whereas HuCCT1 cells were treated with repeated doses every 48 h for 7 days. (D) The proliferation of CD8+ T cells co-cultured with CCA cells in an E:T ratio of 10:1 was evaluated at 72 h. The mean ± standard deviation from three replicates is shown. (ns) p > 0.05; (∗∗∗) p < 0.001; (∗∗∗∗) p < 0.0001.

SiRNA-tMNV-mediated NK cell potentiation

Natural killer (NK) cells are immune cells that are abundantly present within the tumor microenvironment (TME). As these cells express PD-1 on their surface and therefore can interact with tumor cell PD-L1, we hypothesized that knockdown of PD-L1 in CCA cells would modulate NK cell-mediated immune responses. Using an experimental approach similar to that used for T cells, tumor cells were pretreated with CON-tMNVs or siRNA-tMNVs followed by co-culture with NK cells. Remarkably, siRNA-tMNV-pretreated HuCCT1 and SNU1079 cells increased NK cell degranulation (Figures 3A and 3B). Additionally, siRNA-tMNV pretreatment amplified granzyme B release from NK cells (Figure 3C). These effects were observed at both the 5:1 or 10:1 E:T ratios. Furthermore, siRNA-tMNVs enhanced the expression of several cytokines and chemokines that can modulate NK cell activity such as interleukin (IL)8, RANTES, MIP1ɑ, MIP1β, CXCL9, interferon (IFN)-γ, tumor necrosis factor (TNF)-ɑ, and IL18 (Figure 3D). Consistent with these results, siRNA-tMNVs significantly enhanced NK cell-based killing of both HuCCT1 cells at 5:1 and 10:1 E:T ratios and of SNU1079 cells at a 10:1 E:T ratio, compared with CON-tMNV groups (Figures 3E and 3F). These results highlight the effects of siRNA-tMNVs in potentiating NK cell-mediated immune responses against CCA cells.Figure 3 siRNA-tMNV-mediated NK cell modulation

(A) Tumor cells were treated with non-targeting control siRNA-loaded tMNVs (CON-tMNV) or PD-L1-targeted siRNA-loaded tMNVs (siRNA-tMNV) for 48 h, then co-cultured with NK cells in an effector to target (E:T) ratio of 5:1 or 10:1. (B) The percent of CD56+CD107a+ double-positive cells (in Q2) was used as a measure of NK cell degranulation. (C) Granzyme B release during co-culture. (D) Cytokine profiling in the supernatants of CON-tMNV or siRNA-tMNV pretreated HuCCT1 cells that were subsequently co-cultured with NK cells in an E:T ratio of 5:1 was performed at 48 h post-co-culture. The viability of (E) HuCCT1 and (F) SNU1079 cells after a 48 h co-culture with NK cells at an E:T ratio of 5:1 or 10:1. The mean ± standard deviation from three replicates is shown. (ns) non-significant; (∗) p < 0.05; (∗∗) p < 0.01; (∗∗∗) p < 0.001; (∗∗∗∗) p < 0.0001.

RNP-tMNV nanotherapeutics

Direct delivery of the CRISPR-Cas9 system as an RNP complex composed of Cas9 protein and single guide RNA (sgRNA) offers a therapeutic approach based on transient genome editing and reduced off-target effects. However, therapeutic applications are hindered by efficient delivery into target cells. We next evaluated the use of MNV-based delivery for the targeted intracellular delivery of Cas9 RNP. Uptake studies using RNP-tMNVs generated using GFP-tagged Cas9 RNP showed a time-dependent increase in uptake by CCA cells (Figure 4A). Despite efficient uptake, the efficacy of the RNA therapeutic can be limited by poor endosomal escape.19 Evaluation of the release of entrapped RNP from lysosomes following the administration of RNP-tMNVs in CCA cells indicated that the majority of the therapy underwent endosomal escape within 24 h (Figure 4B). Functional characterization of RNP-tMNVs showed significant editing efficiency of the PD-L1 gene in both HuCCT1 and SNU1079 CCA cells (Figures 4C, S2, and S3). Moreover, RNP-tMNV treatment in CCA cells reduced PD-L1 expression compared with untreated and Cas9-tMNV control treatments (Figures 4D and 4E).Figure 4 RNP-tMNV-mediated gene editing

(A and B) HuCCT1 cells were treated with tMNV loaded with GFP-tagged RNP (RNP-tMNV) for 6, 12, or 24 h. Confocal microscopy was performed after staining with lysotracker dye and DAPI (blue). (A) RNP-tMNVs (cas9-GFP) uptake (scale bar, 5 μm) and (B) endosomal escape (scale bar, 10 μm). (C–E). Tumor cells were incubated with unloaded MNVs, Cas9-tMNVs, and RNP-tMNVs every 48 hours for 4 days. (C) The guide RNAs used and percent of indels detected on Sanger sequencing to evaluate gene editing efficiency. Surface PD-L1 expression on (D) HuCCT1 cells and (E) SNU1079 cells was evaluated by flow cytometry. The data represent the mean ± standard deviation from three replicates. ∗∗∗∗p < 0.0001.

To explore the immunomodulatory potential of RNP-tMNVs, HuCCT1 and SNU1079 cells were pretreated with Cas9-tMNVs or RNP-tMNVs, followed by co-culture with T cells or NK cells in an E:T ratio of 5:1 or 10:1. These studies revealed heightened T cell degranulation at both 5:1 and 10:1 E:T ratios when co-cultured with HuCCT1 cells, although no significant changes were noted at a 5:1 E:T ratio with NK cells (Figure 5A). Consistent effects were observed on T cell- and NK cell-mediated cytotoxicity with increased HuCCT1 and SNU1079 cell death at both 5:1 and 10:1 E:T ratios (Figures 5B–5E). These studies demonstrated the efficacy of tMNV platforms as a versatile platform for the delivery of Cas9 RNP-based therapeutics similar to that for delivery of siRNA. Since RNP-tMNVs may require multiple dosing to exert therapeutic effects comparable with a single administration of siRNA-tMNVs, we focused our further assessments on the latter.Figure 5 RNP-tMNV-mediated immune cell modulation

(A) HuCCT1 cells were treated with Cas9-tMNVs or RNP-tMNVs every 48 h for 4 days and co-cultured with T cells or NK cells at an effector: target (E:T) ratio of 5:1 or 10:1. (B) The percent degranulation (in Q2) was quantified for T cell (top) and NK cell (bottom) HuCCT1 co-cultured cells. (C and E) T cell-mediated cytotoxicity HuCCT1 and SNU1079 cells. (D and F). NK cell-mediated cytotoxicity in HuCCT1 and SNU1079 cells. The data represent the mean ± standard deviation from three replicates. ns, non-significant; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001.

tMNV effects on cellular spheroids

Following these observations, we assessed the efficacy of siRNA-tMNVs in multicellular spheroids generated from high PD-L1 expression cancer cells (HuCCT1 and SNU1079) with hepatic stellate cells, fibroblasts, and endothelial cells (Figure 6A).20 The resulting HuCCT1-SFE and SNU1079-SFE spheroids exhibited a compact mass and a viable and proliferative outer core (Figure 6B). Multicellular CSFE spheroids had a higher expression of PD-L1 compared with that observed with unicellular spheroids composed of tumor cells alone (Figure 6C). The effects of MNV-mediated PD-L1 modulation were monitored over time in CSFE spheroids, treated with CON-tMNVs or siRNA-tMNVs and during co-culture with T cells or NK cells at 2.5:1 E:T ratio. Reduction of tumor cell viability was observed during co-culture of T cells with CSFE generated from either HuCCT1 or SNU1079 cells (Figure 6D). After 24 h, cell viability was significantly reduced in HuCCT1-SFE treated with siRNA-tMNVs compared with controls (Figure 6E). Similarly, decreased cell viability was also observed in siRNA-tMNV-treated spheroids co-cultured with NK cells, with the most pronounced effects observed with HuCCT1-SFE at 24 h (Figure S6B). Furthermore, siRNA-tMNV treatment reduced the population of live cells in both HuCCT1-SFE and SNU1079-SFE when co-cultured with CD8+ T cells (Figure 6E). When co-cultured with NK cells, siRNA-tMNVs decreased the live cell population in HuCCT1-SFE spheroids but not with SNU1079-SFE (Figure 6F). These findings could reflect differences in expression of NK cell activating ligands in SNU1079 cells. In parallel, we also sought to evaluate the therapeutic efficacy of RNP-tMNVs in 3D tumor spheroids (Figure S6). First, we assessed the effects of RNP-tMNV treatment on the viability of CCA cells in multicellular tumor spheroids co-cultured with NK or T cells. Compared with Cas9-tMNV, pretreatment of HuCCT1-SFE spheroids with RNP-tMNVs did not potentiate CD8+ T cell cytotoxicity of HuCCT1 cells (Figure S6C). RNP-tMNV-mediated PD-L1 knockout appears to render the HuCCT1-SFE spheroids resistant to CD8+ T cell cytotoxicity. Similar to HuCCT1-SFE spheroids, SNU1079-SFE spheroids pretreated with RNP-tMNVs exhibited a resistant phenotype against CD8+ T cell cytotoxicity as compared with Cas9-tMNV-treated CSFE spheroids (Figure S6C). Furthermore, pretreatment with RNP-tMNVs did not enhance NK cell-mediated cytotoxicity in HuCCT1-SFE or SNU1079-SFE spheroids as compared with the levels observed in Cas9-tMNV-treated spheroids (Figure S6D). Given that PD-L1 is also expressed on other cells in the CCA TME, the collective viability of the cells within the multicellular tumor spheroids was also assessed (Figures 6G–6I). There was no observable difference in the cell viability in HuCCT1-SFE or SNU-SFE viability pretreated with RNP-tMNVs as compared with Cas9-tMNV when co-cultured with CD8+ T cells. The same trend was observed in the CSFE spheroids co-cultured with NK cells. These findings highlight the therapeutic efficacy of siRNA-tMNVs in impacting tumor cell viability in multicellular spheroid models that recapitulate the tumor immune microenvironment. Given the absence of compelling benefit with RNP-tMNVs, only siRNA-tMNVs were selected for further evaluation in vivo in CCA tumor-bearing mice.Figure 6 tMNV effects on immune cell in tumor spheroids

(A) Multicellular spheroids were generated from stellate cells, fibroblasts, and endothelial cells combined with either HuCCT1 (HuCCT1-SFE) or SNU-1079 cells (SNU1079-SFE). Unicellular spheroids were generated from either HuCCT1 or SNU-1079 cells alone. (B) Spheroid compactness after 3 days on light microscopy (top panel, scale bar, 300 μm). The spheroids were stained with calcein AM and ethidium bromide homodimer for live/dead cell imaging by confocal microscopy (bottom panel, scale bar, 200 μm). (C) PD-L1 expression assessed by PCR and using 18S as an endogenous control. (D–F) HuCCT1-SFE or SNU1079-SFE were pretreated for 48 h with non-targeting control-loaded tMNVs (CON-tMNV) or siRNA-loaded tMNVs (siRNA-tMNVs) and subsequently co-cultured with (E) T cells or (F) NK cells at an effector to target (E:T) ratio of 2.5:1. Live/dead cell imaging and quantitation was performed after 24 h (scale bar, 300 μm). (G–I) HuCCT1-SFE and SNU1079-SFE were treated with either cas9-loaded MNVs (Cas9-tMNVs) or cas9-RNP complex loaded MNVs (RNP-tMNVs) and co-cultured with (H) T cells or (I) NK cells at an E:T ratio of 2.5:1, and live/dead cell quantitation performed. The data represent the mean ± standard deviation from three replicates. ns, non-significant; ∗p < 0.05; ∗∗p < 0.01.

siRNA-tMNVs in orthotopic CCA mouse model

To test in vivo efficacy, we used an immunocompetent murine tumor model generated by intrahepatic implantation of syngeneic SB tumor cells. EpCAM and PD-L1 expression on SB cells was confirmed by flow and western blot, respectively (Figures S4B–S4D). EpCAM expression was also confirmed histologically, and high expression noted in tumor lesions in orthotopic SB cell tumor-bearing mice (Figure S4E). The efficiency of uptake was assessed revealing a concentration-dependent increase in tMNV accumulation in the SB cells (Figure S4A). Treatment of SB cells with siRNA-tMNVs resulted in a 20%–30% reduction in PD-L1 compared with control CON-tMNVs (Figure S4). Subsequently, the in vivo efficacy of tMNV treatment was assessed in mice CCA tumors generated following the orthotopic implantation of SB cells (Figure 7A). Gemcitabine increases PD-L1 expression in CCA and other tumor cells.21 Since a combination of gemcitabine and anti-PD-L1 has been approved by the FDA for the treatment of biliary tract cancers, including CCA, our studies focused on evaluating the combination of tMNV and gemcitabine. We observed a decrease in tumor burden with siRNA-tMNV treatment but not with CON-tMNVs when these were administered in combination with gemcitabine at day 30 (Figures 7B and 7C). Moreover, tumor weight was reduced in the siRNA-tMNVs and siRNA-tMNV/gemcitabine groups compared with their respective controls (Figure 7D). Importantly, liver weight remained unchanged (Figure 7E). Histology revealed a reduction in PD-L1 expression and size of tumoral regions in the siRNA-tMNVs and siRNA-tMNV/gemcitabine groups compared with their respective controls (Figures 8A and 8B). There was also a concomitant increase in the percentage of tumor-infiltrating CD8+ and NK1.1+ cells in response to combination treatment of gemcitabine with siRNA-tMNVs (Figures 8C and 8D). The impact of therapeutic modulation of PD-L1 by gemcitabine on antitumor effects was highlighted by the lack of significant alterations in tumor burden with administration of CON-tMNVs or siRNA-tMNVs alone. Subsequently, we examined the systemic effects of siRNA-tMNVs on immune cell populations in vivo. However, an increase in CD4+ T cells, CD8+ T cells, and NK cells, along with decrease in neutrophils and NKT cells was noted indicative of in vivo immune cell modulation by nanovesicle-based RNA immunotherapeutics targeting PD-L1 (Figure 8F). Concomitantly, there were alterations in serum granzyme B levels in siRNA-tMNV-treated mice, with the highest increases in mice that received combination treatment of siRNA-tMNVs with gemcitabine (Figure 8E). At the study endpoint (day 29), there was 20% mortality in the untreated group, and 33% in the group that received CON-tMNVs. In contrast, mortality was 17% in the siRNA-tMNV + Gem group but no mortality was observed in the other treatment groups. In summary, these findings in an aggressive orthotopic CCA mouse model highlight the therapeutic efficacy of siRNA-tMNVs in decreasing tumor growth, likely due to potentiated antitumor responses in response to suppressed expression of the immune checkpoint inhibitor PD-L1.Figure 7 tMNVs and tumor growth in vivo

(A) SB tumor cells (5 × 105/mouse) were orthotopically implanted in the livers of syngeneic mice. After 15 days, mice were randomized to an untreated group (UT) or to a treatment group to receive five doses every 3 days of non-targeting control-loaded tMNVs (CON-tMNV), siRNA-loaded tMNVs (siRNA-tMNV), CON-siRNA + gemcitabine (Gem), or tMNVs + Gem. (B and C) Tumor burden assessed on bioluminescence imaging. At day 29, the study was terminated and (D) tumor weight and (E) liver weight were recorded. The data represent the mean ± standard deviation from five mice per treatment group. (∗) p < 0.05.

Figure 8 Tumoral and systemic immune cell profiling following tMNV treatment

(A) Hematoxylin and eosin and immunohistochemical detection of PD-L1 and CD8+ and NK1.1+ cells within tumor nodules (scale bar, 200 μm). (B–D) Quantitation of (B) PD-L1, (C) CD8, and (D) NK1.1 expression within three randomly selected regions of interest (250 × 250 μm). (E) Serum levels of granzyme B in orthotopic tumor-bearing mice measured by ELISA at study endpoint. (F) Immune cell profiling was performed by imaging mass cytometry in mice receiving either non-targeting control-loaded tMNVs (CON-tMNV), or siRNA-loaded tMNVs (siRNA-tMNV) and concomitant gemcitabine (Gem). The data represent the mean ± standard deviation from five mice in each group. ∗p < 0.05.

Discussion

Targeting PD-L1 in both cancer and immune cells is a promising strategy in cancer immunotherapy.22,23 Within the TME, both T cells and NK cells, express PD-1 and can interact with PD-L1 on cancer cells, blockade of which mediates antitumor immune responses.24 Remarkable clinical responses with improved survival rates have been reported with the use of PD-L1/PD-1 inhibitors in some cancers, including CCA, highlighting the importance of targeting the PD-1/PD-L1 axis as a therapeutic approach.3 In this study, MNVs are shown to be an effective vehicle for the targeted delivery of RNA-based therapies such as PD-L1 siRNA or Cas9 RNP for therapeutic modulation of PD-L1 expression within the TME. Treatment of CCA cells with either siRNA-tMNVs or RNP-tMNVs augmented both T and NK cell responses against CCA in 2D culture, with enhanced T cell degranulation and tumor cell cytotoxicity. Treatment of multicellular tumor spheroids with siRNA-tMNVs achieved an immunomodulatory response, whereas RNP-tMNV treatment did not, making the former an appropriate treatment for therapeutic intervention immunomodulation. The clinical utility of siRNA-tMNVs was further demonstrated in preclinical studies showing an antitumor effect of targeted nanovesicle-mediated delivery of siRNA to PD-L1 with gemcitabine in syngeneic CCA cell xenografts in immune-competent mice. The systemic effect of the therapeutic intervention was evaluated by examining the systemic levels of several immune cells that have been reported as independent positive prognostic factors for CCA.25,26 An increase in the circulating levels of NK cells, CD8+ T cells, and CD4+ T cells was observed in response to PD-L1 siRNA-tMNVs + Gem. In contrast, a slight reduction in NKT cells was observed suggesting the possibility of a greater functional effect of PD-L1 inhibition on NKT cells compared with CD8+ T cells or NK cells.

RNA-based therapeutics are attractive alternatives to the use of monoclonal antibodies for targeting PD-L1. Their ability to selectively knock down intracellular PD-L1 expression enables more specific blockade compared with antibodies that rely on extracellular epitope binding that can have off-target effects. A challenge with anti-PD-L1 therapy is the potential for neutralization by soluble circulating PD-L1. The use of a therapeutic approach that is delivered into the cells avoids mitigation of efficacy by soluble PD-L1. Engineered MNV and similar nanoparticles have other distinctive differences over antibodies that may offer advantages for therapeutic applications. These include their high first-pass uptake within the liver, intrinsic tropism for cell targeting and uptake, ability to display multiple targeting ligands, and weaker immunogenicity. MNV can be loaded with diverse therapeutic cargoes. Furthermore, solid tumor penetration of antibodies may be limited by “binding site barrier” phenomenon in which antibodies bind to tumor cells within superficial layers of the tumor.27 The intracellular action of RNA modalities like siRNA or RNP allow for the modulation of intracellular immune signaling cascades and gene expression in ways not possible through surface protein binding alone.10 RNAs also have lower immunogenic potential compared with monoclonal antibody molecules. Therapeutic RNAs can be rationally designed and chemically modified to modulate molecular stability and half-life. Moreover, RNA-based therapies could be combined to concomitantly target other oncogenic pathways or immune checkpoint inhibitors such as PD-1 or CTLA-4 for synergistic impact. From a bioprocessing standpoint, RNA therapeutics are easier and less expensive to produce compared with the complex processes needed for monoclonal antibody manufacturing. However, their use is currently limited by the susceptibility of RNAs to degradation by nucleases, limitations in internalization and endosomal escape, and their short half-lives in circulation.28

The use of nanovesicle-based approaches offers an effective solution that could help overcome challenges of instability and cellular uptake. Lipid-based nanoparticles have been widely used for clinical applications of RNA therapeutics.29 Our results highlight the utility of biologically derived nanovesicles such as MNVs as delivery vehicles as they can encapsulate therapeutic RNA molecules and deliver them specifically to tumors. Encapsulation of the RNA therapeutics into MNVs protects the RNA cargo from nuclease degradation and immune cell recognition and enhances delivery to target tissues. As MNVs are naturally derived, they are less likely to elicit inflammation or toxicity compared with synthetic nanoparticles and may provoke fewer immune reactions allowing for repeated dosing. An advantage of the use of biological nanoparticles such as MNV over lipid nanoparticles for generating therapeutics is their ability to be engineered to display surface molecules that can bind selective receptors on target cells and thereby provide the cell-specific targeting. In prior studies, we reported enhanced uptake by EpCAM+ cancer cells of MNVs with a 3-way junction (3WJ) RNA construct incorporating an EpCAM-targeting aptamer compared with MNVs with 3WJ but without a targeting aptamer.17 Furthermore, the small size of MNVs facilitates tissue penetration and cellular uptake compared with viral vectors. Additionally, production may eventually be more cost-effective. With further advances in scalable manufacturing and proof of therapeutic benefit in patients, the use of MNV-based RNA therapeutics can surmount several limitations associated with anti-PD-L1 monoclonal antibodies. Continuing innovations will provide additional opportunities through selective targeting, adaptability in design, and multimodal mechanisms enabled by the use of these platforms for delivery of diverse RNA therapeutic modalities ranging from mRNA to siRNA.

In conclusion, the specificity, versatility in design, and favorable bioprocessing characteristics of RNA-based therapies make them a compelling approach to target PD-L1 within the tumor microenvironment. Biological nanovesicles such as MNV provide a suitable delivery formulation that can address key limitations of stability, targeting, and immunogenicity associated with clinical translation of RNA therapies. The use of these nanovesicle-based RNA treatments provides an approach to overcome limitations and improve the efficacy of cancer immunotherapies.

Materials and methods

Cells and culture

HuCCT1 and SNU1079 human CCA cell lines were provided by Dr. John A.Copland, LX2 cells by Dr. Gregory J. Gores, and SB cells by Dr. Sumera Ilyas30 (all from Mayo Clinic), RBE, CCLP, primary dermal fibroblasts (DF), and human umbilical vein-derived endothelial cells (HUVEC) were purchased from ATCC (Manassas, VA). HuCCT1-GFP-Luc and SNU1079-Luc cells were generated by GFP-Luciferase or luciferase vector transduction to HuCCT1 or SNU1079, respectively. To generate SB-Luc cells, pGL4.51[luc2/CMV/Neo] vector was electroporated into SB cells and transfected cells were selected using 100 μg/mL geneticin antibiotic. HuCCT1, HuCCT1-GFP-Luc, SNU1079, SNU1079-Luc, RBE, CCLP, LX2, SB cells, and SB-Luc cells were cultured in Dulbecco’s modified Eagle’s medium-high glucose (DMEM-HG, Hyclone, Logan, Utah) supplemented with 10% fetal bovine serum (FBS) (Benchmark, Tempe, AZ, USA) and 1% penicillin-streptomycin (Gemini Bio, Sacramento, CA). DF and HUVEC were cultured using the supplier’s recommended medium. Primary T cells and NK cells were isolated from human whole blood sourced from the Department of Transfusion Medicine at Mayo Clinic. Peripheral blood mononuclear cells were extracted from whole blood using Lympholyte-H (Cedarlane Labs, Burlington, Canada), and T cells and NK cells isolated using a human CD8+ T cell isolation kit or an NK isolation kit (Miltenyi Biotec, Bergisch Bladbach, Germany). T cells were activated with soluble human anti-CD3 and soluble human anti-CD28 (Thermo Fisher Scientific, Waltham, MA), and grown in RPMI 1640 medium (Corning Inc, Corning, NY) supplemented with 10% FBS, 500 IU/mL IL-2 (Roche Diagnostics, Indianapolis, IN). NK cells were propagated in RPMI 1640 medium supplemented with 10% FBS, 500 IU/mL IL-2, and 140 IU/mL IL-15 (R&D Systems, Minneapolis, MN) for 14 days.

Flow cytometry

Cells were harvested, then washed with 1X PBS, blocked using 2% BSA solution for 30 min on ice, incubated with fluorescent-tagged primary antibody for 1 h on ice, and then washed and resuspended in flow buffer (1% BSA solution) prior to flow cytometry using a NovoCyte flow cytometer. Live cells were selected using Live-or-Dye staining kit (VWR, Randor, PA, USA). The detailed list of antibodies used is provided in Table S1.

Generation of tMNVs

MNVs were isolated from bovine milk as previously reported and loaded with siRNA to PD-L1 or non-targeting control to produce siRNA-tMNVs or CON-tMNVs, respectively.16 The MNVs were subsequently decorated with 10 μM 3WJ EpCAM-targeting aptamer as previously described.17 To generate RNP-tMNVs, Cas9 was combined with guide RNA (Synthego, Redwood City, CA) in a 12:1 ratio in NEB buffer and incubated at 25°C for 10 min to form RNP complexes. RNP (5 μg) was mixed with 10 μL of Cas9 plus reagent (Thermo Fisher Scientific) in 50 μL of optiMEM. Separately, 6 μL of Lipofectamine CRISPRMAX Cas9 reagent (Thermo Fisher Scientific) was mixed in 50 μL of optiMEM, and then amalgamated with the RNP/Cas9 plus at room temperature (RT) for 10 min, added to 2 × 1012 MNVs (particles) and incubated at 4°C for 10 min followed by ultracentrifugation at 100,000 × g for 70 min at 4°C. For Cas9-tMNVs control, Cas9 mRNA was loaded using the same approach. The resulting pellet was resuspended in RIPA Lysis and Extraction Buffer (Thermo Fisher Scientific), and loading efficiency assessed by quantitative immunoblot analysis.31

Cellular uptake of RNP-tMNVs

HuCCT1 cells (5,000/well) were seeded per well of Lab-Tek II chamber slide (Thermo Fisher) and allowed to attach overnight. GFP-RNP was loaded into tMNVs and cells were then treated with 1 × 109 GFP-RNP-tMNV for 3, 6, or 12 hours followed by fixation with 4% paraformaldehyde for 10 min. Subsequently, lysosomes and nuclei were stained with lysotracker and DAPI, respectively, and images were captured using Zeiss confocal microscope.

Gene editing efficiency

HuCCT1 or SNU1079 cells were seeded (50,000/well) in a six-well plate and treated with 2 × 1011 CON-tMNVs or RNP-tMNVs every 48 hours for three doses. Genomic DNA was isolated using DNeasy Blood & Tissue Kits (Qiagen) from treated cells. PCR amplifications were performed using Q5 High-Fidelity DNA Polymerase master mix (NEB) in C1000 Touch Thermal Cycler (Bio-Rad Laboratories, Inc.) at 98°C for 30 s, followed by 40 cycles of 98°C for 10 s, 65°C for 30 s (annealing), 72°C for 40 s, and then final extension at 72°C for 2 min. The PCR was performed using the following primers: forward, 5ʹCAGAGCTAGCAGGTGTTCCCʹ3 reverse: 5ʹGCTAGGGGACAGTGTTAGACATʹ3. The forward primer was used as sequencing primer in the Sanger sequencing. The editing efficiency was calculated by using ICE analysis (Synthego).

T cell proliferation assay

HuCCT1 or SNU1079 cells were seeded in a 24-well plate (50,000/well) and pretreated with 2 × 1011 CON-tMNVs or siRNA-tMNVs for 48 h followed by co-culture with CFSE-stained T cells in 10:1 E:T ratio for 48 h. Cells were stained with anti-CD8-APC (Miltenyi Biotec) for T cells and subjected flow cytometry by NovoCyte flow cytometer.

Degranulation assay

HuCCT1 or SNU1079 cells were seeded (50,000/well) in 24-well plates and allowed to adhere overnight. Cells were pretreated with 2 × 1011 CON-tMNVs or siRNA-tMNVs for 48 h or Cas9-tMNVs or RNP-tMNVs every 48 h for 4 days followed by co-culture with T cells or NK cells at effector:target (E:T) ratios of 5:1 or 10:1 for 6 hours in the presence of GolgiStop (BD Bioscience) and anti-CD-107a-APC antibody (BD Bioscience, XX) in RPMI 1640 medium. The cells were collected and stained with anti-CD8-FITC (Miltenyi Biotec) or anti-CD56-FITC (Miltenyi Biotec) for T cells and NK cells, respectively, and analyzed using a NovoCyte flow cytometer (ACEA Biosciences, Inc., San Diego, CA). Degranulation was determined from CD107a expression. Data were analyzed using FlowJo v10.8.1 (BD Bioscience).

Immune cell-mediated cytotoxicity

HuCCT1-GFP-Luc or SNU1079-Luc cells seeded in 24-well plates were pretreated with 2 × 1011 CON-tMNVs or siRNA-tMNVs for 48 h or Cas9-tMNVs or RNP-tMNVs every 48 h for 4 days. Subsequently, cells were co-cultured with T cells or NK cells for 6 h at 5:1 or 10:1 E:T ratio for 48 h. Luciferase activity was measured using a FLUOstar Omega microplate reader32 (BMG LABTECH, Cary, NC, USA).

Granzyme B and cytokine assays

HuCCT1 or SNU1079 cells were seeded (50,000/well) in 24-well plates and pretreated with 2 × 1011 CON-tMNVs or siRNA-tMNVs for 48 h followed by co-culture with either T cells or NK cells in a 5:1 or 10:1 E:T ratio. Culture supernatant was collected after 24 h. and granzyme B quantitated using ELISA (Fisher scientific) as per the manufacturer’s protocol, while cytokine and chemokine levels were profiled using a multiplex array (48-plex, Eve Technologies; Calgary, Canada).

Tumor cell spheroids

Spheroids representing cells grown in 3D culture were generated by seeding cells (15,000 each) in methylcellulose for 3 days. For unicellular spheroids, one cell type was seeded. For multicellular spheroids (CSFE), luciferase-expressing cancer cells, LX2, dermal fibroblast, and HUVECs in a 5:2:2:2 ratio were seeded. HuCCT1-GFP-Luc was used to generate HuCCT1-SFE while SNU1079-Luc was used to generate SNU1079-SFE. On the third day, spheroids were manually retrieved, plated (one/well) in 96-well plates and cultured in DMEM-HG medium supplemented with 10% FBS and penicillin/streptomycin antibiotics for use in further studies. For live/dead cell imaging, spheroids were washed with PBS and immersed in DMEM-HG media containing 2 μM of calcein AM and 2 μM of ethidium bromide homodimer mixture for 5 min at RT, followed by a wash with PBS prior to imaging using a confocal microscope (LSM880, Zeiss, Oberkochen, Germany) or fluorescent microscope (EVOS M5000, Invitrogen, Waltham, MA, USA). For tMNV-mediated cytotoxicity, spheroids were pretreated with 2 × 1011 particles of CON-tMNVs, siRNA-tMNVs, Cas9-tMNVs, or RNP-tMNVs for 48 h followed by co-culture with T cells or NK cells in an E:T ratio of 2.5:1 for up to 24 h. Luciferase activity was measured using an IVIS imaging system (IVIS, PerkinElmer, Inc., Waltham, MA, USA).

Protein expression

For immunohistochemistry (IHC), tissue samples were fixed using 10% PFA and embedded in paraffin. IHC was performed by the Mayo Clinic Florida Histology Shared Resource. The specimens were deparaffinized and hydrated with pure water. The tissues were later processed for IHC or hematoxylin and eosin staining. Tissues were stained with anti-PD-L1, anti-CD8, or anti-NK1.1 antibodies following antigen retrieval by immersing the slides in EDTA buffer in a 100°C steamer for 25 min. The tissues were then stained with horseradish peroxidase (HRP)-labeled polymer anti-rabbit secondary antibody. Quantification of proteins of interest in the liver tissues and nodules in lung tissues was performed using the Aperio ImageScope program. Three equal-sized regions of interest (250 × 250 μm) were placed in the tumor regions and the positive pixel algorithm was performed on the annotated regions for quantification of tumor PD-L1, tumor infiltrated CD8+ cells, and NK1.1+ cells. For western blots to assess PD-L1, GAPDH, and β-actin expression, proteins were extracted by RIPA lysis buffer and quantified using bicinchoninic acid assay. Proteins were resolved on NuPAGE-Tris Mini Gels (Thermo Fisher Scientific, Walthman, MA, USA) and transferred to a nitrocellulose membrane using the iBlot-2 system (Fisher Scientific, #IB23002) followed by blocking for 1 h at RT using Odyssey blocking buffer (VWR, #927–70001, Radnor, PA). The blots were incubated with primary antibody overnight at 4°C followed by washing with PBS-T. Marker expressions were probed using secondary antibody for 1 h at RT and membrane was washed again before imaging using the Odyssey Imaging System (LI-COR, Lincoln, NE). The expression of the proteins of interest were normalized to β-actin. A detailed list of antibodies used is provided in Table S1.

Quantitative reverse transcription PCR (qRT-PCR)

RNA was isolated using TRIzol (Life Technologies, Grand Island, NY) and quantified by Nanodrop (Nanodrop 2000c, Thermo Scientific). For cDNA synthesis, 500 ng of total RNA was treated with DNase I to eliminate DNA contaminants and reverse transcribed using iScript cDNA synthesis kit (Bio-Rad, Hercules, CA). qRT-PCR was performed using a Light Cycler 96 thermal cycler (Roche, Indianapolis, IN) and TB green advantage qPCR premix 2X was used (Takara, Japan) and PD-L1 primer (Forward (F), TGCCGACTACAAGCGAATTACTG; Reverse (R), CTGCTTGTCCAGATGACTTCGG). The 18S RNA (F, GTAACCCGTTGAACCCCATT; R, CCATCCAATCGGTAGTAGCG) was used to normalize the mRNA expression.

In vivo tumor growth

Six-week-old C57BL/6 mice were obtained from Jackson Laboratory and housed in individually vented cages with bed-o’cobbs bedding (The Andersons, Maumee, Ohio). The mice were fed food (Pico Diet, LabDiet, St Louis, MO) and water ad libitum and maintained in a 12 h/12 h light/dark cycle. All animals received humane care, and the study was conducted in compliance with the Mayo Clinic Institutional Animal Care and Use Committee guidelines under an approved protocol (A00002125-16). SB-luciferase cells (2 × 106 in 50 μL medium) were injected in the left lobe of the liver to establish orthotopic tumors. IVIS imaging was performed every 3 days to monitor tumor growth. At day 14 after cell implantation, tumor-bearing mice were randomized to receive one of five treatments every 3 days for 15 days: Gemcitabine (Gem) alone, siRNA-tMNVs alone or with Gem, and CON-tMNVs alone or with Gem. SiRNA-tMNVs or CON-tMNVs were injected (2 × 1011 MNV particles) via tail vein while Gem (30 mg/kg) was injected subcutaneously. At the end of the study (30 days), whole blood was collected using the cardiac puncture technique and vital organs including tumor-bearing liver were harvested. To evaluate the biodistribution of tMNVs, mice received tail vein injections of 2.00 × 1011 particles of DiR tMNVs, or were left untreated (UT), and the livers collected after 6 h, stained with DAPI, and immunofluorescence microscopy performed for DiR detection.

Mass cytometry-based immune cell profiling

Spleens were harvested from the mice treated with CON-tMNVs/gemcitabine and siRNA-tMNVs/gemcitabine at the end of treatment, with two biological replicates per treatment group. The splenocytes were isolated as previously described.16 Fc receptor blocking of the single-cell suspensions of murine primary splenocytes was performed using heat-inactivated mouse serum. The splenocytes were subsequently barcoded with CD45 for 30 min at RT as follows: one mouse sample from each treatment group was stained with 147Sm-CD45 and the other mouse sample from each treatment group with 89Y-CD45. The cells were washed twice. For live cell discrimination, the barcoded biological replicates for each treatment group were combined and stained with 1.25 μM cisplatin and incubated at RT for 5 min and washed once. Fc receptor blocking was performed and the cells were subsequently incubated with the antibody cocktail for 30 min at RT and washed twice (Table S2). The cells were fixed with 2.6% PFA and stained with 125 nM cell-ID intercalator overnight at 4°C. The cells were washed twice and the data was acquired using a Helios CyTOF instrument (Standard BioTools, San Francisco CA). Data cleanup was performed using the Gaussian discrimination parameters in FlowJo and the samples were de-barcoded. Manual gating was performed to identify immune cell sub-types. Dimensionality reduction analysis was performed in Rstudio using Cytofkit33 using the following parameters: ceil data merging, cytofAsinh data transformation, Rphenograph algorithm with k = 30 and displayed on a t-distributed stochastic neighbor embedding (tSNE) plot. The clusters were immunophenotyped, and the immune cell clusters of interest were reported in color. All other immune cell clusters were reported in gray.

Statistical analysis

All analyses were performed with Prism version 8.0.2 (GraphPad Software, San Diego CA). For comparison of siRNA-tMNVs and CON-tMNVs, a two-tailed, unpaired Student’s t test was performed. For analysis of three or more treatment groups, a one-way ANOVA test was performed. p values less than 0.05 were considered as statistically significant. Data are expressed as the mean ± standard deviation from at least three replicates per condition.

Data and code availability

Materials and protocols will be distributed to qualified scientific researchers for non-commercial, academic purposes. Requests for data reported in this paper or any additional information required can be requested from the corresponding author.

Supplemental information

Document S1. Figures S1–S6, Tables S1, and S2

Document S2. Article plus supplemental information

Acknowledgments

This work was supported in part by National Institutes of Health R01 CA217833. We thank members of the Patel Lab for helpful feedback, and Drs. John (Al) Copland, Gregory J. Gores, and Sumera Ilyas (all from Mayo Clinic) for providing cells. We also thank Brandy Edenfield for assistance with tissue processing for H&E and immunohistochemistry and Margaret Ushman and the Cytometry and Cell Imaging Laboratory, Mayo Clinic, Florida. The graphical abstract was created with BioRender.com.

Author contributions

Conceptualization, P.G. and T.P.; funding acquisition, T.P.; writing – original draft, T.P., A.A.S, and P.G.; writing – review & editing, T.P., P.G., A.A.S, and J.D.; conduct of studies, P.G., A.A.S, A.Z., J.D., and I.K.Y.; formal analysis, P.G., A.A.S, J.D., A.Z., and I.K.Y. All authors reviewed the paper and approved of the final version.

Declaration of interests

The authors declare no conflict of interest.

Declaration of Generative AI and AI-assisted Technologies in the Writing Process

During the preparation of this work the authors used Claude 2 in order to improve readability and language of the writing. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Supplemental information can be found online at https://doi.org/10.1016/j.ymthe.2024.06.006.
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