Biology subjects

Pilon-Thomas, S.

Publications and source records attributed to Pilon-Thomas, S..

11 recordsLinked to original sources

bioRxiv2026

BCL11B targeting in tumor CD8+ T cells amplifies anti-tumor response by blocking exhaustion while promoting stemness and cytotoxicity

Tumor infiltrating CD8+ T cells (TILs) progress to a state of terminal exhaustion (Ttex) which have impaired functionality and are nonrenewable. However their precursors (Tpex) are renewable and can generate efficient effector cells. We started from the observation that melanoma patients undergoing therapy with checkpoint inhibitors show increased survival when their T cells have low BCL11B mRNA. In line with this, ablation of Bcl11b in CD8+ TILs conferred a superior anti-tumor response in murine melanoma and ovarian cancer models. Bcl11b KO TILs failed to progress to the Ttex state and retained elevated stemness. Bcl11b exerted its role by repressing expression of essential transcription factors (TF) controlling stemness, and conversely by promoting expression of exhaustion-associated TFs and inhibitory receptor genes, through complex epigenetic control. In addition, Bcl11b KO CD8+ T cells showed increased Ag-specific cytolytic activity and elevated Gzmb and Prf1 proteins, but no increase in their mRNAs, however presented higher expression of genes with role in translation. Furthermore, CRISPR-CAS9-mediated deletion of BCL11B in human TILs from a patient with poor response to adoptive cell therapy with autologous TILs, improved their cytolytic activity and promoted expression of the stemness-associated TF TCF1, underlying its potential therapeutic use. HIGHLIGHTS- Adoptive transfer of Bcl11b KO CD8+ TILs surpasses WT in tumor burden reduction - Bcl11b ablation reprograms TILs and impairs the progression to Ttex state - Bcl11b KO CD8+ T cells have elevated cytotoxicity and kill only Ag-MHCI targets - BCL11B deletion in nonresponder ACT-TIL improves cytolytic activity and elevates TCF1 GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/742578v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@c3b965org.highwire.dtl.DTLVardef@1c09d75org.highwire.dtl.DTLVardef@a19748org.highwire.dtl.DTLVardef@d2fd6b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Dual stimulation of CD40 and 41BB pathways during ex-vivo TIL expansion enhances CD8+ T cell expansion

BackgroundTumor-infiltrating lymphocyte (TIL) therapy has demonstrated clinical efficacy in malignant melanoma; however, inefficient ex vivo expansion remains a major limitation. We previously showed that stimulation of tumor-infiltrating B cells via CD40-CD40L axis improves TIL expansion, and that direct activation of the 41BB-41BBL pathway on T cells enhances CD8 T cell outgrowth. We hypothesized that adding simultaneous targeting of both pathways would augment the growth and activity of CD8+ cytotoxic T cells. We conducted a study with the objective of determining the feasibility of dual stimulation with human tumors as justification for a Phase I trial. MethodsCD40L variants were generated by yeast display selection and evaluated for B cell binding and activation. The effects of CD40L variants on TIL expansion were evaluated using tumors derived from standard of care resections using fragment method. Based on these findings, a bi-specific molecule was designed and generated fusing a CD40L variant and 41BB to the N- and C-termini of a trimeric leucine zipper. The effects of the bi-specific molecule (termed CD40LEPC6-41BBL) on TIL expansion were evaluated in TIL cultures derived from lung tumor and melanoma fragments. TIL phenotypes were assessed by flow cytometry, including high-dimensional FlowSOM analysis, and tumor reactivity by autologous tumor co-culture assays. ResultsEach of our engineered CD40L variants bound B cells and induced CD80/CD86 expression at levels comparable to wild-type CD40L. Supplementation of TIL cultures with CD40L variants increased the success rate of TIL expansion compared to control. We then developed a bi-specific CD40LEPC6-41BBL molecule capable of binding to both B and T cells. Addition of CD40LEPC6-41BBL significantly increased total TIL yield and improved expansion success rates in both lung tumor and melanoma cultures. In particular, CD40LEPC6-41BBL promoted preferential expansion of CD8 T cells. High-dimensional analysis revealed enrichment of CD8 T cell clusters expressing CD39, CD69, TIM3, and CD56 in cultures supplemented with CD40LEPC6-41BBL. Furthermore, treated cultures displayed increased frequencies of CD27 CD4 T cells. Functional assessment suggested a trend toward enhanced tumor reactivity in melanoma-derived TIL products expanded with CD40LEPC6-41BBL. ConclusionsSimultaneous stimulation of CD40 and 41BB pathways using a novel bi-specific molecule resulted in qualitative and quantitative enhancement of TIL products. These findings support dual targeting of tumor-infiltrating B cells and T cells as a promising strategy to optimize TIL manufacturing for adoptive cell therapy in Phase I trials.

immunology↗

bioRxiv2026

A virtual cohort framework with applications to adoptive cell therapy in bladder cancer

Even under the same treatment, responses can vary. Virtual cohorts build on an available, often limited, dataset can capture these differences and enable the discovery of treatment protocols that work well for a wide variety of individuals. In this paper, we refined current virtual cohort pipelines by improving data handling, ensuring the virtual cohort can be used to stratify individuals into treatment subgroups based on their data, and validating that the virtual cohort matches the observed data variability. To illustrate, we applied this pipeline to a murine data set of orthotopic bladder cancer treated with gemcitabine (Gem) and immunotherapy with OT-1 cells. We generated over 10,000 virtual mice that replicate the dynamics of three cell subpopulations in the tumor (cancer cells, T cells, and myeloid-derived suppressor cells) and data from four experimental cohorts (control, Gem, OT-1, and Gem+OT-1). We also provided a guide for using this pipeline for other treatments.

cancer biology↗

bioRxiv2025

Rethinking the seven-day treatment-free interval in T-cell engager therapy using agent-based modeling

BackgroundThe CD3/CD19 bispecific T cell engager (TCE) blinatumomab has shown efficacy in relapsed/refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL), but response rates are often limited by T cell exhaustion. Recent preclinical studies suggest that incorporating treatment-free intervals (TFIs) into dosing schedules may enhance therapeutic outcomes. MethodsTo systematically evaluate alternative TFI strategies, we developed an agent-based model (ABM) of tumor-T cell interactions under various blinatumomab dosing regimens. The model was calibrated using published in vitro data and incorporated spatial, stochastic, and mechanistic rules governing T cell activation, cytotoxicity, proliferation, and exhaustion. ResultsOur ABM recapitulates experimental observations showing that a 7-day TFI improved T cell function over continuous dosing during the initial 28-day treatment period. However, when simulations were extended to a full 42-day cycle to mimic clinical regimen, this advantage was lost. In contrast, shorter TFIs consistently outperformed both 7-day and continuous schedules, leading to superior tumor control at all timepoints. A translationally oriented Monday-through-Friday (MO_FR) regimen also achieved comparable benefits. ConclusionsOur results indicate that the empirically tested 7-day TFI schedule may not be optimal. TFI with shorter intervals as well as translationally relevant schedules such as MO_FR, may offer greater therapeutic benefit. This work demonstrates the value of ABM in preclinical immunotherapy design and supports model-guided refinement of TCE dosing strategies prior to clinical translation. Future work will focus on validating these predictions in more complex in vivo models and leveraging patient-derived data to guide personalized TCE treatment design.

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↗

bioRxiv2025

Optimizing the Efficacy of Vaccine-Induced Immunotherapy in Melanomas

Cancer therapeutic vaccines are used to strengthen a patients own immune system by amplifying existing immune responses. Intralesional administration of the bacteria-based emm55 vaccine together with the PD1 checkpoint inhibitor produced a strong anti-tumor effect against the B16 melanoma murine model. However, it is not trivial to design an optimal order and frequency of injections for combination therapies. Here, we developed a coupled ordinary differential equations model calibrated to experimental data and used the mesh adaptive direct search method to optimize the treatment protocols of the emm55 vaccine and anti-PD1 combined therapy. This method determined that early consecutive vaccine injections combined with distributed anti-PD1 injections of decreasing separation time yielded the best tumor size reduction. The optimized protocols led to a twofold decrease in tumor area for the vaccine-alone treatment, and a fourfold decrease for the combined therapy. Our results reveal the tumor subpopulation dynamics in the optimal treatment condition, defining the path for efficacious treatment design. Similar computational frameworks can be applied to other tumors and other combination therapies to generate experimentally testable hypotheses in a fairly unrestricted and inexpensive setting.

systems biology↗

bioRxiv2024

Targeted degradation of the HPV oncoprotein E6 reduces tumor burden in cervical cancer

Human Papilloma Virus (HPV)-related cancers are a global health burden, yet there are no targeted therapies available for chronically infected patients. The HPV protein E6 is essential for HPV-mediated tumorigenesis and immune evasion, making it an attractive target for antiviral drug development. In this study, we developed an E6-targeting Proteolysis Targeting Chimera (PROTAC) that inhibits the growth of HPV(+) tumors. To develop E6 antagonists, we generated a panel of nanobodies targeting E6 proteins derived from the oncogenic HPV16 subtype. The highest affinity E6 nanobody, A5, was fused to Von Hippel Lindau protein (VHL) to generate a PROTAC that degrades E6 (PROTACE6). Mutational rescue experiments validated specific degradation via the CRL2VHL E3 ligase. Intralesional administration of the PROTACE6 using a clinically viable DNA vaccine reduced tumor burden in an immunocompetent mouse model of HPV(+) cancer. The inhibitory effect of the PROTACE6 was abrogated by CD4+ and CD8+ T-cell depletion, indicating that the antitumor function of the PROTACE6 relies in part on a host immune response. Overall, these results suggest that the targeted degradation of E6 inhibits its oncogenic function and stimulates a robust immune response against HPV(+) tumors, opening new opportunities for virus-specific therapies in the treatment of HPV-related cancers.

molecular biology↗

bioRxiv2024

Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters

A hybrid off-lattice agent-based model has been developed to reconstruct the tumor tissue oxygenation landscape based on histology images and simulated interactions between vasculature and cells with microenvironment metabolites. Here, we performed a robustness sensitivity analysis of that models physical and computational parameters. We found that changes in the domain boundary conditions, the initial conditions, and the Michaelis constant are negligible and, thus, do not affect the model outputs. The model is also not sensitive to small perturbations of the vascular influx or the maximum consumption rate of oxygen. However, the model is sensitive to large perturbations of these parameters and changes in the tissue boundary condition, emphasizing an imperative aim to measure these parameters experimentally.

systems biology↗

bioRxiv2023

Branched-chain keto acids promote an immune-suppressive and neurodegenerative microenvironment in leptomeningeal disease

Leptomeningeal disease (LMD) occurs when tumors seed into the leptomeningeal space and cerebrospinal fluid (CSF), leading to severe neurological deterioration and poor survival outcomes. We utilized comprehensive multi-omics analyses of CSF from patients with lymphoma LMD to demonstrate an immunosuppressive cellular microenvironment and identified dysregulations in proteins and lipids indicating neurodegenerative processes. Strikingly, we found a significant accumulation of toxic branched-chain keto acids (BCKA) in the CSF of patients with LMD. The BCKA accumulation was found to be a pan-cancer occurrence, evident in lymphoma, breast cancer, and melanoma LMD patients. Functionally, BCKA disrupted the viability and function of endogenous T lymphocytes, chimeric antigen receptor (CAR) T cells, neurons, and meningeal cells. Treatment of LMD mice with BCKA-reducing sodium phenylbutyrate significantly improved neurological function, survival outcomes, and efficacy of anti-CD19 CAR T cell therapy. This is the first report of BCKA accumulation in LMD and provides preclinical evidence that targeting these toxic metabolites improves outcomes.

cancer biology↗

bioRxiv2019

Modeling vaccine-induced immunotherapy: treatment scheduling and robustness with virtual mice cohorts

Therapeutic vaccines are used to boost patients immune system activity by imposing signals that increase T cell proliferation or infiltration. A large population of cytotoxic T cells may then be able to reduce tumor growth. We developed here a mathematical model of vaccine-induced immunotherapy and used it to test the vaccine frequency and doses that can reduce tumor burden. Since tumors are heterogeneous, we examined if the proposed treatments are robust; i.e, are successful for a wide range of tumors. This was assessed by constructing virtual mice cohorts. Together, the optimal and most robust treatment protocol was determined through mathematical modeling.

cancer biology↗

bioRxiv2019

Lymph Nodes Inhibit T-cell Effector Functions Locally by Establishing Acidic Niches

Lymph nodes are an essential component of the adaptive immune response where antigen-presenting cells are closely housed with their cognate effector cells. Protection of lymph node resident cells from activated immune cells in such close quarters would need to be robust and reversible. Effector functions of T-cells are profoundly and reversibly inhibited by an acidic microenvironment. The underlying mechanisms of this inhibition are unknown, but may relate to glycolysis, which is obligatory for expression of effector functions. Here, we demonstrate that acidification rapidly and potently inhibits monocarboxylate transporter-dependent lactic acid efflux, which dually inhibits glycolysis by end-product accumulation and by reducing cytoplasmic pH. Based on the robustness of these responses, we propose that acid-evoked T-cell inhibition is physiologically important, and that lymph nodes are a natural site for such modulation. Using multiple imaging techniques, we show that paracortical T-zones of lymph nodes are highly acidic. We further show that T-cells can be activated by dendritic cells at low pH, and their effector functions are restored rapidly upon increasing pH. Thus, we describe a novel physiological mechanism whereby activated T-cells are kept in stasis by acidosis whilst resident in lymph nodes.

physiology↗