From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

Abstract

Glioblastomas are aggressive primary brain tumors known for their inter-and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment.Glioblastoma, the most common primary brain tumor, is an aggressive and difficult to treat cancer. A key reason is that the tumors can be very heterogeneous, consisting of many different mutants driving distinct cell behaviors. We believe that treatments for this disease could be significantly improved by understanding and quantifying the functional impact of heterogeneity within the tumor. From a clinical standpoint, the larger tissuescale dynamics, like growth rate, can be informed from serial MRI imaging, while the cellscale heterogeneity, can be informed by analysis of biopsies. In this work, we combined information from both scales using a mathematical framework and multiscale data from an animal model of glioblastoma. We found that a wide range of potential tumor compositions matched imaging data alone, as a result the model predicts a wide variety of responses to treatment. Using both imaging and cell-scale data narrowed the range of possible tumor compositions and better predicted responses to treatment. The mathematical model also predicted that while targeting migration alone did not slow tumor growth (in fact it drove a more proliferative tumor), an anti-proliferative/anti-migratory treatment combination improved treatment response. PLOS COMPUTATIONAL BIOLOGYFrom cells to tissue PLOS Computational Biology | https://doi.

Modeling How Heterogeneity in Cell Cycle Length Affects Cancer Cell Growth Dynamics in Response to Treatment

“…The effect of the selection forces is becoming particularly evident in post-treatment recurrence dynamics. When therapy of high mitotic death rates ceases, we observe that the recurrence dynamics are slower relative to untreated populations, an observation that has been seen under antiproliferative treatments in glioblastoma (22). Yet, this is not true for random death probabilities that always favor faster proliferating phenotypes.…”

“…On the other hand, regrowth is faster when random death rates are applied compared to mitotic rates. Interestingly, however, when we compare the growth dynamics between the treated and the untreated population (control), we observe that the selection of slower phenotypes during treatment at high mitotic death rates results in slower recurrent dynamics (Figure 8)-an observation that has been seen in real tumors after antimitotic treatment (22). Note that this is not true for random death rates that always favor the highly proliferative phenotypes.…”

“…Numerous mathematical models have accounted for heterogeneity in the tumor population (11)(12)(13)(22)(23)(24)(25), as it affects various aspects of tumor evolution including growth, invasion, and therapy failure. Among them, many models have also accounted for the natural variability with respect to proliferation rate (11)(12)(13)22). However, the direct effect this heterogeneity plays on tumor evolution without assuming any prior trade-off between fitness and drug sensitivity has not been demonstrated when cytotoxic treatment is applied.…”

Understanding the glioblastoma tumor biology to optimize photodynamic therapy: From molecular to cellular events

“…In 80% of all cases in which tumor recurrence was observed after surgery, such recurrence was found to be within 2 cm of the resected margin (Wallner et al, 1989). As it was described previously, the significant tissue heterogeneity of GBMs, as well as heterogeneity in metabolism within the tumor and in the peritumoral regions could have an impact in the efficiency of PDT (Gallaher et al, 2020;Kampa et al, 2020). These areas have been revealed by complementary techniques such as magnetic resonance…”

“…distributed on the periphery of the structure. In GBMs with diffuse characteristics, it has been analyzed that highly migratory and proliferative cells are found at the edge of the tumor and less migratory cells are found in the nucleus of the tumor (Gallaher et al, 2020).…”