Antibody Therapeutics

Chimeric antigen receptor (CAR) T cells are patient-derived T cells engineered to recognize specific antigens on malignant cells, representing a major breakthrough in personalized cancer immunotherapy. While CAR-T cell therapy is an established treatment for relapsed or refractory hematologic malignancies, understanding the rationale and methodology behind its development is essential for training the next generation of oncology researchers. Here, we describe a novel, hands-on small-enrollment laboratory course designed to teach upper-level undergraduate and graduate students about immunotherapies and CAR-T cell technology by paralleling a typical bench-scale development process. Students isolated T cells from porcine peripheral blood mononuclear cells (PBMCs) via immunomagnetic selection, followed by flow cytometry to assess purity, viability, and yield. Students then used pre-engineered B7H3 CAR-T cells to perform co-culture assays with U87 glioblastoma cells to evaluate tumor cell killing efficacy. Cytotoxicity was qualitatively evaluated using immunofluorescence microscopy, while IFN-gamma release was quantified using Enzyme-linked Immunosorbent Assay (ELISA). Student performance was evaluated through electronic laboratory notebooks, discussion questions, a capstone project, and student-generated data. Our results demonstrate students high academic performance across assignments and the successful execution of complex laboratory workflows. Collectively, these findings establish the feasibility and implementation of a research-integrated curriculum providing foundational exposure to immuno-oncology research tools and CAR-T cell production techniques.

TROP2-targeted antibody-drug conjugates (ADCs) have demonstrated promising clinical activity in triple-negative breast cancer (TNBC) as monotherapies; however, therapeutic benefit varies among patients. Combination strategies pairing TROP2-targeted ADCs with immune checkpoint inhibitors are also being investigated. Elucidating the mechanistic drivers of ADC monotherapy variability and enabling the rational development of combination regimens require computational frameworks that integrate ADC pharmacology with tumor-immune interactions. A quantitative systems pharmacology (QSP) model is presented that incorporates an ADC module into our established immuno-oncology model for TNBC. The module captures ADC and payload pharmacokinetics and pharmacodynamics. TNBC heterogeneity is represented by two tumor cell clones with high and low TROP2 expression, informed by prior characterizations, and differential sensitivity to the ADC payload is incorporated as an intrinsic property of each clone. Although generalizable, the model was applied to the TROP2-targeted ADC sacituzumab govitecan (SG, TRODELVY). A virtual patient cohort was generated using Latin hypercube sampling and calibrated against objective response rate (ORR) data from SG Phase I/II TNBC basket trial. The model predicted an ORR of 33.2% consistent with ASCENT study (NCT02574455). Simulations suggest TROP2-mediated delivery contributes modestly to SG efficacy with tumor exposure driven largely by systemically released SN-38 payload being sufficient to induce cytotoxicity. Tumor heterogeneity emerged as a key determinant of response with ORR increasing as the fraction of payload-sensitive clones increased. Overall, this QSP framework for TROP2-targeted ADCs accounts for TNBC heterogeneity and is extendable to other ADCs and targets enabling interrogation of ADC mechanisms of action in conjunction with tumor-immune interactions.

Antibody developability is determined by multiple physicochemical properties, and the relevance of individual properties varies among experimental assays. Existing computational approaches, however, often provide fixed assessments or rely on computationally intensive descriptors. We present kitAb, an open-source framework for scalable physicochemical characterization of antibody Fv regions and assay-specific developability modeling. kitAb calculates 59 interpretable sequence- and structure-based descriptors and integrates them with automated feature selection and regression. We evaluated kitAb across ten public datasets comprising 46 experimental endpoints, using sequence-aware or predefined splits where feasible. Predictive performance was strongly assay-dependent. In nested evaluations with sequence-aware or predefined data splits, models using kitAb descriptors achieved predictive performance comparable on average to those using PROPERMAB descriptors, while outperforming models based on TAP or sequence-only features. In a single-structure CPU timing benchmark, kitAb descriptor calculation was approximately 11-fold faster than PROPERMAB. Predictive performance was similar across the three structure-generation backends, and broader AutoML searches did not substantially improve mean performance. We further applied the same descriptor representation to approximately 3.4 million paired natural antibodies and 706 Phase II or later therapeutic antibodies. Repertoire-scale analysis revealed systematic differences in surface charge, hydrophobic and aromatic organization, CDR length and germline-associated physicochemical profiles. The kitAb code and analysis workflows are openly available at https://github.com/katyachemistry/kitAb/.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Background Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, and emerging T cell engager (TCE) immunotherapies require predictive preclinical models that capture patient-specific tumour biology and tumour-immune interactions. Although patient-derived organoid (PDO)-immune co-culture systems show promise for evaluating immunotherapy responses, many rely on matrix-embedded cultures that limit scalability and reproducibility. We therefore developed and validated a low-matrix organoid-T cell co-culture platform for functional assessment of TCE activity in patient-derived CRC models. Methods Patient-derived CRC organoids representing diverse molecular and genetic backgrounds were co-cultured with activated allogeneic CD3+ T cells in a suspension low-matrix format. Matrix concentration, medium composition, T cell activation status, and assay duration were optimized. Tumour killing, T cells activation, cytokine secretion, apoptosis, and motility were assessed using flow cytometry, live-cell imaging, cytokine profiling, and immunofluorescence. The platform was evaluated using EGFR- and HER2-targeting bispecific T cell engagers across 21 CRC organoid models and multiple healthy donor-derived T cell populations. Results Optimization identified 1% matrix and a 1:1 organoid: T cell medium that preserved organoid integrity while maintaining T cell viability, activation, and motility. The final workflow enabled reproducible co-culture of 5-day mature organoids with 7-day activated T cells for 72 hours. EGFR-targeting TCEs induced tumour killing, T cell activation, and IFN-{gamma} secretion across donor-organoid combinations. Screening of 21 CRC organoid models revealed substantial inter-patient heterogeneity, with 12 models maintaining [≥]50% baseline viability and supporting functional TCE evaluation. EGFR- and HER2-targeting TCEs produced potent dose-dependent cytotoxicity, with IC50 values ranging from 0.0188 to 54.77 nM across responsive models and up to 75%-85% tumour killing in the most sensitive organoids. These responses were accompanied by increased effector cytokine secretion. Real-time imaging confirmed dynamic T cell engagement and apoptosis-driven organoid destruction following TCE treatment. Conclusions We establish a robust, scalable, human-relevant low-matrix organoid-T cell co-culture platform for functional screening of T cell engagers in patient-derived CRC models. By enabling integrated assessment of tumour killing, immune activation, cytokine responses, and tumour-immune interactions while preserving inter-patient heterogeneity, this system provides a translational framework for immunotherapy development. The platform may support candidate prioritisation, biomarker discovery, patient stratification, and preclinical evaluation of immune-engaging therapeutics.

The immunosuppressive tumor microenvironment (TME) poses formidable challenges for cancer immunotherapies. Among the complex TME landscape, the cytokine TGF-{beta}1 is an attractive soluble target for reinvigoration of anti-tumor immunity due to its pivotal function in immune cell suppression, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) promotion, while immune checkpoint receptors exemplified by PD-1 and CD24 are promising surface targets. Though adaptive immune checkpoint inhibition is well established in clinical practice, TGF-{beta} blockade or innate immune checkpoint blockade is yet to be harnessed for tumor treatment. Here, leveraging CD24 overexpression on multiple tumor tissues and its unique recycling kinetics, we designed an innate Immune Checkpoint TArgeting Chimera (ICTAC) G01, a bifunctional CD24 antibody-TGF-{beta}1 receptor fusion protein that disrupts TGF-{beta}1 signaling by TGF-{beta}1 degradation instead of simple blockade and simultaneously inhibits the "don't eat me" CD24 signaling. We demonstrated that G01 effectively suppressed TGF-{beta}1-Smad signaling, reversed the EMT, and attenuated fibrotic collagen deposition. Additionally, it antagonized CD24 signaling to unleash robust antibody-dependent effector activity, further reprogramming the immunologically cold TME toward inflammatory phenotype. By selectively targeting malignant cells while sparing CD24-expressing B cells, G01 demonstrated a favorable safety profile. Notably, in patient-derived tumor fragments (PDTFs), G01 also inflamed the TME, underscoring its translational potential. Together, these results establish ICTAC as an effective strategy for tumor-specific receptor-mediated extracellular protein depletion, which supports its application for reversing TGF-{beta}1; and innate immune checkpoint-mediated tumor immunosuppression by concurrent targeting of both surface and soluble tumor-promoting factors.

Neurologic autoantibody cell-based assay images are difficult to interpret when fluorescence is weak, focal or dominated by background signal. We developed a visual artificial intelligence overlay that transfers detected cell boundaries from the structural channel to the antibody-staining image and evaluated standalone model concordance and its association with reader classifications. The analysis used 2 763 records for model development and 499 held-out records spanning 14 antibody targets. Four readers assessed the held-out records first without assistance and then with the boundary overlay and model category available. The selected model agreed with the reader-derived three-class reference in 85.2% of records, but recall was lowest for weak-positive or equivocal records. Reader agreement increased in the assisted session, and almost all revised ratings moved towards the model category. The combined display may support consistent visual review while also creating a risk of model-directed anchoring; its role as a second-read aid requires independent validation.

Polymer modification of antibodies has attracted attention for enhancing antibody functionality. Modification with polyethylene glycol (PEG) or biopolymers such as antibody-oligonucleotide conjugates has been investigated. Although direct effects at the modification site have been understood, distal effects of modifications on intrinsic antibody functions such as antigen-binding and Fc receptor-binding activities remain poorly characterized. In this study, we prepared site-specifically PEGylated trastuzumab to evaluate the effects of the modification site and the size of PEG chains on antibody functions. Reporter gene assays revealed a significant reduction in antibody-dependent cell cytotoxicity (ADCC) activity in a site- and size-dependent manner. Both the reduction in interaction with the antigen and Fc{gamma}RIIIa were observed in a size-dependent manner. Interestingly, the effect of the modification site was only observed for ADCC activity, which suggests that the higher-order structure of the modified antibody plays a critical role in effector function. These results provide molecular design guidelines for the polymer modification of antibodies.

Antibody developability is increasingly predictable from sequence, yet software and trained models are rarely made available. We present DELPHI, open software for training developability predictors from labelled antibody assay data, together with ready-to-run, retrainable models. DELPHI compares 25 language-model and classifier combinations under CDR H3-cluster cross-validation that reduces sequence-similarity leakage, measures how performance changes with labelled training-set size, and reports residue-level model attributions. Applied to in-house polyreactivity and size-exclusion (SEC) data, it reaches mean AUC 0.959 and 0.933. Trained on those data alone, it transfers to a 246,293-antibody public library (AUC 0.950 with our deployed model) and ranks polyreactivity at a level similar to the best reported Ginkgo competition point estimate, without training on its data. Any laboratory can screen candidates before running assays, generate residue-level engineering hypotheses, and retrain DELPHI for a new assay.

Purpose: Uveal melanoma (UM) is a rare but aggressive intraocular malignancy originating in the uvea (choroid, iris, and ciliary body). Despite primary treatment, 50% of UM patients develop metastases to the liver, lungs, bones, and skin. Topotecan, a topoisomerase I inhibitor, and SP141, an MDM2 inhibitor, have been studied in cancers, including retinoblastoma. This study explores a combined approach with these drugs in the 92-1 UM cell line. Methods: Primary choroidal melanoma-derived 92-1 cells were treated with topotecan and SP141 individually to determine IC50 values. Cell viability and proliferation were assessed using the MTT assay and the xCELLigence real-time monitoring system. The synergistic effects were analyzed quantitatively using the Loewe Additivity and Highest Single-Agent (HSA) models. Flow cytometry was employed to analyze cell cycle distribution, while cell death and apoptosis were monitored via high-throughput, real-time quantitative analysis using the IncuCyte live-cell kinetic imaging system. Results: Both Topotecan and SP141 induced significant cell death in 92-1 cells. xCELLigence showed dose- and time-dependent inhibition of proliferation. Combining sub-IC50 concentrations of topotecan and SP141 exhibited significant synergistic effects, markedly reducing growth. Cell cycle analysis revealed that topotecan causes a dose-dependent biphasic arrest in G2 and S phases, while SP141 causes G2/M arrest, leading to cell death. Cytotoxicity assays corroborated increased cell death following combination therapy, and the Caspase 3/7 assay indicated that apoptosis is the mechanism underlying cell death. Conclusions: The multi-targeted approach combining topotecan and SP141 drugs demonstrated synergistic therapeutic effects in 92-1 cells, suggesting a potential treatment strategy for managing UM.

Previously, we demonstrated the biosafety of polyethylene glycol-coated iron oxide nanoparticles (MAG@PEG) in a cellular model of viral oncogenesis, supporting their further preclinical evaluation. Herein, we evaluated the safety and biodistribution of MAG@PEG in tumor-bearing mice and assessed the influence of an external magnetic field on nanoparticle distribution. Animals were randomly assigned to three groups: G1, control (PBS); G2, MAG@PEG (10 mg/kg); and G3, MAG@PEG (10 mg/kg) with magnetic exposure for 1 h. Doses were administered intraperitoneally weekly for 1 month. Results showed that animal behavior and body weight were unaffected by MAG@PEG, while tumor growth was significantly reduced only in G2. Biodistribution studies based on tissue magnetization revealed higher magnetic iron levels in the stomach followed by the spleen, lungs, and tumors, particularly in G2. Histological analysis detected Prussian blue-positive staining mainly in the spleen and in lymphoid-associated structures in the lung, stomach and intestine, while a fraction of MAG@PEG remained within the peritoneal cavity. Serum biochemistry showed no evidence of nephrotoxicity. Furthermore, hemocompatibility studies using human blood samples demonstrated that MAG@PEG was non-hemolytic according to the ASTM F756 standard. Taken together, the biodistribution, histological, biochemical, and hemocompatibility findings provide a comprehensive preclinical assessment of the biosafety of MAG@PEG and identify the route of administration and magnetic targeting as key determinants of nanoparticle biodistribution.

Marmosets (MAR, Callithrix jacchus) are nonhuman primates extensively studied in a broad array of preclinical biomedical research areas. However, the limited number of available immunological reagents remains a critical shortcoming of these models. We successfully developed monoclonal antibodies (mAbs) pairs that can be used in immunoassays for the identification and quantification of the biomarkers of inflammation C-reactive protein (CRP), CXCL-10 (IP-10), interleukin (IL)-4, IL-6, and IL-10. Recombinant MAR protein variants were produced in mammalian cells, purified and used for mice immunization. Hybridoma clones were selected first for their capacity to bind the recombinant proteins. All possible pair combinations of these mAbs were then tested for their capacity to identify their targets in MAR-derived samples. These MAR samples consisted of supernatant of PBMCs exposed ex vivo to different stimulants, and plasma samples from animals stimulated in vivo with a low-dose intravenous LPS inoculation. The majority of the mAbs that recognized the recombinant proteins failed to bind to the MAR-derived homologs. However, we found MAR-derived reactive mAbs and selected pairs with optimal sensitivity and signal-to-noise ratio for immunoassays that included Luminex multiplexing assays, ELISA, and ELISpot assays. These immunoreagents and assays will improve the translational value of different MAR biomedical models.

We describe the concept for a new modality to create multi-specific therapeutics that utilizes modified nucleic acids as a scaffold to concatenate multiple warheads (hence called 'catamers'). Catamers are assembled via Watson-Crick interactions into multi-specific molecules from individual components such as ligand-binding modules, protease-cleavage sites, and toxins. Each warhead is covalently bound to an oligonucleotide, which in turn is connected to the catamer scaffold. Post-synthesis of the individual catamer components, they are annealed together such that Watson-Crick base pairs on the oligonucleotides dictate the organization of the warheads on the complete catamer. Notably, catamers are not aptamers, though they can contain aptamers as warheads, along with small molecules and peptides. By design, catamers are intended to address three key problems that have hamstrung the field of multi-specifics: (i) optimization rate; (ii) manufacturing cost; and (iii) unpredictable immunogenicity (Amash et al., 2024 PMID: 39189686). Catamers are purely synthetic and do not require cultured cells to produce. These advantages are based on the underlying design of the catamer and will require experimental validation in future studies. As a proof-of-concept catamer, we focus on a prostate-specific membrane antigen (PSMA)-targeted toxin conjugate. In molecular simulations, this catamer displays structural characteristics to support its intended pharmacology. Importantly, the design addresses one of the key shortcomings of antibody drug-conjugates (ADCs): off-target toxicity caused by release of the toxin after pinocytosis by healthy cells in contact with the blood.

Immunotoxins (ITs) are potent targeted cancer therapeutics that couple tumor-selective receptor binding with efficient intracellular delivery and cytotoxic enzymatic activity of bacterial toxins. While ITs have shown impressive efficacy in hematologic malignancies, dose-limiting toxicity remains a major barrier to their broader clinical application, particularly in solid tumors. The exceptional potency of immunotoxin payloads contributes to off-tumor toxicity because even limited uptake by receptor-low cells can be sufficient to cause cell death. Here, we systematically investigated how receptor-binding domain (RBD) affinity and avidity interact with catalytic payload activity to determine IT selectivity. Using CD123-, HER3-, and HER2-targeted ITs, we found that increasing receptor-binding affinity or avidity altered potency but provided limited control over the selectivity between receptor-high and receptor-low cells. In contrast, attenuating catalytic activity produced profound gains in selectivity when payload potency was appropriately matched to receptor engagement. The optimal level of attenuation varied with receptor context and toxin scaffold, demonstrating that maximal selectivity emerges from balancing cellular delivery with catalytic activity rather than maximizing either property alone. In vivo, payload attenuation increased the maximum tolerated dose of a HER2-targeted IT by 400-fold and enabled robust antitumor activity well below its toxicity limit, whereas the WT counterpart showed no detectable efficacy at tolerated doses. Together, these findings uncover and establish payload activity as a tunable determinant of immunotoxin selectivity and provide a framework for matching receptor engagement with catalytic potency to develop safer and more broadly applicable targeted toxins.

Drug targets and vaccines for bacterial diseases are challenging to develop. Computational subtractive genomics and reverse vaccinology offer a viable substitute for conventional drug and vaccine development approaches. In recent years, Pasteurella multocida, a Gram-negative pathogen responsible for severe infections in humans along with major diseases in livestock and poultry, has developed resistance to multiple drugs. As a result, there is an urgent demand for research into reliable, efficient, highly potent, and cost-effective strategies to combat the rise of Pasteurella multocida infections. To address this, the present study integrated an in silico subtractive genomics and reverse vaccinology workflow to pinpoint proteins with potential value as both drug and vaccine targets. From the complete Pasteurella multocida Pm70 proteome, we identified 446 proteins that had no similarities with human proteins, out of which 129 were found to be essential for the pathogens survival. These 129 proteins are therefore potential candidates for drug development. Further metabolic pathway analysis, virulence potential, subcellular localization, and structural analyses revealed two outer membrane proteins, namely, 47 kDa outer membrane protein and lipopolysaccharide-assembly protein LptD, as promising candidates for therapeutic development. Subsequent antigenicity analysis revealed both outer membrane proteins as potential vaccine targets, exhibiting highly favorable antigenicity scores. Candidate B- and T-cell epitopes were systematically mapped from these two targets based on antigenicity, immunogenicity, toxicity, and allergenicity potential and only high-confidence epitopes were assembled using adjuvants and class-specific linkers to construct the final multi-epitope vaccine (MEV). The designed MEV construct was demonstrated to be highly antigenic, non-toxic and non-allergenic to humans, while possessing favorable structural and physicochemical properties, and exhibited strong binding interactions with human toll-like receptors with minimal molecular deformability. Although further experimental validation is recommended to fully assess their efficacy, the present study establishes a solid foundation for the development of novel therapeutics and vaccine candidates against P. multocida, ultimately contributing to the reduction of its rising infections.

The resurgence of the Mpox virus (MPXV) highlights the urgent need for scalable, mutation-resistant countermeasures. Slow development timelines hinder traditional empirical vaccine discovery, while conventional vaccinology often yields high false-positive rates because it relies on static, linear sequence-based screening. These classical informatics filters consistently fail to predict physical structural stability or the dynamic presentation of immune cells under physiological conditions, creating a distinct translational gap between computational design and in vitro efficacy. To address these limitations, here we engineered an advanced, AI-augmented vaccine discovery pipeline targeting the most virulent components of the MPXV proteome. We systematically screened eight critical viral targets essential for replication, membrane morphogenesis, and host-cell entry to map highly conserved T-cell and B-cell epitopes. Moving beyond standard heuristic filtering, we implemented a high-fidelity deep learning validation layer: ESM-2 protein language models (pLMs) were deployed to calculate pseudo-perplexity scores across chimeric linker junctions (EAAAK, GPGPG) to guarantee structural naturalness, MHCflurry ensemble models successfully verified active intracellular antigen presentation, and structure-informed graph neural networks (GNNs) simulated the physical node-attention dynamics of the immune synapse. Finally, recurrent neural networks (RNNs)-driven mRNA kinetic modeling optimized translation initiation rates and minimum free energy profiles during in silico cloning. This multi-layered pipeline successfully yielded three structurally refined, multi-epitope vaccine constructs optimized for maximum global population coverage. ESM-2 structural validation confirmed exceptionally low perplexity scores at structural junctions, indicating that the engineered chimaeras have native-like stability and a minimized risk of proteolysis. GNN-based immune synapse modeling demonstrated highly stable binding kinetics and robust binding free energies between the target epitopes and human HLA alleles. Furthermore, RNN kinetic optimizations established that the cDNA sequences are optimized for high-yield expression in heterologous expression hosts without risk of ribosomal stalling or translational bottlenecks. This study establishes a deep learning-augmented framework that bridges the gap between theoretical sequence optimization and biopharmaceutical scalability. By validating the structural naturalness, real-world immunogenicity, and manufacturing kinetics of our vaccine chimeras, this framework presents a robust, proactive paradigm for rapid vaccine architecture against emerging orthopoxvirus threats.

Albumin binding can substantially alter the unbound concentration and apparent cellular potency of small-molecule anticancer agents. BAY-1816032 is a selective BUB1 kinase inhibitor with activity in malignant pleural mesothelioma, but its interaction with serum albumin has not been defined. This study combined intrinsic bovine serum albumin (BSA) fluorescence measurements, molecular docking, and cell-viability assays to examine whether albumin association influences BAY-1816032 activity. BAY-1816032 produced pronounced quenching of BSA fluorescence at low drug-to-protein molar ratios, and the quenching response was greater at mildly acidic pH than at mildly basic pH. Docking to BSA (PDB 4F5S) placed the top-ranked pose in Sudlow site I (subdomain IIA), with a Vina score of -7.8 kcal/mol and predicted hydrogen-bond contacts with Tyr149, Arg256, and Asp450. In NCI-H28, NCI-H2052, NCI-H2452, and AB12 mesothelioma cells, supplementation of 10% fetal bovine serum medium with 2% (w/v; 20 mg/mL) BSA shifted BAY-1816032 concentration-response curves to the right. Relative to 10% serum alone, IC50 values increased from 2.4 to 11.1 uM in H28, 3.6 to 22.0 uM in H2052, 2.0 to 4.9 uM in H2452, and 2.4 to 11.1 uM in AB12. These data support an albumin-dependent reduction in the freely available drug fraction and identify site I as the leading computational binding-site hypothesis. Because fluorescence-intensity and docking data do not independently establish a dissociation constant or quenching mechanism, direct unbound-fraction measurements and competitive displacement experiments are required for quantitative confirmation.

Vasculogenic mimicry (VM) describes the ability for tumor cells to mimic endothelial cells and form tumor-lined blood vessels. Although VM is associated with poor prognosis, resistance to therapeutics and metastasis, its presence, relevance and functionality have been debated, largely due to the techniques employed to determine its presence. Dual CD31 and periodic acid-Schiff (PAS) histological staining, the method widely used to detect VM within tumor tissue, is hampered by issues with artifacts of 2D sections and does not assess active blood flow through vessels and hence cannot confirm vessel functionality. Here, we present a 3D imaging pipeline to detect functionally perfused VM and host vessels within mouse tumor models. Mice tumor models are intravenously injected with fluorescently-labelled tomato lectin which binds to the lumen of functional blood vessels. These tumors are thickly sliced, optically-cleared, stained with endothelial markers and imaged using light-sheet microscopy. We apply this imaging modality to image tumors from six breast cancer patient-derived tissue xenograft models, an orthotopic mammary cancer model and two small cell lung cancer circulating-tumor cell explant models, where VM vessels, mosaic vessels and host-VM vessel junctions can be detected. To our knowledge, this is the largest collection of 3D VM vessel images to date, providing convincing evidence of the presence and functionality of VM within tumors and defining the level of evidence necessary to detect VM in vivo.

Cell fusion enables cell-based applications, yet systematic optimization remains constrained by low throughput (microfluidic devices) and manual operation (bulk protocols). Here, an automated microfluidic platform is presented for cell pairing and chemical fusion at a statistically relevant scale. High throughput is achieved by arranging ~350,000 deformability-based cell traps as concentric circular arrays in a radial chip. Using tumor and dendritic cells as a model for cancer vaccine, the device reproducibly co-occupies ~75% of traps (~250,000) of which ~140,000 hold exactly one cell of each type. This exceeds the yield of previously reported in hydrodynamic pairing arrays by more than an order of magnitude. A concentration step-up-and-down PEG profile yielded a fusion efficiency of ~20% (range 17.7-23.4%, n =3) at viability above 90% that results in producing at least ~50,000 hybrids per chip. This efficiency is comparable to that reported for PEG-based fusion in small-scale microfluidic devices but obtained at higher throughput and approximately four-fold higher than the conventional benchtop protocol. Fusion efficiencies were quantified by image analysis and validated by flow cytometry. By combining automation, large-area circular trap arrays and reagent control, the platform is useful for data-rich optimization of cell engineering processes previously constrained by limited microfluidic throughput.

Introduction: Neoepitopes form the basis of tumor-specific immune responses. Tissue biopsy, the primary source for neoepitope detection, is limited and invasive. Therefore, we aimed to identify neoepitopes by mutanome-guided immunopeptidomics from plasma of cancer patients. Methods: Mass spectrometry (MS) data analysis of HLA ligands from plasma (n = 4) was guided by patient-specific mutanomes of cell-free DNA (cfDNA) from plasma or tumor genomic DNA (tgDNA) from tissue. Matched tumor tissue and healthy donor plasma served as controls. Neoepitopes were validated with synthetic peptides, and immunogenicity was assessed using IFN-gamma ELISpot and intracellular cytokine staining. Results: Wild-type immunopeptidomes from tissue and plasma overlapped by 58%, with 91% of plasma HLA ligands rediscovered in tissue. 13 out of 15 tumor-associated HLA ligands detected in plasma were rediscovered in the matching tissue. However, no neoepitopes in plasma were identified by immunopeptidomics guided by cfDNA mutanomes, likely reflecting the limited overlap between cfDNA and tgDNA mutanomes (15%). Using the tgDNA mutanome as a complementary reference, two neoepitopes were detected in one patient's plasma, albeit at the MS detection limit. Both neoepitopes were also discovered in tissue, along with three tissue-exclusive neoepitopes. Two tissue-exclusive neoepitopes induced antigen-specific T cell responses in healthy donor PBMCs. Conclusion: In summary, plasma immunopeptidomics enables profiling of HLA ligands from wild-type proteins, including TAAs. In principle, neoepitope detection from plasma at the peptide level is feasible, but tissue remains the gold standard for variant calling and neoepitope identification. Improved detection methods may enable minimally invasive approaches in the future.