Protocols and characterization data for 2D, 3D, and slice-based tumor models from the PREDECT project

Two-dimensional (2D) culture of cancer cells in vitro does not recapitulate the three-dimensional (3D) architecture, heterogeneity and complexity of human tumors. More representative models are required that better reflect key aspects of tumor biology. These are essential studies of cancer biology and immunology as well as for target validation and drug discovery. The Innovative Medicines Initiative (IMI) consortium PREDECT (www.predect.eu) characterized in vitro models of three solid tumor types with the goal to capture elements of tumor complexity and heterogeneity. 2D culture and 3D mono-and stromal cocultures of increasing complexity, and precision-cut tumor slice models were established. Robust protocols for the generation of these platforms are described. Tissue microarrays were prepared from all the models, permitting immunohistochemical analysis of individual cells, capturing heterogeneity. 3D cultures were also characterized using image analysis. Detailed step-by-step protocols, exemplary datasets from the 2D, 3D, and slice models, and refined analytical methods were established and are presented.Peer reviewe

Non-Invasive In Vivo Imaging of Tumor-Associated CD133/Prominin

detection of cancer stem cells is of great importance. detection of CD133/prominin, a cancer stem cell surface marker for a variety of tumor entities. The CD133-specific monoclonal antibody AC133.1 was used for quantitative fluorescence-based optical imaging of mouse xenograft models based on isogenic pairs of CD133 positive and negative cell lines. A first set consisted of wild-type U251 glioblastoma cells, which do not express CD133, and lentivirally transduced CD133-overexpressing U251 cells. A second set made use of HCT116 colon carcinoma cells, which uniformly express CD133 at levels comparable to primary glioblastoma stem cells, and a CD133-negative HCT116 derivative. Not surprisingly, visualization and quantification of CD133 in overexpressing U251 xenografts was successful; more importantly, however, significant differences were also found in matched HCT116 xenograft pairs, despite the lower CD133 expression levels. The binding of i.v.-injected AC133.1 antibodies to CD133 positive, but not negative, tumor cells isolated from xenografts was confirmed by flow cytometry. imaging of tumor-associated CD133 is feasible and that CD133 antibody-based tumor targeting is efficient. This should facilitate developing clinically applicable cancer stem cell imaging methods and CD133 antibody-based therapeutics

Capturing tumor complexity in vitro : Comparative analysis of 2D and 3D tumor models for drug discovery

Two-dimensional (2D) cell cultures growing on plastic do not recapitulate the three dimensional (3D) architecture and complexity of human tumors. More representative models are required for drug discovery and validation. Here, 2D culture and 3D mono-and stromal co-culture models of increasing complexity have been established and cross-comparisons made using three standard cell carcinoma lines: MCF7, LNCaP, NCI-H1437. Fluorescence-based growth curves, 3D image analysis, immunohistochemistry and treatment responses showed that end points differed according to cell type, stromal co-culture and culture format. The adaptable methodologies described here should guide the choice of appropriate simple and complex in vitro models.Peer reviewe

Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids

In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation

S4_File: 3D image stack, LNCaP cancer spheroids in co-culture with CAFs, control sample (DMSO), well A05, field 3, stack 335

3D image stack of 3D fluorescent multi-cellular spheroid culture existing out of LNCaP human prostate cancer cells (ATCC, Rockville, USA) and CAF-PF179T human cancer associated fibroblasts (a cell line obtained from the Weizmann Institute, via the PREDECT consortium)

Abiraterone switches castration‐resistant prostate cancer dependency from adrenal androgens towards androgen receptor variants and glucocorticoid receptor signalling

INTRODUCTION: Castration‐resistant prostate cancer (CRPC) remains dependent on androgen receptor (AR) signalling, which is largely driven by conversion of adrenal androgen precursors lasting after castration. Abiraterone, an inhibitor of the steroidogenic enzyme CYP17A1, has been demonstrated to reduce adrenal androgen synthesis and prolong CRPC patient survival. To study mechanisms of resistance to castration and abiraterone, we created coculture models using human prostate and adrenal tumours. MATERIALS AND METHODS: Castration‐naïve and CRPC clones of VCaP were incubated with steroid substrates or cocultured with human adrenal cells (H295R) and treated with abiraterone or the antiandrogen enzalutamide. Male mice bearing VCaP xenografts with and without concurrent H295R xenografts were castrated and treated with placebo or abiraterone. Response was assessed by tumour growth and PSA release. Plasma and tumour steroid levels were assessed by LC/MS‐MS. Quantitative polymerase chain reaction determined steroidogenic enzyme, nuclear receptor and AR target gene expression. RESULTS: In vitro, adrenal androgens induced castration‐naïve and CRPC cell growth, while precursors steroids for de novo synthesis did not. In a coculture system, abiraterone blocked H295R‐induced growth of VCaP cells. In vivo, H295R promoted castration‐resistant VCaP growth. Abiraterone only inhibited VCaP growth or PSA production in the presence of H295R. Plasma steroid levels demonstrated CYP17A1 inhibition by abiraterone, whilst CRPC tumour tissue steroid levels showed no evidence of de novo intratumoural androgen production. Castration‐resistant and abiraterone‐resistant VCaP tumours had increased levels of AR, AR variants and glucocorticoid receptor (GR) resulting in equal AR target gene expression levels compared to noncastrate tumours. CONCLUSIONS: In our model, ligand‐dependent AR‐regulated regrowth of CRPC was predominantly supported via adrenal androgen precursor production while there was no evidence for intratumoural androgen synthesis. Abiraterone‐resistant tumours relied on AR overexpression, expression of ligand‐independent AR variants and GR signalling

Three-dimensional models of cancer for pharmacology and cancer cell biology: Capturing tumor complexity in vitro/ex vivo

Cancers are complex and heterogeneous pathological “organs” in a dynamic interplay with their host. Models of human cancer in vitro, used in cancer biology and drug discovery, are generally highly reductionist. These cancer models do not incorporate complexity or heterogeneity. This raises the question as to whether the cancer models' biochemical circuitry (not their genome) represents, with sufficient fidelity, a tumor in situ. Around 95% of new anticancer drugs eventually fail in clinical trial, despite robust indications of activity in existing in vitro pre-clinical models. Innovative models are required that better capture tumor biology. An important feature of all tissues, and tumors, is that cells grow in three dimensions. Advances in generating and characterizing simple and complex (with added stromal components) three-dimensional in vitro models (3D models) are reviewed in this article. The application of stirred bioreactors to permit both scale-up/scale-down of these cancer models and, importantly, methods to permit controlled changes in environment (pH, nutrients, and oxygen) are also described. The challenges of generating thin tumor slices, their utility, and potential advantages and disadvantages are discussed. These in vitro/ex vivo models represent a distinct move to capture the realities of tumor biology in situ, but significant characterization work still remains to be done in order to show that their biochemical circuitry accurately reflects that of a tumor