Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data.

We develop a quantitative single cell-based mathematical model for multi-cellular tumor spheroids (MCTS) of SK-MES-1 cells, a non-small cell lung cancer (NSCLC) cell line, growing under various nutrient conditions: we confront the simulations performed with this model with data on the growth kinetics and spatial labeling patterns for cell proliferation, extracellular matrix (ECM), cell distribution and cell death. We start with a simple model capturing part of the experimental observations. We then show, by performing a sensitivity analysis at each development stage of the model that its complexity needs to be stepwise increased to account for further experimental growth conditions. We thus ultimately arrive at a model that mimics the MCTS growth under multiple conditions to a great extent. Interestingly, the final model, is a minimal model capable of explaining all data simultaneously in the sense, that the number of mechanisms it contains is sufficient to explain the data and missing out any of its mechanisms did not permit fit between all data and the model within physiological parameter ranges. Nevertheless, compared to earlier models it is quite complex i.e., it includes a wide range of mechanisms discussed in biological literature. In this model, the cells lacking oxygen switch from aerobe to anaerobe glycolysis and produce lactate. Too high concentrations of lactate or too low concentrations of ATP promote cell death. Only if the extracellular matrix density overcomes a certain threshold, cells are able to enter the cell cycle. Dying cells produce a diffusive growth inhibitor. Missing out the spatial information would not permit to infer the mechanisms at work. Our findings suggest that this iterative data integration together with intermediate model sensitivity analysis at each model development stage, provide a promising strategy to infer predictive yet minimal (in the above sense) quantitative models of tumor growth, as prospectively of other tissue organization processes. Importantly, calibrating the model with two nutriment-rich growth conditions, the outcome for two nutriment-poor growth conditions could be predicted. As the final model is however quite complex, incorporating many mechanisms, space, time, and stochastic processes, parameter identification is a challenge. This calls for more efficient strategies of imaging and image analysis, as well as of parameter identification in stochastic agent-based simulations.

Regulatory T cells control macrophage accumulation and activation in lymphoma.

Strategies of manipulating immunosuppressive regulatory T cells (Treg) in cancer patients are currently evaluated in clinical trials. Treg suppress immune responses of tumor-specific T cells; yet, relatively little is known about the impact of Treg on innate immune cells in tumor models in vivo. Many tumors lose expression of MHC class I. Therefore, our study aimed at defining strategies to strengthen immune responses against a high tumor burden of the MHC class I-deficient mouse lymphoma RMA-S. We demonstrate that Treg depletion in mice led to tumor rejection that was dependent on T cells, NK cells and IFN-gamma. In the absence of Treg elevated levels of IFN-gamma were produced by tumor-infiltrating T cells and NK cells. Tumor rejection observed in the absence of Treg correlated with a substantial IFN-gamma-dependent increase in the numbers of tumor-infiltrating leukocytes. The most abundant cell population in the tumors was macrophages. Tumor-infiltrating macrophages from Treg-depleted mice expressed increased amounts of MHC class II, produced highly enhanced levels of pro-inflammatory cytokines and inhibited tumor cell proliferation. It was reported that tumor-infiltrating macrophages have multi-faceted functions promoting or counteracting tumor growth. In our study, high numbers of macrophages infiltrating RMA-S tumors in the absence of Treg correlated with tumor rejection suggesting that macrophages are additional targets for Treg-mediated immune suppression in cancer.