23 research outputs found
Study of dose-dependent combination immunotherapy using engineered T cells and IL-2 in cervical cancer
Adoptive T cell based immunotherapy is gaining significant traction in cancer
treatment. Despite its limited success, so far, in treating solid cancers, it
is increasingly successful, demonstrating to have a broader therapeutic
potential. In this paper we develop a mathematical model to study the efficacy
of engineered T cell receptor (TCR) T cell therapy targeting the E7 antigen in
cervical cancer cell lines. We consider a dynamical system that follows the
population of cancer cells, TCR T cells, and IL-2. We demonstrate that there
exists a TCR T cell dosage window for a successful cancer elimination that can
be expressed in terms of the initial tumor size. We obtain the TCR T cell dose
for two cervical cancer cell lines: 4050 and CaSki. Finally, a combination
therapy of TCR T cell and IL-2 treatment is studied. We show that certain
treatment protocols can improve therapy responses in the 4050 cell line, but
not in the CaSki cell line.Comment: 8 pages, 7 figure
Effective Dose Fractionation Schemes of Radiotherapy for Prostate Cancer
Radiation therapy remains as one of the main cancer treatment modalities. Typical regimens for radiotherapy comprise a constant dose administered on weekdays, and no radiation on weekends. In this paper, we examine adaptive dosages of radiation treatment strategies for heterogeneous tumors using a dynamical system model that consist of radiation-resistant and parental populations with unique interactive properties, namely, PC3 and DU145 prostate cancer cell lines. We show that stronger doses of radiation given in longer time intervals, while keeping the overall dosage the same, are effective in PC3 cell lines, but not in DU145 cell lines. In addition, we tested an adaptive dosing schedule by administering a stronger dosage on Friday to compensate for the treatment-off period during the weekend, which was effective in decreasing the final tumor volume of both cell lines. This result creates interesting possibilities for new radiotherapy strategies at clinics that cannot provide treatment on weekends
Bayesian information-theoretic calibration of patient-specific radiotherapy sensitivity parameters for informing effective scanning protocols in cancer
With new advancements in technology, it is now possible to collect data for a
variety of different metrics describing tumor growth, including tumor volume,
composition, and vascularity, among others. For any proposed model of tumor
growth and treatment, we observe large variability among individual patients'
parameter values, particularly those relating to treatment response; thus,
exploiting the use of these various metrics for model calibration can be
helpful to infer such patient-specific parameters both accurately and early, so
that treatment protocols can be adjusted mid-course for maximum efficacy.
However, taking measurements can be costly and invasive, limiting clinicians to
a sparse collection schedule. As such, the determination of optimal times and
metrics for which to collect data in order to best inform proper treatment
protocols could be of great assistance to clinicians. In this investigation, we
employ a Bayesian information-theoretic calibration protocol for experimental
design in order to identify the optimal times at which to collect data for
informing treatment parameters. Within this procedure, data collection times
are chosen sequentially to maximize the reduction in parameter uncertainty with
each added measurement, ensuring that a budget of high-fidelity
experimental measurements results in maximum information gain about the
low-fidelity model parameter values. In addition to investigating the optimal
temporal pattern for data collection, we also develop a framework for deciding
which metrics should be utilized at each data collection point. We illustrate
this framework with a variety of toy examples, each utilizing a radiotherapy
treatment regimen. For each scenario, we analyze the dependence of the
predictive power of the low-fidelity model upon the measurement budget
Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types
The Lotka-Volterra model is widely used to model interactions between two
species. Here, we generate synthetic data mimicking competitive, mutualistic
and antagonistic interactions between two tumor cell lines, and then use the
Lotka-Volterra model to infer the interaction type. Structural identifiability
of the Lotka-Volterra model is confirmed, and practical identifiability is
assessed for three experimental designs: (a) use of a single data set, with a
mixture of both cell lines observed over time, (b) a sequential design where
growth rates and carrying capacities are estimated using data from experiments
in which each cell line is grown in isolation, and then interaction parameters
are estimated from an experiment involving a mixture of both cell lines, and
(c) a parallel experimental design where all model parameters are fitted to
data from two mixtures simultaneously. In addition to assessing each design for
practical identifiability, we investigate how the predictive power of the
model-i.e., its ability to fit data for initial ratios other than those to
which it was calibrated-is affected by the choice of experimental design. The
parallel calibration procedure is found to be optimal and is further tested on
in silico data generated from a spatially-resolved cellular automaton model,
which accounts for oxygen consumption and allows for variation in the intensity
level of the interaction between the two cell lines. We use this study to
highlight the care that must be taken when interpreting parameter estimates for
the spatially-averaged Lotka-Volterra model when it is calibrated against data
produced by the spatially-resolved cellular automaton model, since baseline
competition for space and resources in the CA model may contribute to a
discrepancy between the type of interaction used to generate the CA data and
the type of interaction inferred by the LV model.Comment: 25 pages, 18 figure
An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models
The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated
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Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.
Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit