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We examined the effect of these treatments on the diffuse tumor from Fig 4 as a prime example for an invasive tumor that could benefit from these treatments.

The in silico results show Xepi (Ozenoxacin Cream for Topical Use)- Multum the AM treatment alone is not successful in slowing the growth of most tumors, and the diffuse tumor grows especially fast under this treatment (Fig 9A). The drug is applied continuously at 14d until 28d.

The average response (from 10 runs) to each treatment of the same diffuse tumor from the previous sections is also shown. The response of the diffuse tumor to these treatments is shown as a yellow line. C) Treating just the diffuse tumor example, we show representative spatial cosmetic surgery dental distributions, the measured and potential phenotype distributions (colored according to the key), and the PDGF distribution.

The response of diffuse tumor to each treatment is further examined in Fig 9C. With the AP treatment cells continue to migrate Xepi (Ozenoxacin Cream for Topical Use)- Multum the tissue, and slower proliferating cells are selected for. AM treatment selects for cells with high proliferative and migratory potential since they were previously selected for during growth and already populate the outer edges when migration is shut off (see also S8B Fig). The PDGF concentration also becomes saturated in the tissue mediated by lack of cell dispersal, which further drives tumor growth.

Tumor heterogeneity is fundamental to treatment success or failure. Our results suggest that growth rates alone are not enough to predict drug response; the tumor shape, density, and phenotypic and genotypic compositions can all signify characteristics of the underlying dynamics that affect longer term responses to therapy. We found through experiment and simulation that Xepi (Ozenoxacin Cream for Topical Use)- Multum heterogeneity is highly modulated by the environmental context.

The local environment creates larger scale variations in the observed phenotypes that might be inhibiting, from factors such spectrum lack of space or resources caused by Xepi (Ozenoxacin Cream for Topical Use)- Multum high cell density, or stimulatory, such as an overabundance of growth factors.

These large-scale variations can give insight on environmental niches formed throughout the tumor. At Xepi (Ozenoxacin Cream for Topical Use)- Multum imaging scale, spatial variations can be quantified to reveal habitats and predict treatment response. Our results suggest that tumor heterogeneity is also not strictly a factor determined by the microenvironment, but a combination of cell intrinsic drivers and the environmental context. In silico tumors that were fit to the same growth dynamics with Xepi (Ozenoxacin Cream for Topical Use)- Multum density distributions displayed a huge variation in underlying phenotypes (Fig 4).

Furthermore, measurements at the single cell level do not necessarily match up with the potential behavior that cells could achieve given a different environmental context.

It is often only after big changes in the tumor microenvironment, such as during therapy, that intrinsic variations at the single cell scale become apparent through natural selection (Fig 5). Importantly, our data suggest that more information on single cell heterogeneity before treatment can lead to better treatment decisions. By fitting the in silico model to all of the experimental data, from bulk to single cell metrics, we found a best fit parameter set that resulted in a tumor with Xepi (Ozenoxacin Cream for Topical Use)- Multum in the proliferative and migratory potential (Fig 6).

The best fit responded to an anti-proliferative drug but ultimately resulted in recurrence (Fig 7). Eliminating the potential phenotypic heterogeneity in the best fit tumor did not drastically alter the resulting growth dynamics, yet upon exposure to the anti-proliferative treatment there was a complete response.

Only at the single cell scale level (Fig 6E) were we able to distinguish these two tumors that ultimately had divergent fates. From this result, it is clear that some degree of single cell observation could aid in the prediction of recurrence and a possible alteration of treatment strategy. Based on our mathematical modeling results suggesting a diversity of phenotypes in response to treatment, we carefully investigated the role of anti-proliferative treatments since they form the basis of the vast majority of Xepi (Ozenoxacin Cream for Topical Use)- Multum anti-cancer treatments (e.

When fitting the mathematical model to the cell level and tissue level data, we found a consistent pattern of decreased proliferation in simulated recurrent tumors. This finding was recapitulated when we compared a histological marker for proliferation in human GBM patients at diagnosis and recurrence following chemoradiation (Fig 8).

However, the in silico model suggests that anti-migratory drugs do not help when the tumor is largely driven by environmental factors (Fig 9). Moreover, stopping migration also prevented the widespread dispersal of PDGF, leading to more proliferative tumors due to local accumulation of PDGF.

Package result indicates that, for this type of tumor, anti-migratory therapy alone is not significantly helpful. However, under the right conditions, it might be useful in combination with an anti-proliferative treatment or as a primer for an anti-proliferative drug.

The anti-migratory drug was seen to select for more proliferative cells, so perhaps it calan be used prior Xepi (Ozenoxacin Cream for Topical Use)- Multum an anti-proliferative treatment to select for more sensitive cells.

These are important components in the formation and progression of GBM in particular, however, in order to fit the in silico model to the experimental data, we assumed that these factors played a backseat compared to the driving campbell walsh urology 2020 of PDGF.

This was confirmed by the strong sensitivity of parameter for the consumption rate of PDGF, which was quickly pushed to low values by the estimation algorithm.

The PDGF-driven rat model grows incredibly fast due to fast proliferation, invasion, and recruitment of a large portion of resident progenitor cells by paracrine growth factor stimulation.

While the experimental rat model represents an extreme case compared to human glioblastoma, it is consistent and reproducible, making it a useful tool for controlled data generation to study behavioral heterogeneity.

The in silico model, though calibrated on rat model, sets up an initial framework for addressing heterogeneity in cell traits on multiple scales and within the context of living brain tissue. We also made assumptions on the available phenotypes in this model, focusing on the most apparently important traits in Xepi (Ozenoxacin Cream for Topical Use)- Multum proliferation rate and migration speed.

Though it may make sense in the context of limited resources that a cell must divert energy from one task to another, perhaps a tradeoff should not be observed in this model where the environment is rich in growth factors. Furthermore, we found no dichotomy in the experimental data to warrant this assumption, and in fact the opposite was observed. Cells that had divided within the observation period also had a migration speed distribution shifted toward higher speeds.

On the other hand, in silico tumors with the same size dynamics tended to have measured mean proliferation and migration values that were not often both simultaneously high (Fig 4C), even though individual cells within the population had both fast proliferation and migration rates (S4C Fig).

This was tested computationally, by separating the population into growers (fast proliferators that did not move) and goers (migrating cells that proliferate slowly), and we observed a poorer fit than when no such separation existed (S9 Fig).

While the migration speed distributions fit well, the constraint on the two populations led to a poor fit for other parameters (S10 Fig). The ex vivo data showed that the recruited cells, driven at least initially by the environment, proliferate and migrate faster than infected cells, which was found in the fully fit in silico model, and that the rates of proliferation and migration of recruited progenitor cells also increase over time.

The latter observation could not be reiterated in the in silico model with natural selection alone with any of the assumptions we investigated concerning tumor heterogeneity (S11 Fig). The in silico model allowed us to explore spatial dynamics of a tumor as a population and as individual cells to track heterogeneity over time and match to the experimental model.

It showed that there likely needs to be both environmental and cell autonomous heterogeneity in order to fit to the smaller scale data, but these components are difficult if not impossible to separate by observation alone in a clinical setting. Specifically, there is no easy way to disentangle the drivers of observed phenotypic behaviors, since intrinsic cell autonomous drivers are modified by cell extrinsic environmental signals that themselves are modified by the cells.

Here we have attempted to tackle this question through an integrated approach and hopefully shed light on this complex feedback. Using the hybrid agent-based model, we were able to combine data at different scales to study the environment and phenotypic heterogeneity separately and observe how single cell behavior influenced measurements at different scales.

Although the anti-proliferative treatments showed variable responses in the in silico model, most were not sustaining and resulted in recurrence with slower proliferating, drug resistant phenotypes. Smarter strategies can be employed when more information is known about the tumor heterogeneity on all scales. The nodular, intermediate, and diffuse tumors are found by fitting only to the tumor size data, and the heterogeneous tumor pfizer 2007 found by fitting to all of the data.

The homogeneous tumor is just the heterogeneous tumor with the variation in proliferation and migration set to zero. Each iteration is shown starting at light gray and going to black for the final fit.



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