Single-cell transcriptomics reveals FXR1 as an actionable target for siRNA therapy in ovarian cancer

Domain: biology
Published: 2026-04-03
Editorial outcome: major_revision

Abstract
Ovarian cancer is one of the leading causes of cancer-related mortality among women and remains exceptionally difficult to manage and treat effectively in the clinic. Fragile X-related protein 1 (FXR1) is highly amplified and overexpressed in ovarian and several other cancers. FXR1 is a key regulator of the translation of multiple oncogenes and therefore represents a vulnerable target for cancer therapy. RNA interference (RNAi) of FXR1 using a locked nucleic acid (LNA) form of siRNA (siFXR1-LNA) inhibits tumor growth, ascites formation and metastasis of ovarian cancer more efficiently than the native form of FXR1 siRNA in vivo. LNA modification of siRNA improves resistance to RNase mediated degradation and enhances tumor tissue uptake of siRNA with robust inhibition of target mRNA in tumor tissues. Single-cell RNA sequencing (scRNA-seq) analysis of ascites composed of tumor, stromal and immune cells analysis reveals that FXR1 silencing suppresses tumor cell proliferation and reduces tumor-promoting M2-like macrophages. FXR1 silencing also increases cytotoxic T cells, NK cells, and dendritic cells with anti-tumor characteristics in vivo. Collectively, our data establishes FXR1 as an important regulator of oncogenic processes in cancer tissues and serves as a therapeutic liability. Therefore, FXR1 silencing in tumor tissues provides an effective strategy to treat tumors expressing high levels of FXR1.

Introduction
Ovarian cancer, a leading cause of cancer deaths among women with a 1 in 87 lifetime risk, often goes undetected until its aggressive later stages, leading to low survival rates and frequent recurrences despite early treatment success. Studies from The Cancer Genome Atlas1, us2, and others3 have reported that ovarian cancer is driven by copy number variations (CNVs), such as copy number gain, amplification, or deletion of the genome. Many genes, such as PI3KCA 4, EVI1 5, and ETERC 6 were reported as amplified within the 3q26 locus amplification for their role in ovarian cancer progression. We have reported that non-coding microRNAs, such as the miR569 and miR551b, that are amplified as part of the 3q26.2 locus, contribute to the oncogenesis and progression of breast and ovarian cancers 2, 7, 8. Although many genes were reported for ovarian cancer progression as driver genes or passenger genes, targeted therapy is not well developed for ovarian cancer.

Our recent work identified that fragile X-related protein 1 (FXR1), amplified within the 3q26 genomic region, is a master regulator of stability and translation of several oncogenic mRNAs9. FXR1 is copy-gained or overexpressed in many cancers such as those of lung, ovary, esophagus, head and neck, cervical and uterine 9. Therefore, silencing FXR1 in tumor tissues could offer therapeutic benefits for ovarian and multiple other cancers that exhibit FXR1 amplification and/or overexpression. However, there is no inhibitor readily available that targets and inhibits FXR1 for therapy. Hence, the aim of this study is to develop RNA interference (RNAi) techniques, employing both in vitro and in vivo models, to target and suppress FXR1 for cancer therapy.

RNAi was a Noble Prize-winning discovery made by Dr. Craig Mello and Andrew Fire in 199810, 11. RNAi based therapeutic approaches haven’t received any significant attention for clinical care until 2018 when FDA approved the first RNAi drug Patisiran to treat hereditary transthyretin amyloidosis, and subsequently RNAi drugs Givosiran and Lumasiran got approved for treating hepatic porphyria and hyperoxaluria in 2019 and 2020, respectively 12, 13, 14. Since then, RNA-based therapy has become an important area of research for silencing many genes which were considered undruggable previously. However, the therapeutic potential of RNAi has not met the goal of treating diseases like cancers as expected, potentially due to lack of cancer cell specific RNAi targets and/or effective delivery mechanisms. Additionally, the impact of RNAi on normal cells and immune cell toxicity has not been thoroughly addressed for cancer therapy. Given this background, our research aims to explore the potential of using FXR1 siRNA not only for its ability to inhibit cancer cell growth and induce the death of cancer cells, but also for its immune-supporting effects, which could enhance anti-cancer responses.

Several challenges currently limit the clinical use of siRNA-based therapies, including difficulties in delivering siRNA specifically to target cells, low uptake levels within these cells, and rapid degradation of siRNA by exonucleases and RNases. Another major concern is the off-target effects of siRNA, which silence unintended RNA targets, potentially leading to adverse outcomes15. Introducing chemical modifications in siRNAs evolved as a strategy to improve the stability of small RNAs by reducing the impact of nuclease induced degradation 16, 17. The chemical modifications can be introduced to the 5′- or 3′- terminus, backbone, sugar, or in the nucleotide bases of siRNAs 17, 18. Among the chemical modifications, phosphorothioates (PS), 2'-O-methylation (2′-O-Me), 2′-O-allyl, 2′-deoxy-fluorouridine and locked nucleic acid (LNA) forms were known for improving the stability of siRNA for therapy. LNA modification is a bicyclic nucleic acid where a ribonucleoside monomer is linked between the 2’-oxygen and the 4’-carbon atoms with a methylene unit 19. In contrast to other modifications, the LNA modifications have been reported to substantially increase the potency and activity of siRNAs in both in vitro and in vivo studies with minimal toxicity 20, 21, 22, 23. Another challenge in RNAi research is the development of efficient and safe delivery systems. Recent advances, such as GalNAc conjugates and lipid nanoparticles (LNSPs), have emerged as effective strategies for encapsulating siRNAs in ionizable lipids for delivering them intracellularly for target gene knockdown24, 25.

Although many RNAi studies have reported anti-tumor effects of siRNAs in vitro and in vivo, not many of them were advanced through clinical trials for cancer. This drawback was mainly due to the non-specific targeting of siRNAs on other RNAs and their effects on non-tumor cells. Therefore, it is required to validate the effects of siRNAs in whole tumor tissues comprehensively using advanced technologies like single-cell RNA sequencing (scRNA-seq). However, scRNA-seq has not yet been fully utilized in the current drug discovery approaches including siRNA therapy to evaluate the effects of drugs broadly in all cell populations in the tumor tissues.

Ovarian cancer is a peritoneally progressing cancer where the tumor cells shed into the peritoneal cavity, then circulates through ascites fluid and undergo transcoelomic metastasis a.k.a. peritoneal seeding26, 27. Consequently, tumor cell clusters and spheroids isolated from ascites are considered as a representative model of studying transcriptomic signatures of advanced-stage ovarian cancer28. Given the nature of ovarian cancer, high throughput studies have utilized these ascites-derived clusters and spheroids to perform transcriptomic analyses at the single-cell level, providing insights into tumor heterogeneity and microenvironmental interactions 29, 30, 31.

In this work, we demonstrate that the in vivo delivery of LNA-modified siRNAs targeting FXR1 markedly suppresses ovarian cancer growth and metastasis in both immunocompetent and immunocompromised mouse models. scRNA-seq of ascites from siFXR1-LNA–treated tumor-bearing mice reveals that FXR1 silencing restricts tumor cell expansion while promoting the enrichment of M1-like macrophages and cytotoxic Cd8⁺ T cells, thereby enhancing anti-tumor immune responses. Collectively, these findings provide mechanistic insight into how FXR1-directed RNA interference inhibits ovarian cancer growth and progression in vivo and support FXR1 as a therapeutic vulnerability in ovarian cancer.

Results
Sequence specific screening identifies highly potent siRNAs that enhance FXR1 silencing, inhibit cell growth and promote apoptosis

siRNAs bind to complementary mRNA sequences to inhibit gene expression. Thus, each target sequence is expected to exhibit varying levels of gene silencing. Therefore, we designed five siRNAs numbered as seq1 to seq5 (Table S1), which specifically target coding sequences (CDS) or the sequences in untranslated region (UTR) of FXR1 gene as marked in Supplementary Fig. 1a. First, we used siRNAs (siFXR1) at two different concentrations 2.5 nM and 5 nM and the respective scrambled control siRNA (siCont) at 5 nM and found that the transfection of seq2 and seq3 siRNAs, targeting the CDS exhibited ~70% to ~85% inhibition of FXR1 protein in ovarian cancer cell lines (Fig. 1a). While we observed ~80% inhibition of FXR1 protein by seq5 in HeyA8 and OVCAR8 cells, the level of inhibition was only ~50% in the Kuramochi cell line. In contrast, the seq1 and seq4 sequences exhibited low levels of FXR1 inhibition relative to the other sequences (Fig. 1a). In brief, we observed that CDS-targeting siRNAs demonstrated superior level of FXR1 inhibition compared to the non-CDS targeting regions (Supplementary Fig. 1a and Fig. 1a).

Next, we determined the knockdown effects of all the five siRNAs at their lowest effective concentration of 2.5 nM on cell cycle phases using flow cytometry. Similar to the sequence specific effects of siRNAs on FXR1 inhibition (Fig. 1a), seq2 and seq3 exhibited the most profound effect on G1 phase arrest and a reduction in S phase (Supplementary Fig. 1b). Similarly, those sequences inhibited G1-phase proteins CDK2, CDK4, CDK6, Cyclin D1 and Cyclin E1 levels, most effectively compared to the other sequences (Supplementary Fig. 1c). In conjunction, FXR1 siRNAs seq2 and seq3 induced ~70% cellular apoptosis, accompanied by reduced BCL2 and increased PARP cleavage compared to siCont (Supplementary Fig. 1c, d). In agreement with the level of FXR1 knockdown, a modest increase in apoptosis was only observed with seq1, seq4 and seq5 (Supplementary Fig. 1d). We and others have reported that cMYC is a key target of FXR1 9, 32. Supporting this notion, cMYC protein level was also inhibited mostly by seq2 and seq3 siRNAs (Supplementary Fig. 1c). Similarly, seq2 and seq3 siRNAs also inhibited colony forming ability of ovarian cancer cells (Supplementary Fig. 1e).

FXR1 siRNA preferentially inhibits the growth of FXR1-high expressing cancer cells compared to normal cells

One of the key challenges of siRNA therapy for cancer is its uptake by normal cells, leading to both targeted and non-specific effects. Therefore, it is crucial to assess the growth-inhibitory effects of FXR1 siRNA specifically in cancer cells, ensuring minimal harm to non-tumor cells along with immune cell activation. To address this, we selected the most potent FXR1 siRNAs (seq2 and seq3) and assessed their effects on tumor growth and apoptosis by transfecting normal and cancer cells either independently or in co-culture. In conjunction with FXR1 mRNA expression data (Supplementary Fig. 2a), the transfection of FXR1 siRNA effectively inhibited the growth of tumor cells, which express high levels of FXR1. In contrast, the normal cells that express low levels of FXR1 expression were unaffected by FXR1 siRNA. This data suggests that the growth-inhibitory effects caused by seq2 and seq3 siRNAs are primarily driven through target-specific inhibition of FXR1 (Supplementary Fig. 2b). Next, we employed a co-culture model where GFP-labelled OVCAR8 cells expressing high levels of FXR1 co-cultured with RFP-labeled normal ovarian surface epithelial cells (OSE) or fallopian tube epithelial cells (FTE) expressing very low levels of FXR1 in a single well. As expected, treatment with FXR1 siRNA reduced the overall GFP signal, indicating inhibition of tumor cell growth, while the unchanged RFP signal reflected that non-tumor cells expressing low levels of FXR1 were not affected by FXR1 siRNA treatment (Fig. 1b and Supplementary Fig. 2a, c).

We also conducted a second co-culture assay, where GFP-labelled OVCAR8 cells were incubated with normal OSE or FTE cells. Upon reaching approximately 80% confluence, the OVCAR8-GFP/OSE and OVCAR8-GFP/FTE co-cultures were transfected with either siFXR1 (seq3) or the corresponding siCont. GFP signal was then quantified at 2-day intervals for up to 6 days to assess tumor cell growth. This assay also demonstrated a decrease in the overall percentage of GFP signal from OVCAR8 cells without affecting the viability of non-FXR1 expressing normal OSE cells (Supplementary Fig. 2d).

LNA modified siRNA improve FXR1 silencing and enhance cancer cell death compared to native siRNA

Based on the reports that LNA modifications can improve gene silencing potency with minimal toxicity 20, 21, 22, 23, we introduced LNA modification at the 3’site in both seq2 and seq3 (hereafter referred to as siFXR1-2-LNA and siFXR1-3-LNA respectively) (Fig. 1c, d). Next, we evaluated whether this modification enhanced their growth inhibitory effect in cancer cells. Remarkably, siFXR1-3-LNA achieved over 90% FXR1 knockdown at just 0.5 nM, whereas such effect was obtained with 2 nM of the unmodified (native) siRNA (Fig. 1e). Compared to siFXR1-3-LNA, siFXR1-2-LNA was not able to exert such superior activity in knocking down FXR1 in its low concentrations.

In conjunction, siFXR1-3-LNA was again able to cause G1 arrest more prominently and reduced G2/M phase than native form of siFXR1-3 in both Kuramochi and OVCAR8 cells (Supplementary Fig. 3a). Proteins associated with cell cycle regulators such as Cyclin D1, Cyclin E1, CDK2, CDK4, CDK6, cMYC and BCl2 were also inhibited predominantly by siFXR1-3-LNA than its native form. In contrast, the inhibition induced by siFXR1-2-LNA was less effective in comparison with siFXR1-3-LNA on inhibiting above cell cycle regulators (Supplementary Fig. 3b). Similarly, a subsequent increase was observed in the levels of cleaved PARP, caspase 3/7 activity, and cellular apoptosis when OVCAR8 and Kuramochi cancer cells were transfected with LNA-modified siFXR1-3 compared to its native form (Supplementary Fig. 3b-e). Our comet assay33, 34 also demonstrated a markable increase in DNA damage potentially due to cellular apoptosis in ovarian cancer cells, particularly when the cells were transfected with siFXR1-3-LNA compared to the native siRNA (Fig. 1f).

LNA modified FXR1 siRNA demonstrates superior cellular and tissue uptake with improved stability over nucleases

The primary objective of this study is to assess the therapeutic effects of LNA-modified FXR1 siRNA on tumor growth inhibition via FXR1 silencing in preclinical models. Therefore, we sought to use an intermediary approach of 3D culture model that connects traditional 2D cultures and in vivo systems. Ovarian cancer cells were cultured on an extracellular matrix to form tumor spheroids, allowing us to simulate in vivo tumor growth and assess the growth-inhibitory effects of FXR1 siRNA more accurately. Similar to its effect in 2D culture (Fig. 1), siFXR1-LNA was able to inhibit the growth of ovarian cancer spheroids more efficiently than native siRNA (Fig. 2a). Furthermore, high level uptake of siFXR1-LNA was also observed by tumor spheroids compared to native siRNA (Fig. 2b and Supplementary Movies 1 and 2).

Once internalized, siRNA is often trapped in the endosome and transported to the lysosome, where it gets degraded by lysosomal enzymes quickly, significantly reducing its effectiveness 35, 36. Therefore, we decided to determine whether LNA modification can rescue siRNA from lysosomal degradation. Herein, we used Texas red labeled siRNAs of both native and LNA forms and transfected into cancer cells and the cellular uptake levels were determined by imaging (Fig. 2c). Strikingly, we observed a high level of siFXR1-LNAs uptake within 1 h after transfection in two different cancer cell lines Kuramochi and OVCAR8, whereas only modest level of siRNA uptake was observed when cells were transfected with native siRNA (Fig. 2c and Supplementary Fig. 4a). Consistently, siFXR1-LNAs showed minimum localization within lysosomes, suggesting reduced lysosomal degradation. In contrast, native siRNA displayed a strong lysosomal accumulation compared to LNA form (Fig. 2c and Supplementary Fig. 4a). This data suggests that LNA modified siRNA exhibits greater stability and can evade lysosome mediated degradation. The terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay also showed an increase in the level of cellular apoptosis when siFXR1-LNA transfected cells compared to those transfected with native siRNA (Fig. 2d and Supplementary Fig. 4b). These findings led us to investigate whether LNA modification protects siRNA from RNase activity and if encapsulating LNA form siRNA in nanoparticles further enhances its stability. Here, we used commercially available polyethylenimine (PEI) nanoparticle named jetPEI for encapsulation of siRNAs and found that unmodified siRNA either in free or in encapsulated form were degraded rapidly compared to LNA-modified siRNA (Fig. 2e). Notably, jetPEI encapsulation of siFXR1-LNA enhanced its stability, and demonstrated least degradation in the presence of RNase (Fig. 2e).

We also used serum as a physiologically relevant source of exonucleases and RNases (ribonucleases) to assess whether LNA modification and PEI encapsulation protects siRNA from RNases mediated degradation. Consistent with the earlier results, LNA modifications in siRNAs improved stability over native siRNAs, and this stability was further enhanced upon encapsulation in jetPEI nanoparticles (Fig. 2f). Next, we examined whether siFXR1-LNA exhibits improved uptake in tumor tissues compared to normal organs when injected into tumor-bearing mice. As anticipated, siFXR1-LNA showed enhanced tissue uptake in peritoneal tumors and tumors at organ sites, with minimal uptake in tissues devoid of tumor growth (Fig. 2g). Specifically, a high level of siRNA uptake was observed in tumor tissues and ovaries with tumor presence, whereas no uptake was detected in lungs and heart, which were free of tumor growth. Uptake was also very minimal in the liver, kidneys, and spleen, where a minimal tumor presence was observed (Fig. 2g). Similarly, immunofluorescence of tumor tissue also confirmed a higher level of siFXR1-LNA in tumor tissues compared to native siFXR1 (Fig. 2h).

Collectively, our findings demonstrate that LNA modification of FXR1 siRNA not only protects FXR1 siRNAs from RNase and nuclease, but also enhances its tumor-specific uptake, suggesting improved therapeutic potential for treating cancer in preclinical models.

In vivo administration of LNA-modified FXR1 siRNA effectively suppresses ovarian cancer growth and metastasis

In this aim, we delivered LNA-modified siFXR1 or its native form complexed with jetPEI intraperitoneally (IP) to mice bearing ovarian cancer cells. Briefly, OVCAR8 cells were injected IP in female athymic nude mice and siRNA encapsulated in jetPEI were delivered into the mice twice a week for five weeks after one week of tumor inoculation as depicted in the schema (Fig. 3a). The control group of mice were treated with LNA modified scrambled siRNA (siCont-LNA) encapsulated in jetPEI as above. Bioluminescence imaging was performed once a week to monitor tumor growth. Notably, mice that were treated with native siFXR1 showed a markable reduction in the overall tumor growth, whereas LNA modified siFXR1 displayed a greater level of reduction in the overall tumor growth compared to the native form of siRNAs (Fig. 3b-d and Supplementary Fig. 4c).

To evaluate the silencing effect of the siRNA complex in vivo, we isolated proteins from the tumor specimen and evaluated the protein levels of FXR1 and its target MYC oncogene. As shown in Fig. 3e, tumors treated with the siFXR1-LNA exhibited robust inhibition of both FXR1 and MYC protein compared with tumors treated with jetPEI encapsulated siCont-LNA. Next, we validated the target specific effect of FXR1 siRNA on the downstream effectors of apoptosis in tumor tissues isolated from mice treated with control, native or LNA-modified siRNAs by immunohistochemistry (IHC) analysis. IHC of tumor tissues demonstrated that LNA-modified FXR1 siRNA exhibited a markable effect on inhibiting Ki67 proliferation marker levels and a subsequent upregulation in the levels of apoptosis marker cleaved-caspase 3 (Fig. 3f). TUNEL assay also demonstrated extensive TUNEL staining, as an indication of tumor cell death in the tumor tissues were isolated from mice treated with siFXR1-LNA compared with those treated with native siFXR1 (Supplementary Fig. 4d). Notably, there was no indication of tumor cell death when treated with siCont (Supplementary Fig. 4d).

Next, we validated the tumor inhibitory effects of siFXR1-LNA in a subcutaneous xenograft model using a highly aggressive triple-negative breast cancer cell line MDA-MB-231 which expresses high levels of FXR1 (Fig. 3g and Supplementary Fig. 4e). Consistently, siFXR1-3-LNA demonstrated a more effective knockdown of FXR1 and its downstream target MYC in MDA-MB-231 cells compared to siFXR1-2-LNA (Supplementary Fig. 4f). We selected an MDA-MB-231 TNBC model due to its genomic similarities with High-Grade Serous Ovarian Carcinoma (HGSOC) including frequent TP53 and BRCA mutations, high genomic instability and the prolific growth and metastatic behavior of the MDA-MB-231 cell line.

Consistent with the response observed in OVCAR8 tumors, systemic retro-orbital injections of siFXR1-LNA also significantly inhibited tumor growth of MDA-MB-231 tumors compared to the unmodified siFXR1 treatment (Fig. 3h–3j). Furthermore, histological analysis of lymph nodes used as indicators of early metastasis also revealed no signs of metastasis in the mice that were treated with siFXR1-LNA (Fig. 3k). We further examined whether the delivery of native or LNA modified FXR1 siRNA causes any tissue damage or associated toxicity by performing H&E-staining of liver, lung, heart, and kidney sections. Notably, neither form of FXR1 siRNA induced any observable histological changes compared with the respective control, indicating no detectable tissue damage associated with cellular toxicity (Supplementary Fig. 4g). We also assessed key enzymes and proteins for liver and kidney functions including Aspartate Aminotransferase (AST); Alanine Aminotransferase (ALT); Alkaline Phosphatase (ALP); Total Bilirubin (TBIL); Total Protein (TP) and found that delivery of both native and LNA modified FXR1 siRNA caused no significant changes in the levels of these markers (Table S2).

Taken together, results from our in vitro and in vivo models, along with toxicity assessments, demonstrate that LNA-modified FXR1 siRNA effectively inhibits tumor growth and metastasis by silencing its target FXR1 with minimal or no adverse effects on tissue integrity or organ functions.

Single-cell transcriptome profiling demonstrates marked effects of LNA-modified FXR1 siRNA on tumor growth inhibition and anti-tumor immune responses

One of the major challenges of translating RNAi-based therapies into clinical practice is the risk of immune cell toxicities and unintended effects on stromal components within the tumor microenvironment (TME). Therefore, we performed scRNA-seq method to assess whether there were any adverse effects on transcriptomic changes induced by RNAi treatment in both tumor cells and the cells in TME comprehensively. For this goal, we used both native and LNA-modified forms of FXR1-targeting siRNAs (seq2 or seq3) to knockdown FXR1 in mouse ovarian cancer cell lines, where seq2 showed 92% sequence complementarity and seq3 showed 96% complementarity with mouse FXR1 mRNA (Supplementary Fig. 5a). Consistent with the target knockdown effects in human cell lines and high level sequence complementarity, seq3 siRNA showed superior level of FXR1 inhibition in two mouse ovarian cancer cell lines Br-Luc (genotype: p53-/-; brca1-/-; myc; Akt-myr) and C11 (genotype: p53-/-; myc; KrasG12D) 37. Markedly, the LNA-modified versions exhibited more robust inhibition of FXR1 levels compared to unmodified versions (Supplementary Fig. 5b).

The Br-Luc cell line was selected because of its mutational similarity to human HGSOC for establishing an immunocompetent syngeneic model of ovarian cancer through IP injection of Br-Luc cells into immunocompetent FVB mice 38. Mice were treated with siCont-LNA or siFXR1-LNA encapsulated in jetPEI nanoparticles (10 g/mouse) twice a week for five weeks (Supplementary Fig. 5c). In conjunction with our human xenograft model, we observed a significant reduction in both tumor weight and ascites formation when the mice were treated with jetPEI encapsulated siFXR1-LNA (Supplementary Fig. 5d-f). We also observed that siFXR1-LNA treatment significantly improved the survival of Br-Luc ovarian cancer bearing mice compared with the control siRNA-LNA treated group (Supplementary Fig. 5g).

Ascites samples from tumor-bearing mice treated with the siCont-LNA and siFXR1-LNA were used for scRNA-seq, followed by clustering and CellChat analysis to delineate the role of various cell populations within the TME on tumor progression (Fig. 4a). It has been reported by other scRNA-seq experts that ascites represents a clinically valuable source of transcriptional information of both tumor cells and tumor-associated immune and stromal cells29, 30. These cells can be isolated through minimal invasive procedures, and cell clusters within ascites can be more readily and easily dissociated into single cells compared to solid tumors. Moreover, ascites often reflects advanced metastatic disease and immunosuppressive signaling, providing critical insights into mechanisms of how tumors escape from immune surveillance or therapies39. In brief, 16851 high-quality cells (~11621 cells from siCont-LNA and ~5230 cells from siFXR1-LNA group) were selected for generating single-cell transcriptomic profiles (Supplementary Fig. 5h).

The cell clusters were then visualized using Uniform Manifold Approximation and Projection (UMAP) for the integrated duplicates performed at a resolution of 0.1 annotation level according to sample type with differentially expressed genes (DEGs) (Supplementary Fig. 5i). Integrative analysis identified 11 clusters of cells in the murine TME components in the ascites (Fig. 4b and Supplementary Fig. 5j). Among the cell types, macrophages (cluster 0), epithelial cells (clusters 1, 4), T cells (cluster 3), NK cells (cluster 7), and DCs (cluster 8) showed high level differences between clusters among siCont-LNA and siFXR1-LNA groups (Fig. 4b and Supplementary Fig. 5j). Overall, the composition of cell clusters was variably influenced by siFXR1-LNA treatment compared to the siCont-LNA group (Fig. 4c).

Interestingly, we observed a notable reduction in epithelial cells and a substantial increase in DCs, NK, and T cells in the siFXR1-LNA treated group (Fig. 4c), indicating enhanced immune cell infiltration and a decrease in overall tumor growth potentially due to the direct inhibition of FXR1 in tumor cells as well as the effect through improved immune cell infiltration. While we identified distinct markers among the cells belonging to main clusters in Fig. 4b-d, further analysis identified considerable heterogeneity among the cells present in each cluster. For example, cluster 0 represents macrophage populations since many of the cells in this cluster express genes like, Cd68, Arg1, Adgre1, Apoe and Lyz2 (Fig. 4c, d and Supplementary Fig. 5j) but a considerable level of heterogeneity was observed in this population. Likewise, clusters 1 and 4 were annotated as epithelial cells (tumor cells) based on the high expression of Krt19, Krt18, Krt8, Epcam and Muc16 in those populations (Fig. 4c, d and Supplementary Fig. 5j). Cells in cluster 3 were identified as T cell based on Lef1, Cd3e, Cd3g, Cd3d, Cd4 and Cd8a expression and cluster 5 was determined as B cell based on Cd19, Bank1, Ebf1, Igkc and Ighm expression (Fig. 4c, d and Supplementary Fig. 5j). Cluster 7 showed expression of canonical markers of NK cells expressing Gzmb, Ccl5, Nkg7, Ncr1 and Ilr2b while cluster 8 was classified as DCs due to the presence of Siglech, Cd74, Grm8, Bcl11a, Itgae, and Clec9a (Fig. 4c, d and Supplementary Fig. 5j). Clusters 9 and 10 were relatively small populations and marked as mast cells and fibroblast respectively (Fig. 4c, d and Supplementary Fig. 5j).

Because we identified a considerable decrease in the numbers of epithelial tumor cells and an increase in immune cells (clusters 0, 1, 3, 4, 7 and 8, Fig. 4b-d), we performed CellChat analysis to determine the potential ligand-receptor interactions among cell types40. Notably, the interactions between tumor cells to macrophages and other immune cells were very high in the control group, which significantly reduced upon siFXR1-LNA treatment (Fig. 4e and Supplementary Fig. 5k). Furthermore, communications of T and NK cells with tumor cells were greatly enhanced upon siFXR1-LNA treatment compared to the control suggesting an anti-tumor response mechanism. In addition to T and NK cells, an increase in the interaction between DCs, B cells and tumor cells was observed in the siFXR1-LNA group (Fig. 4e and Supplementary Fig. 5k). This data prompted us to determine the level of cellular interaction by quantitating the magnitude of interaction between ligands and receptors expressed by each cell population using CellChat analysis.

In the CellChat all the ligand-receptor interactions were grouped into ECM-receptor interaction, secreted signaling, and cell-cell contact. We found that ECM-receptor signaling is downregulated and secreted signaling and cell-cell contact signaling are up-regulated in the siFXR1-LNA treated group compared to the siCont-LNA group (Supplementary Fig. 6a). Mostly the ECM signaling regulated through collagen, laminins, and fibronectin were predominant in the control group, where they potentially act as an immunosuppressive signaling through paracrine mechanism or as an oncogenic signaling in tumor cells through autocrine mechanism. In contrast, such signaling was not present in the siFXR1-LNA treated group. Specifically, collagens (encoded by Col4a1, Col4a2, Col4a5, and Col4a6), laminins (Lama3 and Lamc1), and fibronectin (Fn1) were expressed by epithelial cells and the receptor partner such as various integrins, Cd44, and Sdc4 were observed in tumor cells, and immune cells in the control group (Supplementary Fig. 6b). Other ECM-related outgoing signaling from macrophages, involving Fn1 and its interactions with integrins, Cd44, and Sdc4 receptors, were observed in both siFXR1-LNA and siCont-LNA treated groups (Supplementary Fig. 6b).

We also observed a gain in cell-to-cell contact signaling through MHC-I/II signaling components such as H2-t23, H2-aa, H2-ab1, H2-dma, H2-eb1, H2-oa and H2-ob among immune cells with notable magnitude from macrophages, DCs, B cells in partnership with T and NK cells mainly in the siFXR1-LNA treated group. In contrast, we did not observe such interactions in the siCont-LNA group (Supplementary Fig. 6c). This data further confirms that macrophages, DCs, and B cells are actively engaged as antigen-presenting cells (APCs) and deliver MHC-II molecules to T and NK cells for anti-tumor responses in the siFXR1-LNA treated group (Supplementary Fig. 6c). Similarly, the control group showed low expression of ligands like Cd40lg, Cd80, and Cd86 with their respective receptors Cd40 and Cd28, which are important for activating T and NK cells (Supplementary Fig. 6c). Moreover, APCs like macrophages, B cells, and DCs in the siFXR1-LNA group exhibited robust Cd80-Cd28 and Cd86-Cd28 interaction, suggesting that T cells and NK cells are the primary recipients of signals from APCs, which again confirmed the existence of strong anti-tumor effects upon siFXR1-LNA treatment (Supplementary Fig. 6c). Cd40-Cd40lg interaction was also observed in high level in the siFXR1-LNA group (Supplementary Fig. 6c), which is known for T cell proliferation, survival, and differentiation, as well as for enhancing the antibody production capabilities of B cells as reported before 41.

Il-18 interactions with Il-18r1 and Il-8 receptor accessory protein (Il-18rap) through macrophages to NK and T cells act as the recipients were also observed to be high in the siFXR1-LNA group suggest Il-18 mediated activation of NK and T cells (Supplementary Fig. 6d). Tgf-β-associated cellular interactions, such as Tgfb1 signaling through the Tgfbr1 and Tgfbr2 receptors, as well as Tgfb1 signaling through the Acvr1 and Tgfbr2 receptors as immune cell activation, were also observed high in the siFXR1-LNA treated group (Supplementary Fig. 6d).

Targeted silencing of FXR1 in vivo efficiently suppresses oncogenic signaling mechanisms and inhibits tumor growth

scRNA-seq analysis identified that the control siRNA treated group had a large number of epithelial cells that includes tumor cells, whereas siFXR1-LNA treatment reduced the numbers of epithelial cells (Fig. 4c). Considering that the epithelial cells could consist of both tumor cells and nonmalignant epithelial cells, we used the CopyKAT algorithm42 to identify tumor cells based on aneuploid status, determined by copy number variation (CNV). As expected, the proportion of tumor cells (aneuploid) were very high over normal epithelial cells (diploid) in the control group, whereas the aneuploid cell numbers were markedly diminished without affecting diploid cells when treated with siFXR1-LNA (Fig. 4f and Supplementary Fig. 7a). Consistently, flow cytometry of ascites and immunofluorescence analysis of tumor tissues demonstrated a reduction in EpCAM⁺ cells accompanied by a markable increase in Cd45⁺ immune cells in siFXR1-LNA treated samples compared to the siCont-LNA group (Supplementary Fig. 7c, d).

Additionally, transcriptome profile of cell cycle phase also showed a decrease in S phase cells and an increase in G1 phase genes in siFXR1-LNA treated group compared to those treated with control siRNA (Fig. 4g and Supplementary Fig. 7b). We also observed down-regulation of tumor cell markers, including Epcam, Muc16, Krt19, Krt18, and Krt8, following siFXR1-LNA treatment (Fig. 4h). Furthermore, a significant downregulation of genes associated with G1-phase such as Ccnd1 and Cdk4, along with a notable upregulation of the cell cycle arrest gene Cdkn1b (p27Kip1) were observed in the cells belonging to the siFXR1-LNA treated group (Fig. 4h). We also observed a significant decrease in the expression of the apoptosis-related gene Parp1, along with an increase in the expression of Casp3 and Casp8 upon FXR1 silencing (Fig. 4h). A reduction in the expression of the proliferation marker gene MKi67 (Fig. 4h) and genes associated with DNA replication such as Top2a and Mcm6 were also observed upon siFXR1-LNA treatment (Supplementary Fig. 7e). Eukaryotic translation-related genes Eif4g1 and Eif4e, which are the direct targets of FXR1, were also markedly reduced after siFXR1-LNA treatment (Fig. 4h). Most importantly, Fxr1 expression was significantly downregulated in siFXR1-LNA treated epithelial cells compared to those treated with control siRNA (Supplementary Fig. 7f), again confirmed the specificity of siFXR1-LNA on silencing FXR1in tumor cells.

Ingenuity pathway analysis (IPA) of epithelial cluster genes (Supplementary Data 1) further revealed enrichment of gap junction signaling, cell cycle control of chromosomal replication, cell cycle regulation etc., in the siFXR1-LNA group compared to control (Supplementary Fig. 7g). Consistent with our previous work reported the role of FXR1 in oncogenic translation mechanism9, we observed FXR1 silencing significantly suppressed the expression of genes involved in translation initiation, elongation and termination in the tumor cells compared to the controls (Supplementary Fig. 7g). Pathways related to mTOR, Keap-Nfe2l2, and PPAR family signaling pathways, which are crucial for oncogenic metabolism43, 44, 45 were also downregulated upon treatment with siFXR1-LNA (Supplementary Fig. 7g).

In vivo FXR1 silencing enhances anti-tumor response via macrophage polarization and dendritic cell infiltration

Tumor associated macrophages (TAMs) are known for their effects in cancer progression and metastasis46, 47, 48, 49, 50. TAMs are also known for inducing immune suppressive effects on T cells and NK cells by modulating their functions either by secreting anti-inflammatory cytokines, chemokines or by modulating the functions of immune checkpoint proteins51, 52, 53, 54. Notably, our primary analysis identified macrophages as the dominant cell population in cluster 0 comprising ~35% of the total cell population (Fig. 4b, c). UMAP clustering of this population at 0.1 resolution generated about 3 distinct clusters as M1-like, M2-like, and M1/M2/monocyte mixed lineage population in the siCont-LNA and siFXR1-LNA groups (Fig. 5a, b and Supplementary Fig. 8a). In brief, cluster 0 were designated as M2-like TAMs with high expression of Arg1, Cd163, Mrc1, and Adgre1 and cluster 1 was identified as M1-like macrophages expressing anti-tumor factors including Ccl2, Ccl3, Ccl7, Cxcl9, Cxcl10, and Cxcl16 (Fig. 5a, b). The remaining cells in cluster 3 was identified as mixed lineage of M1/M2/monocyte with the presence of monocyte markers Lyz1, Ly6c2, and Cd14 along with M1 and M2 markers (Fig. 5a, b).

Next, we examined the expression of Ccl2 and its receptor Ccr2, as well as Ccl7, Cxcl10, Ccl5, and its receptor Ccr5 in each macrophage subtype. M1-like macrophages (cluster 1) exhibited significantly higher expression of these genes compared to M2-like macrophages (cluster 0), which facilitates the recruitment and activation of T cells (Supplementary Fig. 8b). While tumor promoting M2-like macrophage populations were enriched over M1-like macrophages in the control group, siFXR1-LNA treatment resulted in enrichment of M1-like macrophages with less abundance of M2-like macrophages (Fig. 5c and Supplementary Fig. 8c). To further characterize the macrophage population in detail, we computed M1-like and M2-like signatures score using gene signature scores based on gene sets derived from CIBERSORT55. These scores were then integrated to generate module scores using the AddModuleScore function. As anticipated, the M1-like macrophage signature was significantly higher and the tumor promoting M2 macrophage signature scores were very low in the siFXR1-LNA treated group compared to the controls (Fig. 5d). To further validate the transcriptomic data identified by scRNA-seq, we assessed the protein levels of M2 macrophage markers using flow cytometry and immunofluorescence. Consistent with the scRNA-seq data, flow cytometry revealed enrichment of macrophages with M2-like markers Mrc1, Cd163 and Pdl1 in the control group, while siFXR1-LNA treatment significantly reduced the expression of these markers significantly (Fig. 5e and Supplementary Fig. 8d). We also observed a significant decrease in Pdl1+ TAMs in the siFXR1-LNA treated group, which again confirms the anti-tumor effects of siFXR1-LNA (Fig. 5e and Supplementary Fig. 8d). Immunofluorescence of tumor tissues further confirmed a decrease in the infiltration of M2 macrophages as evidenced by low expression of Arg1 and Mrc1 in the tumor samples isolated from mice were treated with siFXR1-LNA compared to the controls (Supplementary Fig. 8e).

Next, we analyzed the dendritic cells (DCs), which are known as the sentinels of the immune system and could be modulated for immunotherapeutic treatment strategies 56. Thus, we analyzed the population of DCs for their antigen-presenting capabilities such as recognizing, processing, and presenting "threat signals" obtained upon siFXR1-LNA treatment. Our analysis identified distinct expression profiles of DCs isolated from mice treated with control siRNA and siFXR1 LNA (Supplementary Fig. 8f). Notably, DCs expressing high levels of genes associated with DC maturation and migration, namely Cd74, Cd80, Ccr7, Cd83, and Cd86 were observed in the group treated with siFXR1-LNA (Fig. 5f). Because these cells express a specific gene signature for DC activation and maturation, we performed a more detailed subcluster analysis and found three distinct subclusters of DCs in the tumor (Fig. 5g-j). Cluster 0 was identified as the monocyte derived DCs (MoDCs) expressing Cst3, Mrc1 and Arg1 genes, cluster 1 as plasmacytoid DCs (pDC) expressing Bcl11a, Siglech, Bst2 and Ly6d and cluster 2 as type1 conventional DCs (cDC1) expressing Btla and Clec9a (Fig. 5g-j). We also observed that the ratio of MoDCs, a subset of DCs that differentiate in response to inflammatory stimuli and are recruited to inflammatory sites such as the TME, was significantly reduced in siFXR1-LNA treated sample groups (Fig. 5h). Importantly, the proportion of pDCs, which promote anti-tumor immunity via type I interferons (IFN-I) 57, 58, and facilitates cDC1 maturation to enhance Cd8+ T cell and NK cell cytotoxicity 59, was significantly increased in the tumor samples from mice treated with siFXR1-LNA (Fig. 5h). However, no significant changes were observed in the cDC1 population (Fig. 5h).

Next, we performed CellChat analysis to investigate the interactions between pDC and NK cells as well as Cd8+ and Cd4+ cytotoxic T cells, to assess their impact within TME. In the siFXR1-LNA group, pDC ligands (Klrb1a, Klrb1b, Klrb1c, Klrb1f) were found to interact with Clec family receptors on NK cells (Supplementary Fig. 8g). These interactions are expected to enhance NK cell activation and functionality, promoting the recognition and destruction of tumor cells60. Additionally, pDCs which are known for APC functions by providing signals to Cd8+ and Cd4+ cytotoxic T cells through interactions with MHC class I and II receptors were predominant in the siFXR1-LNA group (Supplementary Fig. 8g). Overall, our analysis demonstrates that these pDCs-mediated interactions, which are important for bridging innate and adaptive immunity, facilitate T cell priming and activation in the presence of tumor antigens enhanced upon siFXR1-LNA treatment.

In vivo silencing of FXR1 potentiates anti-tumor immunity by improving cytotoxic T and NK cells infiltration

Our primary analysis showed an increase in T and NK cell populations in samples treated with siFXR1-LNA compared to those treated with control siRNA (Fig. 4c). Unsupervised clustering based on expression of Cd3d, Cd3e, and Cd3g for tumor-infiltrating lymphocytes (TILs) and Nkg7 for NK cells identified six distinct T cell clusters and three NK cell clusters in our sample sets (Fig. 6a and Supplementary Fig. 9a). Specifically, our T-cell subtyping identified subclusters 0 and 1 as naïve Cd4+ and Cd8+ T cells, characterized by expression of Lef1, Tcf, Sel and Ccr7, while subclusters 2 and 3 were identified as cytotoxic Cd4+ and Cd8+ T cells marked by Nkg7 and Gzmb. Subcluster 4 included a small fraction of Cd4+ exhausted T cells/Tregs characterized by Tigit/Foxp3 expression, and subcluster 5 was identified as effector memory Cd4+/Cd8+ T cells, marked by Isg15 expression (Fig. 6b). Quantification of all cell populations in both groups detected abundance of Cd4+ T cells over Cd8+ T and other T cell populations following FXR1 siRNA treatment (Fig. 6c). Most strikingly, we observed a shift in the overall population of naïve Cd4+ T cells and Cd8+ T cells along with the cytotoxic form of Cd4+ T cells and Cd8+ T cells. In brief, we found a decrease in naïve Cd4+ T cells and Cd8+ T cells. In contrast, we found an increase in the enrichment of Cd8+ T cells and Cd4+ T cells with cytotoxic function indicated by the marker genes Nkg7, Prf1, and Gzmb upon FXR1 siRNA treatment (Fig. 6d and Supplementary Fig. 9b).

We also identified a small cluster of Cd4+ T cells that express markers associated with exhausted T cells and Tregs in our T cell clustering (cluster 4; Fig. 6a, b). However, we did not observe any significant differences in the proportions of Cd4+ Treg cells between the siFXR1-LNA and control groups (Fig. 6d). In complement to scRNA-seq analysis, our IHC analysis of tumor tissue samples also demonstrated high levels of T cell infiltration in the tumor tissues upon FXR1 silencing compared to controls (Fig. 6e). Additionally, flow cytometry analysis of the ascites samples collected from the mice treated with control or FXR1 siRNA also exhibited an increase in Cd4+ and Cd8+ T cells upon siFXR1-LNA treatment compared to the siCont-LNA group (Fig. 6f and Supplementary Fig. 9c). Overall, our findings demonstrate a substantial increase in total T cells, particularly an increase in the cytotoxic forms of Cd4+, Cd8+ T cells in the tumor bearing mice treated with siFXR1-LNA.

Next, we performed Cell Chat analysis40 to determine cell-cell communication between cytotoxic Cd8+ and Cd4+ T cells populations with macrophages and tumor cells (epithelial cells), which identified numerous interactions between macrophages, tumor cells and T cells (Supplementary Fig. 9d). Among them, the interaction of Ccl5 ligand with its receptor Ccr1 and Ccr5 between Cd8+ cytotoxic and Cd4+ cytotoxic T cell subtypes, M1- and M2-like macrophages and tumor cells were observed high as a mechanism of T cell mediated cell death in the siFXR1-LNA treated group compared to the control (Supplementary Fig. 9d). We also found that the interaction of adhesion molecules such as Icam1 with Itgal, Itgab2, and Itgam, along with Thy1's interaction with Adgre5, Itgam, and Itgb2 between macrophages and tumor cells with T cells were observed only in the siFXR1-LNA group as a potential mechanism of T cell activation and recruitment of macrophages into tumor sites for the elimination of tumor cells (Supplementary Fig. 9d). We also observed prominent interactions between Cd40lg with Itga5, Itgam, Itgb2 and Cd40, which are critical for T cell activation, and T cell mediated anti-tumor immune response in siFXR1-LNA group (Supplementary Fig. 9d).

Cell Chat analysis also identified other ligands and receptor interactions within M1/M2 macrophages and Cd8+ cytotoxic and Cd4+ cytotoxic T cell subtypes majorly through interaction with MHC-I/II signaling genes (such as H2-d1, H2-aa, H2-k1, H2-dma, H2-m3 and H2-t23) in siFXR1-LNA treated group (Supplementary Fig. 9e and Supplementary Fig. 6c), as part of immune response mechanisms in siFXR1-LNA group. The cytokines and chemokines from cytotoxic Cd4+ and Cd8+ cytotoxic cells interact with their receptors (Ccl12-Ccr2, Ccl2-Ccr2, Ccl7-Ccr2, Il18-Il18r1/Il18rap) on M1-like macrophages were also identified in siFXR1-LNA group (Supplementary Fig. 9e), potentially for enhancing the immune response. Furthermore, interactions between Cd80, Cd86 from macrophages to Cd28 receptor present on cytotoxic T cells again indicating a trigger for anti-tumor response by cytotoxic T cells in siFXR1-LNA treated group (Supplementary Fig. 9e and Supplementary Fig. 6c).

Natural Killer (NK) cells possess a unique capacity to directly engage and eliminate tumor cells, setting them apart from other immune cell types. Studies in both experimental mouse tumor models and in cancer patients have demonstrated crucial roles of NK cells in controlling tumor progression and inhibiting metastasis in patients 61, 62, 63, 64. Supporting this notion, treatment of siFXR1-LNA resulted in a notable increase in NK cells expressing Ifng, Gzmb, Cd244a, Itga2, Il2rb, and Nkg7, indicative of their cytotoxic and activated states against tumor cells (Fig. 6h, i). Our analysis of NK cell population further identified three distinct sub-clusters (Fig. 6j) defined by markers associated with cytotoxic and activated phenotypes. Among these, cluster 0 representing cytotoxic and activated form of NK cells was markedly enriched (~2-fold increase) in samples from siFXR1-LNA treated group (Fig. 6k). We also observed that cluster 1, representing a mixed-lineage population, and cluster 2, composed of partially cytotoxic and activated NK cells, were reduced in the siFXR1-LNA treated group (Fig. 6k). Altogether, our data highlights that siFXR1-LNA treatment enriched the cytotoxic and activated form of NK cells in vivo.

Our results demonstrate that the delivery of siFXR1-LNA encapsulated in jetPEI effectively evades RNase- and lysosome-mediated degradation, enabling efficient silencing of its target mRNA, FXR1, within tumor cells. Administration of the siFXR1-LNA complex also triggered multiple anti-tumor immune responses within the TME. Specifically, siFXR1-LNA treatment promoted the enrichment of Cd4⁺ and Cd8⁺ T cells, activated and cytotoxic NK cell subsets, and DCs with anti-tumor functions and suppressed the growth of tumor epithelial cells. Additionally, TAMs, which typically exhibit immunosuppressive phenotypes, were reprogrammed toward pro-inflammatory, anti-tumor phenotypes following siFXR1-LNA treatment (Fig.7).

Methods
Ethics declarations

All animal studies performed in this study were approved by the Institutional Animal Care Committee (IACUC) at the Medical College of Wisconsin and conducted according to the committee’s guidelines. Tumor growth/burden was monitored regularly, and mice were euthanized before tumors reached the maximum allowable size of 2 cm in maximal diameter recommended by our institutional ethics committee. Blood samples from human were collected for PBMC isolation in de-identified manner with a written informed consent in compliance with the institutional review board approved protocol (PRO00041601) by the Ethics Committee of Froedtert Hospital and Medical College of Wisconsin. All samples were handled in compliance with the Declaration of Helsinki.

Cell culture

HeyA8 cells were received from the Characterized Cell Line core at MD Anderson Cancer Center, Houston, TX, USA. Kuramochi cells were received from Taru Muranen at Beth Israel Deaconess Medical Center, Boston, MA, USA. OVCAR8 cells were purchased from the National Cancer Institute (NCI) cell line repository. NIH-OVCAR3 and MDA-MB-231 cells were purchased from the ATCC repository. Br-Luc and C-11 murine cell lines were received as a kind gift from Dr. Sandra Orsulic, University of California, Los Angeles, CA 37. Ovarian surface epithelial (OSE) cells were isolated as described earlier 74. FTE187 was received from Dr. Jinsong Liu at MD Anderson Cancer Center, Houston, Texas, USA. HeyA8, OVCAR8, Br-Luc, MDA-MB-231 cell lines were cultured in DMEM medium (Sigma-Aldrich) supplemented with 10% fetal bovine serum (FBS, VWR),1% Antibiotics (Anti-Anti 100X, Thermo Scientific). NIH-OVCAR-3 cells were maintained in the RPMI-1640 medium (ATCC, Manassas, VA, USA) supplemented with 10% fetal bovine serum (FBS, Omega Scientific),1% Antibiotics (Anti-Anti 100X, Thermo Scientific). OSE and FTE187 were maintained in cell culture medium consisting of 1:1 Medium 199 and MCDB-105 medium (Sigma-Aldrich) with 10% FBS, 1% antibiotics, 10 ug/mL Insulin Solution (Sigma-Aldrich), and 10 ng/mL hEGF (Peperotech). Cells were routinely tested and deemed free of PlasmotestTM Mycoplasma Detection Kit (InvivoGen, San Diego, CA). Authenticity of the cell lines used were confirmed by STR characterization at IDEXX Bioanalytics Services (Columbia, MO).

Isolation of CD8+ T cells from the PBMCs

T cells were isolated using the EasySep™ Human CD8+ T Cell Isolation Kit (STEMCELL Technologies, Cambridge, MA, USA). Briefly, the frozen peripheral blood mononuclear cells (PBMCs) isolated from the whole blood were resuspended in the RPMI medium and centrifuged at 252 x g for 5 min. The resulting pellet was resuspended in RoboSep™ Buffer to which the Isolation Cocktail containing the antibody complexes was added in the concentration of 50 uL/mL of sample. This mixture was further subjected to negative selection by the addition of EasySep™ Dextran RapidSpheres. This resulted in the labelling of unwanted cells with antibodies and magnetic particles and the subsequent separation of the CD8+ T cells which were poured off into another tube and was used for further analysis.

Western blotting

For preparing cell lysates, the cells were washed twice with 1x ice-cold PBS and lysed on ice in 1X RIPA lysis buffer containing freshly added 1x protease inhibitor cocktails (Sigma-Aldrich). For preparing the tissue lysates, the tumor tissues were homogenized in 1X RIPA lysis buffer over ice. After 30 min of incubation, the lysates were collected by centrifugation at 4°C for 10 min at 11,180 x g. The amount of total protein was determined using a BCA protein assay kit (Pierce, Rockford, IL, USA). An equal amount of total protein (30 μg) was resolved on precast 4-12% SDS-PAGE gels (Biorad, Hercules, CA, USA), transferred onto PVDF membranes, and incubated with desired primary antibodies, followed washing and incubation with HRP conjugated secondary antibodies (Cell Signaling Technology) and detecting of protein bands using chemiluminescence kit (Pierce, Rockford, IL, USA).

siRNA transfection

All siRNA duplexes for human FXR1 and negative control were purchased from Integrated DNA Technologies, Inc. (IDT, Inc. Coralville, IW). siRNA sequences are listed in Table S1. Reverse transfections were performed using the Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific Inc., Waltham, MA). At 48 h post-transfection, cells were harvested for further analysis. Unlabeled and Texas red labeled LNA modified siRNAs were synthesized by Eurogentec (Liege, Belgium) and purified by high performance liquid chromatography (HPLC).

Cell proliferation and colony formation assays

Cell viability was measured with the Cell Counting Kit- 8 (CCK-8) (Dojindo, Shanghai, China) according to the manufacturer’s instructions. Cells were plated at a density of 1×103 cells per well in 96-well plates and incubated at 37 ºC. Proliferation rates were determined at 24, 48, 72 h post-transfection, and quantification was performed on a microtiter plate reader (Tecan, Mannedorf, Switzerland) at 450 nm.

For colony formation assay, transfected cells were plated in six-well plates at a density of 1000 cells per well. After 10 days, cells were rinsed with PBS, fixed in 5% glutaraldehyde for 10 min and then stained with 0.5% crystal violet (Sigma Aldrich, MO, USA) for 20 min. Plates were washed with water and dried before imaging.

Cell cycle analysis

siFXR1s transfected ovarian cancer (Kuramochi and OVCAR8) cells were seeded at a density of 5 x 105 in 6-well plates. When cells reached 70–80% confluence, they were washed with PBS, trypsinized, collected and fixed with 70% ethanol overnight. Next day, cells were treated with 1 mg/ml RNase A (Sigma-Aldrich, Saint Louis, MO) at 37 °C for 30 min and then resuspended in 0.5 ml of PBS and stained with 50 μg/ml propidium iodide (PI) (Sigma-Aldrich, Saint Louis, MO). The cells were analyzed using a FACScan flow cytometer (Becton–Dickinson, Mansfield, MA) and ModFit LT software (Verity Software, Topsham, ME).

Annexin V/PI staining for apoptosis

Cellular apoptosis was measured with FITC Annexin V Apoptosis Detection Kit I (BD Pharmingen, San Diego, CA, USA) as per as manufacturer’s protocol. In brief, Kuramochi and OVCAR8 cells were reverse transfected with siRNAs (siCont and siFXR1s). After 48h, the cells were trypsinized, washed with PBS and resuspended in Annexin V binding buffer at a concentration of 106 cells/ml. Annexin V– FITC (5 μl) was added, vortex-mixed gently and incubated for 15 min at 4 °C in the dark. Cells were stained with 5 μl of PI for another 5 min at 4 °C in the dark. Stained cells were acquired on a FACS Calibur flow cytometer (Becton–Dickinson, Mansfield, MA) and data were analyzed with Flowjo software version 10.6.1 (TreeStar, Ashland, OR, USA).

Caspase3/7 activity assay

Following treatments with siFXR1s for 48h, cells were subjected to Caspase 3/7 activity measurement with Caspase-Glo assay kit (Promega, Madison USA). Briefly, the plates containing cells were removed from the incubator and allowed to equilibrate to room temperature for 30 min. 100 μl of Caspase-Glo reagent was added to each well, the content of well was gently mixed with a plate shaker at 28 x g for 30 sec. The plate was then incubated at room temperature for 2 hours. The luminescence of each sample was measured in SpectraMax i3x Multi-Mode Microplate Reader (Molecular Devices, Japan Co., Ltd., Tokyo, Japan).

Cellular uptake and lysosomal escape of siRNA

To examine the uptake and escape of the siRNAs from lysosomes, native siFXR1 and siFXR1-LNA labelled with Texas-red, kuramochi and OVCAR8 cells were seeded in the 35 mm glass bottom tissue culture plates (Cellvis LLC) and allowed to attach overnight. On the second day, cells were transfected with respective siRNAs using Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific Inc). Cells were washed twice with 1x PBS and stained with Hoechst 33342 and LYSO-ID® Green detection kit (Enzo Life Sciences, New York, USA) before imaging. Images of the live cells were captured at 1 h, 3 h, 6 h and 24 h post siRNAs treatment with a 40X objective using a confocal laser scanning microscope (LSM 510; Zeiss, Oberkochen, Germany).

At 48 h of siRNAs treatment, a TUNEL assay was conducted using an In Situ Cell Death Detection Kit (Roche Applied Science, IN) according to the instructions to detect apoptotic cells. Images from each treatment group were evaluated using a confocal laser scanning microscope (LSM 510; Zeiss, Oberkochen, Germany) and TUNEL-positive cells were quantified using Aim 4.2 software LSM 510.

Serum and enzymatic stability assays

To study the resistance to RNase I, native siFXR1 and LNA modified siFXR1 complexed with JetPEI containing a total amount of 1 µM siRNA were incubated with 1 unit of RNase I at 37 °C for 30 min, followed by incubation at 90 °C for 30 min to inactivate the enzyme. Heparin (200 μg mL−1) was added to the samples, and the mixture was incubated for an additional 30 min at room temperature to release the siRNA from the lipid complex. 1% TAE-Agarose gel electrophoresis was used to determine siRNA integrity and siRNA band intensity was quantified using ImageJ software.

Serum stability study was performed with human and mouse sera as previously reported 9. The native siFXR1 and LNA modified siFXR1 complexed with JetPEI incubated with 10% human or mouse serum for different time intervals at 37 °C. siRNAs were then released from the complex by incubation with heparin (200 μg mL−1) for 30 min and then analyzed by electrophoresis in a 20% TBE-polyacrylamide gel electrophoresis.

Live cell incucyte assay

OSE and FTE cells were nuclear-labeled red with the Incucyte® Nuclight Rapid Red Dye for Live-Cell Nuclear Labeling (Sartorius, USA) according to the manufacturer’s protocol. GFP labelled OVCAR8 were individually co-cultured with OSE and FTE NucLight Red cells in 1:1 ratio and allowed to attach overnight. The media were removed, and cells were transfected with siCont-LNA and siFXR1-3-LNA using Lipofectamine RNAiMAX transfection reagent. After 24 h, cocultured cells were trypsinized and 5x103 cells were reseeded in quadruplicates on a 96-well plate containing complete medium. Live images were taken every 6 h for a period of 72 h using The IncuCyte® Live Cell imaging system (Sartorius, USA). Green cell counts based on number of live cells were performed using IncuCyte® S3 Software (Sartorius, USA).

Co-culture assay

24 h prior to transfection with siCont-LNA or siFXR1-3-LNA, OSE and FTE cells and GFP expressing OVCAR8 cells were seeded together into a 6 well, glass bottom slides (Cellvis LLC) plate at 1:1 ratio, respectively. Prior to imaging, cells were stained with Hoechst (Life Technologies; 5 ng/mL). Plates were then imaged at day 0 (day of transfection), 2 d, 4 d, and 6 d after transfection using a with a 40X objective using a confocal laser scanning microscope (LSM 510; Zeiss, Oberkochen, Germany). Counting on the GFP channel was done using the Aim 4.2 software LSM 510 and used to quantify the number of OVCAR8 cells in the culture. Total cell populations were quantified by counting Hoechst+ nuclei using the Aim 4.2 software LSM 510. The proportion of OVCAR8 cells to total cells in each well was quantified by dividing the number of GFP+ cells by total Hoechst+ cells.

Comet assay

To evaluate DNA damage in a cell, we performed an alkaline comet assay using Comet SCGE assay kit according to the manufacturer’s instructions (Enzo Life Sciences, New York, USA). Briefly, Kuramochi and OVCAR8 cells were transfected with siCont, siFXR1-3 and siFXR1-3-LNA using Lipofectamine RNAiMAX transfection reagent. After 48 h, cells were gently scraped and suspended at 1 × 105 cells/mL in ice cold PBS. 50 µL of cells combined with molten LM Agarose at a ratio of 1:10 (v/v) were dropped and spread onto Comet Slide. After gelling LM Agarose, slides were treated with lysis solution for 1 h at 4° C and alkaline unwinding solution for 20 min at room temperature. The slides were placed in electrophoresis tray containing TBE electrophoresis solution and subjected to electrophoresis at 21 volts for 15 min. After staining cells with CYGREEN green Nucleic acid dye, cell images were obtained at 20X magnification using EVOS M5000 imaging system (Thermo Fisher Scientific). Percent DNA in the tail and tail moment or comet-like structures from at least 50 randomly selected cells per sample were analyzed using OpenComet v1.3.1 software 75.

In vivo study

We performed sequence alignment analysis for both siFXR1 sequences (seq2 and seq3) using CLUSTALW before using them for in vivo study. This analysis revealed that seq3 has 96% sequence homology between human (NCBI Reference Sequence: NM_001013439.3) and mouse (NCBI Reference Sequence: NM_008053.4) FXR1, supporting its use in the murine model.

Athymic nude mice (J:NU, Strain #007850) and FVB/NJ mice (Strain #001800), female and approximately 4–6 weeks old, were purchased from Jackson Laboratories. Ovarian cancer affects only females; therefore, only female mice were used in this study. The animals were maintained under a 12 h light/dark cycle with unrestricted access to food and water at the temperature around 20–24 °C with 40–60% humidity. For the tumorigenicity study, OVCAR8 ovarian cancer cells expressing luciferase (0.5 × 10⁶ cells/mouse) were injected IP into mice (n = 7 per group) to establish a human ovarian cancer xenograft model. After 7 days, tumor-bearing mice were randomized into three groups and treated IP with 10 µg/mouse of siCont-LNA, siFXR1-LNA, or siFXR1, each complexed with 16 µL of in vivo-jetPEI transfection reagent (PolyPlus Transfection, Illkirch, France) in a final volume of 200 µL of 5% glucose. Treatments were administered twice weekly for 28 days.76. Mice were regularly monitored for tumor growth by bioluminescence imaging. For bioluminescence imaging, mice were IP injected with 150 mg/kg of D-luciferin in 200 μL PBS, and bioluminescence signal was captured using an IVIS Lumina II in vivo imaging System (Caliper Life Sciences).

To evaluate siRNA toxicity, athymic nude mice were randomly assigned to three groups (n = 5 per group) and administered IP injections twice weekly for 4 weeks with 10 µg/mouse of siCont-LNA, native siFXR1, or siFXR1-LNA, each formulated with jetPEI nanoparticles. After the final injection, major organs (kidney, lungs, liver, and brain) were collected, fixed, sectioned, and stained with H&E for histopathological assessment. Blood serum was also collected and submitted to IDEXX BioAnalytics (Columbia, MO) for hematological and biochemical analyses, including measurements of ALT, AST, bilirubin, albumin, and creatinine.

For the breast tumorigenicity study, female athymic nude mice were injected subcutaneously with MDA-MB-231 cells (5 × 10⁶ cells/mouse) mixed 1:1 with Matrigel (Corning). Once tumors reached 70–100 mm³, mice were randomized into three groups (n = 5 per group) and treated via retro-orbital injection with 10 µg/mouse of siCont-LNA, siFXR1, or siFXR1-LNA, each complexed with 16 µL of in vivo-jetPEI transfection reagent (PolyPlus Transfection, Illkirch, France) in 100 µL of 5% glucose. Treatments were given twice weekly for 7 weeks. Tumor size was measured weekly using a vernier caliper, and tumor volume was calculated as: volume (mm³) = length × (width²)/2. Mice were euthanized at week 7.

For the scRNA-seq study, FVB/NJ mice were IP injected with 0.5 × 10⁶ Br-Luc cells. Seven days after tumor inoculation, mice received IP injections of siCont-LNA (n = 10) or siFXR1-LNA (n = 10), the latter designed to target the mouse FXR1 sequence (NCBI Reference Sequence: NM_008053.4), each formulated with jetPEI as described above. For the survival study, mice exhibiting moribund conditions with ascites formation were considered to have reached the experimental endpoint and were euthanized. Ascites and tumor tissues were collected from each group for downstream analyses.

Bioluminescence imaging

In vivo bioluminescent imaging was performed using the IVIS Lumina II Bioluminescence and Fluorescence Imaging System (Caliper Life Sciences). Mice were injected D-luciferin (150 mg/kg; Gold Biotechnology, St. Louis, MO) intraperitoneally (IP), and imaging was conducted 10 minutes later, corresponding to the peak signal. Tumor images were captured under the following settings: exposure time, 0.5 seconds; f/stop, 16; medium binning; and a 12.5 × 12.5 cm² field of view. Bioluminescent signals were quantified using Living Image software and reported as tissue radiance (photons/s/cm²/sr).

For the in vivo uptake and biodistribution study, tumor-bearing mice were imaged using the IVIS Spectrum System (PerkinElmer, Shelton) at 24 h after a single IP injection of Texas Red–labeled siFXR1-LNA. Following imaging, mice were euthanized, and tumors and major organs were collected and imaged to assess siRNA distribution.

Immunohistochemistry (IHC) and TUNEL assay

For this purpose, the slides were dewaxed in xylene and rehydrated through graded ethanol to distilled water. Antigen retrieval for the slide specimens were performed using IHC-Tek epitope retrieval solution and steamer set (IHC World, LLC.). The slides were then immersed in 3% H O for 10 min to quench endogenous peroxidase followed by blocking with 10% goat serum for 1 h. Tissue sections were then stained overnight at 4 °C with the indicated primary antibodies. The slides were counterstained with Harris modified hematoxylin (Thermo Fisher Scientific Inc., Rockford, IL), dehydrated with graded ethanol and xylene, and finally mounted with paramount. IHC slides were then digitally scanned using Pannoramic 250 FLASH III scanner (3D HISTECH ltd. Version 2.0) and, using the Case Viewer software (3D HISTECH ltd. Version 2.0) was used to view the images.

A TUNEL assay kit (Roche, Basel, Switzerland) was used to determine apoptotic cells in tumor specimen according to manufacturer’s instructions. Images from each treatment group were evaluated using a confocal laser scanning microscope (LSM 510; Zeiss, Oberkochen, Germany) and TUNEL-positive cells were quantified using Aim 4.2 software LSM 510.

3D tumor spheroid invasion and penetration study

3D tumor spheroids were prepared with growth factor reduced matrigel (GFR Matrigel, Corning Life Sciences, NY, USA) as previously reported. Thousand OVACR8 cells were suspended in 100 µL of spheroid formation ECM and added into a Corning 96-well ultralow attachment microplate, followed by centrifugation at 200 x g for 3 min at 4 °C. The plate was incubated at 37 °C for 24 h to induce the formation of spheroids. The tumor spheroids were incubated with siFXR1-LNA labelled with Texas-red to evaluate the tumor penetration capability of siRNA.

For spheroid invasion assay, the spheroids were embedded in matrix from the Cultrex Spheroid Cell Invasion Assay kit (Trivigen, Gaithersburg, MD) at 37 °C for 60 min and transfected with the siCont-LNA and siFXR1-LNA using Lipofectamine RNAiMAX transfection reagent for 24 h. The medium was then replaced by DMEM containing 10% FBS with 5 ng/mL hEGF, and the spheroids were incubated at 37 °C for up to 4 days. Invasion of the spheroids in the matrix was photographed every 24 h using an inverted microscope (Nikon Ti2E).

scRNA-seq sample preparation, data acquisition and pre-processing

Ascites collected from three biological replicates were pooled together as one sample, and a set of two samples per treatment group (siCont-LNA or siFXR1-LNA) were used for scRNA sequencing. Samples were then processed for single cell using tissue dissociation enzymes. scRNA-seq was performed using the Chromium Next GEM Single Cell 3ʹ Reagent Kits v3.1 (Dual Index). Briefly, cells were loaded into the 10× Chromium Controller (10× Genomics) for barcoding. scRNA-seq libraries were then generated according to the manufacturer’s protocol. After the scRNA-seq library construction, Agilent 4200 Tape Station system and High Sensitivity D5000 and D1000 Screen Tapes were used to assess the size profiles of the amplified cDNA. A NextSeq 500/550 High Output Kit v2.5 (150 cycles; 20024907; Illumina) with 28 cycles for read 1, 10 cycles for i7 index, 10 cycles for i5 index and 90 cycles for read 2 and was used to sequence the samples. Raw sequencing data were downloaded from Illumina BaseSpace, then demultiplexed and converted to gene-barcode matrices using the “mkfastq” and “count” functions in Cell Ranger v8.0 (10× Genomics). To reduce potential batch effects among samples, all cDNA libraries were constructed using the same reagent kit and protocol.

scRNA-seq data analysis for cell type clustering and annotation

After aligning raw sequencing reads to mouse mm10 reference genome, cell barcode and unique molecular index (UMI) count matrices were generated. Pre-processing of the data and downstream analyses were performed in R (v.4.3.3) using Seurat package (v.5.1.0). Cells expressing less than 100 or higher than 4800 genes or expressing higher than 15% of UMI counts from mitochondrial genes were removed due to poor quality or potential doubles. After quality control, a total of 16851 cells remained for downstream analysis. We used log2 transformation with 10,000 as scaling factor for normalization, and then selected the top 2000 most variable genes, followed by performing principal components analysis (PCA) for denoising the data into 50 PCs.

During our analysis we found that one of the replicates among the siFXR1-LNA group exhibited fewer sequencing reads which resulted into identifying less cell numbers presumably due to the therapeutic effects of siFXR1 LNA and associated decrease in overall tumor burden and ascites (Supplementary Fig. 5e and f). This sample was excluded for further analysis. In brief, we identified 16851 high-quality cells (~11621 cells from siCont-LNA and ~5230 cells from siFXR1-LNA group) for further subtyping (Supplementary Fig. 5h).

Shared nearest neighbor (SNN) of each cell was constructed for each cell and then clustered using the Louvain-Jaccard graph-based algorithm, implemented by functions FindNeighbours and FindClusters with the resolution parameter set to 0.1. We used Uniform Manifold Approximation and Projection (UMAP) as dimensional reduction algorithm to visualize the data in two-dimension along with cluster results from the clustering algorithm. Cluster marker genes were identified by using Wilcoxon rank sum tests with adjusted p-value less than 0.05 (with Bonferroni correction for multiple testing). The main cell types were defined using cluster marker genes along with canonical markers.

Dimension reduction plots, heatmaps, violin plots, and dot plots were generated using functions DimPlot, DoHeatmap, VlnPlot, and DotPlot provided in Seurat, respectively. Bar plots were made by using the R package ggplot2 (version 3.5.1).

The scoring of cell function based on a specific gene set was performed through the built-in “AddModuleScore” function of the Seurat package.

Subset analysis for epithelial, macrophage, T, NK, and Dendritic cells

Specific subgroup of cells was extracted from original dataset and re-ran the whole analysis pipeline as aforementioned, including normalization, select highly variable genes, denoising procedure by PCA, clustering analysis and dimension reduction by UMAP. We performed pathway enrichment analysis with the differentially expressed gene list (Supplementary Data 1) from each epithelial cell cluster using the IPA software.

CNV estimation and cell cycle analysis

To identify malignant cells from epithelial cells, we used the R package CopyKAT (version 1.1.0) 42 to estimate the copy number variations (CNVs) for each cell and predict if it is aneuploid or diploid. We assessed the cell cycle phase of the cells using the CellCycleScoring function77 provided by the Seurat package.

Cell‒cell communication analysis

The R package CellChat (v2.1.2) 40 was used to analyze cell-to-cell communication between tumor cells and other cell types. First, a CellChat object was created by grouping defined clusters. The ligand‒receptor interaction database we used for analysis was “CellChatDB.mouse”, without additional supplementation. Preprocessing steps were all conducted with default parameters. The functions computeCommunProb and computeCommunProbPathway were applied to infer the network of each ligand‒receptor pair and each signaling pathway separately. A circle plot was also generated as part of this analysis. Number of interaction and strength plots were made by using netVisual_diffInteraction function.

Flow cytometry of ascites, TAM and T cells

After blocking with Fc Receptor Blocking Solution (Biolegend), cell surface staining was performed in FACS buffer containing antibody cocktails (Epcam, Cd45, F4/80, Cd11b, Pdl1, Mrc1 (Cd206), Cd163, Cd4, Cd8) on ice for 1 h. After washing twice with FACS buffer, the cells were fixed using BD Cytofix/cytoperm solution (BD Bioscience) 20 min on ice. Cells were washed with BD perm/wash buffer (BD Bioscience), intracellular blocking with mouse IgG and Miltenyi FcR blocking, then intracellular staining was performed using PE-anti-LAMP3 (BD Biosciences), or PE-isotype control (BD Biosciences) for 1 h on ice. Cells were washed twice with perm/wash buffer and then analyzed on the BD Fortessa.

Tissue IF staining

FVB mouse tumor tissue slices from siCont and siFXR1-LNA groups were dewaxed in xylene and rehydrated through graded ethanol distilled water. Antigen retrieval for the slide specimens was performed using IHC-Tek epitope retrieval solution and steamer set (IHC World, LLC.). The slides were then immersed in 3% H O for 10 min to quench endogenous peroxidase followed by blocking with 10% BSA for 1 h. Tissue sections were then stained overnight at 4°C with the indicated primary antibodies. After washing with PBS, tissue sections were incubated with secondary antibodies, Alexa Fluor goat anti-mouse 488 (Cat#38731, Life Technologies, Carlsbad, CA) and Alexa Fluor 568 goat anti-rabbit (Cat#35646, Life Technologies, Carlsbad, CA) for 1 h at room temperature. Glass slides were mounted using ProLong Gold Antifade Reagent (Life Technologies, Carlsbad, CA) containing DAPI. Images were acquired with a 40X objective using a confocal laser scanning microscope (LSM 510; Zeiss, Oberkochen, Germany) and analyzed using the Aim 4.2 software LSM510.

Statistical analysis

In most cases, data obtained from three or four biological replicates were analyzed, unless indicated otherwise in the figure legends. The statistical tests used for each experiment are included in the corresponding figure legends. Graphpad Prism 7 (GraphPad, San Diego, CA) was used to perform statistical analysis and p-value determinations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors demonstrated that FXR1 is a promising therapeutic target for the elimination of ovarian cancer cells. They showed that siRNA-mediated silencing of FXR1 selectively induces apoptosis and G1 phase cell cycle arrest in ovarian cancer cells, but not in normal ovarian surface epithelial (OSE) and fallopian tube epithelial (FTE) cells—likely due to the relatively low expression of FXR1 in these normal cells. To enhance therapeutic efficacy, the authors incorporated locked nucleic acid (LNA) modifications and polyethylenimine (PEI) encapsulation of the siRNAs, which improved their stability, cellular and tissue uptake, and overall in vivo therapeutic performance. Finally, using single-cell RNA sequencing (scRNA-seq), they demonstrated that delivery of LNA-modified FXR1 siRNAs in mice triggered an anti-tumor immune response, characterized by tumor-associated macrophage (TAM) polarization and increased infiltration of dendritic cells (DCs), natural killer (NK) cells, and T cells.\nAlthough the authors have performed extensive analyses of the effects of siFXR1 both in vitro and in vivo, the storyline is difficult to follow as it drowns to the vast amount of oftentimes unjustified or redundant analyses shown in 9 main and 10 supplementary figures. In addition, the choice of methods and models are not consistently justified, statistics are lacking, claims are not always supported by the data shown, and the novelty of results shown in first figures is limited. Hence, we do not recommend the manuscript for publication in Nature Communications. We delineate our criticism below and hope the authors can use these to improve their manuscript for following submission(s) to another journal.\n1. Introduction does not sufficiently refer to previous work on using siRNA modifications in cancer research, including in vivo models and even clinical trials (see e.g., reviews doi.org/10.1038/s41392-020-0207-x,\ndoi.org/10.1016/j.jconrel.2023.07.054). This leads to overstatement of the novelty of the conceptual idea behind this article.\n2. The part of FXR1 siRNA selectively inducing apoptosis and G1 phase cell cycle arrest in ovarian cancer cells is clear, but the novelty is compromised since the authors have reported similar phenotypes previously (ref 9).\n3. As staed above, the current form of nine figures is laborious for the reader and way too lengthy for the relatively simple message of the manuscript. The core data of Figures 1–3 on the optimisation of the siRNA could be combined to one figure, and the scRNA-seq analysis in Figures 5–9 to one to two figures.\n4. The advantages of LNA modification and PEI encapsulation of siRNA appear promising, particularly in enhancing stability and delivery. However, the in vivo results were not fully convincing, as the native FXR1 siRNA alone already led to a significant reduction in tumor burden, making it difficult to clearly attribute additional therapeutic benefit to the modifications.\n5. There are several results that lack sufficient statistical analyses, and hence conclusions presented based on them are not justified by the data. For example, Fig. 1c, and Fig. 3g and Fig. S4c lack quantification and statistical analyses; hence, the claims made on these data are not justified. Also, e.g., Figs. 5d, 8c, and 9f lack error bars and statistics (the data should contain two repeats according to Fig. 5a).\n6. The mouse models containing KRAS^G12D^ are not optimal, as KRAS mutations are extremely rare in HGSC. Also, the authors assessed two mouse cell lines, one with and one without MYC amplification, but chose the cell line without MYC amplification for in vivo experiments without any justification. Importantly, the authors use FXR1’s role in MYC translation as a justification for its mechanistic importance in tumorigenesis but do not address it at all, even though they have a syngeneic pair of cell lines with and without MYC to directly do this.\n7. Throughout the manuscript, the authors make unjustified claims based on CellChat analysis. CellChat results do not “determine the ligand-receptor interactions”; they present potential for interactions. CellChat results can neither reveal mechanisms but rather potential mechanisms.\n8. The use of CIBERSORT, meant for bulk RNA-seq signal deconvolution, to what authors state as “a more detailed characterization” when analysing differences in cell-type signature scores between specimens is not well justified. There are many widely used scoring methods—such as AUCell, ssGSEA, AddModuleScore—for this exact purpose.\n9. The scRNA-seq analyses of Figure 6 are mostly obscure. For example, why do the authors calculate “overall tumor scores” to differentiate between tumor versus normal cells, when they have CopyKAT data (or can perform inferCNV) to robustly separate tumor cells from normal cells?\n10. From the scRNA-seq data, the authors mentioned that there were about 3500 epithelial cells (Fig. S5h). Among them, about 3000 cells from siCont-LNA and 500 from siFXR1-LNA (Fig. 5d). But from lines 415–419, “highest numbers of aneuploid epithelial cells (5400) over normal epithelial cells (120 diploid cells)”, the cell number of epithelial cells is very unclear and does not match to Fig. 6a. And there were almost no tumor epithelial cells at all from siFXR1-LNA group, making us to wonder the sense of CellChat analysis between normal rather than tumor epithelial cells and TME.\n11. The author designed 5 siRNAs to target human FXR1, of which seq2 and seq3 were demonstrated with high efficiency. Later, the author used them (seq2 and seq3) also for targeting mouse FXR1, achieving good knockdown efficiency (Fig. S5a). However, the BLAST results seem they (seq2 and seq3) are not 100% matching to mouse FXR1. Have the authors checked the potential off-targets when using human FXR1 siRNAs for mouse FXR1?\n12. For the in vivo experiments, it would be helpful to test a less frequent dosing schedule (e.g., extended intervals between siRNA administrations) to more clearly demonstrate the advantage of using LNA-modified siRNAs over the native form. This would better reflect the improved stability and durability that LNA modifications are intended to provide.\n13. For scRNA-seq experiments, why the author chose ascites and not the tumor mass? This should be clarified.\nMinor Comments\n15. The manuscript requires a proper language check.\n16. Abstract: “In tumor microenvironment (TME), siFXR1-LNA diminished tumor cell proliferation” should read “In vivo, siFXR1-LNA diminished tumor cell proliferation …”\n17. Abstract: “such as copy number gain, amplification, or deletion of genome.” should be corrected to “...deletion of genomic elements.”\n18. The ‘native’ siRNAs (siFXR1-2, siFXR1-3) have significant effects on cell cycle in Fig. 1b but not in Fig. 2d; please explain this inconsistency.\n19. It is not clear what is the difference between Fig. 3g and Fig. S4c. Also, as referred to above, the result text states in both that tissues with tumor have more Texas Red signal from the siRNA, but the amount of tumor is not shown or quantified.\n20. Fig. 4g: how can the proportion of TUNEL-positive cells be over 100%? Doesn’t this suggest that some of the TUNEL signal is artifactual?\n21. Line 358–361: the authors said “the interactions were notably higher in the control group”, is that true?\n22. Line 412: there is no “Fig. S5k”.\n23. Fig. 5d y-axis label is marked “%”, which does not match the actual data. A similar problem appears in Fig. 8f.\n24. Fig. S3b flow cytometry figure is not properly presented for visualization.\n25. In Figure S7a, the x-axis labels “0, 1, 2\" are unclear — could the authors clarify what these values represent?\n26. “Therefore, FXR1 is considered as an excellent molecular and actionable target for ovarian and other cancer therapy.” Isn’t this a bit overstatement? One would assume that excellent targets are targetable by small molecules.", "reviewer_2": "This is a nicely executed study demonstrating the therapeutic potential of a locked nucleic acid (LNA)-based siRNA targeting FXR1 for ovarian cancer treatment. The engineered siFXR1-LNA showed enhanced stability against RNase degradation, improved tumor uptake, and potent suppression of FXR1 expression compared to native siRNA. Its delivery via polyethylenimine significantly reduced tumor growth and peritoneal metastasis in both nude and immunocompetent mouse models, without observable toxicity. Single-cell RNA sequencing revealed not only direct inhibition of FXR1 in cancer cells but also disruption of oncogenic translation pathways and normalizing the tumor microenvironment’s immune function. A range of complementary methods supports the authors’ conclusions, which are well-reasoned and convincing.\nCritique:\nPlease state whether sc RNA-seq dataset is available and the data bank where it is deposited.\nPlease comment on why the single-cell analysis was done with ascites and not tumors. The benefit of using ascites is less variability in cell types, but the benefit of tumors is the spatial context, which is crucial for cell signaling in immune infiltrates. A good part of the work was the description of cell-cell signaling inference by CellChat. Is it assumed that even after these immune cells shed into the ascites, they retain the transcripts of immune activation signaling?\nLane 413 and Fig. 6 a, c, and h: I am confused by the definition of tumor cells and nonmalignant epithelial cells. I understand that this distinction was made using CopyKAT CMV inference. Based on the CNV inference, most of the cells in the ascites are nonmalignant cells. What are these epithelial cells if they are not tumor cells? Mesothelial cells?\nFig. 4h: It appears that there is more blood in FXR1-treated lungs compared to the control. Is this by chance in the selected photographs or there is actually more blood when whole lungs are examined?\nThe three figures below could benefit from a panel showing H&E staining, which would clarify some of the questions below:\nFig. 4f: Two of the panels stained with Ki67 seem to have high background – all of the cells are positive, which is unlikely even in very aggressive tumors.\nFig. 7g: There are two sets of staining shown for both control and FXR1-treated mice. It is unclear whether these represent two distinct tumors per treatment group. The nuclear staining does not clearly identify the tissue context—are these epithelial tumor cells or immune infiltrates? In the FXR1-treated group, the left panels appear to show necrotic debris rather than intact nuclei, while the right panels suggest an area of immune cell infiltration. In contrast, the control panels appear to represent tumor tissue. Additionally, the F4/80 staining appears nonspecific.\nFig. 8e: The panels showing tumors from control mice have different hematoxylin staining from the siFXR1-LNA-treated mice. Do these sections represent tumors or necrotic cells?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "This is anoutstanding and very innovative study,"}, "author_response": "Response: Suggested articles are now included in the introduction. A total of twelve key references (Ref. 12–23, 58) have now been incorporated into the Introduction and Discussion sections to highlight foundational studies demonstrating the therapeutic potential of siRNAs. However, none of these prior studies have explored targeting FXR1 or other RNA-binding proteins (RBPs), likely reflecting the longstanding belief that RBPs are undruggable entities. The central objective of our study is to challenge this paradigm by demonstrating that targeting RBPs; specifically, FXR1 in tumor tissues represents a viable and innovative therapeutic strategy. Given the current lack of available small molecule inhibitors for FXR1, we employed a locked nucleic acid (LNA)-modified siRNA to achieve target-specific and efficient silencing of FXR1. Our data reveal that FXR1 knockdown not only suppresses tumor growth but also reprograms the tumor microenvironment by enhancing macrophage and immune cell populations with anti-tumor characteristics. Unlike prior studies, our work integrates single-cell transcriptomic analysis to elucidate the in vivo cellular responses to RNA interference at a single-cell resolution. By addressing this critical gap, our study provides a comprehensive framework for advancing chemically modified FXR1-targeting siRNAs as novel therapeutics for cancer.\nWe have now highlighted the above description in the introduction and discussion in the revised manuscript.\nResponse: We appreciate the reviewer’s comment highlights our previous work on the phenotypic effects of FXR1 silencing. However, we haven’t evaluated the effects of LNA-modified siRNAs in comparison with native form of siRNA in our previous work in consideration with RNA stability, and its impact on the cells in tumor microenvironment in vivo. Indeed, we observed that the LNA form of FXR1-siRNAs are highly stable in both in vitro and in vivo experimental settings.\nIn agreement with reviewer’s comment, we have moved such preliminary data to revised Supplementary Fig 1.\nResponse: To address this comment, we have condensed our data into 6 main figures as below.\n• The core data from the original Figures 1 and 2 have been combined into a single revised figure, and now presented as Figure 1.\n• siRNA stability and siRNA uptake data presented as Figure 2.\n• The in vivo results demonstrating FXR1 inhibition via LNA-formulated siRNA are presented in Figure 3. In this figure, we included the therapeutic effects of the LNA-modified siRNA targeting FXR1 (siFXR1) in two independent in vivo models: (1) intraperitoneal injection of siFXR1 in mice bearing intraperitoneally implanted OVCAR8 ovarian cancer cells, and (2) systemic administration in mice orthotopically implanted with the aggressive MDAMB231 triple-negative breast cancer (TNBC) cells in the mammary fat pad.\nWe leveraged the TNBC model because of its common genomic features with high-Grade Serous Ovarian Carcinoma (HGSOC) such as frequent p53 and BRCA mutations and high level of genomic instability.\nWe have also considered the advantage of measuring tumor volume in this orthotopic model while the siRNA was delivered systemically via retro-orbital injections.\n• Overall analysis of single cell transcriptome profiling demonstrated an anti-tumor immune response upon LNA-modified FXR1 siRNA delivery, along with cell chat analysis shows cellular communications and copy number variations (CNV) as Figure 4 in the revised manuscript.\n• Sc-RNA seq data of macrophages and dendritic cell populations are now combined as a single figure and presented as Figure 5 in the revised manuscript.\n• Sc-RNA seq data of T-cells and NK cells are now combined as a single figure and presented as Figure 6 in the revised manuscript.\nResponse to comments 4 and 12: We appreciate reviewer’s comment (comment #4) and a thoughtful suggestion (comment #12) of testing less frequent dosing schedule between siRNA administrations to more clearly demonstrate the advantage of using LNA-modified siRNAs over the native form. This would better reflect the improved stability and durability that LNA modifications are intended to provide.\nAs suggested (comment#12), a new study was performed, where we injected native and LNA form of siRNA once a week for six weeks (Figure-1a -1c in the rebuttal letter). As expected, we found a better difference in the total tumor weight when the tumor bearing mice were treated with LNA form of FXR1 siRNA over native FXR1 siRNA. Similarly, a reduction in the luminescence of overall tumor signal from LNA form of FXR1 siRNA treated group over native FXR1 siRNA was observed (Fig-1d -1f in the rebuttal letter).\nWe also used a mutated sequence of siRNA to further confirm whether the delivery of the mutated sequence of siFXR1 does not exhibit any tumor suppressive effects. Herein, we mutated eight nucleotides as highlighted in red letters in Fig. 1a (rebuttal letter) in siFXR1-2 and siFXR1-3 sequences. As expected, the transfection of mutated siRNAs (seq2 and seq3) did not exhibit any silencing of FXR1 in OVCAR8 cells compared and did not affect any change in the overall tumor growth (Fig. 1b in the rebuttal letter).\nResponse: As suggested, we have now included quantification data based on densitometric analyses in Fig. 1c (New Sup Fig-1c in the manuscript), Fig. 3g (New Sup Fig-3b in the manuscript), and S4c (is now combined with New Fig-2g in the manuscript).\nWe have also revised Fig. 5d (New Fig.4c), Fig. 8c (New Fig. 6c), and Fig. 9f (New Fig. 6j) and all similar panels show scRNA-seq data with appropriate statistical comparison between control and treatment group with p-values in the manuscript.\nAscites from two biological replicates were pooled to generate one sample, and two such pooled samples per treatment group (siCont-LNA and siFXR1-LNA) were used for single-cell RNA sequencing. Single-cell suspensions were prepared using tissue dissociation enzymes. During data analysis, we observed that one of the siFXR1-LNA samples exhibited reduced sequencing depth and a markedly lower number of detectable cells. This reduction is likely attributable to the therapeutic effect of siFXR1-LNA, which resulted in decreased tumor burden and ascites volume (Revised Supplementary Fig. 5e, 5f in the manuscript). To assess the treatment-associated changes in cellular composition, we statistically compared the percentage frequency of each cell type between the siCont-LNA and siFXR1-LNA groups (Fig. 2 in the rebuttal letter). In total, 16,851 high-quality cells were retained for downstream analysis, comprising approximately 11,621 cells from the siCont-LNA group and 5,230 cells from the siFXR1-LNA group (Revised Supplementary Fig. 5h in the manuscript). We have expanded our description in the results and method section for clarity in the revised manuscript.\nResponse: We respectfully disagree partially with some of the points related to this comment raised by the reviewer.\n(1). There are only very few syngeneic ovarian cancer cell lines currently available that reliably establish tumors in immunocompetent mouse models. While, we have selected two of those lines such as Br-Luc (p53-/-,brca-/-,Myc) and C-11 (p53-/-, Myc, & KRASG12D) for in vitro experiments, the cell line that we selected Br-Luc line does not have KRASG12D was used for mouse model-based studies.\n(2). We also would like to draw reviewer’s attention that both lines Br-Luc and C-11, we used harbor Myc amplification. More importantly, Br-Luc cell line used for mouse model exhibits MYC amplification.\nThe appropriate reference as below regarding MYC status has now been included in the revised manuscript.\nXing D, et al. A mouse model for the molecular characterization of brca1-associated ovarian carcinoma. Cancer Res. 2006.\n(3). Unfortunately, we could not identify a syngeneic pair of line with and without MYC amplification to perform the suggested experiments in the given time. To further address this comment, we have included additional data examining MYC expression levels in MYC-amplified cell lines (Br-Luc and C-11) following siRNA-mediated FXR1 knockdown (Revised Supplementary Fig. 5b in the manuscript). As expected, MYC level was decreased in a direct correlative manner in both Br-Luc and C-11 cell lines.\nResponse: Thank you for the suggestion. We have now revised the text appropriately.\nResponse: We would like to clarify that the CIBERSORT analysis was used only to identify the gene signature, which were then combined subsequently to calculate module scores using the ‘AddModuleScore’ function.\nWe have now revised the description in the Results section to avoid any confusion related to the previous text. Additionally, we have added a description of the AddModuleScore analysis in the Methods section.\nResponse: To avoid confusion and redundancy, we have removed overall tumor score analysis from the current manuscript.\nResponse: We thank the reviewer for highlighting the discrepancy in the epithelial cell numbers in the text line number 415–419, where we stated that 5400 aneuploid vs. 120 diploid epithelial cells. This discrepancy happened due to a typographical error. This error has been corrected in the revised manuscript as 892 aneuploid cells and 806 diploid cells in the control group, whereas 2 aneuploid cells and 180 diploid cells were identified in the treatment group.\nRegarding the cell chat, our goal was to identify the oncogenic signaling such as ECM-receptor signaling. In conjunction with overall results, we observed ECM-receptor signaling was in higher magnitude in the control, whereas siFXR1 treatment reduced the magnitude of ECM-receptor signaling.\nPrecisely we observed that collagens (encoded by Col4a1, Col4a2, Col4a5, and Col4a6), laminins (Lama3 and Lamc1), and fibronectin (Fn1) from tumor cells interact with their respective receptors in immune cells, potentially as a pro-tumorigenic mechanism in the control group. In contrast, cell-to-cell contact driven by MHC components and ligands activate T/NK cell functions and secreted signaling driven by IL-18, and TGF-B1 for immune cell activation were observed high in the siFXR1 treated group.\nWe have now revised the text to highlight the results that we obtained from cell chat analysis.\nResponse: Seems there is a confusion regarding the alignment match of seq2 and seq3 with mouse FXR1 at reviewer’s side.\nWe have already considered the sequence homology before our animal experiments to ensure that the siRNA sequence we selected are matching well with mouse FXR1 for our in vivo experiments.\nHere we confirm again that Seq2 siRNA shows 90% match with mouse FXR1 sequence. More importantly, Seq3 siRNA shows 100% match with mouse FXR1 sequence (Figure- 3 in the rebuttal letter). We have used Seq3 siRNA in all our in vivo experiments, which is included in the experiment for sc-RNA sequencing (Revised Fig. 3, Fig. 4 and Supplementary Fig. 5 in the manuscript).\nReference sequence ID of mouse FXR1 gene was already described in the method section relevant to in vivo and sc-RNA seq experiments.\nResponse: Thank you for the comment. This concern has already addressed in conjunction with comment # 4. Please see the response above and the Figure-1 in the rebuttal letter.\nAs suggested, less frequent dosing schedule of FXR1 siRNA more clearly demonstrated the advantage on tumor growth inhibition when using LNA-modified siRNAs over the native form.\nResponse: Unlike other cancers, ovarian cancer is a peritoneally progressing cancer where the tumor cells shed into the peritoneal cavity, then circulates through ascites fluid before metastasizing peritoneally. While peritoneally progress, ovarian cancer promotes ascites formation, where the tumor cells grow as a cluster of stromal cells, and immune cells together. Therefore, cell clusters and tumor spheroids isolated from ascites fluid represent the tumors of advanced ovarian cancer. Supporting this notion, the studies listed below have also used cell clusters from ascites to characterize the transcriptomic changes at the single cell level.\n(e.g., https://www.nature.com/articles/s41591-020-0926-0, Nat Med 26,1271–1279 (2020) and https://www.nature.com/articles/s41591-020-0844-1 Nat Med 26, 792–802 (2020)).\nWe have explained the rationale of selecting ascitic fluid in our single-cell analysis and included a description in the introduction and relevant sections of results and cited appropriate references in the revised manuscript.\nResponse: Edited and revised.\nResponse: Corrected as suggested.\nResponse: Revised as suggested.\nResponse: In our Fig. 1 experiments in the first version, we used two concentrations of siRNAs, which are relatively higher for our initial screening and then determined the effects of siRNAs on FXR1 protein at 2.5nM and 5nM concentrations [Please see Fig-1a in the revised manuscript and Figure-4 in the rebuttal letter]. We then continued using the lowest effective concentration 2.5nM on cell cycle phases using flow cytometry, Western blot analysis of proteins associated with G1 cell cycle phase, CDKs, and proteins associated with cell death.\nIn the next set of experiments, we evaluated the knockdown effects of FXR1 of selected siRNAs from first set of experiments such as sequence#2 (siFXR1-2) and sequence#3 (siFXR1-3) at the lowest possible concentration as 0.5, 1 and 2 nM and then selected 0.5 nM concentration of native form and its LNA form [Please see Fig-1e in the revised manuscript and Figure-5 in the rebuttal letter]. to determine their effects on cell cycle phases using flow cytometry, Western blot analysis of proteins associated with G1 cell cycle phase, CDKs, and proteins associated with cell death.\nIn brief, the changes in the effects on cell cycle phases between old Fig. 1b (Supplementary Fig. 1b in the revised manuscript: -siRNA concentration used 2.5 nM) and old Fig. 2d (Supplementary Fig. 3a in the revised manuscript: -siRNA concentration used 0.5 nM) are due to the difference in concentrations.\nResponse: We apologize for this confusion. Supplementary Fig. S4c is the control group of main Fig-3g. This experiment was performed to show the level of uptake of native and LNA form of FXR1-siRNA immediately (1 hr) after the injection of siRNA. We have now marked the tumors within yellow dotted box in both groups and moved this Fig. S4c to the main figure as Fig. 2g in the revised form of manuscript. Quantification for Texas red intensity is also provided as Fig. 2h.\nResponse: Sorry for the typographic error of % mark in the label. We have removed this error and revised the ‘y axis’ as average TUNEL+ cell number (Supplementary Fig. 4c in the revised manuscript).\nResponse: We have rewritten this statement to “the interactions between tumor cells to macrophages and other immune cells were decreased significantly upon the treatment with siFXR1-LNA” compared to the control for clarity.\nResponse: Removed this error and revised the manuscript.\nResponse: We revised the ‘y axis’ as either actual numbers as either frequency or average cell number (Fig. 4c, Fig. 6f in the revised form of manuscript).\nResponse: Sorry for this error happened while the actual image was converted to pdf while submission. Figure is now reformatted for accuracy. (Supplementary Fig. 3c in the revised form of manuscript).\nResponse: These populations are either aneuploid, diploid or not any defined group of cells (Supplementary Fig. 6a in the revised form of manuscript). We have included cell type information in the panel legend in the revised manuscript.\nResponse: We have now revised this statement as ‘Our data suggest that FXR1 is a potential target for treatment for ovarian and other cancers exhibits FXR1 amplification’."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have improved the paper but still the issues are such that I cannot recommend the publication of the manuscript in Nature Communications.\nA major weakness of the paper is the conclusions drawn from the scRNA-seq analysis. The analysis is displayed still very extensively in Figures 4 to 6, with conclusions used in Fig. 7. Yet, it is mostly irrelevant since it is comparing tumor microenvironment to a non-tumor microenvironment, as the authors state that the treated group has only 2 aneuploid (=cancer) cells. That means that based on the data shown, one cannot specify which of the observed changes in for example immune interactions are due to the FXR knockdown, and which are simply due to the loss of cancer. Here, the results are presented and concluded upon as if they would inform us of the effects of loss of FXR1 expression, which is misleading.\nOverall, the effect of the modified siRNA on tumor growth is clearly shown in the mouse model used, with also convincing evidence of how the stabilising modification improves the response when dosing is more sparse. Also the effect on Myc expression is now clear. However, these more solid aspects form just a half of the manuscript.\nResponse to comment 11:\nThe match of seq3 is not 100% as the authors state in their response. The authors fail to highlight the “AGA” at the end of the sequence in their response, although it is present in Table S1: “Seq3: GCUUACUUGAUAAUACAGAAUCAGA”. Hence, their response is incorrect.\nMinor:\nResponse to comment 6.\nThe authors now state that the model used, Br-Luc does not have KRAS mutation, even though their previous manuscript version that was reviewed stated on rows 311-312: Br-Luc (genotype: p53-/-; BRCA1-/-; myc; KrasG12D; Akt-myr) and C11 (genotype: p53-/-; BRCA1-/-; myc; KrasG12D). If there was an error in this version the authors could have simply acknowledged that there was an error.\nResponse to comment 11:\nIn addition to the misleading highlighting of the target sequence, the mouse and human sequences were swapped in their response (I suppose the latter is by mistake).", "reviewer_2": "I am OK with the revisions."}, "author_response": "Response: GEO accession numbers were already provided as part of the reporting summary along with this submission as below:\nAll sc-RNA seq data generated for this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under private accession number GSE292799 with a secure token code: mbihuummzvyxrsp\nResponse: Related question was raised by Reviewer:1 as comment #13. Please see the response to the above comment for the rationale of using ascites samples for single cell RNA sequencing.\nUnlike other cancers, ovarian cancer is a peritoneally progressing cancer where the tumor cells shed into the peritoneal cavity, then circulates through ascites fluid before metastasizing peritoneally. While peritoneally progress, ovarian cancer promotes ascites formation, where the tumor cells grow as a cluster of stromal cells, and immune cells together. Therefore, cell clusters and tumor spheroids isolated from ascites fluid represent the tumors of advanced ovarian cancer. Supporting this notion, the studies listed below have also used cell clusters from ascites to characterize the transcriptomic changes at the single cell level.\nOther high-impact peer reviewed publications have also employed tumor cell clusters isolated from ascites for single cell RNA sequencing. We agree that looking the transcriptomic profile using sc-RNA seq or spatial transcriptomic data from tumor tissues will be complementary to the transcriptomic profiles obtained from ascites. However, such analyses are currently under development as part of our future work and such plans are explained in the discussion section of the revised manuscript.\nResponse: We would like to clarify that in the control group, CNV inference using CopyKAT identified 892 aneuploid cells, 806 diploid cells, and 1,545 undefined cells. Notably, treatment with siFXR1-LNA resulted in a significant reduction in the number of aneuploid cells. In the siFXR1-LNA–treated group, the majority of cells were classified as either diploid or undefined. Based on their gene expression profiles, the diploid population likely comprises epithelial cells preserved diploid characteristics (e.g., expressing Epcam, Krt8, Krt18, and Krt19), or normal-like epithelial cells exhibiting a hybrid epithelial-mesenchymal phenotype as indicated by Zeb1, Fn1, and Cdh1 expression upon FXR1 silencing therapy and some mesothelial cells based Upk3b, Nkain4 expression (Figure-6 in the rebuttal letter).\nResponse: The increased blood signal in the lungs of the FXR1-siRNA–treated group was not consistently observed across all samples and in all microscopic fields. Upon reviewing the full set of lung tissues from both treatment groups, we found that the appearance of increased blood was limited to certain field in the tissue section and does not a generalizable effect upon the treatment of FXR1-siRNA. We have included triplicate images from each group as below (Figure- 7 in the rebuttal letter).\nWe also included H&E-stained images for Fig. 3f, and Fig-6e, in the revised manuscript.\nResponse: Thank you for this point. Images were now recaptured in higher magnification with low background settings and presented as Fig. 3f in the revised manuscript. Revised images are able to distinguish the Ki67 positive cells over Ki67 negative cells accurately.\nResponse: We appreciate the reviewer’s thoughtful evaluation of the immunostaining data and the opportunity to clarify these key points.\nTo avoid confusion, two sets of magnified high-resolution images for macrophages stained for Arg1 and Mrc1 markers are now shown for each group (siCont-LNA- and siFXR1-LNA-treated groups). These will address the concerns of necrosis, intragroup heterogeneity of immune cell infiltration.\nIn brief, the observed nuclear distribution primarily reflects tumor epithelial cells, with notable infiltration of M2-type macrophages based on tissue architecture and co-staining with macrophage markers in the control group. In contrast, siFXR1-LNA-treated tumors displayed uniformly distributed, intact nuclei, consistent with preserved tissue architecture and a marked reduction in M2 macrophage presence, aligning with an enhanced anti-tumor immune response.\nWe also acknowledge the concern regarding the specificity of F4/80 staining. To address this, we have included new images of tumor sections from both treatment groups, immunostained for F4/80, Arg1 and Mrc1, we also captured images at higher magnification to more clearly visualize macrophage infiltration. The new high-resolution images are now included in the revised manuscript as Supplementary Fig 8e in the manuscript.\nResponse: Here we clarify that the images shown in Fig. 8e (Fig.6e in the revised manuscript) are not H&E-stained sections. Those images are immunohistochemical staining for Cd4 and Cd8a in both the siCont-LNA and siFXR1-LNA treatment groups. This information was already included in the legend and text. To avoid confusion, we have included the corresponding H&E-stained images for the same tumor sections in the revised manuscript (Fig 6e).\nResponse: We thank the Reviewer for all the constructive criticism and positive comments including the effects of modified siRNA on tumor growth, how the modification improves stabilization, improved therapeutic response when modified siRNA delivered less frequently in vivo. We also appreciate the positive comments related to previous concern related to cMyc.\nWe agree that the presence of only a small number of aneuploid cells in the FXR1 knockdown group limits the ability to distinguish between changes in tumor microenvironment particularly the immune interactions are either due to the effects of FXR1 knockdown or due to overall loss of cancer.\nReviewer 1 came into this conclusion primarily from the CNV analysis performed using copyKAT analysis.\nCopyKAT is a computational tool that uses single-cell RNA sequencing data to infer genomic copy number changes in a tumor which may not fully accurate due to the limitations of this analysis method such as:\n(i) not all cancer cells have high level aneuploid copy number events that can be used to distinguish normal and tumor cells such that the copy number gains like mild copy number events might be missed from the CopyKAT analysis.\n(ii) CopyKAT is also considered more suitable for the analysis of subclones in tumors where many cells have expanded and share similar genotypes, rather than the analysis of replicating cancer cells are very low when cancer cells are treated with therapeutic agents like FXR1 siRNA in this instance.\nTherefore, it is important to use other complementary approaches like flow cytometry to confirm the data obtained from an algorithm like CopyKAT that predict CNV numbers from gene expression data particularly when the number of tumor cells are low in the samples.\nTo this end, we performed two independent and complementary approaches: flow cytometry of ascites samples and immunofluorescence (IF) staining on tumor tissues collected from the mice were treated with control siRNA and FXR1 siRNA using Epcam antibody to detect cancer cells and Cd45 antibody to detect immune cells [Figure-Ia, b in the rebuttal letter].\nWe selected Epcam because it is typically upregulated in tumor cells and can be easily detected using flow cytometry, whereas it is weakly expressed by normal epithelial cells (Imrich S et al, 2012, PMID: 22647938 and Keller L et al, PMID: 31225512).\nIn conjunction with the sc-RNA seq data [Fig-4c in the manuscript], flow cytometry of control samples also showed high number of tumor cells (~18000) but less immune cells (~9000). Notably, the treatment of the samples collected from mice were treated with FXR1 siRNA reduced the number of tumor cells to ~5000 with an increase in immune cells to ~21000.\nSimilarly, the immunofluorescence (IF) of tumor nodules also showed high number of Epcam+ tumor cells and a few Cd45+ immune cells in the control group. In conjunction with scRNA-seq data and flow cytometry, the number of Epcam+ tumor cells was low in the FXR1 siRNA treated group with a high number of Cd45+ immune cells in the IF imaging of tumor tissues [Figure-Ib in the rebuttal letter].\nWe further corroborated this data in our scRNA-seq analysis, where we found an overall decrease EpCAM+ expression with a concomitant increase in Cd45+ cells in the FXR1 siRNA treated group compared to controls [Figure-Ic in the rebuttal letter].\nWe further validated the changes in overall macrophages and T-cell population using flow cytometry [Figure- Id, e in the rebuttal letter], which again showed a decrease in macrophage population and an increase in the overall T-cell population similarly as we identified in our scRNA-seq data [Fig-4c in the manuscript].\nIn summary, both our flow cytometry and IF analysis indeed identified a significant number of tumor cell population along with an increase in infiltrated immune cells in the samples were collected from the mice were treated with FXR1 siRNA [Figure-Ia, b in the rebuttal letter]. These data further suggest that the tumor-immune cell interactions that we identified are important and valid.\nThis data prompted us to presume that CopyKAT analysis may not be sufficient to identify all aneuploid cells when perform the analysis in default settings where the samples contains a small number of cancer cells or when the samples possess diploid-like genomes with a few copy number alterations particularly due to therapeutic interventions.\nTherefore, two aneuploid cells were identified in the FXR1-siRNA treated group by CopyKAT could be due to the low sensitivity of this algorithm under default settings. To address this concern, we improved CopyKAT’s KS-based segmentation threshold by setting KS.cut = 0.05 without changing any other inputs and parameters. KS-based segmentation mainly controls the stringency of breakpoint detection to classify the cells as aneuploid based on how many CNV segments are detected in the analysis. Strikingly, such improvement in the settings estimated more aneuploid cell fraction in the sample group were treated with FXR1 siRNA without affecting the qualitative conclusion [Figure- II in the rebuttal letter].\nOf note, our refined CopyKAT analysis identified 93 aneuploid cells/315 epithelial cells (~30%) in the siFXR1-LNA treated group versus the control group where we found 1609 aneuploid cells/3243 epithelial cells (~50%). While a decrease in the aneuploid cells were observed in the siFXR1-LNA treated group; an increase in the normal like diploid cells was also observed in the siFXR1-LNA treated group compared to the control.\nTaken together our flowcytometric and immunofluorescence analysis along with a refined CopyKAT analysis confirmed that there is some tumor cells present in the mice even after the FXR1 siRNA treatment. Therefore, the composition of cells in the TME and the immune cell interactions with both tumor cells and normal epithelial cells are important and biologically relevant.\nResponse:\nWe apologize for the confusion and partial response in our previous rebuttal letter. We agree that one nucleotide is not match with mouse gene in terms of complementarity when consider the whole 25 nucleotides of our siRNA, which is now corrected in the result section of the manuscript (Page-14).\nTo confirm that the actions of siFXR1 is operated through knocking down of FXR1, we rescued FXR1 in the mouse Br-Luc cell line using wildtype FXR1 expressing plasmid and also a mutated FXR1 plasmids where four nucleotides were changed to reduce the complementarity with FXR1 siRNA that disrupts siRNA binding without affecting amino acid order. Notably, the loss of FXR1 upon FXR1 siRNA reduced cell proliferation, which was abrogated when 4 nucleotides were mutated to reduce the complementary with siFXR1 [Figure IIIa to c, rebuttal letter].\nWe also like to highlight that we observed a robust and reproducible knockdown of Fxr1 mRNA and protein in two different mouse ovarian cancer cells despite this single nucleotide mismatch [Figure IIId , e rebuttal letter and in Sup Fig-5 and Sup Fig-7].\nWe also determined the levels of MYC, which we have reported as one of the main target of FXR1 (George et al, 2021; Cell Reports, PMID: 34731628) and found that FXR1 silencing using our siRNA inhibited the levels of MYC gene. It was also reported by Shoba Vasudevan and colleagues that FXR1 causes RNA nuclear protein complex formation where it represses the expression of TNFα (Shobha Vasudevan and colleagues, Cell, 2007 PMID: 17382880). In complement to this hypothesis, we found that FXR1 silencing using our siRNA upregulated the levels of TNFα in both cell lines [Figure IIId, e rebuttal letter]. We also extended our analysis to a few non-targets of FXR1 based on the AUA Rich Elements (ARE) and found that none of those genes were affected by FXR1 knockdown [Figure IIId, e, rebuttal letter].\nAdditionally, we also considered to use long nucleotide sequence siRNAs better knockdown efficiency, with less off target effects and better stability than short sequence RNAs. The standard size of an siRNA is considered about 21 nucleotides in length, with a range of 19 to 25 nucleotides in length. In this case, we used a highest possible length of 25-base siRNA, because studies have demonstrated that siRNAs with longer nucleotide length can serve as natural substrate for the Dicer enzyme and enhanced Risc (RNA induced silencing complex) incorporation for gene silencing with less off-target effects (John Rossi and colleagues, Nature Biotechnology, 2005, PMID: 15619617).\nMoreover, with their high potency, Dicer-substrate siRNAs can be used at very low concentrations, which again minimizes the potential for off-target silencing, where the siRNA inadvertently binds to and silences unintended genes that have partial sequence homology (John Rossi and colleagues, Nature Biotechnology, 2005, PMID: 15619617).\nConsidering all of these points, we used very low siRNA concentrations in our in vitro experiments in both human and mouse cell lines (0.5 nM to 2 nM) to minimize off-target effects. Previous studies have also shown that lower siRNA doses reduce off-target silencing of non-complementary transcripts, including the inadvertent induction of immune-response genes (PMID: 21750714, 16682561).\nIn our in vivo studies, we further limited the siRNA dose to 500 µg/kg body weight, whereas most studies from industrial groups developing RNAi therapeutics for clinical trials typically use 10–50 mg/kg body weight in their preclinical models (PMID: 38549376, 40349108).\nTaken together, we have carefully implemented multiple precautions to minimize off-target effects in both human and mouse models.\nResponse: We acknowledge this as an error happened during the first submission. The correct genotype of the Br-Luc model is p53-/-; brca1-/-; myc; Akt-myr and does not exhibit KrasG12D. This has been corrected in the revised manuscript. We apologize for the confusion and appreciate the opportunity to clarify this point.\nResponse: We apologize and thank you to the Reviewer for finding this error, which is corrected in the revised manuscript and presented as Supplementary Figure 5A.\nResponse: We sincerely thank Reviewer #2 for their time, thoughtful evaluation, and positive feedback.\nWe sincerely hope both reviewers and the Editors will find the revisions are satisfactory and this manuscript is ready for acceptance subsequently."}, {"round": 3, "reviewer_comments": {"reviewer_1": "1. The authors could mention in the Introduction section that FXR1 gene resides on 3q26; now they describe both this region and FXR1 gene but do not explicitly connect them. They could also cite and mention previous studies’ estimates on 3q26 amplification prevalence in HGSC.\n2. The authors have further modified their analyses of epithelial content and tumor purity. However, they should not indicate Epcam+ cells as ’Tumor cells’ as they do in Suppl Fig 7; this is contradictory to described results of CopyKAT analysis suggesting that there are also non-cancerous epithelial cells.\nOtherwise, their updated analyses suggest that there are still aneuploid cells left after siFXR1 and thus the term “tumor microenvironment\" can be used.\n3. Fig. 4f and g; the UMAPs, or their legends do not indicate which cells are from siCtrl and which from siFXR.\n4. Also chemotherapy modifies HGSC immune TME to increased T cell infiltration and macrophage polarization shift from M2 to M1 like (e.g. DOI: 10.1016/j.ccell.2024.11.005), suggesting that therapies that weaken cancer cells may in general also relieve cancer-inflicted immune suppression. Therefore, one cannot separate whether the effects on the immune system are specific to siFXR1, or a more general consequence of reduced tumor burden and tumor cell fitness. Please mention this in the Discussion section when describing the siFXR associated immune changes.\n5. There are still grammatical errors / broken sentences in the manuscript, please proof-read and correct."}, "author_response": "Response: Revised as suggested and included appropriate references.\nResponse: We have revised the label to Epcam+ cells in Suppl Fig 7 and revised the corresponding description appropriately in the result section.\nResponse: Revised the legends for the main figures 4f and 4g as suggested. To clarify further, two new supplementary panels with color code legends are included as Supplementary Figure 7a and 7b in the revised manuscript.\nResponse: Thanks for the suggestion. We have now included this point in the discussion section and cited the mentioned manuscript respectively.\nResponse: Manuscript has been proof-read and revised accordingly."}]

Decision letter