Platforms that allow parallel, quantitative analysis of single cells will be integral to realizing the potential of postgenomic biology. In stem cell biology, the study of clonal stem cells in multiwell formats is currently both inefficient and time-consuming. Thus, to investigate low-frequency events of interest, large sample sizes must be interrogated. We report a simple, versatile, and efficient micropatterned arraying system conducive to the culture and dynamic monitoring of stem cell proliferation. This platform enables: 1) parallel, automated, long-term ( approximately days to weeks), live-cell microscopy of single cells in culture; 2) tracking of individual cell fates over time (proliferation, apoptosis); and 3) correlation of differentiated progeny with founder clones. To achieve these goals, we used microfabrication techniques to create an array of approximately 10,000 microwells on a glass coverslip. The dimensions of the wells are tunable, ranging from 20 to >500 microm in diameter and 10-500 microm in height. The microarray can be coated with adhesive proteins and is integrated into a culture chamber that permits rapid (approximately min), addressable monitoring of each well using a standard programmable microscope stage. All cells share the same media (including paracrine survival signals), as opposed to cells in multiwell formats. The incorporation of a coverslip as a substrate also renders the platform compatible with conventional, high-magnification light and fluorescent microscopy. We validated this approach by analyzing the proliferation dynamics of a heterogeneous adult rat neural stem cell population. Using this platform, one can further interrogate the response of distinct stem cell subpopulations to microenvironmental cues (mitogens, cell-cell interactions, and cell-extracellular matrix interactions) that govern their behavior. In the future, the platform may also be adapted for the study of other cell types by tailoring the surface coatings, microwell dimensions, and culture environment, thereby enabling parallel investigation of many distinct cellular responses.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

In this paper, we investigate the pharmacokinetics and effect of doxorubicin and cisplatin in vascularized tumors through two-dimensional simulations. We take into account especially vascular and morphological heterogeneity as well as cellular and lesion-level pharmacokinetic determinants like P-glycoprotein (Pgp) efflux and cell density. To do this we construct a multi-compartment PKPD model calibrated from published experimental data and simulate 2-h bolus administrations followed by 18-h drug washout. Our results show that lesion-scale drug and nutrient distribution may significantly impact therapeutic efficacy and should be considered as carefully as genetic determinants modulating, for example, the production of multidrug-resistance protein or topoisomerase II. We visualize and rigorously quantify distributions of nutrient, drug, and resulting cell inhibition. A main result is the existence of significant heterogeneity in all three, yielding poor inhibition in a large fraction of the lesion, and commensurately increased serum drug concentration necessary for an average 50% inhibition throughout the lesion (the IC50 concentration). For doxorubicin the effect of hypoxia and hypoglycemia (“nutrient effect”) is isolated and shown to further increase cell inhibition heterogeneity and double the IC50, both undesirable. We also show how the therapeutic effectiveness of doxorubicin penetration therapy depends upon other determinants affecting drug distribution, such as cellular efflux and density, offering some insight into the conditions under which otherwise promising therapies may fail and, more importantly, when they will succeed. Cisplatin is used as a contrast to doxorubicin since both published experimental data and our simulations indicate its lesion distribution is more uniform than that of doxorubicin. Because of this some of the complexity in predicting its therapeutic efficacy is mitigated. Using this advantage, we show results suggesting that in vitro monolayer assays using this drug may more accurately predict in vivo performance than for drugs like doxorubicin. The nonlinear interaction among various determinants representing cell and lesion phenotype as well as therapeutic strategies is a unifying theme of our results. Throughout it can be appreciated that macroscopic environmental conditions, notably drug and nutrient distributions, give rise to considerable variation in lesion response, hence clinical resistance. Moreover, the synergy or antagonism of combined therapeutic strategies depends heavily upon this environment.

The complex, constantly evolving and multifaceted nature of cancer has made it difficult to identify unique molecular and pathophysiological signatures for each disease variant, consequently hindering development of effective therapies. Mathematical modeling and computer simulation are tools that can provide a robust framework to better understand cancer progression and response to chemotherapy. Successful therapeutic agents must overcome biological barriers occurring at multiple space and time scales and still reach targets at sufficient concentrations. A multiscale computer simulator founded on the integration of experimental data and mathematical models can provide valuable insights into these processes and establish a technology platform for analyzing the effectiveness of chemotherapeutic drugs, with the potential to cost-effectively and efficiently screen drug candidates during the drug-development process.

BackgroundPathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms.MethodsWe combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal.ResultsWe demonstrate that the two separately introduced ER- breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2.ConclusionWe propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed.

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#1165 Background: Models of cancer growth have been developed that predict tumor size and growth dynamics for invasive tumors. However, it has been difficult to model ductal carcinoma in situ (DCIS) because of the constraints introduced by its containment within the duct system.
 Materials and Methods: We have developed a spherical model of growth of solid type DCIS using chemical engineering models of reaction and diffusion in porous media to represent the spread of DCIS in the duct systems. The model predicts tumor diameter based on four input parameters: the ratio of the apoptosis rate to the proliferation rate (A), the diffusion penetration length for nutrient to sustain the tumor growth (L), the volume fraction that tumor cells occupied within the involved breast tissue (V), and the time taken for a cell to complete mitosis(T). We have estimated L, V, and T from the literature, and then back-calcuated A for a range of diameters. We have used these four parameters as inputs and studied the time dependence of the evolution of DCIS.
 Results: We have found that the range of the values of A that we determined are within an adeqaute physiological range based on rates of proliferation and apoptosis taken from the literature. Using the model, the time to reach at least 95% of the maximum size ranges from less than 30 days for DCIS measuring 0.5 cm to almost 80 days for DCIS measuring 6 cm in diameter.
 
 Discussion: There has been little understanding of how long it takes for DCIS to grow, and whether it reaches a steady state size. Our simulations show that DCIS can grow to sizes as large as 6 cm in less than 3 months if it has the correct properties, including a high proliferation rate relative to the apoptosis rate and appropriate access to nutrients. This finding may help to explain why many cases of DCIS are not diagnosed before they progress to invasive carcinoma. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 1165.

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