Biology subjects

Stewart, W. C.

Publications and source records attributed to Stewart, W. C..

7 recordsLinked to original sources

bioRxiv2025

Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers

Solid tumors are characterized by a spatially heterogeneous mixture of cancer cells, immune cells and other non-tumor cells. Recent characterization of the heterogeneity at the single cell level has revealed spatial patterns of different cell types often lacking a simple geometric structure associated with cancer progression. Here we investigated the occurrence of physical fencing of tumor cells by specific immune and non-immune phenotypes in the tumor microenvironment (TME) and the association of these clusters to cancer progression in a wide range solid tumors formed in different organs. We analyzed published patient response and imaging mass cytometry (IMC) datasets from tumor microarrays obtained from tumor tissues in triple-negative breast cancer (279 patients), lung cancer (416 patients), melanoma (30 patients), colorectal cancer (9 patients), glioma (185 patients), and head and neck cancer (139 patients) to characterize the presence of fencing clusters of various cell types and their association with differing patient outcomes. Devising and employing simple mechanistic and stochastic spatially-resolved computational models we quantify the dependence of the pro- and anti-tumor roles of a fencing cluster on the size and the lifetime of the cluster, as well as the chemokine gradient in the local environment. We unveiled that spatial patterns of immune cells, especially through fencing tumor boundary, affects tumor progression and treatment responsiveness to immunotherapy.

cancer biology↗

bioRxiv2025

Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

Brain cancer is one of the most aggressive forms of cancer in the central nervous system occurring as primary or metastatic tumors. Sequencing of resected tissues from glioblastoma (GBM) and brain metastases (BrMET) reveals high heterogeneity in neoantigens and T cell receptor (TCR) repertoires. Our analysis of published sequencing data in different spatial regions of tumors GBM and BrMET patients show the presence of T cell clones of sizes with a heavy right-tailed distribution spanning several orders of magnitude (e.g., 1 - 1000 cells) with a few (<10) large clone sizes and many small clones. We investigated how neoantigens in the tumor microenvironment (TME) drive T cell expansion in GBM and BrMET by developing a mechanistic mathematical model based on the interaction of T cells and the neoantigens that incorporates their stochastic proliferation in the immunosuppressive environment and trained it to predict the emergence of T cell clones in different spatial regions. The model accurately predicts the distribution of observed T cell clone sizes and reveals that the strength of interaction between TCR and neoantigen-MHC complex and stochastic T cell proliferation crucially regulates T cell expansion in the TME. It also suggests higher rate of T cell proliferation BrMET compared to GBM. An extended version of the model predicts the ability of individual neoantigens to generate T cell clones in the periphery in patients receiving personalized neoantigen vaccines. Our model may facilitate the discovery of improved peptide combinations in neoantigen vaccine studies. Significance StatementNeoantigen-driven T cell responses are key to immune defense against solid tumors. Multi-region sequencing of brain tumors reveals spatial heterogeneity in neoantigens and T cell repertoires. To understand whether neoantigen-driven T cell expansion underlies the TCR repertoire heterogeneities, we developed a stochastic, mechanistic model of T cell proliferation using published TCR and neoantigen data from primary and metastatic brain tumors. The model accurately predicts clone size distributions, showing faster T cell proliferation in metastases and stronger responses to clonal (shared) neoantigens than to private (region-specific) ones. The model is extended to describe T cell clonal expansion in the periphery in response to neoantigen vaccine in glioblastoma patients. This framework may help design optimal peptide combinations in neoantigen vaccine development.

cancer biology↗

bioRxiv2025

Natural Killer Cell Receptor Signaling and Activation Depend on Cell Cycle Stages

Receptor signaling in Natural Killer (NK) cells leads to post-translational modification (e.g., phosphorylation) of sub-cellular signaling proteins within minutes of receptor stimulation that eventually give rise to diverse effector functions including cell proliferation. Recent single-cell mass cytometry (i.e., CyTOF) experiments in macrophages showed variations of abundances of phosphorylated signaling proteins across cell cycle states indicating a dependence of cell signaling kinetics on an order of magnitude slower kinetics (~ several hours) of cell cycle transitions. We investigated cell cycle dependence of NKG2D signaling kinetics in NK cells by CyTOF measurements performed on IL-2-treated NKG2D-stimulated primary human CD56dim NK cells. The CyTOF experiments revealed monotonic or semi-monotonic increases of the average protein abundances of the majority of signaling proteins such as pCrkL, pPLC{gamma}2, and pErk, and the degranulation marker protein CD107a with progressing cell cycle states at specific time points post-NKG2D stimulation; however, several proteins such as pVav1, pS6, and pAkt, and early activation marker protein CD69 also showed non-monotonic variations in the average abundances with progressing cell cycle states. We used minimal mathematical and computational models coupling signaling and cell cycle processes to show that non-monotonic variations in the signaling protein abundances with progressing cell cycle stages are likely to arise in situations where protein synthesis and degradation and signaling kinetics are actively regulated by cell cycle processes.

immunology↗

bioRxiv2025

An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data

Recent single-cell experiments that measure copy numbers of over 40 proteins in individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the time-evolution of protein abundances that could yield mechanisms that underlie signaling kinetics. We recently developed a generalized method of moments (GMM) based approach that estimates parameters of mechanistic models using TSS data. However, when multiple mechanistic models potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. Popular approaches like Kullback-Leibler divergence and Akaikes Information Criterion are difficult to implement because the distribution that gave rise to the "noisy" data is only known numerically and approximately. To perform model selection in this situation, we introduce an entropy-based approach that incorporates our GMM based parameter estimation and commonly used estimators in kernel density estimation. Using simulated TSS data, we show that our approach can select the "ground truth" from a set of competing mechanistic models. Furthermore, we use a bootstrap procedure to compute model selection probabilities, which can be useful when measuring the relative support of a candidate model.

biophysics↗

bioRxiv2025

A framework integrating multiscale in-silico modeling and experimental data predicts CD33 CAR-NK cytotoxicity across target cell types

Uncovering mechanisms and predicting tumor cell responses to CAR-NK cytotoxicity is essential for improving therapeutic efficacy. Currently, the complexity of these effector-target interactions and the donor-to-donor variations in NK cell receptor (NKR) repertoire require functional assays to be performed experimentally for each manufactured CAR-NK cell product and target combination. Here, we developed a computational mechanistic multiscale model which considers heterogenous expression of CARs, NKRs, adhesion receptors and their cognate ligands, signal transduction, and NK cell-target cell population kinetics. The model trained with quantitative flow cytometry and in vitro cytotoxicity data accurately predicts the short- and long- term cytotoxicity of CD33CAR-NK cells against leukemia cell lines across multiple CAR designs. Furthermore, using Pareto optimization we explored the effect of CAR proportion and NK cell signaling on the differential cytotoxicity of CD33CAR-NK cells to cancer and healthy cells. This model can be extended to predict CAR-NK cytotoxicity across many antigens and tumor targets.

cancer biology↗

bioRxiv2024

Impact of IL-21 on Natural Killer cell proliferation and Function - a mathematical and functional assessment

Natural killer (NK) cells are currently in use as immunotherapeutic agents for cancer. Many different cytokines are used to generate NK cells including IL-2, IL-12, IL-15 and IL-18 in solution and membrane bound IL-21. These cytokines drive NK cell activation through the integration of STAT and NF-{kappa}B pathways, which overlap and synergize, making it challenging to predict optimal cytokine combinations. We integrated functional assays for NK cells cultured in a variety of cytokine combinations with feature selection and mechanistic regression models. Our regression model successfully predicts NK cell proliferation for different cytokine combinations and indicates synergy between STAT3 and NF-{kappa}B transcription factors. Use of IL-21 in solution in the priming, but not post-priming phase of NK cell culture resulted in optimal NK cell proliferation, without compromising cytotoxicity or IFN-{gamma} secretion against hepatocellular carcinoma cell lines. Our work provides a mathematical framework for interrogating NK cell activation for cancer immunotherapy.

immunology↗

bioRxiv2022

BioNetGMMFit: a Parameter Estimation Tool for BioNetGen using Single-Cell Snapshot Data from Cell Populations Evolving over Time

BackgroundMechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. ResultsBioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, timestamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetG-MMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. ConclusionBy streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

systems biology↗