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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

The impact of respiratory gating on improving volume measurement of murine lung tumors in micro-CT imaging

Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging faces unique challenges that emphasize the gap between mouse and man. One example is the difference in breathing patterns and breath-holding ability, which can dramatically affect tumor burden assessment in lung tissue. As part of a co-clinical trial studying immunotherapy and radiotherapy in sarcomas, we are using micro-CT of the lungs to detect and measure metastases as a metric of disease progression. To effectively utilize metastatic disease detection as a metric of progression, we have addressed the impact of respiratory gating during micro-CT acquisition on improving lung tumor detection and volume quantitation. Accuracy and precision of lung tumor measurements with and without respiratory gating were studied by performing experiments with in vivo images, simulations, and a pocket phantom. When performing test-retest studies in vivo, the variance in volume calculations was 5.9% in gated images and 15.8% in non-gated images, compared to 2.9% in post-mortem images. Sensitivity of detection was examined in images with simulated tumors, demonstrating that reliable sensitivity (true positive rate (TPR) [≥] 90%) was achievable down to 1.0 mm3 lesions with respiratory gating, but was limited to [≥] 8.0 mm3 in non-gated images. Finally, a clinically-inspired \"pocket phantom\" was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.

cancer biology↗

bioRxiv2024

Automatic detection of seizures within zebrafish larvae epilepsy models using single-channel electroencephalography

Researchers continue to pursue new drugs capable of treating intractable, or drug- resistant, epilepsy as a large number of patients do not see a reduction in the number of seizures from current treatments. To quicken the pace of drug research, zebrafish (Danio rerio) have been utilized as a model organism for testing anticonvulsant drugs before clinical trials. However, the lengthy task of labeling electroencephalography (EEG) data slows the pace of this line of research and limits its full potential. This study investigated the creation of automatic seizure detection algorithms for electroencephalogram data recorded from seizure-induced zebrafish to detect seizure, artifact, and neurotypical events. Four unique seizure detection algorithms were proposed and implemented based on k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) classifiers. These four techniques were tested using the same input features and their results compared. The best-performing algorithm was identified as the Single Stage KNN with an 83.8% accuracy, followed by the Single Stage ANN with an 80.3% accuracy. The results indicate that a single-stage, multiclass classification architecture may be beneficial to automatically labeling epilepsy data, thus enhancing efficiency. Furthermore, the results for the algorithms which separate the multiclass classification into a series of binary classifications suggest that there are advantages in research and clinical settings to implement a detection algorithm that can delineate neurotypical and non-neurotypical data to assist with manual labeling.

bioengineering↗

bioRxiv2024

Monoclonal antibodies derived from B cells in subjects with cystic fibrosis reduce Pseudomonas aeruginosa burden in mice

Pseudomonas aeruginosa (PA) is an opportunistic, frequently multidrug-resistant pathogen that can cause severe infections in hospitalized patients. Antibodies against the PA virulence factor, PcrV, protect from death and disease in a variety of animal models. However, clinical trials of PcrV-binding antibody-based products have thus far failed to demonstrate benefit. Prior candidates were derivations of antibodies identified using protein-immunized animal systems and required extensive engineering to optimize binding and/or reduce immunogenicity. Of note, PA infections are common in people with cystic fibrosis (pwCF), who are generally believed to mount normal adaptive immune responses. Here we utilized a tetramer reagent to detect and isolate PcrV-specific B cells in pwCF and, via single-cell sorting and paired-chain sequencing, identified the B cell receptor (BCR) variable region sequences that confer PcrV-specificity. We derived multiple high affinity anti-PcrV monoclonal antibodies (mAbs) from PcrV-specific B cells across 3 donors, including mAbs that exhibit potent anti-PA activity in a murine pneumonia model. This robust strategy for mAb discovery expands what is known about PA-specific B cells in pwCF and yields novel mAbs with potential for future clinical use.

immunology↗

bioRxiv2025

High levels of DNA replication initiation factors indicate ATR inhibitor sensitivity via excessive origin firing

Inhibitors of ATR, a central kinase controlling DNA replication origin firing and cellular checkpoint activity, are currently in multiple clinical trials, yet mechanisms underpinning sensitivity and robust patient stratification biomarkers are lacking. We used functional genomics approaches to identify molecular mechanisms driving sensitivity to the ATR inhibitor (ATRi) ceralasertib. Replication stress-associated patterns of DNA copy number alterations identified a subset of sensitive breast cancer cell lines. In parallel, we performed proteomics, phosphoproteomics and gene expression analyses and discovered that sensitive cell lines had higher expression of DNA replication origin firing factors, and massively increased origin firing in response to ATRi. ATRi sensitivity was partly rescued upon co-treatment with XL-413, a CDC7 inhibitor that decreases origin firing. High expression of replication initiation factors correlated with ATRi sensitivity across multiple cancer types, and in acute myeloid leukemia patient samples. Together, this study reveals a novel contribution of lethal origin firing capacity in determining the sensitivity of cancer cells to ATR inhibition and demonstrates the predictive potential of mechanism-specific copy number alterations, providing key steps towards developing a multimodal clinically applicable biomarker for ATR inhibitors.

cancer biology↗

bioRxiv2023

Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior

ImportanceValid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts. ObjectiveTo determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD). DesignA longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up. ParticipantsEighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session. Main Outcomes and MeasuresOutcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison. ResultsModels using the non-speech variables showed the best predictive performance at three(r>0.45, P<2x10-3) and six months follow-up (r>0.37, P<3x10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3x10-2), craving (r=0.72, P=5x10-5), days of abstinence (r=0.76, P=1x10-5), and cocaine use in the past 90 days (r=0.61, P=2x10-3), significantly outperforming the other models for abstinence prediction. Conclusions and RelevanceAt short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity. Key PointsO_ST_ABSQuestionC_ST_ABSCan natural language processing be leveraged to predict longitudinal drug use outcomes in individuals with substance use disorder? FindingsIn this prospective study that included initially abstinent individuals with cocaine use disorder (iCUD), models using demographics, neuropsychological measures and drug use patterns at baseline were compared to those using minimally structured short natural speech samples relating the positive consequences of abstinence and the negative consequences of using drugs, showing a differential prediction of outcomes measured up to one year later. At three and six months, the former outperformed speech models, including approximately 50% of the variability in craving and 40% in abstinence duration. At 12 months from baseline, speech models were superior, predicting 50% of the variability in abstinence duration. MeaningSpeech variables derived through natural language processing can predict clinically meaningful drug use outcome measures in addiction, with greater value at longer intervals. The applicability of language modeling to aid in assessing treatment response and risk in drug addiction warrants further investigation in clinical settings.

neuroscience↗

bioRxiv2024

Unbiased preclinical phenotyping reveals neuroprotective properties of pioglitazone

Animal models are essential for assessing the preclinical efficacy of candidate drugs, but animal data often fails to replicate in human clinical trials. This translational gulf is due in part to the use of models that do not accurately replicate human disease processes and phenotyping strategies that do not capture sensitive, disease-relevant measures. To address these challenges with the aim of validating candidate neuroprotective drugs, we combined a mouse prion (RML scrapie) model that recapitulates the key common features of human neurodegenerative disease including bona fide neuronal loss, with unbiased and machine learning-assisted behavioural phenotyping. We found that this approach measured subtle, stereotyped, and progressive changes in motor behaviour over the disease time course that correlated with the earliest detectable histopathological changes in the mouse brain. To validate the utility of this model system, we tested whether the anti-diabetic drug pioglitazone could slow prion disease progression. Pioglitazone crosses the blood-brain-barrier and has been shown to reduce neurodegenerative disease severity in other mouse models. We found that in addition to significantly slowing the emergence of early-stage clinical signs of neurodegeneration, pioglitazone significantly improved motor coordination throughout the disease time course and reduced neuronal endoplasmic reticulum stress. Together, these findings suggest that pioglitazone could have neuroprotective properties in humans, confirm the utility of the scrapie mouse model of neurodegeneration, and provide generalisable experimental and analysis methods for the generation of data-rich behavioural data to accelerate and improve preclinical validation.

neuroscience↗

bioRxiv2026

Adaptive Resilience: Agarikon Mycelium Modulates Immune Responses and Provides Oxidative Stress Buffering in Human Immune Cells

Agarikon (Fomitopsis officinalis, syn. Laricifomes officinalis) is a fungus with millennia of traditional use across many cultures with modern research supporting the antimicrobial, antiviral, anticancer, antioxidant, and immune-modulating properties of both mycelium and fruit body. Due to the slow growth and old-growth forest habitat of agarikon fruit bodies, its mycelium represents an easily cultivated immunomodulating and stress buffering preparation, underscored by recent clinical trials of a blend of agarikon and Trametes versicolor mycelium. We investigated the transcriptomic effects of agarikon mycelium in human peripheral blood mononuclear cells (PBMCs) under both basal and LPS-stimulated conditions, alongside evaluations of antioxidant, iron chelating, and kinase binding activity. Two agarikon fractions were also assessed for effects on cell viability and proliferation and induction of select cytokine targets. Under basal conditions, agarikon mycelium selectively engaged the innate immune response through IL-1 and NF-{kappa}B axes, balanced by increases in anti-inflammatory mediators such as IL-1RA and decreases in toll-like receptor transcripts. Under LPS-stimulated conditions, this innate immune response was modified, with measured increases in immune effectors (including TLR5) observed in response to induced stress, alongside accompanying transcript decreases in cytokine pathways that can overstimulate the immune system. Overall, agarikon mycelium demonstrated a coordinated, context-dependent immune response profile, supporting its stress-buffering and immune-modulating potential and warranting continued clinical validation.

immunology↗

bioRxiv2026

Preclinical efficacy of a systemically-administered, second-generation STING agonist that promotes antitumour immunity in combination with radiotherapy

As potent triggers of innate immunity, STING agonists hold promise as active immunotherapeutic agents for cancer treatment. Second-generation STING agonists, suitable for systemic delivery, are being investigated in preclinical research and have entered clinical trials. Here, the novel synthetic STING agonist, BI-1703880 (STINGa), which was designed for intravenous delivery, was investigated for anti-tumour and immunological effects. We show that STINGa activates the STING pathway and results in a transient and dose-dependent upregulation and secretion of interferons and proinflammatory cytokines in vitro and in vivo. We show that intravenous administration of repeated dosing with low-dose STINGa is well tolerated. We report that radiotherapy (RT) and STING agonism synergizes to generate innate immune cell and CD8+ T cell responses that control tumour growth. Anti-tumour activity induced by combined RT / STINGa was reduced in mice lacking a functional immune system. RT / STINGa combination treatment also initiated development of protective immune memory. RT / STINGa upregulated PD-L1, PD-1 and CTLA-4 in the tumour microenvironment. Our findings show that combining RT / STINGa with immune checkpoint inhibitors further increases therapeutic benefit. Our data confirm STING as a therapeutic target in cancer and support the clinical development of BI-1703880 STING agonist, thereby suggesting radiotherapy as a potential combination for enhancing anti-tumour efficacy.

cancer biology↗

bioRxiv2022

Machine learning approaches based on fibroblast morphometry confidently identify stress but have limited ability to predict ALS

ObjectiveAmyotrophic lateral sclerosis (ALS) is a devastating neuromuscular disease with limited therapeutic options. Diagnostic and surrogate endpoint biomarkers are needed for early disease detection, clinical trial design, and personalized medicine. MethodsWe tested the predictive power of a large set of primary skin fibroblast (n=443) from sporadic and familial ALS patients and healthy controls. We measured morphometric features of endoplasmic reticulum, mitochondria, and lysosomes by imaging with vital dyes. We also analysed immunofluorescence images of ALS-linked proteins, including TDP-43 and stress granule components. We studied fibroblasts under basal conditions and under metabolic (galactose medium), oxidative (arsenite), and heat stress conditions. We then employed machine learning (ML) techniques on the dataset to develop biomarkers. ResultsStress perturbations caused robust changes in the measured features, such as organellar morphology, stress granule formation, and TDP-43 mislocalization. ML approaches were able to predict the perturbation with near perfect performance (ROC-AUC > 0.99). However, when trying to predict disease state or disease groups (e.g., sporadic, or familial ALS), the performance of the ML algorithm was more modest (ROC-AUC Control vs ALS = 0.63). We also detected modest but significant scores when predicting clinical features, such as age of onset (ROC-AUC late vs early = 0.60). ConclusionsOur findings indicate that the ML morphometry we developed can accurately predict if human fibroblasts are under stress, but the differences between ALS and controls, while statistically significant, are small and pose a challenge for the development of biomarkers for clinical use by these approaches.

neuroscience↗

bioRxiv2025

BRAVE: a highly accurate method for predicting HIV-1 antibody resistance using large language models for proteins

MotivationBroadly neutralizing antibodies (bNAbs) that target the envelope glycoprotein (Env) of human immunodeficiency virus-1 (HIV-1) have been utilized in clinical trials aimed at preventing and treating HIV-1 infections. However, the emergence of neutralization resistance to bNAbs occurs rapidly due to the high mutation rate of HIV-1. Previous studies have suggested the use of in silico methods to effectively predict the resistance of HIV-1 isolates to bNAbs. In this study, we present a novel machine learning approach called BRAVE (Bnab Resistance Analysis Via Evolutionary scale modeling 2) designed to predict HIV-1 resistance against 33 known bNAbs. This innovative tool employs a Random Forests classifier that uses a protein language model to reliably capture protein features. ResultsBRAVE outperformed leading resistance prediction tools on various performance metrics, attaining the highest performance in established classification measures including accuracy, area under the curve, logarithmic loss, and F1-score. Importantly, rigorous statistical comparisons (p<0.001) show that BRAVE is significantly more accurate than state-of-the-art neutralization prediction tools. BRAVE will facilitate informed decisions of antibody usage and sequence-based monitoring of viral escape in clinical settings. Availability and implementationBRAVE software is available for download under GitHub (https://github.com/kiryst/BRAVE/tree/master). Contactreda.rawi@nih.gov Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Exploration of clinical breakpoint of Danofloxacin for Glaesserella parasuis in plasma and in PELF

BackgroundTo establish the clinical breakpoint (CBP) of danofloxacin to G. parasuis, three cutoff values, including epidemiological cutoff value (ECV), pharmacodynamic cutoff value (COPD) and clinical cutoff value (COCL), was obtained in the present study. MethodsThe ECV was calculated using ECOFFinder base on MIC distribution of 347 G. parasuis collected from disease pigs. The COPD was established base on in vivo and ex vivo pharmacokinetic (PK) - pharmacodynamic (PD) modeling of danofloxacin both in plasma and pulmonary epithelial lining fluid (PELF) using Hill formula and Monte Carlo analysis. The COCL was established based on the relationship between possibility of cure (POC) and MIC in the clinical trials using "WindoW" approach, nonlinear regression and CART analysis. ResultsThe MIC50 and MIC90 of danofloxacin against 347 G. parasuis were 2 g/mL and 8 g/mL, respectively. The ECV value was set up as 8 g/mL using ECOFFinder. Concentration-time curve of danofloxacin indicated a two-compartment model for PK analysis. The PK parameters of the maximum concentration (Cmax) and area under concentration-time curve (AUC) in PELF were 3.67 {+/-} 0.25 g/mL and 24.28 {+/-} 2.70 h{middle dot}g/mL, higher than those in plasma (0.67 {+/-} 0.01g/mL and 4.47 {+/-} 0.51 h{middle dot}g/mL). The peak time (Tmax) in plasma was 0.23 {+/-} 0.07 h, shorter than that in PELF (1.61 {+/-} 0.15 h). The COPD in plasma and PELF were 0.125 g/mL and 0.5 g/mL, respectively. The COCL calculated by WindoW approach, nonlinear regression and CART analysis were 0.125[~]4 g/mL, 0.428 g/mL and 0.56 g/mL, respectively. The 0.5 g/mL was selected as eligible COCL. The ECV is much higher than the COPD and COCL, and the clinical breakpoint based on data in plasma was large different with that of in PELF. ConclusionsOur study firstly established three cutoff values of danofloxacin against G. parasuis. It suggested that epidemiological danofloxacin-resistant G. parasuis may lead to the ineffective treatment by danofloxacin. ImportanceG. parasuis, a gram-negative respiratory pathogen, can colonize in the upper respiratory tract in swine and cause Glassers disease. As the abuse of antibiotics, antimicrobial resistant G. parasuis emerged in different degrees, which brought serious threat to global economy and public health. Danofloxacin in quinolones are one of the best choices for treatment of G. parasuis infection, because of their strong bactericidal activity and good absorption into blood and great distribution in the lung. However, the clinical breakpoint (CBP) for danofloxacin against G. parasuis had not yet been established by clinical laboratory of standard Institute (CLSI) and European Commission of antimicrobial susceptibility testing (EUCAST). Our study firstly established three cutoff values of danofloxacin against G. parasuis. It suggested that epidemiological danofloxacin-resistant G. parasuis may lead to the ineffective treatment by danofloxacin.

microbiology↗

bioRxiv2022

Developing and optimizing a personalized probiotic treatment regime for nasal endogenous Staphylococcus aureus decolonization

AbstractDespite the increasing burden of antibiotic resistance and persistence, current approaches to eradicate nasal pathobionts such as Staphylococcus aureus and Streptococcus pneumoniae are based on the use of antibacterial agents. An alternative approach is the artificial inoculation of commensal bacteria, i.e., probiotic treatment, which is supported by the increasing evidence for commensal-mediated inhibition of pathogens. To systematically investigate the potential and the limitations of this approach, we developed a quantitative framework simulating the dynamics of the nasal bacterial microbiome by combining mathematical modeling with longitudinal microbiota data. By inferring the microbial interaction parameters using 16S rRNA amplicon sequencing data and simulating the nasal microbial dynamics of patients colonized with S. aureus, we compared the decolonization performance of probiotic and antibiotic treatments under different assumptions on patients bacterial community composition and susceptibility profile. To further compare the robustness of these treatments, we simulated a S. aureus challenge following each treatment and quantified the recolonization probability. Eventually, using nasal swabs of adults colonized with S. aureus, we confirmed that after antibiotic treatment, recolonization of S. aureus was inhibited in samples treated with a probiotic mixture compared to the non-treated control. Our results suggest that probiotic treatment clearly outperforms antibiotics in terms of decolonization performance, recolonization robustness, and leads to less collateral reduction of the microbiome diversity. Moreover, we find that recolonization robustness is highest in those patients that were not initially colonized by Dolosigranulum pigrum. Thus, probiotic treatment may provide a promising alternative to combat antibiotic resistance, with the additional advantage of personalized treatment options via using the patients own metagenomic data to tailor the intervention. The combination of an in silico framework with in vitro confirmatory experiments using clinical samples reported in this work is an important step forward to further investigate this alternative in clinical trials. ImportanceThe development of new antimicrobial agents is declining while antibiotic resistance is rising, which is particularly concerning for upper respiratory tract pathogens S. pneumoniae and S. aureus. Combating such resistant infections will only become more challenging unless alternative treatment strategies are explored. Despite the accumulating evidence on using commensal bacteria for pathobiont decolonization, it is still not commonly practiced. To investigate the potential of commensal-mediated inhibition of pathogens systematically, we developed a quantitative framework describing the dynamics of the nasal microbiome by merging mathematical modeling and metagenomic data. We show that probiotic treatment outperforms antibiotics regarding decolonization performance and recolonization robustness while preserving the microbiome diversity with the additional advantage of personalized treatment options via using the patients own microbiota data. Moreover, we validated the approach by using nasal swabs from adults with nasal S. aureus colonization, demonstrating that probiotic treatment prevents recolonization with S. aureus in vitro. The framework developed in this work is an important step forward for the translation of experimental and clinical data into mainstream clinical practice in a systematic and controlled manner.

microbiology↗

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗

bioRxiv2023

Immunosuppressive tumor microenvironment of osteosarcoma

Osteosarcoma is the most common malignant bone tumor in children, characterized by a high degree of genomic instability, resulting in copy-number alterations and genomic rearrangements without disease-defining recurrent mutations. Clinical trials based on molecular characterization have failed to find new effective therapies or improve outcomes over the last 40 years. To better understand the immune microenvironment of osteosarcoma, we performed single-cell RNA sequencing on six tumor biopsy samples, combined with a previously-published cohort of six samples. Additional osteosarcoma samples were profiled using spatial transcriptomics for validation of discovered subtypes and to add spatial context. Analysis revealed immunosuppressive cells, including myeloid-derived suppressor cells (MDSCs), regulatory and exhausted T-cells, and LAMP3+ dendritic cells. Using cell-cell communication modeling, we identified robust interactions between MDSCs and other cells, leading to NF-{kappa}B upregulation and an immunosuppressive microenvironment, as well as interactions involving regulatory T-cells and osteosarcoma cells that promoted tumor progression and a proangiogenic niche. Statement of SignificanceOsteosarcoma patient survival has remained stagnant for several decades due to lack of successful therapy for high-risk patients, including those with metastatic disease. Identifying novel therapeutics including immunotherapies is of great clinical importance. Our study highlights several important immunosuppressive mechanisms within osteosarcoma that should be considered when developing future immunotherapies.

cancer biology↗

bioRxiv2022

Combined blockade of VEGF, Angiopoietin-2, and PD1 reprograms glioblastoma endothelial cells into quasi-antigen-presenting cells

Glioblastoma (GBM) remains a highly aggressive and uniformly fatal tumor, with a 5-year survival of <10%. Moreover, all randomized clinical trials with immune-checkpoint blockers have failed to date. Here we report that tumor endothelial cell (EC) dysfunction confers resistance to immunotherapy in preclinical GBM models. Anti-VEGF-therapy-induced vascular normalization is insufficient to fully restore the EC function and alleviate inflammatory edema induced by blocking programmed cell death protein 1 (PD1). Strikingly, concomitant blockade of angiopoietin 2 (Ang2), vascular endothelial growth factor (VEGF), and programmed cell death protein 1 (PD1) reprograms dysfunctional ECs to quasi-antigen presenting cells and upregulates receptors required for cytotoxic T lymphocyte entry into the tumor. Blocking VEGF, Ang2, and PD1 induces durable anti-tumor response while controlling edema. Upregulation of the transcription factor T-bet is necessary for generating resident memory T cells elicited by this combination therapy. Moreover, a human GBM organoid model formed from human cancer cells, ECs, CD14 and CD8 cells recapitulated these findings. Our study reveals the role of Ang2 in resistance to VEGF and/or PD1-blockade and provides a compelling rationale for clinical evaluation of this combination in GBM patients.

cancer biology↗

bioRxiv2023

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

cancer biology↗

bioRxiv2025

A Machine Learning Model Optimized for Local Data Stratifies Patients for the Adoptive Cell Therapy with Tumor Infiltrating Lymphocytes in Bladder Tumors

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TIL) is a form of personalized immunotherapy that requires ex vivo expansion of autologous TILs and their reinfusion back into the patient. Predicting TIL expansion at the time of diagnosis may improve selection of patients that can benefit from ACT-TIL. It can also prevent high treatment-related costs and delays in treatment of patients whose cancer specimens would not yield successful TIL growth. We developed PETIL, a machine-learning model optimized for data of a medium size to determine a minimal combination of features (demographic, clinical, and biological specimen-based) that is predictive of expansion of TILs from a resected bladder cancer. We used a retrospectively identified set of data from bladder cancer patients at Moffitt Cancer Center for the training and testing cohorts. Additionally, we used data from a recent feasibility clinical trial at Moffitt Cancer Center as a blinded validation cohort. PETIL uses random forest method to identify a combination of robust predictive features, support vector machine model to determine the optimal classification hyperparameters, and Matthews correlation coefficient method to adjust the decision-boundary threshold for imbalanced data. Our model yielded AUC=0.740 for the testing cohort and AUC=0.857 for blinded validation cohort. Thus, our PETIL model optimized for data of medium size has favorable performance metrics for predicting TIL expansion from a given tumor. Authors SummaryTreatment with autologous tumor-infiltrating lymphocytes (TIL) that are expanded ex vivo from a given tumor and then reinfused into the patient is a promising personalized immunotherapy. However, the TIL expansion takes about 4-6 weeks, thus developing tools that predict whether TIL growth will be successful can help to avoid delays in treatment of patients whose cancer specimens would not yield successful TIL expansion. Our Predictor of Expansion of TIL (PETIL) is a machine-learning model that uses patients demographic information, clinical tumor classification, and biological tumor specimen-based measurements to determine a minimal set of these data features that are predictive of TIL expansion outcome. We applied this model to data from bladder cancer patients collected at Moffitt Cancer Center and showed that PETIL has favorable performance metrics for the dataset of a moderate size. This computational predictor can support clinicians in determining which patients are candidates for TIL immunotherapy. The developed PETIL pipeline can also be adjusted to data from other solid tumors.

immunology↗

bioRxiv2026

PRMT5 inhibitors actively promote metastatic progression of lung adenocarcinoma

Epigenetic changes are a major driver of cancer progression, placing considerable focus on epigenetic regulators as therapeutic targets. Protein arginine methyltransferase 5 (PRMT5) is one such regulator, and numerous PRMT5 inhibitors (PRMT5i) in clinical trials. Despite this, the mechanisms and consequences of PRMT5i-resistance are unknown. Here, we demonstrate that aggressive cancer progression is an inbuilt feature of PRMT5i-resistance acquisition in lung adenocarcinoma (LUAD). Independently-generated resistant cell lines gain dedifferentiation signatures that typify late-stage disease and show increased metastatic potential in vivo. We establish that these state shifts are a direct consequence of PRMT5i action; treatment induces rapid and widespread chromatin rewiring, enabling derepression of late-stage disease states that are stably established in resistant cells. Notably, treatment of lung tumor-bearing mice drives rapid disease advancement without decreasing tumor burden, showing that drug-induced disease progression supersedes any benefits from PRMT5 inhibition in vivo. Furthermore, analyses of human cell lines and patient cohorts supports the notion of PRMT5 inhibition-mediated dedifferentiation. Collectively our data show that PRMT5i can actively promote self-resistance and disease progression in different tumor types. This raises serious concerns for the use of PRMT5i in patients, arguing that clinical studies should consider the possibility of drug-induced plasticity, resistance, and disease advancement.

cancer biology↗