Biology subjects

Rejniak, K. A.

Publications and source records attributed to Rejniak, K. A..

13 recordsLinked to original sources

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

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

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↗

bioRxiv2024

Visualizing the Spatio-Temporal Dynamics of Clonal Evolution with LinG3D software

Cancer clonal evolution, especially following anti-cancer treatments, depends on the locations of the mutated cells within the tumor tissue. Cells near the vessels, exposed to higher concentrations of drugs, will undergo a different evolutionary path than cells residing far from the vasculature in the areas of lower drug levels. However, classical representations of cell lineage trees do not account for this spatial component of emerging cancer clones. Here, we propose the LinG3D (Lineage Graphs in 3D) algorithms to trace clonal evolution in space and time. These are an open-source collection of routines (in MATLAB, Python, and R) that enables spatio-temporal visualization of clonal evolution in a two-dimensional tumor slice from computer simulations of the tumor evolution models. These routines draw traces of tumor clones in both time and space, with an option to include a projection of a selected microenvironmental factor, such as the drug or oxygen distribution within the tumor. The utility of LinG3D has been demonstrated through examples of simulated tumors with different number of clones and, additionally, in experimental colony growth assay. This routine package extends the classical lineage trees, that show cellular clone relationships in time, by adding the space component to show the locations of cellular clones within the 2D tumor tissue patch from computer simulations of tumor evolution models.

systems biology↗

bioRxiv2021

Dynamics of fibril collagen remodeling by tumor cells using individual cell-based mathematical modeling

Many solid tumors are characterized by dense extracellular matrix (ECM) composed of various ECM fibril proteins that provide structural support and biological context for the residing cells. The growing tumor cell colonies are capable of remodeling the ECM structure in tumor immediate vicinity to form specific microenvironmental niches. The changes in fibril patterns of the collagen (one of the ECM proteins) surrounding the tumor can be visualized experimentally using both histology and fluorescent imaging. In particular, three diverse tumor associated collagen signatures (TACS) were identified and related to tumor behavior, such as benign growth or invasion. Here we will use an off-lattice hybrid agent-based model (MultiCell-LF) to identify the rules of cell-ECM interactions that guide the development of various patterns of alignment of the ECM fibrils.

systems biology↗

bioRxiv2021

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, the models simulating individual cells behavior are needed. Here, we provided a link between the average data value recorded for radiologic image voxels and the cellular and vascular architecture of the tissue that fills these voxels. Using hybrid agent-based modeling, we generated a set of tissue morphologies capable of reproducing tissue oxygenation levels observed in radiologic images. We applied this approach to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies showed that intravascular changes in oxygen supply can reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, large magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in biologically feasible manner. Additionally, we developed a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications this approach can be used to generate a set of tissues representative for radiology images and to simulate tumor response to various anti-cancer treatments on the tissue-scale level. Authors SummaryLow levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells which are resistant to chemo-, radio- and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuation, however they operate on a macro-scale voxel level. To examine the causes of rapid oxygen fluctuations on the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that could be responsible for these cyclic effects in tissue oxygenation: variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This will also provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average voxel values. In the future applications, this approach can be used to generate a set of tissues representative of radiology images and to simulate tumor response to various anticancer treatments on the cell-scale level.

systems biology↗

bioRxiv2020

Comparison of drug inhibitory effects (IC50) in monolayer and spheroid cultures

Traditionally, the monolayer (two-dimensional) cell cultures are used for initial evaluation of the ef-fectiveness of anticancer drugs. In particular, these experiments provide the IC50 curves that determine drug concentration that can inhibit growth of a tumor colony by half when compared to the cells grown with no exposure to the drug. Low IC50 value means that the drug is effective at low concentrations, and thus will show lower systemic toxicity when administered to the patient. However, in these experiments cells are grown in a monolayer, all well exposed to the drug, while in vivo tumors expand as three-dimensional multicellular masses, where inner cells have a limited access to the drug. Therefore, we performed computational studies to compare the IC50 curves for cells grown as a two-dimensional monolayer and a cross section through a three-dimensional spheroid. Our results identified conditions (drug diffusivity, drug action mechanisms and cell proliferation capabilities) under which these IC50 curves differ significantly. This will help experimentalists to better determine drug dosage for future in vivo experiments and clinical trials.

systems biology↗

bioRxiv2020

Drug-induced resistance in micrometastases: analysis of spatio-temporal cell lineages

Resistance to anti-cancer drugs is a major cause of treatment failure. While several intracellular mechanisms of resistance have been postulated, the role of extrinsic factors in the development of resistance in individual tumor cells is still not fully understood. Here we used a hybrid agent-based model to investigate how sensitive tumor cells develop drug resistance in the heterogeneous tumor microenvironment. We characterized the spatio-temporal evolution of lineages of the resistant cells and examined how resistance at the single-cell level contributes to the overall tumor resistance. We also developed new methods to track tumor cell adaptation, to trace cell viability trajectories and to examine the three-dimensional spatio-temporal lineage trees. Our findings indicate that drug-induced resistance can result from cells adaptation to the changes in drug distribution. Two modes of cell adaptation were identified that coincide with microenvironmental niches--areas sheltered by cell micro-communities (protectorates) or regions with limited drug penetration (refuga or sanctuaries). We also recognized that certain cells gave rise to lineages of resistant cells (precursors of resistance) and pinpointed three temporal periods and spatial locations at which such cells emerged. This supports the hypothesis that tumor micrometastases do not need to harbor cell populations with pre-existing resistance, but that individual tumor cells can adapt and develop resistance induced by the drug during the treatment.

systems biology↗

bioRxiv2020

High school Internship Program in Integrated Mathematical Oncology (HIP IMO) - five-year experience at Moffitt Cancer Center

Modern cancer research, and the wealth of data across multiple spatial and temporal scales, has created the need for researchers that are well-versed in the life sciences (cancer biology, developmental biology, immunology), medical sciences (oncology) and natural sciences (mathematics, physics, engineering, computer sciences). College undergraduate education is traditionally provided in disciplinary silos, which creates a steep learning curve at the graduate and postdoctoral levels that increasingly bridge multiple disciplines. Numerous colleges have begun to embrace interdisciplinary curricula, but students who double-major in mathematics (or other quantitative sciences) and biology (or medicine) remain scarce. We identified the need to educate junior and senior high school students about integrating mathematical and biological skills, through the lens of mathematical oncology, to better prepare students for future careers at the interdisciplinary interface. The High school Internship Program in Integrated Mathematical Oncology (HIP IMO) at Moffitt Cancer Center has so far trained 59 students between 2015 and 2019. We report here on the program structure, training deliverables, curriculum, and outcomes. We hope to promote such interdisciplinary educational activities early in a students career.

scientific communication and education↗

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

Hypoxia in cancer chemo- and immunotherapy: foe or friend?

Hypoxia, a low level of oxygen in the tissue, is a feature of most solid tumors. It arises due to an imbalance between the oxygen supply from the abnormal vasculature and oxygen demand by the large number of tumor and stromal cells. Hypoxia has been implicated in the development of aggressive tumors and tumor resistance to various therapies. This makes hypoxia a negative marker of patients survival. However, recent advances in designing new hypoxia-activated pro-drugs and adoptive T cell therapies provide an opportunity for exploiting hypoxia in order to improve cancer treatment. We used novel mathematical models of micro-pharmacology and computational optimization techniques for determining the most effective treatment protocols that take advantage of heterogeneous and dynamically changing oxygenation in in vivo tumors. These models were applied to design schedules for a combination of three therapeutic compounds in pancreatic cancers and determine the most effective adoptive T cell therapy protocols in melanomas.

systems biology↗

bioRxiv2017

Assessment of patient-specific efficacy of chemo- and targeted-therapies: a micropharmacology approach

Both targeted and standard chemotherapy drugs are subject to various intratumoral barriers that impede their effectiveness. The tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, and nonuniform expression of cell membrane receptors hinder drug molecule transport and perturb its cellular uptake. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine these aspects of drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microPKPD models and coupled them with patient-specific data to test personalized treatments.

cancer biology↗