Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions.

Over the course of tumor growth , cancer cells interact with normal cells via processes that are difficult to understand by experiment alone.

Ultimately, the immunological state (or contexture) at the tumor site predicted tumor survival long before an impact on tumor growth was evident.

If the establishment of a tumor-supporting local network state (vs. one that controls tumor growth ) is supported through positive feedback, then any pro-tumor influence, even one that is stochastic, could tip the balance towards favoring an M2 phenotype that promotes tumor survival.

More specifically, both strategies that suppressed the polarization of engineered macrophages to an M2 phenotype were effective at controlling tumor growth , even when these engineered cells comprised a minority of macrophages at the tumor site and polarization of native macrophages was not manipulated.

As ubiquitous cells of the innate immune system, macrophages are present at the earliest stages of tumor establishment, are found in great numbers at tumor sites (along with the related myeloid-derived suppressor cells), and have the capacity to functionally “polarize” to phenotypes that alternatively suppress or promote tumor growth [18—20].

1B), and the effects of oxygen and vascularization on both tumor growth and death, as well as on macrophage recruitment (Fig.

The resulting limited oxygen availability led to asymmetric tumor growth , which is also a general feature of growing tumors [34,45].

Our study suggests that downstream signaling from IGFI to H|F1 a, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth .

When we conducted the glioblastoma growth reduction analyses of the LN229 and U87 cell lines, there was almost no change in growth observed in the U87 cell lines, while the LN229 showed a reduction in the glioblastoma tumors’ growth .

In order to analyze the contribution of each rate constant to glioblastoma growth, the sensitivity index was calculated for each rate constant, for LN229 tumor growth (shown in Table 4 in descending order).