搜索结果：Translational oncology

Accurate detection of KRAS codon mutations is essential for precision oncology in colorectal cancer (CRC), yet conventional liquid biopsy methods often lack sufficient sensitivity for rare ctDNA variants, particularly in early diseases. We developed a three-dimensional (3D) plasmonic KRAS microarray integrating blocked recombinase polymerase amplification with plasmon-enhanced fluorescence. Quencher-modified blocking probes suppress wild-type DNA while selectively enabling mutant signal amplification. A single primer-probe set per codon allows comprehensive detection of all substitutions within KRAS codons 12/13, 61, and 146. The platform achieved detection down to 1 fM by direct hybridization and 100 zM after blocked amplification, exceeding conventional PCR and next-generation sequencing sensitivity. Codon-level specificity was validated in CRC cell lines, with distinct signals for each mutation. Clinical analysis of 58 patients showed 100% concordance between tissue, plasma, and urine in mutation-positive malignant cases when sufficient input was available, indicating accurate reflection of tumor profiles. In benign tumors, detection was rare despite tissue mutations, likely due to limited ctDNA release.This plasmonic microarray enables ultra-sensitive, specific, and non-invasive detection, supporting early diagnosis, minimal residual disease monitoring, and longitudinal CRC management.

Skin cancer carries a significant global health concern; the incidence is further rising due to environmental exposure and genetic predisposition. In this chapter, nanotechnology is explored in the context of its practical and clinical importance for skin cancer diagnosis, treatment, and prevention. It discusses, through real-world applications and case-based insights, how nanotechnology played a role in precise drug delivery, improved immunohistochemistry (i.e., for diagnosis and marking cells), and the manufacture of diagnostic tools for early detection. The manuscript emphasizes the use of nano enabled strategies like nanovaccines that modulate the immune system against tumor antigens, nano biosensors/ biomarker detectors, nano photothermal and photodynamic therapies. Furthermore, preventive nano-devices for high-risk people are presented in terms of wearable nano-devices that monitor ultraviolet exposure. Current literature and clinical outcomes allow this to be supported for each application, showcasing the integration of advanced materials science with oncology and how this benefits patient care. The chapter bridges theoretical innovation to clinical utility in dermatologic oncology through the example of these translational advances.

Extrachromosomal DNA (ecDNA) constitutes a principal factor in the amplification of oncogenes and the progression of tumors in solid malignancies. This review synthesizes emerging mechanistic, genomic, and immunologic evidence across multiple tumor types, including glioblastoma, lung, breast, gastrointestinal, hepatobiliary, urothelial, prostate, gynecologic, pediatric, and head-and-neck cancers, with the goal of clarifying the role of ecDNA in immune escape and therapy resistance and outlining its translational implications for precision oncology. ecDNA comprises substantial acentromeric circular elements that serve as transcriptional hubs, modulate enhancer-promoter interactions, and undergo dynamic copy-number cycling, thereby fostering intratumoral heterogeneity and resistance to therapy. Recurrent oncogenic cargos, including epidermal growth factor receptor (EGFR), v-myc avian myelocytomatosis viral oncogene homolog (MYC), erb-b2 receptor tyrosine kinase 2, also known as human epidermal growth factor receptor 2 (ERBB2/HER2), and cyclin D1 (CCND1), are frequently located in ecDNA. They can interconvert with intrachromosomal homogeneously staining regions (HSRs) under treatment pressure. Emerging evidence links ecDNA to an immune-cold phenotype, characterized by downregulation of antigen presentation and decreased responsiveness to immune checkpoint inhibitors. We further emphasize diagnostic and translational methodologies that incorporate ecDNA detection through liquid biopsy and the spatial mapping of tumor topology. Finally, we propose a comprehensive clinical implementation framework that integrates ecDNA profiling, longitudinal monitoring, and immune microenvironment assessment to guide precision therapy. Gaining a deeper understanding of ecDNA biology has the potential to ultimately transform it from merely a prognostic biomarker into a targetable element within cancer therapy.

Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the "black box" nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway-uniting computer scientists, clinicians, and regulatory bodies-required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.

The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways-including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades-have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback loops, shared intermediates, and compensatory activation, creating a complex network that enables tumor cells to adapt and metastasize. New developments in AI and ML have enabled modeling and prediction of these interactions for pathway discovery, mapping oncogenic crosstalk, predicting drug resistance and therapeutic responses, and complex data analysis. Novel technologies such as feature selection algorithms and convolutional neural networks have demonstrated immense translational potential to bridge computational predictions in cancer genomics with clinical applications. Similar models have also proven useful for learning from genomic datasets and reducing multidimensionality in heterogeneous multiomics data. As current AI/ML approaches continue to develop, it is also important to consider the limitations of batch effects, model generalizability, and potential bias in training datasets. This review aims to integrate the most recent AI and ML applications in uncovering the hidden interactions within oncogenic networks that drive tumorigenesis, heterogeneity, and resistance to therapies. Moreover, this review aims to synthesize the functionality of emerging computational methods that elucidate these insights, as well as the transformative implications of AI-guided systems biology on precision oncology and combinatorial therapies.

Cardiovascular disease (CVD) remains the leading cause of mortality and disability worldwide, imposing a substantial burden on individuals, families, and healthcare systems. Despite major advances in controlling conventional risk factors (e.g., blood pressure, glycaemia, and lipids), a considerable residual risk persists, highlighting the need to elucidate additional pathogenic mechanisms and to develop more effective preventive and therapeutic strategies. Accumulating experimental and clinical evidence indicates that immune dysregulation and chronic low-grade inflammation are not merely associated with CVD but actively drive disease progression-from lesion initiation to acute thrombotic events. These processes are further shaped by metabolic status, lifestyle factors, psychosocial stress, and environmental exposures, and age-related genetic immune changes such as clonal hematopoiesis of indeterminate potential (CHIP). Atherosclerosis, the predominant pathological substrate of most CVDs, is now widely recognized as a chronic immune-inflammatory disease. Emerging concepts including immunometabolic reprogramming, trained immunity(distinguished by central and peripheral subtypes), the thrombo-inflammatory axis, and allostatic load provide an integrative framework for understanding CVD as a systemic disorder. Here, we synthesize recent advances in innate and adaptive immune mechanisms, immunometabolic dysregulation, and inflammation-thrombosis crosstalk that collectively govern plaque formation, destabilization, and clinical events. We also discuss how lifestyle-related factors (e.g., diet, fasting, physical activity, and stress) may modulate long-term cardiovascular risk through trained immunity and inflammatory pathways, and we highlight progress in immune biomarkers and anti-inflammatory interventions, and the immunometabolic effects of modern cardiometabolic drugs (GLP-1 receptor agonists, SGLT2 inhibitors). Additionally, we elaborate on the translational potential of short chain fatty acid derivatives in reversing innate immune inflammatory memory, and clarify the distinct cardiovascular toxic mechanisms of immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T-cell (CAR-T) therapy in cardio-oncology. Conceptualizing CVD as a systemic immune-metabolic-inflammatory disease may facilitate improved risk stratification and inform precision prevention and treatment strategies.

Over the past several decades, immunotherapy has emerged as a transformative paradigm in oncology. Within this domain, vaccines targeting tumor-specific neoantigens represent one of the most advanced approaches, engineered to activate the host immune system and elicit potent, antigen-specific T-cell responses. By stimulating both CD8+ cytotoxic and CD4+ helper T cells, these vaccines enable highly selective tumor cell elimination while establishing durable immunological memory. Despite their promise, the rational development and clinical translation of neoantigen-based vaccines remain constrained by substantial challenges that limit their broad therapeutic impact. This review provides a comprehensive synthesis of the field, tracing the entire pipeline from the molecular origin and computational prediction of neoantigens to the design principles guiding vaccine formulation. It examines mechanisms of action across diverse platforms-including mRNA, peptide, and dendritic cell vaccines-and explores synergistic strategies that combine adjuvants or immune checkpoint blockade to enhance efficacy. In addition, we critically evaluate key barriers to success, such as immunosuppressive tumor microenvironments, T-cell dysfunction, and antigenic escape. Finally, we highlight recent clinical advances aimed at overcoming these barriers, thereby outlining a framework for optimizing neoantigen vaccine design to maximize their therapeutic potential in cancer treatment. Notably, encouraging progress has been reported in malignancies such as non-small cell lung cancer and melanoma, underscoring the translational promise of this strategy.

Sézary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8⁺ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n=26) and PCAECTCL (n=13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Chi-square or Fisher's exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. Conversational AI agents, AI-HOPE, were used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics.

Skin cancer is considered one of the most significant malignancies worldwide, arising primarily from epidermal keratinocytes or melanocytes and influenced by genetic, environmental, and lifestyle factors. Conventional therapies, including chemical treatments, chemotherapy, radiotherapy, and antibody immunotherapy, though most effective in the early stages, often present significant limitations including such as systemic toxicity, recurrence, drug resistance, and cosmetic or psychological impacts. In recent years, nanotechnology has emerged as a promising strategy to overcome these challenges by enabling targeted, efficient, and minimally invasive therapeutic approaches. Nanoparticles, with their tunable size, surface properties, and biocompatibility, facilitate site-specific drug delivery, improve solubility of poorly soluble agents, and prolong drug circulation time while minimizing off-target effects. Diverse nanocarrier systems-liposomes, niosomes, dendrimers, micelles, nanospheres, nanoemulsions, and metallic nanoparticles-have been investigated for skin cancer therapy, offering improved penetration across the stratum corneum, controlled release, and enhanced drug retention. Functional modifications such as PEGylation and ligand attachment further optimize stability, immune evasion, and receptor-mediated targeting. Moreover, nanotechnology integrates diagnostic and therapeutic potential through theranostic applications, enabling simultaneous imaging, monitoring, and treatment of skin malignancies. Despite regulatory and translational challenges, advancements in nanoparticle-based therapeutics represent a paradigm shift in precision dermatologic oncology, offering safer, more effective, and patient-friendly interventions. This chapter highlights recent progress, clinical perspectives, and future directions in nanotechnology-enabled skin cancer therapies.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with poor prognosis and rising incidence. Late detection and limited responsiveness to standard treatment translates into a 5-year overall survival of less than 12%. The pathology contributes to a desmoplastic tumor microenvironment that creates a physical barrier, leading to a dense, hypoxic environment that promotes further tumorigenesis, limited immunogenicity, and chemoresistance, resulting in a still significant translational gap in PDAC research. Feasible techniques to further elucidate tumorigenesis are indispensable because of the frequently limited predictive value of current preclinical models. PDAC organoids offer a powerful tool that can be rapidly generated from resected tumors and biopsies. This review summarizes the current technical and scientific knowledge and highlights the importance of the tumor microenvironment, the use of realistic oxygen conditions, and the role of the hypoxia-inducible factors. Additionally, various protocols based on different media and scaffolds are displayed, and it is illustrated how PDAC organoids can help to improve both diagnosis and treatment options. Finally, critical bottlenecks in modeling PDAC tumor-stromal interactions are identified, and integrated co-culture platforms are proposed as a promising solution for translational applications.

Nanotechnology has transformed healthcare, leading to the clinical adoption of numerous nanomedical products. To evaluate their clinical translation, we analyzed all trials registered on ClinicalTrials.gov using a novel nanomedicine lexicon developed through expert curation and generative AI. This approach identified 4114 nanomedical clinical trials (out of more than 500,000) forming the Nanomedical Clinical Trials (NanoCT) dataset. Our analysis reveals a 38 % rise in nanomedical trials in recent years. While oncology remains dominant (30 %), emerging applications-particularly in infectious diseases, driven by the rise of mRNA vaccines-demonstrate the field's expanding therapeutic scope. This diversification is further evidenced by the growing use of micelles, polymeric, and metallic nanoparticles, marking a shift from the dominance of liposomal formulations. Despite significant advancements, nanomedical trials account for only 0.8 % of all registered clinical trials, highlighting key translational challenges such as regulatory complexities, high production costs, and clinical design limitations. Addressing these barriers requires the establishment of a universally accepted nanomedical lexicon to enhance data harmonization, streamline regulatory pathways, and improve interdisciplinary communication. This comprehensive analysis provides critical insights into the trajectory of nanohealth, identifies obstacles to clinical translation, and outlines strategies to maximize its future impact in medicine.

Enfortumab vedotin (EV) is approved for the treatment of metastatic urothelial carcinoma (mUC), as monotherapy or in combination with immune checkpoint inhibitors (ICIs), following the results of recent practice-changing clinical trials, such as EV-301 and EV-302. However, EV-301 included only patients with Eastern Cooperative Oncology Group Performance Status (ECOG-PS) 0 or 1, while ECOG-PS 2 mUC patients were excluded. In clinical settings, the benefit of EV for this group of vulnerable patients remains a significant and yet unresolved question. The aim of our study was to evaluate the impact of ECOG-PS on survival outcomes in patients treated with EV using the ARON global real-world database. A total of 483 mUC patients with ECOG-PS 0-1 and 85 with ECOG-PS 2 and treated with EV were included. The coprimary endpoints were Overall Survival (OS) and Progression-Free Survival (PFS) to compare the clinical outcomes of mUC patients with ECOG-PS 2 versus ECOG-PS 0 or 1. The secondary endpoints included the comparison of OS and PFS in these two patient groups according to metastatic sites (liver, bone, lung, lymph nodes, brain, soft tissue). The median OS was 13.63 months (95% CI 11.9-15.57) and 6.34 months (95% CI 4.96-8.48) in mUC patients with ECOG-PS 0-1 and ECOG-PS 2, respectively. Patients with ECOG-PS 2 receiving EV reported statistically significantly shorter OS compared to those with ECOG-PS 0-1 (HR 2.24; 95% CI 1.64-3.06; p&#x2009;<&#x2009;0.001). The median PFS was 7.39 months (95% CI 6.60-8.04) and 3.98 months (95% CI 3.21-5.95) in mUC patients with ECOG-PS 0-1 and ECOG-PS 2, respectively. Patients with ECOG-PS 2 receiving EV reported statistically significantly shorter PFS compared to those with ECOG-PS 0-1 (HR 1.71; 95% CI 1.29-2.27; p&#x2009;<&#x2009;0.001). Similarly, shorter OS and PFS was observed in ECOG-PS 2 patients with liver, bone, lung, and lymph nodes metastases, while shorter PFS was associated with lymph nodes and bone metastases. Our analysis showed worse survival outcomes in pretreated mUC with ECOG-PS 2 receiving EV monotherapy; however, given the retrospective design and baseline imbalances between ECOG groups, these findings should not be interpreted as evidence of reduced intrinsic EV efficacy. The outcomes of EV monotherapy in ECOG-PS 2 patients remains uncertain and can only be inferred from non-randomized prospective trials and studies based on real-world evidence. Further studies and multicentric translational collaborations are fundamental to validate these findings.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type 2 diabetes (T2D) remains clinically challenging. We investigated whether plasma proteomic profiling combined with machine learning could discriminate these conditions. Plasma samples from individuals with PDAC (with and without DM), long-standing T2D, and controls were analyzed by MALDI-TOF mass spectrometry. Spectral features were processed through a nested cross-validation framework to prevent data leakage, and model interpretability was explored using SHAP values. In parallel, low-molecular-weight proteins were characterized by GeLC-MS followed by LC-MS/MS and differential abundance analysis. Machine learning models distinguished PDAC-associated DM from T2D with a balanced accuracy of 85%. Proteomic analyses identified distinct signatures in PDAC- associated DM, including downregulation of erythrocyte-related proteins and PPBP, and upregulation of acute-phase reactants such as FGA, CP, and SERPINA3. Treatment-na&#xef;ve cases displayed increased circulating epithelial and keratin-associated proteins, which were attenuated after therapy, suggesting dynamic tumor-related remodeling. These findings demonstrate that integrating MALDI-TOF profiling with machine learning can capture plasma signatures associated with PDAC-associated DM. Although exploratory, this approach supports further validation in prospective cohorts aimed at improving PDAC risk stratification among individuals with NODM. SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, primarily due to late-stage diagnosis. The frequent occurrence of new-onset diabetes mellitus (NODM) as a paraneoplastic syndrome offers a critical window for early detection. However, the clinical challenge of distinguishing PDAC-associated diabetes (PDAC-DM) from type 2 diabetes mellitus (T2D) has hindered the implementation of effective screening strategies. This study addresses this significant clinical problem by leveraging a multi-faceted proteomics approach. We demonstrate that the integration of MALDI-TOF mass spectrometry peptide profiling with machine learning algorithms can accurately discriminate PDAC-DM from T2D with 85% accuracy. Furthermore, we used LC-MS/MS to identify specific low molecular weight proteins that are differentially regulated between these conditions, providing a molecular basis for the observed discrimination. Our work is significant as it presents a novel, high-throughput pipeline for biomarker discovery that combines the scalability of MALDI-TOF with the analytical power of LC-MS/MS and machine learning. The identified plasma signatures hold strong translational potential to improve risk stratification in patients with new-onset diabetes, ultimately enabling earlier diagnosis of PDAC and improving patient survival prospects. This research directly contributes to the field of clinical proteomics by providing a robust methodological framework and candidate biomarkers for the early detection of one of oncology's most challenging diseases.

Nanotechnology has emerged as a promising frontier in the identification and treatment of skin cancer by offering innovative platforms for targeted drug administration, real-time imaging, and enhanced therapeutic efficacy. Though preclinical results are promising and scientific enthusiasm is rising, the shift of nanotechnological breakthroughs from research laboratories to clinical environments remains hampered. The main translational problems preventing the clinical acceptance of nanomedicine in skin cancer treatment are investigated in this chapter. It explores obstacles like manufacturing scalability, reproducibility, regulatory uncertainty, clinical trial design restrictions, financial limits, and intellectual property complexity. Moreover, the chapter describes strategic ways to get beyond these obstacles: multidisciplinary cooperation, regulatory harmonization, and the inclusion of digital technologies into development pipelines together with artificial intelligence (AI). This chapter seeks to give a complete knowledge of what it takes to propel nanotechnology beyond the bench and into pragmatic, patient-centred applications in oncology by closely analysing both the challenges and possible solutions.

Chimeric antigen receptor T (CAR-T) cell therapy has achieved transformative success in hematological malignancies; however, its translation to solid tumors remains severely limited by tumor heterogeneity, immunosuppressive microenvironments, and safety concerns such as on-target/off-tumor toxicity. A major contributor to these challenges is the lack of preclinical models capable of faithfully recapitulating human tumor architecture and tumor-immune interactions. Conventional two-dimensional cell cultures and animal models frequently fail to predict CAR-T efficacy, resistance, and toxicity observed in patients. Organoid technology, particularly patient-derived organoids (PDOs) and immune-integrated organoid systems, has emerged as a next-generation platform that bridges this translational gap. By preserving patient-specific genetic, phenotypic, and spatial heterogeneity, organoids provide a physiologically relevant and scalable system for interrogating CAR-T cell behavior in human-like tumor contexts. Recent advances in tumor-immune co-culture, vascularized organoids, and microfluidic organoid-on-a-chip platforms have further expanded their utility for dynamic assessment of CAR-T infiltration, cytotoxicity, cytokine release, and adaptive resistance mechanisms. In this review, we comprehensively examine how organoid-based models are reshaping the CAR-T development pipeline, spanning target discovery and validation, functional efficacy assessment, safety profiling, and optimization of combination therapies. We further discuss emerging applications of organoids as patient-specific "avatars" for personalized CAR-T selection and response prediction. Finally, we highlight current technical limitations and future bioengineering directions required to enable clinical translation. Collectively, organoid platforms represent a transformative tool for accelerating precision development of next-generation CAR-T cell therapies and advancing human-relevant immuno-oncology research.

The heterogeneity of the tumor microenvironment poses a significant challenge to the success of anti-cancer therapeutics. Consequently, there is an urgent need to generate comprehensive data to elucidate the mechanisms underlying tumor resistance and to inform the rational and systematic application of anti-cancer drugs to mitigate drug resistance. In-vitro tumor models based on established cell lines are widely employed in studying the mechanisms of action of drugs; however, these traditional models often fail to accurately recapitulate the complexity of native tumors, particularly in terms of the heterogenous and intricate tumor microenvironment, including cellular composition, intercellular communication, and interactions between cells and extracellular matrix. As a result, preclinical data often diverges from clinical outcomes. In recent years, the emergence of patient-derived three-dimensional (3D) models including spheroids, organoids, tumor-on-a-chip systems, and 3D bioprinting has offered promising alternatives for addressing these limitations and enhancing the predictive power of tumor drug screening. In this review, we explore the relationship between the complexity of the tumor microenvironment, tumor drug resistance, and then introduce the current biofabrication techniques enabling the reconstruction of 3D tumor models in vitro. We delve deeper into a myriad of applications of such models for a wide range of cancer indications. These models offer a morfailures andlly relevant platform for evaluating anti-cancer drugs with the potential to improve translational accuracy, reduce drug development failures, and accelerate the discovery of cancer therapies.

Stereotactic body radiation therapy (SBRT) provides excellent local control for localized prostate cancer (PC); however, systemic relapse remains the primary cause of mortality in high-risk patients, underscoring the need to understand and therapeutically address radiation-induced immune suppression. Here, we identify a previously unrecognized myeloid checkpoint pathway driven by GPNMB&#x207a; myeloid-derived suppressor cells (MDSCs) as a dominant systemic response to clinical SBRT and demonstrate a tractable strategy to counter it. We used paired peripheral blood samples from patients treated with SBRT to measure systemic MDSCs by flow cytometry, followed by ex vivo functional assays with patient PBMCs. For further characterization, we used a syngeneic PC RM-9 tumor model. RT-PCR and luciferase assays determined the mechanism observed. We observed a selective and reproducible expansion of GPNMB&#x207a; MDSCs accompanied by enhanced T-cell suppression. GPNMB blockade in patient's PBMCs rapidly and consistently restored T-cell activity, directly supporting the clinical feasibility of targeting this pathway. Likewise, in tumor-bearing mice, radiation upregulated GPNMB on MDSCs and its ligand SDC4 on tumor-infiltrating T cells. Therapeutically, combining anti-GPNMB antibody with radiation significantly improved local tumor control and reduced metastatic burden compared with radiation alone and outperformed PD-L1 blockade. Transcriptomic and mechanistic analyses identified MITF as a key regulator of radiation-induced GPNMB expression. Together, these findings define an actionable RT&#x202f;&#x2192;&#x202f;MDSC&#x202f;&#x2192;&#x202f;GPNMB myeloid checkpoint that suppresses T-cell immunity in prostate cancer and demonstrate that targeting this pathway reverses radiation-induced immune suppression across human and murine systems. This work establishes a strong translational rationale for integrating MDSC-targeted therapy with SBRT to improve systemic control in localized high-risk prostate cancer.

BRCA1-associated protein 1 (BAP1) is frequently inactivated in pleural mesothelioma and functions as a tumor suppressor through its deubiquitinating activity. In this study, we investigated the context-dependent interplay between BAP1 and ubiquitin-specific protease 1 (USP1) in mesothelioma cells, focusing on their roles in regulating FANCD2, cell proliferation, and DNA damage responses. Genetic suppression of USP1 selectively inhibited cell proliferation in BAP1-deficient mesothelioma cells, whereas reintroduction of wild-type BAP1 rescued this growth defect; notably, a catalytically inactive BAP1 mutant failed to do so, indicating that BAP1 deubiquitinase activity is required for this compensation. In contrast, depletion of FANCD2 suppressed cell proliferation irrespective of BAP1 status, underscoring the essential role of FANCD2 in mesothelioma cell survival. Although both BAP1 and USP1 were capable of deubiquitinating FANCD2 in vitro, USP1 suppression in mesothelioma cells did not provide clear biochemical evidence of altered FANCD2 ubiquitination. Instead, USP1 knockdown was associated with reduced FANCD2 transcript and protein levels, without markedly affecting FANCD2 mRNA stability. At the cellular level, USP1 depletion impaired FANCD2 focus formation and reduced its colocalization with &#x3b3;-H2AX in BAP1-deficient cells, consistent with defective DNA damage signaling. Despite these changes, homologous recombination (HR) efficiency was largely preserved, whereas non-homologous end joining activity was modestly increased upon USP1 suppression. Consistent with these in vitro findings, USP1 knockdown suppressed tumor growth in an intrathoracic xenograft model. Collectively, our results indicate that BAP1 and USP1 appear to regulate FANCD2 through distinct, context-dependent mechanisms, with USP1 primarily influencing FANCD2 expression and BAP1 modulating FANCD2 function at the post-translational level. Together, these findings identify USP1 as a context-dependent therapeutic vulnerability in BAP1-deficient mesothelioma and support a working model in which USP1-dependent maintenance of FANCD2 function becomes critical in the absence of functional BAP1.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related phenomenon. CHIP is prevalent in multiple myeloma (MM) patients, and several lines of investigations suggest it might be relevant for MM pathogenesis and clinical course. Phylogenetic studies indicate that CHIP and MM do not share a clonal origin. CHIP does not consistently affect survival in MM. However, CHIP has been associated with increased treatment-related toxicities. Recent single-cell RNA sequencing data suggest that CHIP is associated with a more inflammatory and immunosuppressive tumor microenvironment (TME), characterized by dysfunctional myeloid and T cells. Furthermore, growing evidence highlights how anti-MM therapies such as alkylators and immunomodulatory drugs can favor the expansion of pre-existing mutant clones. Collectively, these data suggest a bidirectional interplay in which CHIP, acting as a modifier of the TME, may amplify inflammation driven by MM plasma cells and therapy, ultimately affecting MM progression and therapeutic response, while treatment pressure may reshape CHIP clonal architecture. Understanding CHIP dynamics in the context of MM treatment is therefore crucial to optimize therapeutic strategies, anticipate toxicities, and guide tailored approaches. This review summarizes current evidence supporting a translational and clinical impact of CHIP in MM.