Integrative Biology

Protein tyrosine phosphatase receptor J (PTPRJ) restrains cell proliferation and migration by dephosphorylating receptor tyrosine kinases (RTKs) including the epidermal growth factor receptor (EGFR). PTPRJ is a purported tumor suppressor, and alterations to its expression and/or function are associated with colorectal, breast, lung, and other cancers. While there is interest in controlling PTPRJ-regulated phenotypes, efforts are limited by the complexity of PTPRJ-mediated signaling. PTPRJ dephosphorylates multiple RTKs, and the degree to which PTPRJ control of signaling and phenotypes depends on local cellular RTK activation profiles is unknown. To probe the context dependence of PTPRJ signaling regulation, we collected signaling measurements across 16 pathway nodes at two time points in a panel of HSC3 carcinoma cells engineered with different PTPRJ expression profiles. Cells were treated with three different RTK ligands, and paired phenotype measurements (viability, wound healing, xCELLigence cell index) were made. Partial least squares regression models were developed to predict relationships between PTPRJ-regulated signaling pathways and cell phenotypes. The model effectively separated contributions to variance arising from the PTPRJ expression background and growth factor context. In testing model predictions, we demonstrated that PTPRJ suppressed MET-induced cell cell proliferation via regulation of a HER3/AKT signaling axis that stabilized PTPRJ expression through an unanticipated feedback mechanism. We also found that PTPRJ regulated HSC3 cell migration via JNK signaling that was preferentially activated by MET. Our results identify new regulatory nodes through which PTPRJ influences cancer cell phenotypes and demonstrates that these processes preferentially occur in the context of distinct RTK activation states.

Anticancer drug resistance is challenging to overcome because it can arise through both intrinsic and acquired mechanisms, each driven by distinct cellular machinery. In particular, there is a sharp need for therapies that target hormone-insensitive prostate tumors due to the growing incidence of castration-resistant prostate cancer. Optimizing the pathways that regulate apoptosis in prostate cancer offers a promising strategy to induce apoptosis and inhibit tumor progression, since these mechanisms do not depend on hormonal signaling. Here, we identified strategies to enhance apoptosis in prostate cancer cells. We used several computational tools (including sensitivity analysis, particle swarm optimization, and ImageJ) to design an ordinary differential equation model of caspase-mediated prostate cancer apoptosis signaling. We apply the model to identify key modalities that increase the propensity toward apoptosis across three separate pro-apoptotic drugs (Tocopheryloxybutyrate, Narciclasine, and Celecoxib). Overall, we demonstrate that apoptosis dynamics can be accurately captured in response to each of the three drugs and identify which features of the model represent viable targets for overcoming intrinsic drug resistance.

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recur-rently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

The tumor microenvironment (TME) is a complex ecosystem composed of tumor cells, cancer-associated fibroblasts (CAFs), immune suppressive cells, and the extracellular matrix (ECM), playing a crucial role in tumor development and CAR-T cell therapy efficacy. CAR-T therapy has shown promise in hematological malignancies but faces challenges in solid tumors due to the TMEs ability to suppress CAR-T cell infiltration, proliferation, and cytotoxicity. Traditional drug evaluation models, such as 2D cell cultures and animal models, have significant limitations due to oversimplification of the in vivo environment or physiological differences between species. Organoid models offer a more biomimetic approach but often fail to fully recapitulate the TMEs complexity and heterogeneity. Our research developed a tumor organoid and CAF co-culture model using the IBAC co-culture chip, demonstrating that CAFs significantly impact CAR-T cell therapy efficacy by forming physical (e.g., fibronectin) and chemical (e.g., IL-10) barriers that prevent CAR-T cell infiltration and cytotoxicity. This model provides a high-biomimetic platform for investigating the TMEs effects on CAR-T therapy and highlights the importance of incorporating a comprehensive stromal component into in vitro models to enhance their predictive power for cancer treatment.

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

BackgroundThe complexity of gene regulatory networks, involving thousands of genes, poses a fundamental challenge to understanding cancer phenotype reversal. However, recent evidence suggests that the effective dimensionality of normal and tumor transcriptional manifolds is low, and that small panels of genes can discriminate perfectly between normal and tumor samples. MethodsWe build upon two previously developed concepts: (i) highly accurate normal and tumor gene markers (namely, N-and T-markers), defined as genes with exclusive expression intervals in normal and tumor samples, respectively; and (ii) gene deregulation networks (GDNs), represented as directed acyclic graphs encoding causal relationships between gene deregulation events. A subset of genes appearing in both marker classes (NT-markers) act as bridging nodes between the N-and T-GDNs. Starting from these elements, we introduce a quantitative dynamical model based on node frequency and connectivity to assess how gene intervention effects propagate through the GDN and thereby predict their overall impact on the tumor tissue. ResultsAccording to the model, interventions on pure T-markers (T-markers that are not NT-markers) produce effects largely confined to the T-GDN, with a minimal perturbation of the N-network. Interventions on pure N-markers (N-markers that are not NT-markers) generate a perturbation of both networks, but with limited effect. In contrast, interventions on NT-markers with high activation frequency in both tumors and normal state (e.g., AGER in lung adenocarcinoma: 98% in tumor samples, 75% in normal samples) can induce bidirectional phenotype shifts. For an effective combination of targets, coverage across tumor samples must be maximized. At the same time, in the T-GDN the number of nodes unreached by the reverse cascade following the intervention must be minimized, as these regions may act as escape routes for the tumor. Escape probability further depends on the tumor stage and the tumors activation rate of new T-genes. When targeting NT genes, high frequency in normal samples should also be prioritized. ConclusionsHigh-frequency NT-genes, due to dual network connectivity and tissue relevance, represent optimal targets for achieving at least partial phenotype reversal. This framework provides a quantitative guide for prioritizing gene therapy targets and designing combination strategies that balance coverage, escape minimization, and normal tissue relevance.

IntroductionCancer progression is driven not only by tumor cells but also by interactions between the extracellular matrix (ECM), stromal cells, and immune cells within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are major drivers of ECM remodeling, assembling ECM with aberrant organization. Extra domain A fibronectin (EDA-FN), a cellular FN containing an extra type III domain, is upregulated in the TME. EDA-FN regulates cellular behavior and has been associated with poor patient prognosis. Macrophages are among the most abundant immune cells within the TME, where they contribute to TME remodeling and inflammation to promote cancer cell invasion and metastasis. However, how tumor-associated matrix-specific cues regulate macrophage behavior remains largely understudied. PurposeHere, we developed a fibroblast-derived matrix platform that captures the structural imprint of tumor-associated EDA-enriched matrices and investigated how matrix-specific cues regulate macrophage behavior in the absence of ongoing soluble factor cues. MethodHuman mammary fibroblasts (HMFs) preconditioned in incubated low-serum media (lNC, or control) and MDA-MB231 metastatic breast cancer cell-conditioned media (mTCM) were cultured on polyacrylamide gels of 2 kPa and 20 kPa, respectively, followed by decellularization. Matrix organization, including fiber alignment, width, and intrafibrillar spacing, was quantified from confocal images. Decellularized EDA-FN-enriched matrices were subsequently reseeded with macrophages to assess macrophage morphology, phenotype, and matrix interactions. ResultsThe combined effects of tumor-derived soluble factors and pathological stiffness induced a CAF-like phenotype in HMFs, accompanied by cytoskeletal reorganization and microarchitectural alterations of EDA-FN-enriched matrices. Tumor-associated matrices exhibited increased alignment, narrower fiber width, and enlarged intrafibrillar spacing compared to control matrices. These aberrant, tumor-associated matrix-derived features were associated with altered macrophage behavior, including heterogeneous morphology, enhanced localized EDA-FN matrix loss beneath the cell body, and a hybrid phenotype with a shift toward a CD206-dominant profile. ConclusionsThese findings demonstrate the feasibility of obtaining EDA-FN-enriched matrices to isolate matrix-specific cues for investigating macrophage-ECM interactions. Furthermore, this platform can be leveraged to identify matrix-targeting therapeutic approaches for modulating macrophage function within the TME.

Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment that facilitate a supportive niche for cancer progression and metastasis. Experimental evidence suggests that CAFs can facilitate estrogen-independent tumor growth, thereby reducing the efficacy of anti-hormonal therapies. Understanding and quantifying the complex interactions between tumor cells, hormonal signalling, and the microenvironment are crucial for designing more effective and individualized treatment strategies. We propose a mathematical framework to explore the influence of CAFs on ER+ breast cancer progression and to evaluate strategies to mitigate their impact. We develop a system of nonlinear ordinary differential equations that substantiates the experimental observations by providing a mechanistic basis for the role of CAFs in regulating estrogen-independent growth dynamics. We then employ optimal control theory to evaluate distinct therapeutic approaches involving monotherapy or combinations of: (i) inhibition of tumor-to-CAF signaling, (ii) inhibition of CAF-to-tumor proliferative signaling, and (iii) endocrine therapy. Taken together, our results demonstrate that CAF-targeted strategies can enhance treatment efficacy across various estrogen dosing regimens. Our study provides new insights into the potential of CAF as a therapeutic target that could help to improve existing approaches for endocrine therapies.

BackgroundActivated platelets release polyphosphate (polyP), a linear polymer of inorganic phosphate residues, from dense granules. Experiments performed under no-flow conditions show that polyP alters the kinetics of tissue factor (TF) pathway reactions, accelerating FXI activation by thrombin and FV activation by FXa and thrombin, and may impact inhibition by tissue factor pathway inhibitor (TFPI). How polyP influences this pathway in conjunction with platelet deposition under flow remains understudied. ObjectivesTo investigate how polyP-mediated acceleration of FV and FXI activation modulates thrombin generation under flow in TF-initiated coagulation. MethodsWe extended a previously validated mathematical model of platelet deposition and coagulation under flow to examine polyP-mediated effects following a small vascular injury during intravascular clotting. Simulations varied the surface density of TF exposed, wall shear rate, and plasma TFPI concentration. ResultsPolyP shifts the threshold TF density for a thrombin burst to lower TF densities. For TF densities above this threshold, polyP shortens the lag time to thrombin generation in a TF- and shear-rate-dependent manner. Although no explicit effect of polyP on TFPI function was included in the model, thrombin generation was much less sensitive to TFPI concentration with polyP, in a TF-dependent manner. Relative contributions of accelerations of FV and FXI activations depend on incompletely known enhancements by polyP. ConclusionsThe experimentally observed influence of polyP on TFPI function depends on TF density and may arise indirectly from accelerated FV and FXI activation, with the dominant effect arising through accelerated thrombin-mediated conversion of FV to FVa.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of Cyp1a1 and Ahrr mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

Tissue engineered constructs are increasingly used for both modeling organs and disease in vitro as well as for therapeutic intervention. In addition to collagen, these constructs commonly include native extracellular matrix proteins (ECM), such as fibronectin and laminin. Given the critical role of inflammatory pathways in disease and in response to implanted materials, it is important to understand the role these proteins play in regulating the inflammatory environment. Fibronectin and laminin influence neutrophil function and endothelial activation in 2D, but their regulation of the inflammatory environment in 3D engineered constructs is not clear. For this study, we used an inflammation-on-a-chip device that includes a model blood vessel surrounded by a collagen I hydrogel with fibronectin and/or laminin. We investigated the additive effects of both proteins and a range of concentrations for each protein to determine concentration dependence. Both fibronectin and laminin have concertation dependent effects on neutrophils and the endothelium. High concentrations (50 {micro}g/mL) of fibronectin reduced neutrophil migration, while 20 {micro}g/mL laminin reduced neutrophil extravasation and migration, potentially due to lower ICAM-1 expression by the endothelium. Interestingly, 50 {micro}g/mL of laminin significantly disrupted endothelial vessel formation and reduced ICAM-1 and VE-cadherin expression, likely due to significant changes in the collagen architecture. The inclusion of fibronectin and laminin, even at physiological levels, results in significant effects on neutrophil behavior, endothelial vessel formation, and collagen architecture. These proteins impact the inflammatory environment and thus need to be considered when modeling diseases and designing therapeutics, especially when neutrophils or an endothelium are involved. Translational Impact StatementThis work uses an inflammation-on-a-chip device to study how fibronectin and laminin impact neutrophil behavior and vascular inflammation as these proteins are commonly used in engineered constructs. We found that fibronectin impairs neutrophil migration, while laminin decreases neutrophil extravasation and migration and at higher concentrations also prevents endothelial vessel formation. Therefore, researchers should be aware that these proteins will alter the inflammatory environment when including them in engineered constructs.

Accurate cell counting is essential in tissue engineering and cancer research. The ongoing transition towards advanced 3D in vitro tumour models raises a question about the validity of the standard cell counting protocols, particularly in the systems containing extracellular matrix-based scaffolds. Here, we provide a quantitative analysis of the performance of three popular plate reader-based cell counting/viability assays, such as the Alamar Blue, MTT, CellTiter Glo 3D assays, in 2D monolayer and 3D scaffold-based cultures of U251 human glioblastoma cells, including cell-laden Matrigel plugs, and original tissue engineering constructs based on the decellularised sheep brain scaffolds. We quantitatively characterized the assays linearity, precision, biological and technical reproducibility, proportionality, and inter-assay agreement. The study revealed that assays performance is highly platform-dependent, with 2D cultures allowing significantly more precise and reliable measurements than in 3D ECM scaffold-based cultures. The numerical results provided in this study can help researchers make informed decisions when working with 3D scaffold-based in vitro tumour models and for other tissue engineering purposes where precise cell counting is essential. ToC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/720021v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16018d9org.highwire.dtl.DTLVardef@1ff7d6dorg.highwire.dtl.DTLVardef@838021org.highwire.dtl.DTLVardef@1510d5b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cells communicate via extracellular ligands, such as hormones, which bind to plasma membrane receptors and trigger intracellular signaling cascades. G Protein-Coupled Receptors (GPCRs) exemplify this mechanism by initiating signaling both at the cell surface and, from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking-including internalization, endosomal sorting, and recycling-remains a pivotal but often overlooked component of theoretical GPCR models. In this study, we present a mathematical framework that integrates receptor trafficking and signaling compartmentalization into generic GPCR dynamic models. Using a compartmentalized approach based on systems of ordinary differential equations (Chemical Reaction Networks), we analyze how receptor internalization and recycling modulate ligand-induced responses. Our results show that the balance between plasma membrane and endosomal signaling can significantly enhance or diminish ligand efficacy. Calibrated with high-throughput kinetic data, our model offers a refined tool for ligand pharmacological characterization and advances the understanding of GPCR signaling spatial organization.

BackgroundCytokines are immunomodulatory proteins that play central roles in regulating immune responses and represent attractive targets for cancer therapy. However, as single agents, cytokines have shown limited clinical benefit due to systemic toxicities and a short in vivo half-life. Our group has focused on engineering fusion cytokines (fusokines) that couple two cytokines into a single biologic to reprogram immune cell responses by enforcing non-canonical receptor engagement and signaling. A chimeric IL-6/IL-1{beta} fusokine was engineered to test the hypothesis that enforced co-engagement of IL-6 and IL-1{beta} signaling pathways would confer a gain-of-function phenotype in T cells and promote robust anti-tumor immunity. Here, we describe the immunomodulatory properties of IL6/1 fusokine and a method to deliver this fusokine to produce inhibition of ovarian tumor growth in a pre-clinical mouse model. MethodsLentiviral vectors encoding murine or human IL6/1 were designed using Vector Builder and expressed in either HEK293, CHO or ID8-F3 (p53-/-) cells depending on the downstream experiment to be conducted. IL6/1 expression was validated by ELISA and flow cytometry. Effects of human IL6/1 (hIL6/1) on T cell function (proliferation, memory phenotype, activation induced apoptosis) were monitored by flow cytometry. For in vivo studies, ID8-F3 murine ovarian cancer cells expressing mouse IL6/1 (mIL6/1) were administered intraperitoneally (I.P.) as a cell-based therapy to C57BL/6 female mice bearing established ID8-F3 luciferase tumors. Tumor progression was monitored by bioluminescence (BLI) imaging, and overall survival was evaluated. ResultshIL6/1 significantly enhanced T cell survival and selectively promoted activation and expansion of CD45RO memory T cells. mIL6/1 expressing ID8-F3 cells (ID8IL6/1) demonstrated stable transduction and sustained cytokine secretion. In vivo, ID8IL6/1 cell therapy significantly reduced tumor growth and improved overall survival compared to control groups, with 2 of 8 mice achieving complete tumor clearance. ConclusionThese findings indicate that IL6/1 fusokine enhances T cell survival and proliferation while promoting memory responses. Engineered cancer cells (ID8-F3) expressing mIL6/1 fusokine induced a strong anti-tumor response when delivered as a therapeutic vaccine in ovarian cancer mouse model. What is already known on this topicO_LIFusokines are a class of bifunctional proteins designed to achieve synergistic immune modulation. Previous studies in our lab have shown fusokine exhibit gain-of-function immunomodulating activity. Individually, IL-6 and IL-1{beta} are recognized for their roles in promoting T-cell proliferation and effector function. However, the potential for a fused IL-6/1 fusokine to reprogram the immune system and elicit a superior anti-tumor response in vivo in ovarian cancer model is not yet studied. C_LI What this study addsO_LIThis study develops a novel fusion cytokine (fusokine), combining IL-6 and IL-1{beta}, and demonstrate robust activation of T cells. In a preclinical ovarian cancer model, engineered cancer cells expressing IL6/1 used as a therapeutic vaccine showed significant tumor reduction and improved overall survival. C_LI How this study might affect research, practice or policyO_LIThis study demonstrates that in comparison to individual cytokines, fusokines have greater potential to activate T cell function and when delivered as a cell therapy, achieve clear therapeutic efficacy in an ovarian cancer model. Further translational and clinical studies may enable the development of novel and more effective fusokine cell therapy approaches for patients with ovarian cancer. C_LI

AbstractsO_ST_ABSBackgroundC_ST_ABSQuantitative systems pharmacology (QSP) models provide mechanistic insight into drug response but are limited by labor-intensive, expert-driven workflows. We developed an AI-assisted QSP (AI-QSP) framework that integrates large language models (LLMs) with SBML-based modeling to enable automated reconstruction, extension, and calibration of mechanistic models. MethodsThe framework was applied to a published CAR-T QSP model. The model was reconstructed in SBML and extended via LLM-guided prompts to incorporate key resistance mechanisms: T-cell exhaustion, PD-1/PD-L1 checkpoint regulation, and tumor antigen escape. Model development followed an iterative expert-in-the-loop workflow. The resulting model (21 reactions, 9 species) was calibrated to synthetic benchmark data using 19-parameter optimization. Model credibility was assessed using ASME V&V 40 and ICH M15 principles, including global sensitivity and profile-likelihood analyses. ResultsThe calibrated model reproduced benchmark dynamics with high accuracy (mean log-RMSE = 0.132). Sensitivity analysis identified CAR-T killing and bystander cytotoxicity as dominant drivers of tumor response. Profile-likelihood analysis showed 71% of parameters were practically identifiable, with remaining parameters prioritised for future data-driven refinement. ConclusionsAI-assisted QSP modeling enables reproducible, scalable model reconstruction and evolution while maintaining mechanistic transparency and regulatory alignment. This framework provides a foundation for accelerating model-informed drug development in cell and gene therapies.

Glioblastoma is an aggressive brain cancer whose cells can switch between different modes of invasion in response to their surroundings, making the disease difficult to predict and treat. How physical forces influence this adaptability remains poorly understood. Here, we used simulated microgravity together with engineered hydrogels that independently control adhesion, degradability, and mechanical properties to test how gravity affects glioblastoma invasion. Microgravity strongly reduced invasion and shifted cells from elongated, protrusive behavior to a more cohesive state. Proteomic analysis showed reduced invasive signaling together with increased cell-matrix and cell-cell adhesion, consistent with a redistribution of contractile forces toward the cell edge. Under normal gravity, blocking CD44, integrin {beta}1, or N-cadherin reduced matrix-dependent invasion. In contrast, under microgravity, inhibiting these same adhesion pathways restored invasion, indicating that microgravity traps cells in an overly adhesive, cohesive state that limits movement rather than motility itself. These findings show that gravity is an important regulator of cancer cell plasticity and reveal a mechanically induced vulnerability in glioblastoma invasion. More broadly, combining defined biomaterials with gravitational modulation provides a new way to study how physical forces shape tumor behavior.

The endometrium and menstrual disorders, such as endometriosis and adenomyosis, are difficult to study, partly because menstruation depends on interactions between multiple cell types through complex molecular mechanisms. To help understand this system, researchers need humanized experimental and computational models that can interrogate how endometrial cell populations impact each other in both physiological and pathological conditions. Here, we use ordinary differential equations (ODEs) to model changes in the rates of endometrial cell proliferation and death in response to hormones, cytokines, and the specific cell types present. To calibrate this computational model, we used previous-published experimental datasets from a 3D co-culture platform supporting primary human endometrial epithelial organoids and endometrial stromal cells. Our ODE-based model can simulate the size of endometrial epithelial organoids and the density of stromal cells over time under multiple hormone/cytokine conditions in mono- and co-cultures. We further created a second, partial differential equation (PDE)-based model that simulates the diffusion of molecules added to these 3D cultures and their uptake by proliferating endometrial cells using the predicted cell densities from the ODE model as inputs to the PDE simulations. We show that the exposure to hormones and cytokines used in the experiments is reasonably homogenous throughout the 3D culture and identify conditions where this would not be true. Altogether we use these models to quantify the influence of stromal cells on epithelial cell proliferation and vice versa, to identify differences across cells from different donors, and to provide a quantitative assessment of experimental designs.

Adoptive cell therapies are transforming the treatment of cancer and autoimmunity by enhancing patients own immune cells to fight disease. In cell therapy manufacturing, immunomagnetic beads are used to isolate and activate target cells for gene transfer but must be removed downstream to [&le;]10 beads per 300,000 cells. Current quantification requires time-intensive and error-prone manual counting using brightfield microscopy, while existing automated approaches struggle with variable bead-cell morphology and tedious sample preparation steps. Raman spectroscopy offers rapid, morphology-independent detection using molecular signatures generated by inelastic light scattering. Here, we leverage immunomagnetic beads strong Raman signatures to quantify them in area scans from dried samples, achieving single bead resolution and accurate counting of bead clusters with and without cells. Using low power ([&le;]7 mW) and exposure times ([&ge;]0.5 s), the average area under 3 signature Raman peaks (1110 cm-1, 1346 cm-1, and 1595 cm-1) are measured and input to a linear regression model, achieving a mean squared error (MSE) of <0.2 beads. Our results show Raman spectroscopy as a robust, automated approach for bead counting in existing pipelines with potential to improve the safety and throughput of cell therapies.