LLNL scientists use 3D bioprinting to better understand cancerous tumor formation

Lawrence Livermore National Laboratory (LLNL) researchers have combined 3D bioprinting with computational flow simulations to better understand the spread of cancer during metastasis. 

Working alongside scientists from Duke University, the team injected tumorous cells into a 3D printed brain cell structure. By applying fluid dynamics analysis to the process, the team were able to identify where the tumors became attached, paving the way for a potential predictive model. Leveraging the researchers’ novel computer-modelling-based approach, future clinicians could be able to anticipate the spread of cancer cells within individual patients. 

“Computational modeling is definitely a useful tool, but you still need to benchmark it against something real,” said Monica Moya, principal investigator of the LLNL study. “With this approach, we can make the biology as simple and clean as it needs to be to validate the models, and we can increase the complexity, both in the biology and the computational model.

“Physics matters in biology, and this paper really provides a framework for how you can use these in vitro models, paired with simulations, to really bring strength to the field.” 

Computer analysis carried out at LLNL revealed a pattern in where the cancer cells gathered in the 3D printed vascular structure. Image via LLNL.
Computer analysis carried out at LLNL revealed a pattern in where the cancer cells gathered in the 3D printed vascular structure. Image via LLNL.

The urgent need for 3D printed cancer solutions 

It has been known for more than 150 years that cancerous cells can invade secondary sites and cause tumors, but predicting the exact trajectory of these growths has remained impossible. The current inability of doctors to locate and treat cancers early on, has made them very difficult to treat, and when growths occur in the brain, they are almost always fatal. 

Moya described the process whereby cancerous cells spread, attach and grow to a vessel’s lining, as like a seed planting itself in soil. “Tumor cells tend to escape from a primary tumor and travel through the vasculature,” explained Moya. “They eventually attach to a vessel wall, pass through the endothelium into the tissue and grow like a seed in soil.”

Gaining further insight into where the cells land via studies, has proved almost impossible due to the number of differentiating factors between each vascular system tested. Measuring mechanical forces such as dynamic fluid flow in-vivo also requires a significant simplification of existing testing models, limiting their usefulness in drawing conclusions. 

While a large amount of previous research into cancerous growths has involved computer modelling, it’s important to validate these hypotheses via in-situ testing. If proven correct, the theories could hold the key to understanding the role of flow patterns, vascular geometry and tissue compliance in endovascular seeding. Having a highly-detailed model is therefore vital to getting the most from insight from cancer research. 

Despite the importance of validation, methods are often limited to using microfluidic devices, which don’t feature tubular channels or vessel compliance, two important characteristics of in-vivo models. Microfluidic devices also lack the full range of attachment sites available to Circulating Tumor Cells (CTCs), and their geometries cannot be reproduced exactly between models. 

The LLNL team's vascular structures demonstrated the same storage modulus as real human brain tissue. Image via LLNL.
The LLNL team’s vascular structures demonstrated the same storage modulus as real human brain tissue. Image via LLNL.

The LLNL team’s hydrogel-based device

To overcome the limitations of existing microfluidic devices, the LLNL team developed a hydrogel-based vascular flow device. Leveraging a custom-built extrusion-based 3D printer, a sacrificial bio-ink was patterned and embedded into a gelatin-fibrin hydrogel. Luminal channels were then evacuated and seeded with immortalized human cerebral endothelial cells, forming vascular-like tissue. 

Although a range of vascular geometries could be achieved with their hydrogel, the researchers initially opted to fabricate simplified straight and branching geometries. By starting with basic structures, the team aimed to draw wider conclusions based on a number of continuous small changes during testing. Vessels were summarily produced to the size of small arteries, with two hierarchical 45° branching points and daughter vessels featuring increasingly smaller diameters. 

In order to test their additive blood vascular system, the LLNL team connected it to a pneumatic fluidic feeding system. After seven days, the endothelial cells had completely covered all exposed channel surfaces, forming a confluent layer of endothelial lining. The vessels were subsequently subjected to a range of flow rate tests and imaged with confocal microscopy to evaluate their response to varying flow rates. 

The researchers concluded that their combined validation method had been successful in finding patterns in cancer cell behaviours. Image via LLNL.
The researchers concluded that their combined validation method had been successful in finding patterns in cancer cell behaviours. Image via LLNL.

The final storage modulus of the gel was found to be similar to that reported in human brain tissue, making it ideal for testing the probability of CTC attachment. Metastatic mammary gland carcinoma cells were filtered and circulated through the bioprinted devices at an average flow rate of 1690 μl/min for an hour. The devices were then fixed, stained, and imaged to determine where CTCs attached. 

Testing showed that the CTCs had a preference for attaching at the vessel’s branching points rather than its straight portions. Continued simulations revealed that the Wall Shear Stress (WSS) levels of the 3D printed arteries were also an important factor in attachment rate. Despite the greater shear stresses applied to the cancer cells in straight channels, the higher WSS observed in smaller regions was found to enhance their anchorage. 

Based on their findings, the LLNL team concluded that their strategy of carrying out increasingly complex computational fluid dynamics simulations to be a success. The researchers consider their approach to be a first step in using computational models to identify how cancerous cells spread to distant organs. 

“Using this approach, we were able to test, observe and measure a biological phenomenon that was previously impossible,” summarized lead author and LLNL research staff engineer William “Rick” Hynes. “By pairing our engineered platform with computational modeling, we can directly interrogate the behavior of metastatic cells and the rules that govern them far more rapidly than through experimentation alone.”

Utilizing 3D printing in the fight against cancer

Additive manufacturing has often been deployed by researchers as a method of discovering more about cancerous cells, with the ultimate aim of finding ways to combat the deadly disease. 

Scientists from Japanese-based Nagoya City University have developed a new type of 3D printed cancer drug delivery system. Using a polymer hydrogel, the team fabricated an implantable patch which proved capable of carrying the Liposomal Doxorubicin medication. 

An assistant professor from Virginia Commonwealth University has used 3D printing to create live models of tumor cells. The breakthrough could enable cancer researchers to better understand the disease’s progression.

Researchers from the USA and Germany have produced a 3D bioprinted model of glioblastoma (GBM), an aggressive type of brain cancer. The biofabricated cell structures have the potential to help clinicians develop a better understanding of the disease, and accelerate the discovery of new drugs to fight it.

The researchers’ findings are detailed in their paper titled “Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model,” which was published in the Advanced Functional Materials journal. The report was co-authored by W. F. Hynes, M. Pepona, C. Robertson, J. Alvarado, K. Dubbin, M. Triplett, J. J. Adorno, A. Randles and M. L. Moya. 

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Featured image shows an image captured by the researchers during the computer analysis of their 3D printed vascular structure. Image via LLNL.