KEYWORDS: Tumors, 3D modeling, Tumor growth modeling, Photodynamic therapy, Cancer, Animal model studies, 3D image processing, Systems modeling, In vitro testing, 3D displays
The development and translational potential of therapeutic strategies for cancer is limited, in part, by a lack of biological
models that capture important aspects of tumor growth and treatment response. It is also becoming increasingly evident
that no single treatment will be curative for this complex disease. Rationally-designed combination regimens that impact
multiple targets provide the best hope of significantly improving clinical outcomes for cancer patients. Rapidly
identifying treatments that cooperatively enhance treatment efficacy from the vast library of candidate interventions is
not feasible, however, with current systems. There is a vital, unmet need to create cell-based research platforms that
more accurately mimic the complex biology of human tumors than monolayer cultures, while providing the ability to
screen therapeutic combinations more rapidly than animal models. We have developed a highly reproducible in vitro
three-dimensional (3D) tumor model for micrometastatic ovarian cancer (OvCa), which in conjunction with quantitative
image analysis routines to batch-process large datasets, serves as a high throughput reporter to screen rationally-designed
combination regimens. We use this system to assess mechanism-based combination regimens with photodynamic
therapy (PDT), which sensitizes OvCa to chemo and biologic agents, and has shown promise in clinic trials. We show
that PDT synergistically enhances carboplatin efficacy in a sequence dependent manner. In printed heterocellular
cultures we demonstrate that proximity of fibroblasts enhances 3D tumor growth and investigate co-cultures with
endothelial cells. The principles described here could inform the design and evaluation of mechanism-based therapeutic
options for a broad spectrum of metastatic solid tumors.
Three-dimensional in vitro tumor models have emerged as powerful research tools in cancer biology, though the vast
potential of these systems as high-throughput, biologically relevant reporters of treatment response has yet to be
adequately explored. Here, building on previous studies, we demonstrate the utility of using 3D models for ovarian and
pancreatic cancers in conjunction with quantitative image processing to reveal aspects of growth behavior and treatment
response that would not be evident without either modeling or quantitative analysis component. In this report we
specifically focus on recent improvements in the imaging component of this integrative research platform and emphasize
analysis to establish reproducible growth properties in 3D tumor arrays, a key consideration in establishing the utility of
this platform as a reliable reporter of therapeutic response. Building on previous studies using automated segmentation
of low magnification image fields containing large numbers of nodules to study size dependent treatment effects, we
introduce an improvement to this method using multiresolution decomposition to remove gradient background from
transmitted light images for more reliable feature identification. This approach facilitates the development of a new
treatment response metric, disruption fraction (Dfrac), which quantifies dose dependent distribution shifts from nodular
fragmentation induced by cytotoxic therapies. Using this approach we show that PDT treatment is associated with
significant dose-dependent increases in Dfrac, while this is not observed with carboplatin treatment. The ability to
quantify this response to therapy could play a key role in design of combination regimens involving these two
modalities.
Advances in imaging and spectroscopic technologies have enabled the optimization of many therapeutic modalities in
cancer and noncancer pathologies either by earlier disease detection or by allowing therapy monitoring. Amongst the
therapeutic options benefiting from developments in imaging technologies, photodynamic therapy (PDT) is exceptional.
PDT is a photochemistry-based therapeutic approach where a light-sensitive molecule (photosensitizer) is activated with
light of appropriate energy (wavelength) to produce reactive molecular species such as free radicals and singlet oxygen.
These molecular entities then react with biological targets such as DNA, membranes and other cellular components to
impair their function and lead to eventual cell and tissue death. Development of PDT-based imaging also provides a
platform for rapid screening of new therapeutics in novel in vitro models prior to expensive and labor-intensive animal
studies. In this study we demonstrate how an imaging platform can be used for strategizing a novel combination
treatment strategy for multifocal ovarian cancer. Using an in vitro 3D model for micrometastatic ovarian cancer in
conjunction with quantitative imaging we examine dose and scheduling strategies for PDT in combination with
carboplatin, a chemotherapeutic agent presently in clinical use for management of this deadly form of cancer.
Three-dimensional tumor models have emerged as valuable in vitro research tools, though the power of such systems as quantitative reporters of tumor growth and treatment response has not been adequately explored. We introduce an approach combining a 3-D model of disseminated ovarian cancer with high-throughput processing of image data for quantification of growth characteristics and cytotoxic response. We developed custom MATLAB routines to analyze longitudinally acquired dark-field microscopy images containing thousands of 3-D nodules. These data reveal a reproducible bimodal log-normal size distribution. Growth behavior is driven by migration and assembly, causing an exponential decay in spatial density concomitant with increasing mean size. At day 10, cultures are treated with either carboplatin or photodynamic therapy (PDT). We quantify size-dependent cytotoxic response for each treatment on a nodule by nodule basis using automated segmentation combined with ratiometric batch-processing of calcein and ethidium bromide fluorescence intensity data (indicating live and dead cells, respectively). Both treatments reduce viability, though carboplatin leaves micronodules largely structurally intact with a size distribution similar to untreated cultures. In contrast, PDT treatment disrupts micronodular structure, causing punctate regions of toxicity, shifting the distribution toward smaller sizes, and potentially increasing vulnerability to subsequent chemotherapeutic treatment.
Ovarian epithelial cancer has a high morbidity due to its propensity to metastasize onto surfaces in the abdomen. In order to effectively treat these metastatic lesions with photodynamic therapy (PDT), it is critical to understand the detailed dynamics of the PDT response. 3D in vitro models of ovarian cancer are a promising system for studying the response to PDT of these lesions, as they replicate the size, appearance, and characteristics of metastatic disease observed in the clinic. An ideal approach capable of non-purturbative, 3D imaging of this model is optical coherence tomography (OCT). An ultrahigh resolution time-lapse OCT (TL-OCT) system was used to visualize the photodynamic therapeutic
response in the hours and days following treatment. Tumor nodules were observed to experience rapid cell death within
the first 24 hours post-treatment using benzophorphyrin derivative monoacid A (BPD), characterized by structural breakdown of the model nodules. Highly scattering bodies were observed with OCT contrast to form at the periphery of the tumor nodules. These highly scattering moieties were identified as apoptotic bodies, indicating that OCT is capable of tracking the PDT-induced apoptosis in real-time without the need for labels.
We introduce a new platform to study treatment response in adherent micrometastatic ovarian cancer, combining an in vitro 3D model, with custom quantitative analysis routines to report growth and cytotoxic response in large sets of image data. OVCAR-5 human ovarian cancer cells were grown on a bed of Growth Factor Reduced MatrigelTM (GFR MatrigelTM). Using batch analysis routines to analyze longitudinal image data we show that in vitro tumor growth leads to a reproducible log-normal size distribution with two well-defined peaks. These distinct growth modes correspond to a
population with approximately constant diameter of 20μm over the time probed, while the other peak corresponds to a more rapidly assembling sub-distribution of micronodules which shifts towards larger peak center positions with mean equivalent diameters of 92μm, 120μm and 150μm at days 7, 10 and 17 following plating. At day 10, 3D and monolayer cultures were treated with a regimen of either carboplatin or photodynamic therapy. Using a quantitative fluorescence imaging approach we report dose response curves and demonstrate that 3D nodules are significantly less sensitive to treatment than the same cells grown in monolayer. 3D cultures subject to 5J/cm2 PDT (250nM BPD-MA) exhibited a
mean viability of 80% (95% CI = 73% to 82%) relative to no treatment control. 3D cultures subject to carboplatin treatment at 100μM concentration exhibited a mean viability of 92% (95% CI =86% to 97%). A combination treatment of 5J/cm2 PDT followed by 100μM carboplatin yielded an enhanced cytotoxic effect with mean viability of 46%, 95% confidence interval (CI) = (35 % to 46%).
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