A high-content image-based method for quantitatively studying context-dependent cell population dynamics.

Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.

The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells.

Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of a drug. Using a combined experimental and computational approach, we developed a mathematical model of preexisting drug resistance describing multiple cellular compartments, each representing a specific tumor environmental niche. This model was parameterized using a novel experimental dataset derived from the HCC827 erlotinib-sensitive and -resistant NSCLC cell lines. We found that, in contrast to in the drug-free environment, resistant cells may hold a fitness advantage compared to parental cells in microenvironments deficient in oxygen and nutrients. We then utilized the model to predict the impact of drug and nutrient gradients on tumor composition and recurrence times, demonstrating that these endpoints are strongly dependent on the microenvironment. Our interdisciplinary approach provides a model system to quantitatively investigate the impact of microenvironmental effects on the evolutionary dynamics of tumor cells.

A physical sciences network characterization of non-tumorigenic and metastatic cells.

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer.

Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance.

Long-term in vivo imaging and measurement of dendritic shrinkage of retinal ganglion cells.

PURPOSE: To monitor and measure dendritic shrinkage of retinal ganglion cells (RGCs) in a strain of transgenic mice (Thy-1 YFP) that expresses yellow fluorescent proteins in neurons under the control of a Thy-1 promoter. METHODS: A total of 125 RGCs from 16 eyes of Thy-1 YFP transgenic mice were serially imaged with a confocal scanning laser ophthalmoscope for 6 months after optic nerve crush. Quantitative analysis of cell body area, axon diameter, dendritic field, number of terminal branches, total dendritic branch length, branching complexity, symmetry, and distance from the optic disc was used to characterize the morphology of RGCs, describe the patterns of axonal and dendritic degeneration, identify the morphologic predictors for cell survival, and estimate the rate of dendritic shrinkage. RESULTS: RGC damage was observed prospectively to begin with progressive dendritic shrinkage, followed by loss of the axon and the cell body. In a small proportion of RGCs, progressive axonal changes including fragmentation, beading, retraction, and bulb formation were also observed. RGCs with a larger dendritic field and a longer total dendritic branch length in general have a better survival probability. The rate of dendritic shrinkage was variable with a slower rate observed in cells having a larger dendritic field, a longer total dendritic branch length, and a greater distance from the optic disc. CONCLUSIONS: Estimating the probability of RGC survival and measuring the rate of dendritic shrinkage could become a new paradigm for investigating neuronal degeneration and evaluating the response of neuroprotective treatment.

Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: pattern of RNFL defects in glaucoma.

OBJECTIVE: To characterize the distribution pattern, angular width, and area of retinal nerve fiber layer (RNFL) defects in glaucoma using spectral-domain optical coherence tomography (OCT). DESIGN: Prospective, cross-sectional study. PARTICIPANTS: We included 113 normal subjects and 116 glaucoma patients. METHODS: One eye from each individual was randomly selected for Cirrus HD-OCT (Carl Zeiss Meditec Inc., Dublin, CA) RNFL imaging of the 6 × 6-mm² parapapillary region. The RNFL defects were identified in the RNFL thickness deviation map as superpixels coded in red. The angular location and the angular width of RNFL defects were measured. The proportion of area with RNFL measurements within the normal ranges in the RNFL thickness deviation map was expressed as the RNFL area index (RAI): 1 - [area of superpixels coded in red/(6 × 6 - optic disc and parapapillary atrophic area)]. The diagnostic performance between RAI and average RNFL thickness was compared with the area under the receiver operating characteristic curve after adjusting refraction, signal strength, optic disc, and parapapillary atrophic areas. MAIN OUTCOME MEASURES: Frequency distribution profiles and distribution patterns of RNFL defects, diagnostic sensitivity and specificity of RAI, and average RNFL thickness. RESULTS: The RNFL defects in glaucoma were most frequently found at the inferotemporal meridian at 284° (80.4%), followed by the superotemporal meridians at 73° (54.2%). The respective proportions of localized (angular width ≤ 30°) and diffuse (angular width > 30°) RNFL defects were 11.4% and 70.5% in mild glaucoma (MD ≥ 6 dB), and 4.2% and 94.5% in moderate to advanced glaucoma (MD < -6 dB). The RAI was 90.2 ± 6.4% and 83.6 ± 7.4% in the mild and moderate to advanced glaucoma groups, respectively. At a specificity of 90.0%, the respective diagnostic sensitivity of RAI and average RNFL thickness was 95.7% (95% confidence interval, 92.2-99.1%) and 94.0% (90.1-99.1%). CONCLUSIONS: Analysis of the pattern of RNFL defects with spectral domain OCT imaging offers important insights in understanding the characteristics of RNFL damage. As RNFL defects expand in size as the disease progresses, measurement of the angular width and area of RNFL defects can provide an additional dimension for evaluation of glaucoma.