New advances in the era of precision medicine: an example of architecturally defined 3D bioprinted models of human tumors.
Tags: 3D Bioprinting
3D bioprinted in vitro models have received increasing attention as a potential means to improve in vitro cancer research by bridging the gap between 2D in vitro and animal in vivo platforms and mimicking more accurately human cancer biology. In vivo cell behavior is modulated through different interactions, including mechanical and chemical signals between cells themselves and their extracellular microenvironment (extracellular matrix and stromal cells). These signals are responsible for cellular growth, self-organization and cell-dependent deposit of matrix proteins, and are impaired in tumors. Indeed, tumor microenvironment determines different cancer phenotypes for a same tissue, and subsequent therapeutic response or resistance. In line with in vivoevidence and in contrast to 2D monoculture, a number of studies have shown that 3D microenvironment substantially alters the tumor response to anti-cancer drugs; perhaps, due to the absence of a well-established microenvironment and defined tissue architecture in 2D models. Therefore, relevant 3D in vitro platforms that enable the formation of pertinent interactions between cancer cells and their microenvironment are required for the assessment of different anti-tumorigenic strategies. Furthermore, modeling the complicated tumor architecture in a very precise and controlled way is currently possible with innovative bioprinting techniques, which has opened new venues for the creation of more realistic 3D cancer in vitro platforms.
Recently, Langer et al. (2019) demonstrated that 3D bioprinting might be used to generate multicellular architecturally defined tissue models of human tumors capable of effectively mimicking in vivo tissue biology. In particular, they modeled different 3D tumor phenotypes with a variety of cancer cell types, including multiple human breast cancer and pancreatic cell lines, and, importantly, primary tissue from pancreatic cancer patients. The generated platforms were used to interrogate both intrinsic and extrinsic signals from the diverse cell types, including their corresponding responses to different microenvironments and a range of anti-cancer drugs.
The models were initially created as bio-printed structures comprising the corresponding cancer cell type, different stromal cell types and alginate-containing hydrogel as bio-ink, which was removed during subsequent culture. The resulting tumor tissue showed similarities to in vivo solid tumor architecture, involving a tumor cell core surrounded by normal stromal cells. These cells were able to survive, self-organize, proliferate, migrate and, importantly, interact with one another to form tissue-like structures once the bio-ink was removed. For instance, an estrogen receptor-positive model of breast cancer was created with a mix of the MCF-7 cancer cell line, stromal fibroblasts, and human umbilical vein endothelial cells. In this model, close interaction between epithelial cancer cells and stromal fibroblasts, and progressive formation of endothelial networks was evidenced several days after the bio-ink was removed.
Additionally, the study compared the proliferation rate of different models of breast cancer containing the same stromal cell mix. The proliferation rate was highest in the platform containing the claudin-low MDA-MB-231 breast cancer phenotype, which has been widely recognized as highly invasive in vivo. Conversely, no significant differences were reported when the equivalent 2D platforms were compared.
The authors also demonstrated that distinct microenvironments might be modelled in their architecturally defined breast cancer models, by incorporating additional cell types known to be present in human breast tumors to the initial stromal cell mix. These included mesenchymal stem cells, which have been shown to affect tumor progression, growth, and migration.
Importantly, Langer et al. evidenced that their bio-printed 3D tissues can be used to assess therapeutic efficacy, better recapitulating in vivo phenotypes than 2D monocultures. As an example, an equivalent 2D monoculture to one of their breast cancer models was treated with the targeted PI3K/mTOR inhibitor BEZ235, which led to a substantial decrease in cell proliferation. In contrast, the same experiment didn’t lead to any significant changes in the bio-printed 3D tissue.
The present 3D bio-printing methodology was applied to additional tumor types (i.e. pancreatic ductal adenocarcinoma) both in the human pancreatic cell line HPAFIIA and primary tissue from patients. In order to demonstrate that these models respond to microenvironmental signals, they treated the HPAFIIA 3D platform with transforming growth factor beta (TGFbeta), a well-studied cytokine that activates pancreatic stellate cells thereby inducing their proliferation and tumor cell intrinsic migratory capacity in vivo. In line with this, pancreatic stellate cell proliferation was increased in the developed 3D platforms treated with TGF-beta, as well as the percentage of HPAFFII cancer cells that migrated into the surrounding stroma. Finally, primary tumor tissues from pancreatic cancer patients that were able to recapitulate in vivo morphology were generated. The morphology, connectivity between cells and proliferative capacity of these innovative tissue platforms were indeed similar to structures found in primary tissue and xenografts of the corresponding patients.
All in all, Langer and colleagues have generated physiologically realistic 3D bio-printed tumor tissues from distinct subtypes of breast and pancreatic cancer in relevant microenvironments and demonstrated that the same technique can model patient-specific tumors using primary cells. Moreover, the models can be used to mimic different tumorigenic reactions to multiple stimuli, including drug resistance or response in distinct cancer phenotypes. In the future, bio-printed tissues containing cells from patient tumors could be used for translational studies aiming at generating personalized therapies for the treatment of cancer.
Source: Langer et al. Modeling Tumor Phenotypes In Vitro with Three-Dimensional Bioprinting. Cell Reports, 2019.
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