Cell Reports Medicine

Severe preeclampsia (sPE) is a major cause of maternal and fetal morbidity worldwide, yet its placental molecular heterogeneity remains poorly defined by current clinical diagnosis. To resolve the molecular architecture of sPE, here we integrated DNA methylation and proteomic profiling from a multi-ethnical cohort of 444 placentas from the Hawaiian Biorepository (HiBR), including 169 sPE cases, matched preterm controls and full-term controls. To address cellular heterogeneity in bulk placental tissue, we developed HOMED (Hierarchically Optimized Methylation Deconvolution), a single-cell-guided hierarchical framework for inferring placental cell-type composition from DNA methylation data. HOMED-adjusted integrative analyses identified extensive subtype-specific alterations involving hypoxia, angiogenesis, immune activation, trophoblast differentiation and metabolic remodeling. Molecular stratification revealed two reproducible sPE subtypes with divergent placental aging trajectories. One subtype exhibited a pre-mature placental state marked by accelerated placental aging, whereas the other displayed slower accelerated placental aging but a substantially increased risk of small-for-gestational-age birth (P = 0.028). These subtypes were independently replicated across six external cohorts and further supported by proteomic signatures achieving a classification accuracy of 0.88. Integrative epigenomic and proteomic analyses linked the growth-restricted subtype to hypoxia-associated glycolytic remodeling, suggesting distinct pathogenic mechanisms underlying clinically diagnosed sPE. Together, our findings redefine severe preeclampsia as a biologically heterogeneous placental disorder composed of molecularly distinct subtypes with divergent aging trajectories and fetal growth outcomes, providing a framework for mechanism-based stratification and precision obstetric medicine.

Alpha-gal Syndrome (AGS) is a potentially life-threatening allergy caused by an IgE-mediated immune response to galactose--1,3-galactose (alpha-gal), a carbohydrate epitope present in most mammalian meats. Currently, strict avoidance of mammalian meat remains the primary management strategy for affected individuals, and alpha-gal-free beef is not commercially available. Here, we leverage cultivated meat as a biotechnology plat-form to address this unmet clinical need by engineering alpha-gal-free bovine muscle cells. Using CRISPR/Cas9 genome editing, we disrupted GGTA1, the gene encoding 1,3-galactosyltransferase, in immortalized bovine satellite cells (iBSCs). High-efficiency editing produced clonal GGTA1 knockout iBSCs harboring a homozygous frameshift mutation. Flow cytometry and immunofluorescence confirmed loss of the alpha-gal epitope, while bulk RNA-seq indicated minimal disruption of global gene expression and preserved myogenic differentiation capacity. Importantly, lysates from GGTA1 knockout iBSCs elicited substantially reduced basophil activation in assays using plasma from a patient with AGS, indicating reduced basophil activation consistent with reduced allergenic potential. Together, these findings establish a proof of concept for engineering AGS-compatible cultivated meat and demonstrate the potential of cultivated meat technologies to address human health challenges. HIGHLIGHTS{circ} CRISPR/Cas9-mediated disruption of GGTA1 eliminated alpha-gal from bovine satellite cells {circ}GGTA1 knockout cells retained myogenic identity and differentiation capacity {circ}GGTA1 knockout reduced basophil activation in an alpha-gal syndrome immune assay {circ}Genome-edited bovine cells provide a proof of concept for AGS-compatible cultivated meat

To better understand how pregnancy impacts humoral immunity, we conducted an in-depth longitudinal analysis of the kinetics and characteristics of vaccine responses in a prospective cohort of pregnant and non-pregnant women. Humoral immune responses observed among pregnant participants who received the mRNA-delivered SARS-CoV-2 vaccination, including their effector functions, were in some cases marginally lower than those among non-pregnant controls, while prior infection was associated with some potentiation in humoral responses. Importantly, vaccine-induced antibodies were efficiently transferred across the placenta, providing the fetus with passive immunity and underscoring the dual benefit of maternal vaccination for both mother and neonate against COVID-19. Delayed induction of spike-specific IgG4 following the primary two-dose vaccination series was observed in vaccine recipients, independent of pregnancy status. In a subset (n=6) of pregnant women whose spike-specific serum IgG repertoires were extensively profiled at the clonotypic level over time as part of another study, we proteomically identified secreted IgG clonotypes that had class-switched to IgG4. Matching of these clonotypes detected as IgG4 to those defined as SARS-CoV-2 spike-specific revealed that, while a minority of total clonotypes, they were elicited early in the immunization series and tended to be more highly mutated, more prevalent, and more persistent than clonotypes in the serological repertoire that were not detected as IgG4. Consistent with the increase in secreted vaccine-specific IgG4 over time, but its poorer placental transfer, these clonotypes were detected at greater levels in maternal but not cord blood at the time of delivery as compared to 28 days post the second vaccine dose. These findings indicate some impact in the kinetics, characteristics, and functions of the humoral response that may be associated with pregnancy-related immune modulation. Conservation of the late class-switch recombination to IgG4 that has previously been associated with mRNA-based SARS-CoV-2 vaccines raises questions about how different immunological states and vaccine components influence short- and long-term characteristics of the humoral immune response.

Dexamethasone is widely used to control cerebral edema and inflammation in glioblastoma, but its benefits are limited by systemic toxicities and adverse prognostic associations. We evaluated local administration of dexamethasone via convection-enhanced delivery (CED) to maximize intratumoral anti-inflammatory effects by increasing local corticosteroid exposure while minimizing systemic exposure. In two glioma mouse models, continuous intraparenchymal infusion of dexamethasone was well tolerated with no adverse effects. Pharmacokinetic analyses supported preferential intratumoral distribution and reduced systemic exposure with CED compared with systemic dosing. Single-nucleus RNA sequencing (snRNA-seq) and immunohistochemistry showed attenuation of glioma-associated inflammation with downregulation of reactive microglial/macrophage programs and reduced tumor-infiltrating myeloid cells with a morphology consistent with a less activated state. Experiments in human induced pluripotent stem cell (iPSC)-derived microglia confirmed that dexamethasone directly suppresses inflammatory gene expression, indicating a conserved mechanism across species. This inflammatory suppression was recapitulated in both immortalized microglial (HMC3) and macrophage (THP1) cell lines. These findings suggest that localized dexamethasone delivered by CED reprograms the glioma immune microenvironment and achieves control of inflammation without the systemic adverse effects associated with standard systemic dexamethasone therapy. This clinically translatable strategy may improve symptom management and provide a platform for integrating local immunomodulation with future glioblastoma therapies.

Polycystic ovary syndrome (PCOS) is linked to adverse pregnancy outcomes and increased cardiometabolic risk in offspring, yet the placental mechanisms underlying these risks remain poorly understood. Metformin is prescribed during PCOS pregnancies despite limited mechanistic justification. Using multi-modal molecular analyses of placentas from healthy controls and women with PCOS randomized to placebo or metformin (PregMet trial), restricted to uncomplicated pregnancies, we characterized direct PCOS associated placental alterations independent of confounding complications. PCOS placentas showed transcriptional downregulation across multiple cell types and shifts in cell type proportions. Specifically, syncytiotrophoblasts exhibited reduced expression activity of growth hormone receptor signaling and glycosaminoglycan biosynthesis. Endothelial cells displayed diminished receptor tyrosine kinase pathway activity, including VEGFC, despite increased cell proportion and hypervascularity. Intercellular communication networks were globally suppressed, including reductions in PDGF signaling from Hofbauer cells to fibroblasts. Notably, metformin did not reverse most PCOS-associated molecular alterations and induced transcriptional changes correlated to birth weight and childhood BMI. These findings indicate that PCOS-associated placental features are driven by cell type specific dysregulation of growth factor, angiogenic signaling pathways that are largely unresponsive to metformin. This underscores the need to develop mechanism based, placenta targeted therapeutic alternatives for future pregnancy management.

BackgroundPrimary and secondary resistance to immune checkpoint blockade (ICB) remains a critical challenge in advanced melanoma. Oncolytic Viruses (OV) selectively lyse tumor cells while generating systemic anti-tumor immune responses with minimal side effects. Yet their clinical use is limited to refractory melanoma patients and are only given in combination with second-line ICB regimens. ICB can both help and hinder OV efficacy depending on the source of checkpoint interactions across the tumor-immune microenvironment (TiME). However, functional checkpoint interactions are typically inferred from gene or protein expression and rarely contextualized within myeloid- and antigen presenting cell-associated immune niches during OV therapy, despite these populations dominating melanoma TiMEs and serving as key regulators of anti-viral immunity. MethodsAn integrated multi-omics framework combining Nanostring GeoMx digital spatial profiling (DSP), COMET sequential immunofluorescence (seqIF) and functional oncology mapping (FuncOmap) was applied to melanoma patient tissues collected pre- and post-neoadjuvant Talimogene Laherparepvec (T-VEC) to characterize immune remodeling and directly quantify checkpoint interaction dynamics associated with pathologic responses to OV therapy. ResultsT-VEC induced broad lymphocyte- and myeloid-associated immune transcriptional activation across melanoma TiMEs; however, pathologic responses could not be defined by bulk transcriptomics or cellular deconvolution alone. COMET seqIF analysis identified that HSV-associated M1/APC-like tumor-associated macrophages (TAMs) were enriched in complete pathologic response (CR) tissues and were a major source of PD-1/PD-L1 interaction niches. While partial (PR) and non-pathologic response (NR) tissues retained melanoma-centered PD-1/PD-L1 interaction niches and were enriched for B cell and M2-like TAM populations. FuncOmap analysis indicated that post-T-VEC PD-1/PD-L1 interaction states were consistently elevated in tumor bed, but not in lymph node tissues, across all pathologic response groups. Suggesting that immune checkpoint interactions may benefit T-VEC therapeutic responses depending on their spatial and immune context relative to OV infection. ConclusionsThese findings highlight the importance of integrated transcriptomic and functional proteomic analyses for resolving the spatial distribution and functional status of immune niches during OV therapy. Resolving PD-1/PD-L1 interaction states to specific M1/APC-like TAM and B cell niches may define mechanisms of responses and resistance to OV therapy.

BRAF-targeted therapy (BRAFi/MEKi) and immune checkpoint blockade (anti-PD-1/anti-CTLA-4) have transformed the treatment of BRAF-mutant metastatic melanoma. While most patients who respond to targeted therapy eventually progress, a subset derives durable benefit, and biomarkers to identify this subset would inform optimal treatment selection. In this study, we analyzed pre-treatment tumor samples from a clinically annotated cohort of 155 patients with BRAF-mutant metastatic melanoma treated with first-line BRAFi/MEKi and followed for up to five years. We stratified patients into durable responders (PFS [&ge;] 24 months) and rapid progressors (PFS < 6 months with progression) and found that a global metric of tumor genomic heterogeneity, rather than individual gene alterations, distinguished these groups. Combining genomic heterogeneity with baseline tumor burden (e.g., lactate dehydrogenase (LDH) or radiographic lesion dimensions), we developed a parsimonious model that predicted durable responders with high precision and specificity. Notably, the analogous population of patients treated instead with immunotherapy were not durable responders, suggesting that the selected predictors of durable responders are targeted therapy specific. Spatial profiling of a subset of pre-treatment biopsies (n = 47) demonstrated that high intratumoral, but not peritumoral, CD8+ T-cell infiltration correlated with prolonged survival on BRAF-targeted therapy and served as an independent predictive factor when considered with genomic heterogeneity and features of clinical tumor burden. Together, these findings highlight the distinct baseline intrinsic and extrinsic features underlying durable response to BRAF-targeted therapy and support their potential implication in guiding treatment selection for patients with BRAF-mutant metastatic melanoma. One-Sentence SummaryIntegrated clinical, tumor genomic, and immune microenvironmental features predict durable responses to BRAF-targeted therapy.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly increasing worldwide, yet effective targeted therapies remain limited. To better understand the molecular mechanisms underlying MASLD, we performed an integrated proteogenomic analysis of human liver tissue. Using mass spectrometry, we quantified 2,744 proteins in 504 liver biopsies from the Quebec Obesity Biobank and examined changes across disease stages. To investigate causality, we integrated liver proteomics with RNA sequencing and genome-wide genotyping to map thousands of protein quantitative trait loci (pQTLs) and expression quantitative trait loci (eQTLs). These molecular data were combined with summary statistics from a meta-analysis of genome-wide association studies including 16,532 MASLD cases and 1,240,188 controls. Mendelian randomization and genetic colocalization analyses revealed that most proteins differentially expressed across MASLD stages were not causally implicated in disease risk, whereas several genetically predicted liver proteins showed evidence of causal effects. Among these, higher hepatic levels of the MTARC1 protein were causally associated with MASLD and hepatic fat accumulation. Phenome-wide analyses suggested that MTARC1 inhibition may reduce the risk of cirrhosis, hepatocellular carcinoma, and cholelithiasis while improving lipid profiles. Notably, the causal MTARC1 variant influenced liver protein levels but not gene expression. Genetic analyses also identified ERLIN1 and HSD17B13 as potential therapeutic targets. In contrast, eQTLs and pQTLs at other loci such as GCKR showed opposite effects on MASLD risk. These findings highlight the importance of integrating tissue proteomics with human genetics to distinguish biomarkers from causal drivers and to identify promising therapeutic targets for MASLD.

High grade serous ovarian cancer patients initially respond to platinum-based chemotherapy, but usually relapse within two years and ultimately develop therapy resistance. Management of response and effective clinical decisions are currently based on unspecific biomarkers and limited imaging techniques, illustrating the clear clinical need for reliable predictors of response. In this work, we evaluated the performance of patient-derived organoids generated from ascitic fluid and functionally tested in parallel to the patients clinical course, in the prediction of treatment response, and guiding clinical decision-making in a patient-specific manner. Ascites derived organoids reliably recapitulated the histological and molecular features of a paradigmatic HGSOC patient with an apparent dissociated response, and demonstrated chemoresistance months before laparoscopy confirmed persistent inoperable disease with poor pathological response. Drug screening identified alternative therapeutic options, while multi-omics provided additional insights into the tumor-specific biological features, to assist in the personalized clinical management in ovarian cancer.

Leptomeningeal disease (LMD) is a rapidly fatal complication of systemic cancer for which sensitive diagnostic tools and informative biomarkers remain limited. Here, we introduce CSF-Seq, a method for whole-transcriptome sequencing of cell-free RNA (cfRNA) from human cerebrospinal fluid (CSF), designed to enable molecular profiling of LMD and other central nervous system (CNS) conditions. Using a prospectively collected CSF biobank, we analyzed 125 samples spanning multiple pathologies, including breast and lung LMD, glioblastoma, traumatic brain injury, and non-cancer neurological controls. Through optimized RNA extraction, library preparation, and deep sequencing, CSF-Seq generated robust and reproducible transcriptome-wide profiles despite the low abundance and fragmentation of cfRNA in CSF. CSF transcriptomes exhibited disease-specific expression, separating LMD from non-cancer controls and from non-LMD cancers, independent of CSF collection modality. Tumor-associated epithelial transcripts, including CEACAM6 and MUC1, were consistently enriched in LMD samples, whereas immune and CNS-associated transcripts were broadly detected across disease states, consistent with contributions from both tumor and non-tumor sources. Cross-site processing of matched samples demonstrated high concordance, indicating preservation of sample-specific transcriptional signatures across independent workflows. Importantly, we identified a collection method- independent LMD gene expression signature that was significantly associated with overall survival, supporting its potential prognostic relevance. Together, these findings establish CSF-Seq as a technically robust and clinically informative platform for transcriptomic biomarker discovery in CNS metastatic disease, offering a minimally invasive approach for disease characterization, risk stratification, and longitudinal monitoring in patients with LMD.

The limiting step in immune-active drug development is increasingly not candidate generation but testing whether a candidate therapy is effective in a system that preserves tissue architecture, vascular exposure, multicellular interaction, and repeated pharmacodynamic sampling without patient exposure. We developed an acellular normothermic machine-perfusion platform for intact porcine spleen designed as a translational immune-organ assay. Across independent acellular perfusions, the circuit maintained physiologic parameters, preserved red- and white-pulp histology, and yielded viable effluent cells suitable for serial flow cytometry and multiomics. High-dose methylprednisolone was used as a clinically familiar perturbation to determine whether the platform could resolve steroid immunosuppression at mechanistically distinct levels. Effluent RNA-seq identified canonical glucocorticoid-responsive transcriptional programs, including DUSP1, FKBP5, PER1, DDIT4, SGK1, KLF9, ANXA1, NF-{kappa}B feedback regulators, and JAK/STAT suppressor pathways. SOCS3 was a prominent early signal in the perfusion transcriptome and was validated orthogonally at the protein level in prednisone-treated, CD3/CD28-activated primary murine splenocytes, strengthening its role as a candidate pharmacodynamic marker. In parallel, data independent acquisition (DIA) proteomics of effluent cell pellets nominated a non-transcriptional protein-level response: a Sus scrofa LGALS13-annotated, CLC/Galectin-10-like galectin detected despite absence of the corresponding effluent-cell transcript. Because this porcine LGALS13-annotated protein group is treated here as an orthologous CLC/Galectin-10-like signal rather than as canonical human placental Galectin-13/PP13, we tested recombinant human Galectin-10 in vitro. Human Galectin-10 induced marked apoptosis of CD3/CD28-stimulated Jurkat cells, prioritizing this axis for future mechanistic testing without proving causality in the perfused spleen. These data establish acellular spleen perfusion as a serial multiomic platform for translational immunopharmacology and motivate deployment with otherwise-discarded human donor spleens. One sentence summaryAn acellular intact-spleen perfusion platform enables serial cellular, transcriptomic, proteomic, and functional pharmacodynamic sampling that identifies steroid-responsive transcriptional programs, validates SOCS3 protein induction, and nominates a CLC/Galectin-10-like non-transcriptional immunosuppressive axis for translation to discarded human donor spleens.

Immune checkpoint inhibition (ICI) is clinically active against multiple cancers, including urothelial cancer at the non-muscle invasive, muscle-invasive, and metastatic stages. Despite this, large numbers of patients experience disease progression and relapse after treatment with ICI-containing regimens. Tumor antigen-specific T cells are critical to ICI response, however few studies have evaluated the breadth and magnitude of tumor antigen-specific T cell responses with ICI therapy. In this study, we mapped the tumor antigen immunodominance hierarchy in the BBN963 model of murine basal-like bladder cancer for endogenous tumor neoantigens expressed physiologically. We used a high-throughput matrixed ELISpot assay to detect CD8+ T cell responses to predicted BBN963 tumor antigens derived from multiple mutational genomic sources. We found CD8+ T cell responses were directed against a subset of tumor antigens forming a stable and reproducible immunodominance hierarchy across individual mice. Treatment with anti-PD-1 or anti-CTLA-4 did not substantially reshape this hierarchy or broadly shift dominant responses to previously defined subdominant epitopes. Predicted peptide MHC binding stability and affinity was associated with antigen immunogenicity. Cancer-testis antigens, endogenous retroviral antigens, and SNV-derived tumor antigens that were immunogenic were found across tumor subclones. By diversifying the immunogenic antigen repertoire beyond SNVs, we achieved nearly 100% tumor subclone coverage, suggesting that broader antigen selection could help immunotherapy target more tumor subclones. In conclusion, this study supports the stability of the immunodominance hierarchy under ICI therapy and a role for broadening antigen discovery to multiple expressional sources in immunotherapy design.

Background. Plasma and serum metabolomic studies of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) have repeatedly implicated hypometabolic, lipid, mitochondrial, redox and tryptophan-kynurenine pathways, but prior cohorts have been modest in size and have used heterogeneous case definitions. Whether similar pathway-level signals are detectable at scale in dried blood spots (DBS), across questionnaire-derived fatigue constructs and across orthogonal LC gradients in the same individuals remains unresolved. Methods. We profiled DBS extracts from 1,784 community-cohort adults by reverse-phase LC-MS using paired 5 min and 15 min gradients. Six questionnaire-derived endpoints captured a pragmatic self-reported PEM-like phenotype, a DSQ-derived PEM-like construct, high or review clinical status, temporal fatigue state, comorbid fatigue and self-reported chronic fatigue. The locked primary endpoint for Phase 1 was pragmatic_fatigue_pem with 226 cases and 914 controls after excluding major metabolic comorbidity. We tested a biology-first panel comprising 22 literature-curated metabolites represented by four participant-level descriptors each, and evaluated three discovery extensions: a targeted m/z search of additional literature candidates, a hypothesis-free univariate screen across 4,553 5 min and 5,625 15 min consensus features, and pairwise z-difference ratios. Endpoint-specific Ridge classifiers were evaluated by five-fold out-of-fold AUC with bootstrap stability filtering. Cross-gradient agreement was assessed by per-metabolite AUC concordance between paired 5 min and 15 min profiles. Severity was modelled as an ordinal grade derived from the number of fatigue criteria met and chronic-fatigue-form status. Results. The biology-first DBS panel achieved out-of-fold AUC 0.81 for the pragmatic self-reported PEM-like endpoint (226 cases / 914 controls). The DSQ-derived PEM-like construct reached AUC 0.60 (57 cases / 201 controls) on the un-filtered set and AUC 0.778 (SD 0.013, twenty seeds) in a post-hoc signature-decomposition follow-up restricted to participants without a self-declared major-metabolic-history tag (29 cases / 230 controls); both are treated as construct-validity anchors rather than as provoked or clinically adjudicated PEM. An optimised operationalisation of the same construct (panel-self normalisation, restriction to non-comorbid participants and demographic covariates) reached AUC 0.71 (95 % CI 0.55 to 0.76), and an exploratory age-stratified signature decomposition suggested age-dependent pathway composition that requires confirmation given small per-stratum case counts. Stable contributors mapped to carnitine-shuttle, TCA-cycle, redox-thiol and tryptophan-kynurenine pathways. Cross-gradient analysis of 22 matched metabolites yielded Pearson r = 0.62 for signed univariate effects (p = 0.002; 68 % directional agreement). The metabolomic score increased with severity grade (Spearman rho = 0.45, p = 4 x 10^-91; median scores 0.24, 0.51 and 0.75 across grades 0, 1 and 2). Sensitivity analyses on the covariate-complete subset (n = 565; 138 cases / 427 controls) showed that the DBS signal was robust to adjustment for age, sex, BMI and medication burden (DBS-only AUC 0.76, DBS plus covariates 0.78, covariates only 0.64), and produced a metabolomic-specific lift of approximately 0.13 AUC over the strongest anti-leak declarative cross-form questionnaire baseline (AUC 0.63). DBS-only AUC was stable across sex, age and BMI subgroups, and a 1:4 nearest-neighbour matched analysis on age, sex and BMI yielded AUC 0.72 (95 % CI 0.67 to 0.77). The observed pattern supported pathway-level convergence with prior ME/CFS metabolomics literature, including carnitine shuttle, fatty-acid beta-oxidation, TCA cycle, redox-thiol, urea cycle, glycerophospholipid and tryptophan-kynurenine axes. In contrast, the hypothesis-free 15 min screen produced high-AUC features that mapped predominantly to environmental or technical signals, including pesticide, industrial-amine and mobile-phase artifact annotations; only one of eight top leads, a truncated oxidised phospholipid, was biologically plausible, and none had tandem-MS support. Conclusions. In this large community cohort, a literature-curated DBS metabolomic panel captured pathway-level biology associated with a questionnaire-derived PEM-like fatigue phenotype, showed directional concordance across LC gradients, scaled with symptom severity and remained robust to key demographic, anthropometric and anti-leak questionnaire baselines. The findings converge with several metabolic axes previously reported in ME/CFS plasma and serum studies, including carnitine-shuttle, TCA-cycle, redox-thiol, urea-cycle, glycerophospholipid and tryptophan-kynurenine pathways. They should not be interpreted as clinical validation of a diagnostic test, screening tool or objective provoked-PEM biomarker. Rather, they support at-home-compatible DBS metabolomics as a biologically grounded platform for future clinically adjudicated validation, decision-support development and longitudinal monitoring in fatigue and PEM-like syndromes. Because DBS contains cellular and plasma-derived components, matrix effects must be considered when comparing individual metabolites with venous plasma or serum studies, and hypothesis-free screening at this scale can preferentially surface exposome or technical variance unless molecular identification is enforced before biological interpretation.

Circulating tumor DNA (ctDNA) from blood plasma has emerged as a promising biomarker for noninvasive profiling of tumor mutational landscapes and disease monitoring across cancers. In this study, we developed a targeted next-generation sequencing approach to explore the role of ctDNA for comprehensive tumor genotyping, early response prediction, and characterization of clonal heterogeneity in patients with advanced and rare cancers treated within molecular tumor boards. We applied our technology to 157 plasma specimens from 57 patients at distinct disease milestones and detected tumor variants in 96% of baseline samples, with 65% of them harboring actionable aberrations. Longitudinal monitoring of baseline mutations in on-treatment plasma revealed that ctDNA dynamics were significantly associated with clinical outcomes and enabled early prediction of disease progression. Finally, we observed substantial clonal heterogeneity over time, identifying emerging mutations in all analyzed plasma samples obtained at progression, including potentially targetable variants for subsequent personalized therapies.

Glioblastoma is a highly aggressive brain tumour with poor prognosis, whose aetiology, progression, and therapeutic resistance remain incompletely understood. While the microbiota-gut-brain axis has emerged as a key regulator of neurological disorders, its role in glioblastoma biology and treatment response is still largely unexplored. Using a clinically relevant immunocompetent murine model combining glioblastoma stem cell implantation, dextran sodium sulfate-induced gut inflammation, and a full Stupp-like therapeutic protocol, we investigated bidirectional gut-brain communication in glioblastoma. Tumour growth and recurrence were monitored by bioluminescence imaging, tumour transcriptomic profiles were analysed by RNA sequencing, and brain and colon tissues were subjected to histological and molecular analyses. Gut microbiota composition was assessed by 16S rRNA sequencing, while systemic metabolites and cytokines were quantified in plasma. Cross-compartment association bioinformatic analyses were performed to correlate multi-organ readouts. Gut inflammation enhanced glioblastoma growth and promoted tumour recurrence following therapy. Tumour progression was associated with increased infiltration of immunosuppressive macrophages, whereas recurrence correlated with elevated oxidative DNA damage. Remarkably, glioblastoma exerted systemic immunomodulatory effects, attenuating intestinal and systemic inflammatory responses, and induced profound remodelling of gut microbiota composition and predicted metabolic function, including enrichment of Akkermansia and depletion of Lactobacillus. Systemic metabolic profiling was investigated as a route of communication within the gut-brain axis and revealed adaptations in DSS-treated mice associated with tumour burden and therapeutic response. Multi-compartment correlation and multivariable association analyses identified specific bacterial genera and circulating metabolites associated with tumour volume, intestinal inflammation, and genomic instability. These findings uncover a dynamic, bidirectional microbiota-gut-brain axis in glioblastoma and identify intestinal inflammation as a critical determinant of tumour progression and therapeutic outcome. Targeting gut disturbances and microbiota-associated metabolic pathways may represent novel strategies to modulate glioblastoma aggressiveness and treatment response.

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

TP53-mutant cancers often accumulate p53 protein, creating a potential abundance-amplified therapeutic target, but wild-type p53 can also rise in stressed normal cells. This study defines the quantitative design requirements for an intracellular strategy targeting RHSVV/PAb240-like conformational exposure of p53 in TP53-mutant/high-p53 cancer. We integrated DepMap cell-line annotations, p53 abundance data, NCI TP53 mutation resources, and Human Protein Atlas normal-tissue immunohistochemistry, and modeled RHSVV/PAb240-like exposure as a latent design variable. Abundance-only models were compared with latent RHSVV exposure-discriminated models across target-engagement affinity, intracellular concentration, exposure ratio, output nonlinearity, and wild-type stress conditions. Abundance-only targeting was stress-fragile, with no feasible designs at or above the default 3.0x wild-type stress background. In contrast, RHSVV exposure-discriminated models retained feasible regions across the full stress sweep, requiring minimum target-to-stressed-normal exposure ratios of 3 at lower stress, 10 at 5x stress, and 30 at extreme 10x stress. Robust feasible bands required exposure ratios of 10-1000, effective Kd values of 1- 300 nM, intracellular concentration proxies of 10-1000 nM, and output sharpness of 1.0-1.25. These findings define falsifiable molecular requirements for future RHSVV/PAb240-inspired p53-state-selective therapeutic design.

Adeno-associated virus (AAV)-delivered anti-HIV-1 broadly neutralizing antibodies (bNAbs) could prevent and treat HIV-1 infection but are limited by host immune responses, specifically anti-drug antibodies (ADA). We tested whether PD-L1-mediated immune shielding could improve the consistency of AAV-delivered bNAb expression from muscle tissue in rhesus macaques. AAV9.PD-L1 co-delivery with AAV9.3BNC117 reduced the occurrence of ADA and T cell responses and improved the durability of 3BNC117 expression for one year post administration. Importantly, 5 of 6 macaques that received co-delivered AAV9.PD-L1 vectors were protected against ten repeated SHIVAD8-EO challenges. Histopathological and spatial transcriptomic profiling showed that AAV9.PD-L1 co-delivery prevented severe local inflammation, muscle injury, and tertiary lymphoid structure formation at the administration site. Thus, immune shielding could serve as a strategy to prolong transgene expression from muscle-directed AAV-delivered biologics.

BRCA-deficient high-grade serous ovarian cancer is characterized by profound genomic instability and elevated replication-associated DNA damage, rendering these tumors initially sensitive to platinum-based chemotherapy and PARP inhibition. However, despite this vulnerability, most patients ultimately develop resistance, underscoring the need for therapeutic strategies that extend beyond DNA repair-targeted mechanisms. Here, we introduce the MTDH-SND1 complex as a complementary therapeutic target that may expose additional stress vulnerabilities in ovarian cancer cells. We show that pharmacological disruption of the MTDH-SND1 interaction using C26-A6 increases susceptibility to ferroptosis-associated stress, an iron-dependent form of regulated cell death and that BRCA-deficient models are particularly more sensitive to this perturbation. Notably, when combined with PARP inhibition, MTDH-SND1 disruption is associated with increased MHC class I expression in tumor cells, suggesting enhanced tumor visibility to the immune system. Together, these findings support a combination strategy that couples DNA repair disruption with metabolic and immunogenic remodeling in BRCA-deficient ovarian cancer.

Background. Type 2 diabetes mellitus (T2D) is defined by progressive pancreatic {beta}-cell dysfunction whose molecular underpinnings remain incompletely understood. Single-cohort transcriptomic analyses of donor islets have yielded heterogeneous gene lists of limited cross-study reproducibility, constraining both mechanistic interpretation and biomarker development. Methods. We combined two complementary analytical strategies applied to four public human islet transcriptomic cohorts (GSE25724, GSE20966, GSE38642, and GSE164416; n = 7-57 donors per contrast). For the integrative arm, three microarray datasets and one bulk RNA-seq dataset were processed independently and unified through gene-level random-effects meta-analysis, hallmark pathway scoring (GSVA/MSigDB), and iterative module refinement, yielding a two-axis disease framework. For the diagnostic arm, a consensus multi-method machine learning pipeline, combining LASSO penalized logistic regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest importance scoring, was applied to 184 differentially expressed genes from the RNA-seq cohort, with all normalization steps performed within leave-one-out cross-validation (LOOCV) folds to prevent data leakage. Machine learning classification of the RNA-seq cohort was additionally subjected to external transportability testing in the independent bulk human islet RNA-seq cohort GSE50244 using an overlap-restricted reduced score and a threshold fixed in the discovery cohort. Results. Meta-analysis across all four cohorts identified 337 high-confidence T2D-associated genes (96.1% directional concordance in beta-cell-enriched tissue). These were distilled into two refined 14-gene modules: ImmuneStress (MICB, HLA-DRA, HLA-DPA1, IL1R2, and others) and BetaCellIdentitySecretion (RASGRP1, PPP1R1A, SLC2A2, and others), whose composite IsletDysfunctionScore provided the most stable cross-platform separation of non-diabetic from T2D islets (Hedges' g = 1.80, p = 9.83 x $10^-17$, $\text{I}^2$= 0%). Consistent with progressive disease, IsletDysfunctionScore increased monotonically from non-diabetic to impaired glucose tolerance to T2D. Separately, the machine learning pipeline derived a 10-gene diagnostic panel: GABRA2, SLC2A2, ARG2, DKK3, PRIMA1, TAFA4, HHATL, PARVG, RNU1-70P, and the novel lncRNA ENSG00000284653, that achieved perfect discrimination in LOOCV (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, zero misclassifications across all 57 donors). A leakage-verification experiment confirmed that this performance reflected genuine biological signal: global quantile normalization prior to cross-validation collapsed AUC to 0.380. External testing showed that 8 of the 10 panel genes were measurable in GSE50244. The frozen 8-gene reduced score retained strong discrimination (external AUC = 0.907), with 6 of 8 genes preserving directional concordance, but the discovery-derived threshold did not transfer because the external score distribution was shifted upward and compressed, yielding complete sensitivity but zero specificity at the frozen cutoff Conclusions. Integrating pathway-level meta-analysis with machine learning classification, we present a coherent two-axis model: immune/stress activation and loss of beta-cell identity/secretory competence, together with a compact, biologically interpretable 10-gene diagnostic signature. Panel genes converge on GABA signaling, glucose transport, arginine metabolism, WNT pathway inhibition, and a novel lncRNA, providing both mechanistic hypotheses and high-priority targets for external validation. These findings offer a reproducible transcriptomic scaffold for future mechanistic, biomarker, and clinical translation studies of human islet dysfunction. They also support external transportability of the core biological signal, while indicating that absolute operating thresholds are cohort-dependent and would require recalibration before deployment in independent datasets.