Cell-to-cell variations contribute to drug resistance with consequent therapy failure in cancer. Experimental techniques have been developed to monitor tumor heterogeneity, but estimates of cell-to-cell variation typically fail to account for the expected spatiotemporal variations during the cell growth process. To fully capture the extent of such dynamic variations, we developed a mechanistic mathematical model supported by in vitro experiments with an ovarian cancer cell line. We introduce the notion of dynamic baseline cell-to-cell variation, showing how the emerging spatiotemporal heterogeneity of one cell population can be attributed to differences in local cell density and cell-cycle. Manipulation of the geometric arrangement and spatial density of cancer cells revealed that given a fixed global cell density, significant differences in growth, proliferation, and paclitaxel-induced apoptosis rates were observed based solely on cell movement and local conditions. We conclude that any statistical estimate of changes in the level of heterogeneity should be integrated with the dynamics and spatial effects of the baseline system. This approach incorporates experimental and theoretical methods to systematically analyze biological phenomena, and merits consideration as an underlying reference model for cell biology studies that investigate dynamic processes affecting cancer cell behavior.

Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli (“phenotype stability”). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.

We report the findings of small CNVs in two newborns in the genomic imprinting regions. They exemplified the challenge of interpreting small CNVs in diagnostic samples. Careful detection of small CNVs in the imprinting regions and effective genetic counseling are of clinical and reproductive significance

<p>Detailed information of the mathematical model simulation results. Panel A) Sample initial and final configurations of the ABM used for comparison with 24 hour image control data. Panel B) Spatial deviation parameter search. Panel C) Rate of progression through the cell-cycle as a function of density and dosage. Panel D) 10 Monte Carlo long-time simulations of the ABM.</p>

<p>Detailed information of the mathematical model calibrations . Panel A) Initial and final configurations of the experimental data used for model comparison. Panel B) Attraction and repulsion potentials used in the SDE model. Panel C) Quiescent transition rates.</p>