Copy number variation (CNV), which alters the number of genomic segments, is a major driver of intratumor heterogeneity, characterized by spatially organized and genetically distinct cell populations. Recent advances in spatially resolved transcriptomic (SRT) technologies, which profile gene expression across thousands of spatially indexed tissue locations, offer a powerful opportunity to reconstruct the CNV architecture and dissect the spatial organization of cancer subclones. Here, we introduce SPICE ( sp atial i nference of C NV e vents), a probabilistic method for identifying somatic CNVs and allele-specific copy number (ASCN) profiles from SRT data. A key feature of SPICE is its ability to integrate multiple complementary information available in SRT data, including gene expression, spatial coordinates, and heterozygous SNPs inferred from transcriptomic reads, to substantially enhance the accuracy and power of CNV detection. Using datasets generated across different SRT platforms, we first assess the reliability of SNPs derived from SRT data to ensure robust downstream inference. We then demonstrate that SPICE effectively integrates these modalities to deliver accurate and spatially coherent reconstruction of CNV landscapes and subclonal architecture, while maintaining excellent control of false discoveries. Together, SPICE provides a robust and effective solution for dissecting genomic heterogeneity in SRT studies of cancer.

Invasion plasticity allows malignant cells to toggle between collective, mesenchymal, and amoeboid phenotypes while traversing extracellular matrix (ECM) barriers. Current dogma holds that collective and mesenchymal invasion programs trigger the mobilization of proteinases that digest structural barriers dominated by type I collagen, while amoeboid activity allows cancer cells to marshal mechanical forces to traverse tissues independently of ECM proteolysis. Here, we use cancer spheroid-3-dimensional matrix models, single-cell RNA sequencing, and human tissue explants to identify the mechanisms controlling mesenchymal versus amoeboid invasion. Unexpectedly, collective/mesenchymal-and amoeboid-type invasion programs-though distinct- are each characterized by active tunneling through ECM barriers, with expression of matrix-degradative metalloproteinases. CRISPR/Cas9-mediated targeting of a single membrane-anchored collagenase, MMP14/MT1-MMP, ablates tissue-invasive activity while coregulating cancer cell transcriptional programs. Though changes in matrix architecture, nuclear rigidity, and metabolic stress as well as the presence of cancer-associated fibroblasts are proposed to support amoeboid activity, none of these changes restore invasive activity of MMP14-targeted cancer cells. While a requirement for MMP14 is bypassed in low-density collagen hydrogels, invasion by the proteinase-deleted cells is associated with nuclear envelope and DNA damage, highlighting a proteolytic requirement for maintaining nuclear integrity. Nevertheless, when cancer cells confront explants of live human breast tissue, MMP14 is again required to support invasive activity. Corroborating these results, spatial transcriptomic and immunohistological analyses of human breast cancers identified MMP14 expression in tissue-infiltrating carcinoma cells that were further juxtaposed with proteolyzed type I collagen fragments, underlining the pathophysiologic importance of this proteinase in directing invasive activity in vivo.

Bone metastases cause significant morbidity and ultimately death in some human cancer patients. Although experimental animal models of skeletal metastases have been invaluable for investigating the molecular mechanisms of cancer progression and growth in bones, few of these models recapitulate the heterogeneous nature of bone metastases observed in humans. Animals with naturally occurring cancers have been proposed as ideal and unique models for human cancer research. From this, understanding the incidence, histologic features, clinical presentation, and response to therapy of spontaneous bone metastases in animals with different cancers will help with animal model selection and translational experiments on bone metastasis. This review provides an overview of the natural history of spontaneous bone metastases that occur in animals with spontaneous cancer. The review focuses on companion animals (dogs and cats) and includes rodents and other animal species. The similarities and differences compared to human bone metastases are highlighted, permitting a valuable resource for future skeletal metastasis modeling and therapeutic discovery.

Abstract Renal cell carcinoma (RCC), the most common type of kidney cancer, relies heavily on the complex ecosystem of the Tumor Microenvironment (TME) for its progression. A critical cellular component within the TME is the Tumor-Associated Macrophage (TAM), which often adopts a pro-tumor, M2-like phenotype that supports angiogenesis, invasion, and immune suppression. This study investigates the role of tumor-derived exosomes (TDEs) in mediating this critical reprogramming event. In vitro experiments utilized human (786-O renal cancer cell line and THP-1 monocytes) and mouse (Renca RCC and RAW 264.7 monocytes/macrophage) cell lines. The macrophages were treated with RCC-derived exosomes, and the conditioned medium (CM) was used to treat parental RCC cells. Functional assays demonstrated that exosome-educated macrophage CM significantly increased the proliferation, migration, and invasion of both human and mouse RCC cells. Furthermore, Western blot and ELISA analysis confirmed that TDE treatment successfully reprogrammed macrophages, leading to altered cytokine profiles indicative of polarization toward a pro-tumor phenotype. In vivo, the impact of the TDE-educated macrophage CM was assessed in SCID and BALB/c mouse models using intracardial and intrarenal RCC injections. Bioluminescence imaging (BLI) confirmed that CM from exosome-treated macrophages significantly enhanced tumor progression and distant metastasis compared to controls. In conclusion, this research identifies tumor-derived exosomes as critical signaling vectors that effectively reprogram TAMs into pro-tumorigenic cells. This exosome-mediated communication promotes a highly permissive TME, leading to accelerated renal cancer progression and metastasis. These findings highlight the TDE-TAM axis as a potent therapeutic target for mitigating RCC malignancy. Citation Format: Jinlu Dai, Suguru Kadomoto, Tyler Robinson, Evan T. Keller. Tumor-derived exosomes promote renal cancer progression via reprogramming of tumor-associated macrophages [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3356.

Advances in spatial transcriptomics (ST) technologies enable systematic molecular characterization of tumor microenvironment, tumor gradients and gene regulatory networks. Cancer progression is known to vary along pathological gradients, yet existing network approaches for gene network inference typically ignore hierarchical spatial organization across the tumor. We develop a Bayesian multi-resolution spatial graphical regression (mSGR) framework to infer spatially varying gene networks from multi-resolution ST data. The proposed model allows precision matrices to vary across hierarchically structured spatial domains, capturing both local and global organization within the tumor. To identify spatially varying regulatory relationships, we introduce a spatially structured edge selection strategy that borrows strength across regions according to spatial proximity and pathological gradients, while Gaussian-process priors flexibly model spatial variation in edge strengths. Scalable inference is achieved through an augmented mean-field variational Bayes algorithm with node-wise parallel regressions, enabling efficient estimation in high-dimensional settings. Simulation studies demonstrate improved recovery of network structures compared with competing approaches. Applying mSGR to multi-resolution ST data from kidney cancer reveals stronger regulatory connectivity in transitional regions of epithelial-mesenchymal transition pathway and identifies hub genes along the tumor gradient, illustrating how spatially resolved network analysis can provide key insights into tumor microenvironment organization.

Drug repurposing offers a scalable route to accelerate therapeutic discovery, yet existing approaches based on single-cell RNA sequencing (scRNA-seq) often overlook spatial tissue context, limiting their ability to capture microenvironment-dependent drug responses. Here we present STDrug, a spatially informed computational framework that integrates spatial transcriptomics, graph-based modeling, and multimodal learning to enable patient-specific therapeutic prioritization. STDrug identifies and aligns disease and control spatial domains using graph convolutional networks and coherent point drift, and prioritizes candidate drugs through an integrative scoring scheme combining tumor-reversible gene signatures, perturbation-based reversal scores, and knowledge-guided gene weighting within a machine learning framework. By modeling spatial domain interactions alongside predicted drug efficacy and toxicity, STDrug generates robust patient-level drug scores. Across hepatocellular carcinoma and prostate cancer datasets, STDrug outperforms existing single-cell and spatial transcriptomics-based drug repurposing methods, achieving signficantly improved predictive accuracy (AUCs=0.81-0.82) across patients. Validation using large-scale electronic health records and in vitro assays further supports the translational relevance of top-ranked candidates. Taking together, STDrug establishes a generalizable framework for incorporating spatial omics into therapeutic discovery, advancing spatially informed and personalized drug repurposing.

Hierarchical multiplex imaging approaches generate spatially resolved single-cell measurements across multiple, spatially organized fields of view (FOVs) within patient tumor specimens, thereby enabling systematic investigation of how the organization of the tumor microenvironment varies along biologically meaningful intratumoral gradients. Existing approaches fail to jointly address this multi-resolution data structure needed to recover true biological signals. We propose MoSAIC: multi-resolution spatial regression analysis of cell colocalizations, a hierarchical Bayesian spatial regression model designed for multi-resolution spatial data. MoSAIC decomposes the joint variation into three model components: (i) global tumor-gradient effects, (ii) patient-specific effects to capture inter-patient variability, and (iii) Gaussian process models to account for spatial dependence between FOVs within each patient tumor tissue. Simulations demonstrate MoSAIC has improved prediction and model fit compared to existing spatial and non-spatial model alternatives. Our method is motivated by and applied to a renal cell carcinoma multiplex imaging cohort to investigate immune-tumor colocalization patterns across the epithelial-to-mesenchymal transition (EMT) gradient. MoSAIC identifies increased macrophage-tumor colocalization and decreased cytotoxic T-tumor colocalization progressing across the increasing EMT gradient, consistent with EMT-associated immune suppression and spatially varying immune engagement. Overall, MoSAIC provides an interpretable, multi-resolution framework for quantifying spatial tumor-gradient effects in cancer imaging studies. Software is available on GitHub at jcaldous/MoSAIC.

Abstract Primary central nervous system lymphoma (PCNSL) is histologically a subtype of diffuse large B-cell lymphoma (DLBCL), sharing genetic and transcriptomic similarity, but with distinct clinical features, particularly its confinement to the CNS and higher relapse risk. The microenvironmental basis for its divergence from systemic DLBCL remains unclear. Using spatial transcriptomic approaches (Xenium/GeoMx digital spatial profiling) in a comparative study of PCNSL (n=17) and DLBCL (n=76), we found that PCNSL, unlike systemic DLBCL, is dominated by an immunosuppressive macrophage compartment enriched for cholesterol-metabolism programs. In independent cohorts of PCNSL profiled by single-cell RNA sequencing, we validated the presence of a recurrent population of lipid-laden macrophages (LLMs): TREM2/GPNMB-expressing, lipid-remodeled cells transcriptionally distinct from resident microglia and consistent with an infiltrating monocyte origin, not previously characterized in CNS lymphoma. LLMs formed immunosuppressive niches with regulatory T cells, and using Cellscape hyperplex proteomic imaging we demonstrate that LLM-Treg spatial interactions are associated with chemotherapy response. To test whether LLMs are lymphoma-driven and functionally important, we developed an immunocompetent syngeneic PCNSL mouse model, driven by Myd88 L252P and Cd79b mutations with Bcl2 overexpression. Monocyte-derived macrophages in lymphoma-bearing brain regions acquired an LLM-like state, not seen in lymphoma-free brain regions or in splenic tumors driven by the same oncogenic lesions. TREM2-SYK signaling sustained this state, and SYK inhibition reversed its tumor-supportive activity ex vivo . These findings identify the LLM program as a targetable immunosuppressive myeloid state in CNS lymphoma. Key Points PCNSL is enriched for TREM2 + lipid-laden macrophages (LLMs) forming immunosuppressive niches enriched relative to systemic DLBCL. The first immunocompetent MCD-like-genotype PCNSL model recapitulates human LLMs; SYK inhibition reverses their tumor-supportive activity.

Androgen deprivation therapy (ADT) remains a cornerstone in the treatment of prostate cancer (PCa), yet most tumors eventually develop resistance. Murine models are widely used to study PCa progression and ADT response, but a detailed understanding of the prostate's biological response to androgen deprivation in these models is lacking. Here, we present a spatiotemporal analysis of cellular and transcriptional dynamics in the mouse prostate following orchiectomy (ORX)-induced androgen deprivation with a focus on non-epithelial components. We observed progressive involution across all prostate lobes (dorsal, ventral, lateral, and anterior) and distinct lobe-specific temporal gene expression changes post-ORX. Immune cell infiltration markedly increased over time, highlighting a shift in the prostate's cellular landscape. Single-cell RNA sequencing uncovered a previously undescribed fibroblast subtype-termed ORX-induced fibroblast (OIF)-characterized by high expression of Wnt2, Rorb, and Wif1, with distinct spatial localization. Pathway analysis revealed upregulation of amide and peptide binding functions, alongside suppression of peptidase and endopeptidase activity. Furthermore, dynamic changes in ligand-receptor interactions across lobes underscored the evolving intercellular communication in the post-ORX prostate. By integrating spatial transcriptomics with single-cell profiling, our study generates a high-resolution atlas of the murine prostate's response to androgen deprivation. These findings provide a foundational resource for interpreting ADT responses in preclinical models of PCa.

Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets-where two or more cells are sequenced together-disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.

Introduction Primary central nervous system lymphoma (PCNSL) is an aggressive B-cell lymphoma exhibiting unique central nervous system (CNS) tropism, and high recurrence rates despite sharing morphological and molecular features with systemic Diffuse Large B-Cell Lymphoma (DLBCL). Given the unique immune landscape of the CNS and the critical role of macrophages in neural tissue, we hypothesized that PCNSL may be sustained by a CNS-specific macrophage program distinct from DLBCL, representing a targetable mechanism underlying immune evasion, CNS confinement, and treatment resistance. While tumor-associated macrophages (TAMs), particularly CD163+ M2-like macrophages, are known to be enriched in PCNSL tumor microenvironment (TME), prior spatial studies have focused predominantly on tumor-intrinsic features and T-cell dysfunction, leaving macrophage organization and functional programming poorly characterized. Methods We employed cutting-edge spatial multi-omics using four complementary platforms to comprehensively profile macrophage heterogeneity. Formalin-fixed paraffin-embedded (FFPE) samples in TMA format (26 PCNSL, 89 DLBCL) were analyzed using a Xenium 380-gene immuno-oncology panel. GeoMx digital spatial profiling whole transcriptome analysis (DSP-WTA) was performed in 82 cases (17 PCNSL, 65 DLBCL) using CD3, CD20, and CD68 cell masks. Macrophage signatures identified from DSP were validated across independent single-cell RNA sequencing (scRNA-seq) datasets (PCNSL N=28, DLBCL N=17, reactive lymph nodes N=2), distinguishing microglia from monocyte-derived macrophages. CellScape high-plex imaging was used to confirm phenotypes at the protein level and assess spatial proximity and interactions in 24 PCNSL and 5 tonsil samples using 40 architecture, immune and macrophage markers. Results Xenium spatial profiling revealed significantly higher macrophage abundance in PCNSL versus DLBCL, confirmed by DSP-WTA (p=0.0002). DSP further demonstrated that PCNSL TAMs upregulate immunosuppressive genes (CRYAB, SPP1) while downregulating T-cell recruitment genes (CCL19, IGSF6), with enrichment of glycolysis, cholesterol homeostasis, and peroxisome pathways. The CXCL9:SPP1 expression ratio, a validated macrophage polarity biomarker of prognosis in cancer, was significantly reduced in PCNSL macrophages across both DSP and scRNA-seq datasets (p=0.027). Of specific interest, differentially expressed gene (DEG) projection and BayesPrism deconvolution of CD68+ regions of interest (ROIs) revealed enrichment of TREM2+ macrophages (p=0.00066) and elevated GPNMB+ lipid-laden macrophage (LLM) signatures (p=0.004) in PCNSL. scRNA-seq confirmed higher LLM signatures in PCNSL versus DLBCL (p=0.0052) and lymph nodes (p=1.2e-06), identifying a monocyte-derived subset enriched in cholesterol metabolism and high-density lipoprotein (HDL) binding pathways. Cell-cell communication analysis revealed enhanced interactions between LLMs and regulatory T-cells via secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and intercellular adhesion molecule 1 (ICAM1) signaling axes. CellScape imaging confirmed the presence of GPNMB+CD163+CD274+ macrophages in PCNSL, with spatial analysis showing that LLM-T cell proximity correlated with treatment response. Conclusion We define a CNS-specific macrophage program characterized by TREM2+ GPNMB+ lipid-laden macrophages that may foster CNS tropism in PNCSL, along with immune evasion through metabolic reprogramming and regulatory T-cell activation. This first comprehensive spatial multi-omics characterization of macrophage heterogeneity distinguishing PCNSL from DLBCL identifies TREM2, SPP1 and lipid metabolism as potential therapeutic targets for macrophage-directed immunotherapy in this disease of unmet clinical need.

Inadequate response to androgen deprivation therapy (ADT) frequently arises in prostate cancer, driven by cellular mechanisms that remain poorly understood. Here, we integrated single-cell RNA sequencing, single-cell multiomics, and spatial transcriptomics to define the transcriptional, epigenetic, and spatial basis of cell identity and castration response in the mouse prostate. Leveraging these data along with a meta-analysis of human prostates and prostate cancer (PCa), we identified cellular orthologs and key determinants of ADT response and resistance. Our findings reveal that mouse prostates harbor lobe-specific luminal epithelial cell types distinguished by unique gene regulatory modules and anatomically defined androgen-responsive transcriptional programs, indicative of divergent developmental origins. Androgen-insensitive, stem-like epithelial populations-resembling human club and hillock cells-are notably enriched in the urethra and ventral prostate but are rare in other lobes. Within the ventral prostate, we also uncovered two additional androgen-responsive luminal epithelial cell types, marked by Pbsn or Spink1 expression, which align with human luminal subsets and may define the origin of distinct PCa subtypes. Castration profoundly reshaped luminal epithelial transcriptomes, with castration-resistant luminal epithelial cells activating stress-responsive and stemness programs. These transcriptional signatures are enriched in tumor cells from ADT-treated and castration-resistant PCa patients, underscoring their likely role in driving treatment resistance. Temporal tracking of cells will precisely map disease-associated cellular transitions, and our technical framework facilitates such interrogations. Collectively, our comprehensive cellular atlas of the mouse prostate illuminates the importance of lobe-specific contexts for PCa modeling and reveals potential therapeutic targets to counter castration resistance.

Sarcomatoid renal cell carcinoma (sRCC) is an aggressive transdifferentiation of epithelioid clear cell RCC (ccRCC) tumors that shows heightened response to immunotherapy. The underlying biology leading to sarcomatoid transformation and mechanisms contributing to immunotherapy response are not well understood. Novel single cell spatial techniques were used in ccRCC and sRCC tumors from 40 patients to understand the spatial sRCC transformation and corresponding immune changes. A transcriptional transition state in epithelioid ccRCC cells along a continuum to mesenchymal sRCC was identified which expresses high levels of pro-inflammatory cytokines and an immune infiltrate. In vitro studies demonstrated that M2-like macrophages, recruited to the tumor by the transition state, induce full transition to the sarcomatoid state. A combination of increased PD-L1 expression and T-cells recruited by the transition state was observed, consistent with the increased immunotherapy response. This study enriches our understanding of the mechanisms leading to development and immune responsiveness of sRCC paving the way for novel approaches to diminish RCC progression.

BACKGROUND:Dogs spontaneously develop prostate carcinoma (PC) and share prostate gland anatomy, physiology, and size to men. Over the last 15 years, we have developed and refined a canine model of focal PC to evaluate therapeutic-diagnostic (theranostic) interventions. A comprehensive description of the pathology and synthesis of the various studies has not been performed. The goal of this manuscript was to describe the canine model tumor pathology within the framework of its methodological development to help guide future translational PC research. METHODS:In published and unpublished studies, we previously inoculated prostate glands of immunosuppressed, intact beagle dogs (n = 56) with a canine PC cell line (Ace-1) transduced with human or canine genes for targeted theranostics. Gross tumor assessment and histology were performed in all cases. Molecular tumor and microenvironmental pathology was investigated using digital image analysis, immunohistochemistry, laser-capture microdissection, and quantitative real-time PCR. RESULTS:The model reliably (85.7% engraftment rate) formed prostatic tumors resembling intermediate and high-grade localized PC, with poorly differentiated morphology, stromal invasion, and peripheral growth. Soft tissue metastasis occurred in 13/48 (27.1%) dogs. Most dogs formed multifocal prostatic tumors with occasional tumors outside the prostate gland. Tumor location influenced growth behavior and the microenvironment. Allografts were histologically classified as intraglandular intraprostatic, invasive intraprostatic, capsular, or extraprostatic. Compared to intraprostatic tumors, capsular/extraprostatic tumors had increased proliferation (Ki-67 index), epithelial-to-mesenchymal transition, and microenvironmental alterations that included increased collagenous stroma, fibroplasia, and reduced immune cell infiltration. CONCLUSIONS:The canine model of PC captured important pathologic features of men undergoing curative-intent therapy alongside model- and species-specific characteristics of interest to researchers. Beyond defining pathology, the results highlighted applications of the canine model in studying the tumor microenvironment and advancing preclinical, anti-cancer strategies in a large animal species.

The development of drug-resistant cell lines is essential for understanding the mechanisms of drug resistance and identifying strategies to overcome treatment failure in cancer therapy. Resistance models enable preclinical evaluation of novel compounds, repurposed drugs, and combination therapies. To generate resistant cells, parental cancer cell lines are repeatedly exposed to incrementally increasing concentrations of the target drug over several weeks. Cells that survive and proliferate at each stage are selected, expanded, and exposed to higher drug doses. The development of resistance is confirmed by quantifying and comparing the half-maximal inhibitory concentration (IC50) values between parental and resistant cells using cell viability assays and nonlinear regression analysis. Significantly increased IC50 values indicate successful adaptation to drug pressure and the development of resistance. These drug-resistant cell lines are available for comprehensive analysis, such as microarray and single-cell sequencing, as well as various in vitro or in vivo experiments. These models provide valuable tools for investigating potential therapeutic strategies to overcome drug resistance.

Prostate cancer is the second leading cause of cancer-related death among American men, with a new diagnosis made every 2 min in the United States. Advanced cases are commonly treated with androgen deprivation therapy (ADT). Despite its effectiveness, treatment failure remains inevitable for many patients, necessitating better predictive tools for clinical management of disease. This study presents a data-driven mathematical modeling approach that integrates patient-specific prostate-specific antigen (PSA) time-course data with experimentally measured PSA expression rates to improve the prediction of ADT failure. Our findings suggest that post-nadir PSA dynamics, rather than initial decline, hold greater prognostic value and can inform PSA monitoring schedules. By employing virtual clones of individual patients, our model integrates routinely collected PSA measurements to dynamically predict ADT failure probabilities at future clinic visits. If implemented in clinical practice, this personalized framework could empower oncologists to make proactive, informed treatment decisions and guide timely interventions.

Introduction The epithelial to mesenchymal transition (EMT) is an important pathway in renal cell carcinoma (RCC) progression in which tumor cells lose epithelial features and gain more aggressive mesenchymal features that promote invasion and metastasis. Sarcomatoid RCC (sRCC) is a de-differentiated tumor state which occurs by EMT. sRCC tumors are highly responsive to immunotherapy, although the reasons behind this remain unknown. Here, we demonstrate that EMT modulates tumor cells and their immune environment to confer sensitivity to PD-1/PD-L1 directed immunotherapy. Methods Multiplex immunofluorescent staining (Vectra Polaris) was performed on 29 human RCC specimens for epithelial marker E-cadherin, mesenchymal marker N-cadherin, PD-L1, CD8 (CD8 T-cells), CD4 (CD4 T-cells), and CD163 (macrophages). Spatial correlation of markers was assessed. One specimen underwent single cell spatial transcriptomics for spatially resolved cell type and gene expression using the NanoString CosMx platform. In vitro, expression of EMT markers and PD-L1 were measured using Western Blot in two ccRCC lines and five sRCC lines. EMT was induced in cells through treatment with cytokines TGFB or IFNg, treatment with recombinant HGF or SPP1 (proteins of interest from prior data), and by overexpression of EMT transcription factors Snail or Zeb1. To evaluate the role of PD-1/PD-L1 signaling on EMT, cells were treated with a siRNA against PD-L1, the PD-L1 inhibitor durvalumab, or recombinant PD-1 protein. Effects on EMT-related protein expression were measured using Western Blot. Results In 29 RCC specimens, N-cadherin (mesenchymal marker) was strongly associated with PD-L1 expression (Figure1A). Macrophages in N-cadherin high areas also exhibited high PD-L1 expression and were closely spatially associated with CD8 T-cells (Figure1B-E). Single cell transcriptomic data revealed these CD8 T-cells expressed high levels of PD-1 but otherwise cytotoxic traits (Figure1F). In vitro, PD-L1 expression positively correlated with increasing EMT state in all 7 RCC lines. Induction of EMT in RCC cells by all methods tested led to a concurrent strong upregulation of PD-L1 expression. Co-culture of EMT high RCC cells with macrophages led to upregulation of PD-L1 expression in the macrophages. PD-L1 knock down with siRNA or inhibition with durvalumab, both led to a decreased EMT state in the cells whereas activation of PD-L1 by treatment with recombinant PD-1 led to an enhanced EMT state. Conclusions These findings demonstrate that EMT alters tumor cells and their immune microenvironment to enhance PD-L1 expression and promote co-localization of PD-1 expressing CD8 T-cells. Our data suggests that EMT high RCC tumors may be dependent on PD-1/PD-L1 signaling to maintain or enhance the EMT status of tumor cells which can be blocked with PD-1/PD-L1 inhibition. This provides biologic insight to sRCC's high responsiveness to PD-1/PD-L1 directed immunotherapy, and provides important rationale to explore EMT related factors as biomarkers for immunotherapy response in RCC.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with limited effective therapeutic options readily available. We have previously demonstrated that lovastatin, an FDA-approved lipid-lowering drug, selectively inhibits the stemness properties of TNBC. However, the intracellular targets of lovastatin in TNBC remain largely unknown. Here, we unexpectedly uncovered ribosome biogenesis as the predominant pathway targeted by lovastatin in TNBC. Lovastatin induced the translocation of ribosome biogenesis-related proteins including nucleophosmin (NPM), nucleolar and coiled-body phosphoprotein 1 (NOLC1), and the ribosomal protein RPL3. Lovastatin also suppressed the transcript levels of rRNAs and increased the nuclear protein level and transcriptional activity of p53, a master mediator of nucleolar stress. A prognostic model generated from 10 ribosome biogenesis-related genes showed outstanding performance in predicting the survival of TNBC patients. Mitochondrial ribosomal protein S27 (MRPS27), the top-ranked risky model gene, was highly expressed and correlated with tumor stage and lymph node involvement in TNBC. Mechanistically, MRPS27 knockdown inhibited the stemness properties and the malignant phenotypes of TNBC. Overexpression of MRPS27 attenuated the stemness-inhibitory effect of lovastatin in TNBC cells. Our findings reveal that dysregulated ribosome biogenesis is a targetable vulnerability and targeting MRPS27 could be a novel therapeutic strategy for TNBC patients.

You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology I (PD16)1 May 2024PD16-04 SPATIAL ANALYSIS OF PRIMARY AND METASTATIC LESIONS IN SARCOMATOID RENAL CELL CARCINOMA REVEALS INSIGHT TO TUMOR BIOLOGY AND IMMUNE MICROENVIRONMENT Allison M. May, Claire Williams, Shaye Hagler, Michael McNiff, Tyler Robinson, Simpa Salami, Aaron Udager, and Evan T. Keller Allison M. MayAllison M. May , Claire WilliamsClaire Williams , Shaye HaglerShaye Hagler , Michael McNiffMichael McNiff , Tyler RobinsonTyler Robinson , Simpa SalamiSimpa Salami , Aaron UdagerAaron Udager , and Evan T. KellerEvan T. Keller View All Author Informationhttps://doi.org/10.1097/01.JU.0001009560.23593.56.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Sarcomatoid renal cell carcinoma (sRCC) is a dedifferentiation process that can occur in any kidney tumor, most commonly clear cell RCC (ccRCC). sRCC tumors have a high rate of metastasis. It is unknown whether metastatic lesions initiate from ccRCC or sRCC cells. Our prior work identified the ability to detect cells in a transition state from ccRCC to sRCC which was associated with dense macrophage and CD8 T-cell infiltrate. We hypothesized that clear cells in an early stage of transition to sRCC have more metastatic potential than fully mesenchymal sRCC cells. Here, we use spatial single cell transcriptomics and multiplex immunofluorescent staining to explore the tumor cell biology and corresponding immune microenvironment in matched primary and metastatic sRCC lesions. METHODS: Single cell spatial transcriptomics via NanoString's CosMx platform was performed on 6 specimens from two patients with matched primary and metastatic lesions. Fields of view were selected based on histology, selecting regions of ccRCC, "transition" (areas spatially between clear cell and sarcomatoid areas with an intermediate morphologic appearance), and sRCC. Unsupervised clustering was used to identify cell types which were mapped to spatial location. Immunoflourescent staining of 18 markers was performed on an adjacent cut of the same specimens and immune infiltrate was quantified via the Canopy CellScape platform. RESULTS: Unique tumor cell states were identified in ccRCC and sRCC areas. Transition areas were comprised largely of sRCC cell states with some component of ccRCC, consistent with prior findings. Metastatic lesions were comprised predominantly of ccRCC cell states with high expression of stemness genes such as POU5F1. Primary lesions had high T-cell infiltrate with the majority comprised of memory T-cells while metastatic lesions and much higher effector T-cell infiltrate. Ongoing work includes spatial analysis of tumor cell/immune interactions. CONCLUSIONS: These findings support the hypothesis that initial metastases in sRCC arise from clear cell RCC cells with stem-like properties, which may then progress to develop sarcomatoid features. The immune microenvironment varies from primary to metastatic lesions with a higher density of effector T-cells in metastases. Further exploration of these findings will have important implications for understanding sRCC biology and response to immunotherapy in primary versus metastatic lesions. Source of Funding: Clark Family Kidney Cancer Research FellowshipNCI/NIH T32 Research Fellow, Division of Urologic Oncology at University of MichiganRogel Cancer Center Forbes Institute for Cancer Discovery Scholarship © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e366 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Allison M. May More articles by this author Claire Williams More articles by this author Shaye Hagler More articles by this author Michael McNiff More articles by this author Tyler Robinson More articles by this author Simpa Salami More articles by this author Aaron Udager More articles by this author Evan T. Keller More articles by this author Expand All Advertisement PDF downloadLoading ...