Cancer Cell

BackgroundStomach adenocarcinoma is driven by heterogeneity, limiting therapeutic success. Although ROS acts as a continuous "redox rheostat" for tumor evolution, it is categorized based on binary models that are masked by tumor-microenvironment (TME) confounders. Here, we have defined a continuous, TME-independent ROS axis to help identify intrinsic vulnerabilities and improve patient stratification. MethodsNon-negative matrix factorization (NMF) defined a ROS-Axis in TCGA-STAD which was validated in ACRG Cohort. Multivariate regression model isolated intrinsic signatures via "residual" ROS scores by adjusting for TME confounders. Survival was assessed using Cox hazard models. Drug sensitivities were mapped using GDSC2/ElasticNet modeling with cross-cohort replication. ResultsOur results define a reproducible ROS gradient, driven by effectors like NQO1 and SOD1, characterizing ROS-high tumors as proliferative, epithelial and "immune-cold". High residual ROS score was associated with an improved prognosis, regardless of TNM stage and age. Pharmacogenomic mapping revealed an overlapping sensitivity to mTOR inhibitors in ROS-high gastric cancer tumors which persisted after TME confounder adjustment. ConclusionThe continuous ROS axis provides a functional readout of metabolic dependency that refines traditional anatomical staging. By identifying mTOR dependent cold tumors, our framework offers a precision strategy for immunotherapy-resistant patients like those affected by microsatellite-stable gastric cancer.

Clear cell renal cell carcinoma (ccRCC) is the leading cause of kidney cancer-related death, but how the tumor microenvironment shapes patient survival is not completely understood. Here, we describe the characterization of ccRCC tumor ecosystems from 498 patients using imaging mass cytometry with a focus on tumor, myeloid, and T cell landscapes. Data from more than 3 million single cells is analyzed using machine-learning to identify key ecosystem features that outperform basic clinical data for predicting patient survival. We define three survival ecotypes of ccRCC: Poor ecotypes, correlate with the worst survival, have high levels of ICAM1 and CD44 expression in tumor cells and are enriched in M2-like macrophages and interactions of exhausted CD8+ T cells with macrophages. Favorable ecotypes are characterized by high levels of VHL on tumor cells and of HLADR on myeloid cells and contain Th1-like CD4+ T cells. Medium ecotypes have the highest endothelial cell density and various immune-to-tumor interactions. Multi-omic characterization of these ecotypes using targeted genomic sequencing and metabolic imaging reveals distinct genomic and metabolic features, including BAP1 mutations in Poor and VHL monodriver/wild-type status in Favorable patients. We show that deep learning allows ecotype prediction directly from standard pathology H&E images. We validate the ecotypes and their associated molecular characteristics with orthogonal omics data across five clinical cohorts and more than 2,500 patients. These analyses highlight an overall survival benefit for Medium patients treated with immunotherapy. In summary, our study distills the survival-relevant information encoded in the ccRCC tumor microenvironment into prognostic survival ecotypes, which may inform clinical decision making in the future.

Chimeric antigen receptor (CAR)-modified V{delta}2 T cells are an attractive therapeutic cell platform for cancer immunotherapy. However, their clinical efficacy is limited by short in vivo persistence due to insufficient cytokine support and high susceptibility to activation-induced cell death (AICD). Through comparison of membrane-bound (mb) cytokines, we identified mbIL-18 to support superior anti-tumor activity of CAR-V{delta}2 T cells in vitro and in vivo. To reduce constitutive surface exposure of IL-18 and enable antigen-driven signal 3, we fused MyD88 - the key IL-18R signaling mediator - to an extracellular domain of Fas (Fas88). Antigen stimulation-induced FasL engagement of Fas88 triggered IL-18 signaling while simultaneously protecting V{delta}2 T cells from AICD. Fas88-armed human CAR-V{delta}2 T cells produced superior yet stimulation-dependent in vivo expansion and functional persistence in xenograft models of hematologic and solid malignancies. Together, these findings highlight the importance of IL-18 signaling and AICD resistance for CAR-V{delta}2 T cell activity, enabling a single-transgene modification to limit inflammatory risk and facilitate clinical translation.

Breast cancer is a highly heterogeneous disease shaped by dynamic interactions between malignant cells, immune infiltrates, stromal compartments, and the extracellular matrix. Among the molecular regulators of these interactions, cysteine cathepsins and legumain have emerged as important proteases involved in tissue remodeling, immune regulation, and tumor progression, yet their distribution and functional status across human breast cancer ecosystems remain insufficiently defined. Here, we performed an integrated protease-centric analysis of breast cancer specimens from 66 patients using high-dimensional single-cell mass cytometry of matched peripheral blood and tumor samples, imaging mass cytometry of intact tissues, and activity-based TOF probes for in situ detection of active proteases. Systemic immune profiling identified two patient clusters associated primarily with neoadjuvant therapy and tumor grade, accompanied by distinct cytokine and circulating protease patterns. In tumors, single-cell analysis revealed pronounced interpatient heterogeneity in tissue architecture and immune infiltration, while protease profiling uncovered reproducible cell type-associated modules, including cathepsin B/L-cystatin C and legumain-cystatin E/M axes. Cathepsins B and L were prominent in tumor-infiltrating immune cells and variably expressed in epithelial cells, whereas cathepsin D showed broader tumor distribution and cathepsin S remained more restricted. In epithelial cells, HER2 expression did not consistently coincide with high cathepsin B or L abundance, enabling identification of a limited subgroup of patients with combined HER2-high/protease-high states relevant to protease-cleavable antibody-drug conjugates. Spatial imaging further localized cathepsins B and D to tumor-stroma interfaces and macrophage-rich niches, and activity-based IMC confirmed the presence of catalytically active cathepsin B in human breast tumor tissue. Together, these findings define cysteine cathepsins as spatially and cellularly organized components of breast tumor ecosystems and provide a framework for protease-informed patient stratification and biomarker-protease pairing in targeted therapy.

Classical and basal-like transcriptional subtypes of pancreatic ductal adenocarcinoma (PDAC) are prognostic and may predict response to different chemotherapy regimens and RAS inhibitors. Current subtyping methods rely on tissue biopsies and remain challenging to integrate into clinical workflows. Herein, we present a novel approach for non-invasive subtyping of PDAC based on epigenomic profiling of circulating tumor DNA (ctDNA). In a multi-omics cohort of patient-derived xenografts, we identify highly recurrent regulatory elements associated with classical and basal-like PDAC. We then demonstrate that these epigenomic signatures can identify PDAC subtype from plasma epigenomic profiling in a multi-institutional cohort of patients with metastatic PDAC and integrate information from circulating histone modifications and DNA methylation to develop the Pancreatic Integrated Epigenomic Score (PIES). PIES is concordant with tissue-based labels and captures transcriptional subtype heterogeneity observed within biopsies. Furthermore, it improves prognostication over tissue-based subtyping suggestive of the recovery of ground truth tumor biology from plasma ctDNA. Our work provides a proof-of-concept for a circulating biomarker that enables transcriptional subtyping and informs therapeutic decisions in pancreatic cancer.

Effective predictive biomarkers for immune checkpoint inhibitor (ICI) therapy remain an unmet need across solid tumors. Here, we present an integrated spatial proteomics workflow that combines in situ proximity ligation assay with multiplexed immunofluorescence to directly resolve PD-1/PD-L1 signaling events at the level of defined cellular phenotypes and their spatial organization within intact tumor tissue. Applied as a proof-of-concept to tumor samples from patients with metastatic urothelial carcinoma treated with pembrolizumab, this approach reveals that PD-1/PD-L1 interactions specifically involving cytotoxic CD8 T cells are significantly enriched in complete responders, while such interactions are rare in patients with progressive disease. This interaction-defined T cell subset achieves superior discrimination of clinical response compared to single-marker PD-L1 expression or immune cell abundance alone. By integrating direct detection of protein-protein interactions with high-dimensional single-cell phenotyping, our workflow provides a mechanistically informed, spatially resolved biomarker of functional immune engagement. Beyond urothelial carcinoma, this platform establishes a generalizable framework for translating spatial signaling biology into predictive tools for immunotherapy response across tumor types. One Sentence SummaryIn situ detection of PD1/PD-L1 interactions between cancer cells and cytotoxic T cells predicts response to immunotherapy in urothelial carcinoma

Adoptive cell therapy (ACT) in solid tumors is limited by tumor microenvironment (TME)-imposed resistance mechanisms that are inadequately addressed by conventional systems. We developed tissue-preserving patient-derived explants (PDEs) from lung and ovarian cancer to interrogate redirected T cell immunity in intact human tissue. Using mesothelin-targeting bispecific T cell engager (BiTE(R), Amgen trademark)-secreting T cells, we observed antigen-dependent but heterogeneous responses across lesions. An integrated ex vivo response score stratified responder and non-responder TMEs, revealing that resistance associates with reduced antigen density, stromal dominance, and limited myeloid licensing rather than baseline lymphocyte abundance. Elevated prostaglandin E2 (PGE2) inversely correlated with BiTE-induced T cell activation, identifying the COX/PGE2 axis as a tissue-imposed constraint. COX inhibition amplified interferon-driven immune programs enhanced intratumoral CD8 infiltration, and increased tumor-restricted apoptosis. Spatial transcriptomics localized these effects to tumor-proximal immune hubs in responders, whereas non-responders remained stromally insulated. These findings position PDEs as human-based new approach methodologies enabling combinatorial ACT pharmacodynamics and stratification. Statement of significancePatient-derived explants provide a human-based new approach methodology to interrogate adoptive immunotherapy pharmacodynamics within intact tumor microenvironments in NSCLC and HGSOC. We uncover a COX/PGE2-mediated tissue ceiling that limits BiTE-driven T cell function and demonstrate that COX inhibition reactivates tumor-proximal immune hubs to enhance intratumoral CD8 infiltration and tumor-restricted apoptosis, informing patient stratification and rational combinations.

Antibody-drug conjugates (ADCs) require high and homogeneous target expression for optimal efficacy, yet the spatial, temporal, and cellular heterogeneity of clinically approved ADC targets in high-grade serous ovarian cancer (HGSC) remains incompletely defined. We analyzed bulk RNA-sequencing, single-cell RNA-sequencing, and whole-genome sequencing data from 867 samples across 304 patients enrolled in the real-world DECIDER cohort to systematically evaluate 11 approved ADC targets. FOLR1, TACSTD2, and ERBB2 emerged as highly expressed candidates. Inter-patient variability exceeded intra-patient heterogeneity, which further decreased following neoadjuvant chemotherapy. Target expression was highly concordant across anatomical sites and largely stable from diagnosis to relapse. Single-cell RNA-sequencing results revealed that TACSTD2 and FOLR1 showed the most frequent cancer cell-restricted expression. In rare cases of gene amplification, ERBB2 and F3 emerged as potential targets alongside TACSTD2 and FOLR1. Overall, 80% of patients displayed homogeneous expression of at least one actionable target, with frequent co-expression of TACSTD2 and FOLR1. These findings indicate that ADC target expression in HGSC is broadly stable across space and time and support the prioritization and strategic integration of TACSTD2- and FOLR1-directed ADCs in this disease.

Anti-CD19 chimeric antigen receptor (CAR) T-cell therapy can induce durable remissions in patients with large B-cell lymphoma (LBCL), yet outcomes remain variable. Reliable pre-treatment predictors of durable response remain limited, leaving a critical gap in patient management. To address this, we profiled pre-treatment plasma cell-free RNA (cfRNA) from 91 LBCL patients treated with axicabtagene ciloleucel (axi-cel, Yescarta) across three independent cohorts. We first demonstrated that signatures of "lymph node-like" tumor microenvironments (TMEs), previously identified in tumor biopsies and shown to correlate with favorable outcomes, are specifically elevated in the pre-treatment plasma cfRNA of responders, but not in matched peripheral blood mononuclear cells (PBMCs). These observations indicate that cfRNA captures TME tissue-derived signals not reflected in circulating immune cells. Next, using unbiased approaches, we identified additional cfRNA signatures associated with one-year clinical outcomes that capture the underlying biological landscape of treatment response. Collectively, these findings support pre-treatment plasma cfRNA as a minimally invasive surrogate of TME state to prospectively inform durable CAR T-cell therapy outcomes and guide risk stratification and TME-modulating adjunct therapies.

The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma. Initial bulk transcriptomic and immunohistochemistry analyses suggested links between immune activation, tissue remodeling, and resistance pathways such as transforming growth factor {beta} that were associated with clinical outcome. To further characterize spatial and phenotypic changes at high resolution, artificial intelligence-assisted digital image analysis of hematoxylin and eosin sections and spatial transcriptomics were performed on paired tissue samples. In baseline samples, cells residing in lymphoid aggregates and tertiary lymphoid structures were more abundant in stable disease than in relapse and exhibited gene expression programs associated with improved survival in urothelial carcinoma. Most spatial features reflected shared pharmacodynamic changes between stable disease and relapse; however, carcinoma-endothelial adjacency was reduced significantly following treatment and differed between groups, accompanied by distinct transcriptional programs. Together, these findings indicate that atezolizumab induces localized immune and stromal remodeling within the tumor microenvironment, while non-response despite immune expansion is associated with persistent spatial immune exclusion and carcinoma-endothelial adjacency. Spatial and phenotypic biomarkers identified here may inform rational combination strategies for immune checkpoint inhibitor-refractory urothelial carcinoma.

Colorectal cancer (CRC) remains a major cause of cancer mortality, with limited options for poor-prognosis subtypes such as CMS4. Antigen-targeted therapies show promise but tend to fail due to inadequate target selection and insufficient patient stratification. Effective prioritization requires large harmonized data capturing CRC heterogeneity - a resource that is currently lacking. To address this need, we built a harmonized multi-omic CRC knowledge base and applied a scalable discovery pipeline to identify antigen targets specifically associated with CMS4 biology and with strong translational potential. We constructed a harmonized CRC atlas by integrating 79 transcriptomics datasets (5,033 tumors, 161 normal samples) using proprietary AI-powered data scouting, integration, and curation technologies. Consensus Molecular Subtypes (CMS) were inferred to capture CMS4-specific expression patterns and this atlas was then combined with 3 bulk RNA-seq reference datasets, 2 single-cell atlases, and 8 protein annotation databases to form a unified multi-omic CRC knowledge base of unmatched scale. From this integrated system, we identified genes differentially expressed in CMS4 patients encoding druggable cell-surface proteins, which we then prioritized using a weighted efficacy- and safety-based scoring model. We identified 236 CMS4-enriched candidates, including 124 not detectable at the CRC-wide level, demonstrating the added resolution gained through subtype stratification. Recovery of known investigational CRC (LGR5, MET, TACSTD2) and CMS4-associated targets of clinical emerging interest (PDGFRB, ALK5/TGFBR1, FAP) support the biological and methodological validity of our approach. Benchmarking against thresholds from FDA-approved pan-cancer targets and terminated trials identified 32 candidates with comparable or superior therapeutic profiles. Among these, 11 were enriched for CMS4-defining pathways, including epithelial-mesenchymal transition, angiogenesis, and stromal invasion, and 5 showed strong profile similarity to established CRC and CMS4 benchmarks. After extensive data exploration, particularly promising candidates were shortlisted for further validation. This work shows that CMS4-focused molecular stratification, when combined with an unprecedentedly large harmonized multi-omic knowledge base, yields a refined set of antigen candidates with enhanced specificity, safety, and biological relevance. The prioritized targets illustrate the power of subtype-resolved discovery to uncover clinically actionable insights. Our pipelines modular design can extend to other tumor contexts, offering a robust foundation for accelerating targeted therapy development.

Resistance to systemic therapy is a major unmet challenge in pancreatic cancer. To identify potential mechanisms of resistance, we developed a novel 3D pipeline in clinical samples that uses deep learning to classify sensitive and persistent tumor cell populations based on morphological features, enabling subsequent molecular characterization of intratumoral heterogeneity. We applied this automated 3D pipeline to a cohort of human pancreatic cancer samples treated with neoadjuvant chemotherapy, identifying heterogeneity in response to therapy both between and within tumors. Application of spatial proteomics to these sensitive and persistent regions identified enhanced epithelial-to-mesenchymal transition and non-classical cell states in persistent cells, confirming our morphological classification. Integration of spatial transcriptomics in multiple pancreatic cancer cohorts associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy. Our validated 3D multi-omic pipeline is now poised for application to clinical trials, enabling discovery of resistance mechanisms and design of new therapeutic combinations to circumvent resistance. Statement of significanceWe developed a novel 3D multi-omic pipeline to identify mechanisms of resistance to chemotherapy in clinical samples. This approach associated fibroblast-cancer crosstalk via syndecans with resistance to cytotoxic therapy and is poised for broader application in neoadjuvant clinical trials.

Glycosylation is critical to biopharmaceutical activity, stability, and pharmacokinetics. While production cells can be engineered to produce better protein glycoforms, glycans decorate thousands of host cell proteins, and it remains unclear how glycoengineering impacts the host cell. To decipher the cell response to glycoengineering, we studied a library of 166 glycoengineered CHO-K1 cell clones representing 54 different glycosyltransferase modifications. Through integrated analysis of glycomics, RNA-Seq, and phenotypic data, we discovered that glycoengineered mutants clustered into three distinct groups (wild-type-like, Moderate, and Substantial) based on their glycosylation patterns. Different glycosyltransferase families exhibited distinct phenotypic signatures: St3gal modifications increased growth rate and cell density, B4galt knockouts affected cell size, and Mgat knockouts enhanced cell viability. Notably, we found specific cellular reprogramming associated with each glycosyltransferase family, including alterations in energy metabolism, stress responses, and DNA repair mechanisms. These findings were validated in an independent set of 30 glycoengineered CHO-S cell lines, expressing a panel of 10 recombinant proteins. Our extensive analysis reveals phenotypic changes resulting from glycoengineering, identifies their molecular bases, and provides crucial insights for controlling glycosylation during therapeutic protein production.

Barretts esophagus (BE) is the precursor lesion of esophageal adenocarcinoma (EAC). It affects approximately 5% of adults in the United States and significantly increases the risk of developing EAC. However, current surveillance strategies cannot reliably distinguish patients who will progress from those who will remain stable. Direct studies of progressor BE are extremely limited due to availability of tissue with known progression outcomes, and have largely been restricted to genomic profiling approaches. The premalignant cellular landscape of progressor BE remains poorly understood. Here, we used complementary spatial transcriptomic and proteomic imaging to profile 34 non-dysplastic BE patients under endoscopic surveillance, including those who subsequently progressed to dysplasia or EAC, termed "Progressors" and those who remained stable, termed "Non-progressors". Transcriptomics based Xenium analysis captured 974,604 cells across 70 whole-biopsy regions, while protein based imaging mass cytometry profiled 372,242 cells across 119 selected regions. FUME-TCRseq further quantified T cell clonotypes from matched tissues scrolls. Cellular composition was generally similar between Progressors and Non-progressors. However, Progressors showed increased intestinal Barretts columnar cells, B cells and gastric progenitor-like cells, together with enhanced immune-epithelial interactions, whereas Non-progressors retained coordinated stromal organization. Spatial interaction features strongly outperformed cell composition and density for progression prediction. Combined spatial interaction model achieved an area under the curve (AUC) of 0.97, compared with 0.62 and 0.68 for comparison and density alone. Complementary imaging mass cytometry further resolved the underlying immune programs, identifying cytotoxic and antigen presenting myeloid features enriched in progressors, and CD56 associated memory T cell interactions enriched in non progressors. Together, these findings support a model that BE progression is driven by progressive remodeling of epithelial-immune-stromal architecture rather than emergence of distinct dysplasia-like cell subsets. Increased T cell clonal diversity and recruitment of cytotoxic and antigen-presenting immune niches may also reflect an evolving response to genomic alteration prior to dysplasia. These results establish spatial tissue architecture, rather than specific cell types, captures progression associated microenvironmental states in BE and provides a framework for spatially informed patient stratification and early cancer risk assessment.

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, but they can also induce immune-related adverse events (irAEs). Checkpoint inhibitor-induced liver injury (ChILI) is among the most frequent irAEs, yet its pathophysiology remains poorly understood. Here, we assembled a cohort of liver biopsies from cancer patients with ChILI and used a multi-modal analysis integrating single-cell spatial proteomics, bulk T cell receptor (TCR) sequencing and single-cell spatial transcriptomics to construct the first single-cell spatial atlas of ChILI. Integrating bulk and spatial TCR analyses revealed expanded T cell clones with a cytotoxic CD8+ phenotype that were shared between the liver and tumour. Intercellular communication analyses further indicated close interactions between the shared T cell clones and macrophages involving CCL5-CCR1 signalling. Our work provides in situ evidence of tumour-associated T cell contributions to ChILI. Furthermore, it establishes a framework for gaining mechanistic insights into ChILI and identifying therapeutic targets.

Prostate cancer remains a major health burden with limited success in immune-targeted therapies. To identify immune-cell-specific therapeutic targets, we integrated single-cell cis-eQTL data across 14 immune cell types, bulk eQTLs, GWAS summary statistics from PRACTICAL and FinnGen, and single-cell RNA-seq data from prostate tumors. Using Mendelian randomization and Bayesian colocalization, we prioritized 80 causal eGenes with shared genetic signals, especially in CD4 and CD8 T cells. Functional analyses revealed enrichment in immune-related pathways such as antigen processing and cytokine signaling. Meta-analysis validated 52 robust eGenes across cohorts. Single-cell transcriptomics confirmed cell-type-specific expression of key genes including HLA-DQA2, TXN, and COX6B1 within the tumor microenvironment. Drug repurposing analysis identified potential therapeutic targets such as IGF1R and FAAH, with known drug interactions mapped via DrugBank and STRING. Our integrative framework highlights immune-cell-specific genetic drivers and actionable targets in prostate cancer, offering a high-resolution resource for precision immunotherapy development.

Intravesical Bacillus Calmette-Guerin (BCG) immunotherapy remains the standard treatment for intermediate and high-risk non-muscle invasive bladder cancer (NMIBC), yet more than half of the patients do not respond and experience recurrence or progression. BCG induced humoral immune responses remain poorly defined in patients with NMIBC. Building upon our previous findings showing the pathogenic role of atypical B cells (ABCs) in cancer progression, and following repeated intravesical treatment with BCG, in an aging murine model of bladder cancer, we conducted a longitudinal study to characterize B cell associated local and systemic responses in 45 patients (37 males and 8 females) with high-risk NMIBC who underwent treatment with BCG. Peripheral B cell immune phenotyping, B cell single-cell transcriptomics, spatial multi-omics, and systemic proteomics were performed. We identified expansion of circulating ABCs following the 4th BCG instillation, as a defining feature of patients who experienced early recurrence following BCG therapy. Spatial mapping of corresponding pre- and post-BCG recurrent tumors, at single cell transcriptomic and proteomic levels, revealed preferential enrichment of ABCs within tumor-adjacent stroma and tertiary lymphoid structures, where they co-localized with PD-1 B cells, regulatory T cells, and CD163 macrophages, forming immunosuppressive niches. BCG non-responders exhibited IgG skewed antibody responses at both local and systemic levels with expanded IgG autoantibody repertoires, progressive IgG reactivity against BCG antigens, and higher IgG deposition within the tumor microenvironment. Independent validation in tumors from two independent cohorts (total n = 409) of patients treated with BCG immunotherapy, revealed a significant association between high expression of the ABC specific transcript, FCRL5, and shorter recurrence and progression free survival. Findings from this study demonstrate that a BCG unresponsive state arises within a pre-existing ABC-dominated immune landscape that is further amplified during repeated BCG instillations. Our study identifies a novel role of ABCs as key regulators of local and systemic humoral immune dysfunction in high-risk NMIBC, and highlights ABC signatures as a potential predictive biomarker of response to BCG.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/717853v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@a696f3org.highwire.dtl.DTLVardef@1005c11org.highwire.dtl.DTLVardef@9c65bborg.highwire.dtl.DTLVardef@1dafb2d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG This study establishes an integrative framework that combines paired mRNA/miRNA profiling with immune microenvironmental features to clarify how EML4-ALK fusions shape transcriptomic and post-transcriptional networks in Non-small cell lung cancer (NSCLC). Using paired mRNA-seq and miRNA-seq data generated from the same patients, we compared fusion-positive and fusion-negative NSCLC across three interconnected layers: (i) transcriptome architecture, including differential expression, pathway, and network analyses; (ii) the miRNA-mRNA regulatory axis, encompassing dysregulated miRNAs, target repression and sponging, and fusion-specific regulatory pairs; and (iii) the tumor microenvironment, with emphasis on immune and stromal infiltration, particularly cancer-associated fibroblast (CAF)-linked extracellular matrix (ECM) and adhesion programs. Our analyses revealed a distinct reprogramming pattern in fusion-positive NSCLC, marked by activation of metabolic and proteostasis pathways, including N-glycan metabolism coupled to ER export, together with attenuation of immune-stromal communication, adhesion, ECM, calcium signaling, and PI3K/VEGF-axis transcription relative to fusion-negative NSCLC. We also identified fusion-associated microRNA perturbations, including an exclusively upregulated miR-3065-centered regulatory hub predicted to repress ECM- and adhesion-related genes (PDGFRB, CTSK, COL4A2, SPARC, FBN1, and LUM) in fusion-positive tumors, in contrast to broader miRNA network rewiring in fusion-negative tumors targeting ciliary and mitotic hubs. Tumor microenvironment analysis further distinguished the subtypes, with fusion-positive tumors showing reduced CAF infiltration relative to fusion-negative tumors and concordant gene-CAF associations. By linking mechanistic insight with candidate biomarkers and targetable pathway nodes, this work provides a basis for precision strategies in both fusion-positive and fusion-negative cohorts and broadens the therapeutic perspective beyond kinase inhibition alone.

Meningiomas are the most common primary central nervous system tumors in adults, posing a significant burden to society. Although a large percentage of lower-grade meningiomas are curable by surgery or radiation alone, high-grade and a subset of low-grade meningiomas demonstrate recurrences and complications from treatment. Systemic therapies for meningioma remain ineffective, and no targeted treatments are approved. Despite the central role of YAP1/TAZ-TEAD signaling in NF2-deficient/mutant tumors, no studies have systematically examined TEAD inhibition across molecularly defined meningioma subtypes or investigated mechanisms of resistance in this disease. We have recently shown that YAP1/TAZ signaling is an oncogenic driver of meningioma. Here, using established and patient-derived meningioma cell lines, we demonstrate that genetic ablation of YAP1/TAZ suppresses growth in both NF2 mutant and NF2 wild type cell lines, establishing YAP1/TAZ-TEAD signaling as a shared oncogenic dependency. Pharmacologic TEAD inhibition suppressed growth of benign NF2 mutant and a subset of higher-grade NF2 mutant meningiomas, whereas NF2 wild type meningiomas were generally more resistant. RNA-Seq and Western Blot analysis identified compensatory activation of MEK-ERK, mTOR-S6, and FAK signaling in resistant lines exhibit. Importantly, co-targeting these pathways was able to overcome resistance to TEADi and was superior to MEK/mTOR/FAK inhibition alone. These studies provide a compelling proof-of-concept that TEADi represents a novel therapeutic vulnerability in meningioma and reveal adaptive signaling responses that can be therapeutically exploited.

HighlightsO_LIA pan-cancer proteomic atlas defines the UPS architecture across human cancers C_LIO_LIGenome-wide pQTLs reveal mutation-driven UPS remodeling C_LIO_LILineage- and genotype-specific vulnerabilities created by UPS rewiring C_LIO_LIE3 ligase specificity scores to guide rational design of targeted protein degradation C_LIO_LIAn interactive platform, UbiDash, enables UPS-focused proteogenomic exploration C_LI Components of the Ubiquitin Proteasome System (UPS) are attractive candidates for targeted protein degradation therapies owing to their key roles in maintaining protein homeostasis in healthy and malignant cells. How cancer driver mutations rewire UPS components to support tumor growth and survival remains incompletely understood. By mapping tissue- and cancer-specific expression of UPS components across more than 20 tissues and 10 tumor types using harmonized multiomic datasets, we present an integrated pan-cancer proteogenomic analysis focused on E3 ubiquitin ligases. These analyses uncovered (1) mutation-associated UPS protein level changes; (2) clinically actionable E3s based on recurrent alterations, tissue-enriched expression, and prognostic value; and (3) E3 regulatory networks based on co-expression, co-dependency, and protein-protein interactions. We also introduce UbiDash, an interactive platform for exploring UPS alterations across cancers. This study identifies clinically relevant E3s and mutation-defined proteostatic dependencies and provides resource for mechanistic insight and therapeutic prioritization of UPS components in cancer.