American Association for Cancer Research® (AACR)

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with limited treatment options and poor outcomes. Multikinase inhibitors remain the first-line therapy for advanced HCC. However, therapeutic resistance remains a significant challenge in HCC, and the molecular mechanisms underlying metabolic adaptation to drug resistance remain poorly understood. Here, we identify Y-box binding protein 1 (YBX1) as a key regulator of cholesterol metabolism that promotes tumor growth and drug resistance in HCC. Multi-omics and mechanistic analyses reveal that YBX1 transcriptionally activates Sterol Regulatory Element-Binding Protein 2 (SREBP2), a critical master regulator of cholesterol metabolism, and suppresses the cholesterol efflux transporter ABCA1, resulting in increased expression of cholesterol biosynthetic enzymes and intracellular cholesterol accumulation. Additionally, Cholesterol modulates TGFβ signaling and is implicated in drug resistance. This metabolic rewiring stabilizes membrane receptor tyrosine kinases (RTKs) and sustains downstream PI3K/Akt/mTORC1 and EMT signaling pathways, thereby fostering the development of drug resistance. Genetic silence or pharmacological inhibition of YBX1 or SREBP2 with SU056/Betulin restores sorafenib sensitivity and reduces tumor growth. Clinically, higher levels of YBX1 and SREBP2 expression are associated with poor therapeutic response and decreased overall survival in patients with HCC. These findings uncover a YBX1/SREBP2/Cholesterol metabolic axis that mediates adaptive resistance, offering a new therapeutic target to overcome drug resistance in HCC.
Keywords: Hepatocellular carcinoma, Drug resistance, YBX1, SREBP2, Cholesterol metabolism, Akt/mTOR signaling.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite extensive research, there haven’t been remarkable gains in resolving the seeds of GBM recurrence, and the outcomes for many patients suffering from this devastating disease remain poor. Our knowledge on GBM heterogeneity is mostly restricted to the surgically resectable tumor core, while functional characterization of tumor cells at the infiltrating edge remains largely elusive due to the presence of normal functional brain tissue in the peritumoral lesion. Edge-derived cells exhibit larger capacity for infiltrative expansion and are the main drivers of treatment failure and tumor recurrence, making them action targets for novel treatment approaches.In this study, we present a first-of-its-kind integrative spatial investigation of GBM, combining two complementary spatial omics modalities high-definition spatial transcriptomics (ST – Visium HD) and spatial proteomics (SP – COMET) to achieve a comprehensive morphological, transcriptomic, and proteomic characterization of invasive tumor edge in situ. This multimodal spatial framework enabled to resolve the complex molecular landscape of the GBM periphery and to identify druggable biomarkers specific to edge-derived malignant cell populations.By integrating pathologically annotated H&E images with high-resolution spatial gene expression, we delineated patterns of tissue architecture and captured continuous gradients of gene expression across tumor-brain interface. Using NicheCompass, we dissected tissue hierarchies and spatially localized cellular processes within the infiltrative compartment of GBM. Through unsupervised phenotyping, we identified top spatially variable genes and active gene programs, revealing modules indicative of tumor cell hijacking of neuronal pathways. These findings align with previously described mechanisms of glioma-neuron synaptic coupling and formation of neurite-like tumor microtubes, consistent with enrichment of OPC- and NPC-like cellular states at tumor margin.Complementing the transcriptomic layer, SP was used to guide single-cell segmentation for ST analysis, infer cell types based on canonical phenotypic markers, and characterize cell state and function through protein-level profiling. This dual-modality approach allowed for precise spatial mapping of highly invasive edge cell populations and their functional states.By integrating multiple spatial omic layers, our study provides an unprecedented multimodal view of GBM invasion and identifies novel, spatially defined biomarkers that distinguish malignant edge-derived cells. These findings hold potential translational value as diagnostic and prognostic tools, enabling early assessment of treatment response and facilitating personalized therapeutic strategies aimed at mitigating GBM progression and recurrence.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with high recurrence and resistance to standard therapies. Midkine (MDK), a heparin-binding growth factor that functions as a cytokine, is overexpressed in TNBC and promotes tumor progression, immune evasion, and chemoresistance. We recently developed HBS-101, as a first-in-class small-molecule MDK inhibitor and demonstrated its therapeutic efficacy in preclinical TNBC models. This study evaluates whether MDK inhibition enhances chemo and immunotherapy efficacy as well as the mechanistic understanding of combination therapy effects.
Methods: TNBC cell lines were treated with HBS-101 alone or in combination with chemotherapy (doxorubicin and paclitaxel), and assessed for viability, apoptosis, and synergy. 3D organoids derived from patient-derived xenografts (PDX) were employed to examine the ex vivo effects of combination therapy. The efficacy of HBS-101 with chemotherapy and immunotherapy (PD-L1 inhibitor) was evaluated using human and murine cell line-derived xenograft (CDX) models of TNBC respectively. In vivo, orthotopic TNBC tumors were established in immunocompetent mice and treated with combination treatments. Tumor growth, and immune cell infiltration were analyzed using xenograft models. RNA-seq was performed on HBS-101 treated cells to identify changes in immune and apoptotic pathways. Molecular and immunological effects were examined using RT-qPCR, Western blotting, flow cytometry, and immunohistochemistry.
Results: Combination treatment with HBS-101 and chemotherapy produced synergistic anti-tumor activity in TNBC models, significantly reducing 2D and 3D cell viability, stemness, and tumor growth relative to monotherapies. Similarly, combining HBS-101 with immunotherapy also yielded strong synergistic effects in syngeneic TNBC models, outperforming individual treatments. Mechanistic studies revealed that HBS-101 disrupted MDK-mediated signaling pathways, including STAT3, thereby sensitizing tumor cells to treatment and enhancing immune activation. Combination therapy also increased infiltration of CD8 positive T cells and macrophages, accompanied by elevated levels of IFN-gamma and granzyme B. RNA-seq analysis showed upregulation of immune and apoptotic pathways and suppression of immunosuppressive signals. These molecular changes correlated with reduced tumor burden, and increased apoptosis, supporting the mechanistic synergy of MDK inhibition in combination therapy.
Conclusion: MDK inhibition with HBS-101 enhances the efficacy of chemo-immunotherapy in TNBC by disrupting oncogenic signaling and promoting immune activation. These findings support the therapeutic potential of HBS-101 as part of a combination strategy to overcome resistance and improve outcomes in TNBC.

Resistance and metastasis continue to make breast cancer a clinical challenge. VC2-GM-CSF drives antitumor immunity; paclitaxel remodels tumor environment to boost immune infiltration. Thus, combined use may yield superior therapeutic benefit over the use of either agent alone. This study investigates the potential synergistic interactions of VC2-GM-CSF with paclitaxel using the murine 4T1 breast cancer model in vitro and in vivo. In vitro, 4T1 cells were treated with paclitaxel alone or in combination with VC2-GM-CSF, and cell viability was assessed by MTT assay. Viral entry was quantified using a flow-cytometric assay. We further explored the in vivo antitumor effect of combined VC2-GM-CSF and paclitaxel in an orthotopic 4T1 stage IV metastatic model of breast cancer. Established 4T1 tumors in BALB/c mice were treated intratumorally with VC2-GM-CSF combined with paclitaxel. On day 31, tumors and lungs were collected for histopathological evaluation. Flow cytometric quantification of lymphocytes infiltrating the tumors was performed for CD45⁺, CD3⁺, CD4⁺, and CD8⁺ cells. The combined treatment was substantially more effective in inducing dose-dependent decreases in the viability of 4T1 cells and cell death than either single therapy in vitro. Flow cytometric entry assays showed that paclitaxel does not impair the entry of VC2-GM-CSF into cancer cells. In vivo, the combination significantly reduced primary tumor growth compared with the control treatment. Flow cytometry and immunohistochemistry analyses indicated increased T-cell infiltration in tumors after combination therapy. Together, these results demonstrate an enhanced antitumor effect resulting from the complementary cytotoxic and immunomodulatory mechanisms of paclitaxel and oncolytic virotherapy.

Background
The tumor microenvironment (TME) plays a pivotal role in cancer progression, immune evasion, and therapeutic response. Understanding the spatial organization and functional states of cells within the TME is essential for advancing immuno-oncology and precision medicine [PMID: 40102282]. However, simultaneous visualization of secreted molecules and cellular phenotypes in situ remains a major challenge in the spatial biology field [PMID: 39930476].  

Here, we employed an automated hyperplex multiomics assay to simultaneously detect RNA and protein expressions to spatially map cell phenotypes and their functional states in the TME of multiple cancer types. 

Methods
We examined a formalin-fixed paraffin-embedded tissue microarray (TMA) comprising various human cancer types: prostate, lung, breast, colorectal, melanoma, and lymphoma. A TMA section was stained and imaged on the COMET™ platform, integrating RNAscope™ HiPlex Pro for transcript detection and sequential immunofluorescence (seqIF™) for proteomic analysis [PMID: 22166544; 37813886; 41065276]. Image was analyzed using HORIZON™ software to extract single-cell and spatial features. 

Results
The automated multiomics approach enabled the concomitant in situ detection of over 100 biomolecular targets, including 12 transcripts and more than 90 proteins on the same section. High-resolution spatial profiling of the TME allowed accurate mapping of cancer, stromal, vascular and diverse immune cell subsets. It further revealed key molecular features associated with tumor-suppressor or proto-oncogene activity, including markers of proliferation, apoptosis, and immune checkpoint regulation. Concurrent detection of cytokine and chemokine transcripts highlighted localized immune signaling and cell-cell communication within tumor and stromal compartments.  

Conclusions
This multiomics workflow offers a powerful tool for in-depth characterization of the TME across multiple cancer types, while significantly reducing sample consumption. Spatial profiling provides new opportunities to dissect the tissue architecture and immune dynamics to identify functional cell states and interactions to be exploited in immunotherapy and personalized medicine

Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive desmoplasia, with cancer-associated fibroblasts (CAFs) dominating the tumor microenvironment (TME) and driving collagen-rich extracellular matrix (ECM) deposition. While resident pancreatic fibroblasts exert natural tumor-restrictive functions, CAF/ECM units adopt both tumor-promoting and tumor-suppressive phenotypes, critically shaping tumor progression, therapeutic response, and patient outcomes. Defining inter-patient heterogeneity in CAF/ECM phenotypic traits and understanding their functional shifts in response to therapy is essential for predicting clinical outcomes, predicting therapy responses, and identifying new TME-targeting strategies.
Methods: We developed a human PDAC TME-within-a-chip integrating fresh tissue-derived 3D CAF/ECM units cultured in customized microfluidic chambers on glass slides. High-plex sequential immunofluorescence (seqIF™) on the COMET™ platform standardized ~20 mesenchymal biomarkers to distinguish tumor-supportive from tumor-restrictive CAF phenotypes. Automated image analysis (HORIZON™ software) quantified single-cell, subcellular, and extracellular marker expression patterns.
Results: CAF activation-state interventions revealed marked alterations in canonical TGFβ signaling, reduced Ki67-positive cell proliferation, and changes in ECM architecture and composition, reflecting shifts in stromal functional states. This integrated platform effectively resolved CAF/ECM unit heterogeneity and tracked TME adaptations in response to therapeutic perturbation.
Conclusions: This technological advancement provides a robust, medium-throughput framework for dissecting CAF heterogeneity and exploring stromal biology in PDAC (and other cancers). The platform enables comprehensive profiling from minimal starting material while substantially reducing antibody consumption and processing time compared to conventional methods, establishing a scalable foundation for functional screening and evaluating therapeutic interventions in complex TMEs.

Breast cancer is one of the most common cancers worldwide, but it is more fatal and rarer among men. There are no male patient derived breast cancer cell lines commercially available, limiting research on breast cancers in the male patients. Consequently, most research and therapeutic approaches are largely based on female research subjects because of limited information on male mammary carcinoma. Thus, development of male breast cancer cell lines would provide an excellent biological and preclinical model to study male specific mammary carcinoma. This study aims to establish novel male breast cancer cell lines using tumors derived from male mammary carcinoma patients. Mammary tumors were obtained from three consented male breast cancer patients undergoing mastectomy at the Simmons Cancer Institute. The breast tumor tissues were minced, enzymatically digested overnight at 370c, strained using sterile strainer, and centrifuged to pellet the cells on the following day. The pellets were suspended in HUMEC Ready Mix media and cultured for several weeks until adherent epithelial cells started to grow. The epithelial cell populations were purified by positive flow sorting using EpCAM antibody (epithelial marker) and considered immortal upon reaching passage 20. Out of three tumor samples, two unique male breast cancer cell lines from two different tumor samples have been successfully cultured and named as male breast cancer cell-1(MBCC-1) and male breast cancer cell-2 (MBCC-2). During the flow sorting, MBCC-1, which is currently in passage-50, was noted to be 70% epithelial (EpCAM positive) while MBCC-2 cells, which are currently at passage-19, were 20% EpCAM positive. The average circularity and diameter of the MBCC-1 ranges between 0.6-0.8uM and 12-19 microns while the average population doubling time is forty-two hours. Further, MBCC-1 is capable of anchorage independent growth by soft agar colony formation assay and shows positive expression for Cytokeratin, CD24, CD44, ALDH1, cKIT, ki-67 but is negative for Collagen-IV and fibroblast activation protein (FAP) protein. Studies are ongoing to further characterize both the male breast cancer cell lines, including biomarkers, gene expression profiling, karyotyping, cell proliferation rate, soft agar colony formation assay, nutrient dependency tests, and tumorigenicity assays. In summary, two novel male breast cancer cell lines have been established for studying the biology of male breast cancers.

Adenoid cystic carcinoma (ACC) is a rare malignancy characterized by indolent growth but a high rate of metastasis, with approximately 40-60% of patients developing distant disease. Systemic therapies, including immune checkpoint inhibitors (ICIs), have shown minimal efficacy, and multiple clinical trials have failed to identify effective treatments for recurrent or metastatic ACC. In previous work, we profiled the ACC immune microenvironment using multiplex immunofluorescence (mIF) and found that ACCs are “cold” tumors, with scarce tumor-infiltrating lymphocytes (TILs) and uniformly low expression of B2M and HLA class I across nearly all analyzed samples in our cohort of 24 ACCs. Only metastatic lesions displayed focal HLA class I expression. Spatial transcriptomic analysis revealed that these focally positive regions were associated with an interferon-γ-driven transcriptional program. Short-term ex vivo treatment of ACC tissues with interferon-γ or a STING agonist strongly upregulated HLA class I, B2M, and PD-L1, suggesting that immune visibility of ACC can be pharmacologically restored. However, using surrogate cell lines (given the lack of established ACC cell lines), we observed that STING activation induced a negative feedback loop leading to suppression of STING signaling pathway with reduced STING, IRF3 and TBK1 expression after 24 hours. Co-treatment with a range of agents – including proteasome and lysosome inhibitors, kinase pathway inhibitors, epigenetic modifiers, and modulators of NF-κB or PI3K/AKT signaling – did not prevent this feedback inhibition. Clinically, one ACC patient treated with a combination of a STING agonist dazostinag and pembrolizumab over nine months showed a ~70% reduction in tumor burden, while other patients experienced stable or progressive disease. One hypothesis is that differential activation of the STING feedback mechanism may underlie these varied clinical responses. These findings indicate that while ACC cells retain the machinery to upregulate antigen-presentation pathways, intrinsic mechanisms of STING pathway repression may limit the durability of immunostimulatory responses, underscoring the need for strategies to sustain interferon signaling in ACC. Ongoing work focuses on identifying strategies to overcome this feedback inhibition and on testing alternative agents capable of enhancing antigen presentation without triggering the same suppressive cascade, with the goal of developing more durable immunostimulatory therapies for ACC.

Background
Understanding the complexity of the tumor microenvironment (TME) requires simultaneous insight into multiple biological domains. Studying the molecular interactions that govern intercellular and intracellular signaling in combination with the analysis of cell phenotypes and transcriptional states, can guarantee an improved comprehension of key pathways driving tumor growth or the response of anti-cancer therapies. Protein–protein interactions (PPIs), such as PD-1/PD-L1, are central to immune evasion and targets of important immunotherapies. Despite the success of PD-1/PD-L1 and other checkpoint inhibitors, patient stratification for these therapies has been challenging, and marker expression alone has shown to not fully capture functional engagement or predict an efficient drug response. Here, we present a fully automated workflow that combines three omics layers on the same tissue section: protein-protein proximity, RNA, and protein expression, enabling a more comprehensive view of cellular interplay in cancer and modeling of treatment outcomes.

Methods
The multiomics assay runs on the COMET™ platform. It allows the co-detection of: (i) RNA profiling via RNAscope™ HiPlex Pro, (ii) Protein expression through sequential immunofluorescence (seqIF™, PMID: 37813886), (iii) Protein–protein proximity detection using oligonucleotide-conjugated secondary antibody pairs and RNAscope™ amplification chemistry. 

Proximity signals are interpreted as probabilistic indicators of molecular interactions and supported by multiple controls to ensure their specificity: from the colocalization of seqIF™ signals to negative controls run on the same section. 

Conclusions
In this study, we demonstrated that it is possible to combine the detection of proximity signals for the analysis of intercellular interactions controlling anti-tumoral immune responses, alongside protein markers for cell phenotyping and RNA targets for functional markers and cell activation status. In detail, across multiple human FFPE tumor samples, PD-1/PD-L1 interaction was detected in combination with multiple proteins, for immune and stromal phenotyping, and RNA transcripts responsible for the expression of key secreted molecules like cytokines and chemokines. Furthermore, we showed that multiple iterative cycles allow the detection of more than one PPI on the same FFPE section. 

Tumor progression, immune evasion, and therapy resistance are not driven by single molecules but by complex interaction networks among proteins, signaling pathways, and cellular types. This automated spatial multiomics workflow incorporating protein–protein proximity as a third dimension, can help reveal how these processes are regulated. By combining RNA, protein, and proximity data, the assay offers a powerful approach to support biomarker discovery and improved patient stratification. 

Background
The use of multiplex immunofluorescence (mIF) to study the tumor microenvironment (TME) has significantly advanced our understanding of spatial dynamics within tumors. This technique has emerged as a valuable tool for identifying biomarkers and therapeutic targets. Despite its growing adoption, mIF protocols remain complex and technically demanding. Their manual execution and reliance on dedicated reagents make them time-consuming and expensive. Additionally, concerns persist regarding their reproducibility and transferability across different tissue types. Ready-to-use, validated antibody panels, such as the SPYRE™ Core Panels, help address these challenges. 

Methods
In this study, we demonstrate the development and validation of two new antibody panels covering relevant stromal and vessel biomarkers to enable spatial analysis of the TME on the COMET™ platform across various tissue types as well as two additional antibodies against SOX10 and S100B optimized for melanoma studies. The stroma panel enables simultaneous detection of Vimentin, E-Cadherin, Collagen I, and FAP, while the vessel panel contains CD31, CD34, Podoplanin, and LYVE-1. Formalin-fixed paraffin-embedded human tissue sections from a 24-cores multi-organ tissue microarray and whole-section melanoma samples were stained on COMET™ by fully automated sequential immunofluorescence (seqIF™, PMID: 37813886). Staining and detection are done via indirect immunofluorescence using unlabeled primary antibodies and fluorophore conjugated secondary antibodies. Both panels were developed and validated on several tumoral and non-tumoral tissues at the same time. The sections retrieved from COMET™ after seqIF™, were stained by a histology facility with standard immunohistochemistry (IHC) established for pathological diagnosis to compare seqIF™ and IHC staining patterns and verify antibody specificity. All markers demonstrate accurate detection with specific seqIF™ staining, comparable to gold-standard IHC counterparts, as well as robust performance across multiple tissues. Protocols were optimized to achieve high staining quality for all ten markers in terms of sensitivity and signal-to-background ratio. The repeatability and reproducibility of the automated stainings on the COMET™ platform was verified by day-to-day tests on one instrument and tests among multiple ones.  

Results
Our validated SPYRE™ Stroma and Vessel Focus panels, along with melanoma-specific antibodies, deliver highly specific and reproducible results across diverse tissues. Ready-to-use on the COMET™ platform and designed as modular extensions of the SPYRE™ Core Panels, they enable quantitative marker detection, combination with custom antibodies, and empower researchers with robust and scalable workflows for advanced spatial biology studies. 

High-resolution tissue profiling increasingly relies on integrated spatial multiomic approaches that unify spatial transcriptomics and antibody-based proteomics to reveal coordinated molecular patterns within complex tissues. This enables a detailed exploration of spatial niches, cell-cell interactions, and tissue microenvironments. However, these modalities are often performed on consecutive sections, limiting precise correlation between molecular and spatial features. Here, we present optimized workflows that combine high-plex imaging and sequencing-based spatial transcriptomic assays with antibody-based proteomics from the same tissue section in a coordinated and customizable manner.
We developed and evaluated experimental adaptations to ensure high data quality and optimal tissue handling across multiple platforms, including Xenium, Visium, and COMET. Quality control procedures were implemented to assess antigen retrieval compatibility, as these conditions can be antibody dependent. We examined the balance between epitope exposure, tissue integrity, and background signal, providing specific recommendations tailored to different research objectives.
We further compared the sensitivity of these technologies and offer guidance on selecting and combining commercially available transcriptomic and proteomic workflows in a controlled, flexible setup. As in all multiomic approaches, signal loss can occur, particularly in the second readout of consecutive analyses. For proteomics, photobleaching and antigen retrieval are key considerations, especially for low-abundance or difficult-to-detect targets. Transcriptomic data can be enhanced by using HiPlex RNAscope Pro on COMET to detect lowly expressed transcripts. For data integration, we employed a straightforward pipeline that includes cell segmentation based on protein data, image registration using nuclear staining and/or segmentation masks, and extraction of single-cell transcript counts and pixel intensity data for downstream analyses within the SpatialData framework.
We applied these workflows to tonsil, skin, and colon tissues using immuno-oncology-focused panels. The combined approach improved molecular resolution and reduced data sparsity, enabling more precise definition of cell states, spatial neighborhoods, and functional niches. These spatial multiomics workflows expand the analytical capabilities and facilitate deeper biological interpretation across diverse tissue contexts.

Hepatocellular carcinoma (HCC) is the primary form of liver cancer, because early symptoms may be absent, it is often detected late, resulting in a median survival of less than a year. Metabolically-dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), has been identified as the fastest-growing cause of HCC globally, including in the United States. Vitellius et al. reported that MASLD may account for 35% of HCC cases. Furthermore, Younossi et al. mentioned it is estimated that by 2040, MASLD will be present in 55% of the population. Therefore, understanding the molecular mechanisms by which MASLD progresses to HCC is critical.
In this study, we leveraged the existing whole-transcriptomic data from over 200 patients with MASLD or Metabolic Dysfunction-Associated Steatohepatitis (MASH) to identify all differentially expressed transcripts, compared to normal liver tissue. We examined the expression of unique noncoding transcripts that are associated with metabolism in HCC. To explore their potential functions, we used the Genomic Regions Enrichment of Annotations Tool (GREAT). Currently, we are employing various molecular and phenotypic based assays to identify their function in HCC. We will link their functions to HCC patient outcomes using RNA in situ hybridization in human tissues.
The analysis identified differentially expressed transcripts in MASLD and MASH. We observed overlapping gene expression patterns across both conditions, along with distinct transcriptomic profiles unique to each stage. In MASLD, 4,403 genes were significantly upregulated and 4,411 downregulated compared to controls. Similarly, in MASH, 4,702 genes were upregulated and 4,433 downregulated. Comparative analyses revealed substantial overlap between the two cohorts, while also highlighting transcripts unique to each condition. These findings suggest that as metabolic liver disease progresses from fatty liver to steatohepatitis, many transcriptional alterations are conserved and become increasingly dysregulated. GREAT analyses indicate that these may be linked to various cellular processes known to play critical role in HCC, such as long-chain fatty-acyl-CoA and fatty-acyl-CoA metabolism, cellular catabolism, apoptosis, and kidney development, including mesonephros. Notably, many of these transcripts exhibit distinct expression patterns in HCC, suggesting their potential as diagnostic and therapeutic markers. We have begun elucidating their mechanism of action using loss- and gain-of approaches in HCC and how their expression is regulated in patient tissue samples.

Ewing sarcoma (ES) is a pediatric cancer of the bone and soft tissues with poor outcomes for patients with metastatic or relapsed disease. Ewing sarcoma cells are characterized by the presence of a driver fusion oncogene, most commonly EWSR1::FLI1. In the absence of a genetic animal model due to the severe toxicity of the oncofusion, the developmental aspects of ES initiation, including its cellular origin, have remained poorly understood. To address these questions, we developed a stable zebrafish transgenic model enabling tissue-specific expression of the human EWSR1::FLI1 oncofusion in neural crest cells, one of the proposed cell of origin for ES (Vasileva et al., Cell Reports 2025). Using this model, we demonstrated that expression of human EWSR1::FLI1 oncofusion in neural crest cells can lead to their transformation and the development of tumors in vivo. Single-cell analysis of tumor initiation shows that EWSR1::FLI1 reprograms neural crest-derived cells to a mesoderm-like state, strikingly resulting in ectopic fin formation throughout the body. Such hijacking of the limb development program led to abnormal activation of developmental signaling pathways in EWSR1::FLI1-induced outgrowths, resulting in dysregulation of the FGF signaling cascade and HOX gene expression. EWSR1::FLI1 reprograms neural crest cells by hijacking developmental enhancers and upregulating the expression of mesodermal regulators. One such regulator is tbxta (Brachyury or T), a key transcription factor controlling mesodermal specification. Notably, tbxta/TBXT expression was maintained in a subset of zebrafish and human tumors. Our model provides a mechanism by which a neural crest cell lineage can be transformed into Ewing sarcoma, a malignancy with predominant mesenchymal features. Taken together, these findings show how a single mutation can disrupt normal developmental trajectories, driving neural crest cells reprogramming and initiating malignant transformation.

Background: Immunosuppression is a key characteristic of pancreatic ductal adenocarcinoma (PDAC), contributing to metastasis and poor survival. Our studies have identified tumor cell intrinsic Rho-associated coiled-coil containing protein kinase-2 (ROCK2) as a key regulator of extracellular matrix remodeling. In this study, we investigated how ROCK2 regulates immunosuppression in PDAC by modulating Leukemia inhibitory factor (LIF) and its effects on STAT3.
Methods: TCGA PDAC patient dataset was used to compare the ROCK2 and LIF expression in normal and PDAC tissues. CIBERSORT analysis of the PDAC dataset estimated the proportion of tumor infiltrating immune cell subsets. Genomic editing using the CRISPR/Cas9-system in LSL-Kras^G12D/+; Trp53 ^R172H/+; Pdx1^Cre/+ (KPC) cells was performed to generate KPC Rock2 knockout (Rock2^KO) cells. Cytokine array was performed on Rock2^EV and Rock2^KO conditioned media and ELISA was used to validate the results. Flow cytometry was used to profile LIF receptor (LIFR) expression in different cell types from KPC orthotopic tumors. Bone marrow-derived macrophages from C57BL/6 mice were treated with recombinant LIF (rLIF) and LIFR inhibitor (EC359), then analyzed for polarization by flow cytometry. KPC orthotopic tumors were generated, and Immune cell profiling was performed to evaluate alterations in immune cell subsets following treatment with EC359. Findings from ROCK2 and ROCK2-regulated LIF-STAT3 targeting, both in vitro and in vivo were validated using Immunoblotting and immunohistochemistry.
Results: Analysis of the TCGA dataset revealed that Human PDAC tissues have increased expression and correlation of ROCK2 and LIF. Further analysis showed that macrophages constitute a substantial proportion of the immune cell infiltrate. Cytokine array and ELISA-based studies revealed decreased LIF secretion with ROCK2 knockout, providing evidence for ROCK2 dependent regulation of LIF. KPC orthotopic tumors demonstrated higher LIFR expression in tumor-associated macrophages (TAMs) and EC359 treatment reduced ARG1 and PD-L1 expression on these cells. Additionally, EC359 treatment led to a significant increase in the activated effector and effector memory T cell populations. Furthermore, rLIF treatment increased pSTAT3 levels in macrophages, while EC359 lowered its expression, highlighting the role of ROCK2-regulated LIF-LIFR in STAT3 activation.
Conclusion: These findings demonstrate that tumor cell-intrinsic ROCK2 regulates LIF-STAT3, which mediates immunosuppression and can serve as a potential therapeutic target for PDAC.