Combining cutting-edge bioprinting technology with microfluidic chips, an international interdisciplinary team led by scientists at the University of Waterloo in Canada has successfully created a 3D model of complex cancer, which can help researchers more accurately understand heterogeneous tumors (tumors with multiple cancer cells) and treat cancer faster and cheaper. The relevant research papers were published in the latest issue of the journal "Scientific Reports". In traditional cancer treatment methods, doctors biopsy patients' tumors, extract cells, and then culture them in laboratory culture dishes. But 10 years ago, the use of this method to treat cancer repeatedly failed in human trials, prompting scientists to realize that the 2D model mentioned above cannot capture the true tumor structure in the human body. In view of this, the research team solved this problem by creating a 3D tumor model that not only reflects the complexity of the tumor, but also simulates its surrounding environment. They first created polymer "microfluidic chips", which are tiny structures etched with channels that simulate the blood flow and other fluids around patients' tumors. Subsequently, they cultured various types of cancer cells and suspended these cell cultures in their customized bio ink (mixed with gelatin, alginate, and other nutrients) to maintain the vitality of the cell culture. Finally, they used a squeeze bioprinter to print different types of cancer cells layer by layer onto the microfluidic chips prepared earlier. As a result, they obtained a living 3D model of a complex cancer to test different treatment modes, such as various chemotherapy drugs. Researchers are particularly interested in creating complex breast cancer models. breast cancer is the most common cancer among women after skin cancer. Treatment of breast cancer is extremely challenging, because it is a complex tumor that contains multiple types of cells when it metastasizes. Relying on cells that receive biopsy once or twice to accurately represent the entire tumor may lead to invalid treatment plans. The latest model may be expected to solve this problem. Author: Liu Xia (Liao Xinshe)