Abstract
Cancer is the second leading cause of human death worldwide. Although decades of efforts in engineering in vitro cancer models have advanced drug discovery and the insight into cancer biology, the establishment of preclinical models that enable fully recapitulating the tumor microenvironment remains challenging owing to its intrinsic complexity. Recent progress in engineering techniques has allowed the development of a new generation of in vitro preclinical models that can recreate complex in vivo tumor microenvironments and accurately predict drug responses, including tumor-on-a-chip and spheroids. These biomimetic tumor models are of particular interest as they pave the way for a better understanding of cancer biology and accelerating the development of new anticancer therapeutics with reducing animal use. Here, we developed various in vitro tumor models for revealing the underlying molecular mechanisms associated with tumor metastasis under fluid shear stress (FSS) and precision anticancer drug screening.We first report a microfluidic circulatory system that can emulate the circulating tumor cell microenvironment to research the responses of typical liver cancer cells to varying levels of FSS. We observe that HepG2 cells surviving FSS exhibit a marked overexpression of TLR4 and TPPP3, which are shown to be associated with the colony formation, migration, and anti-apoptosis abilities of HepG2. Furthermore, overexpression of these two genes in another liver cancer cell line with normally low TLR4 and TPPP3 expression, SK-Hep-1 cells, by lentivirus-mediated transfection also confirm the critical role of TLR4 and TPPP3 in improving colony formation, migration, and survival capability under a fluid environment. Interestingly, in vivo experiments show SK-Hep-1 cells, overexpressed with these genes, have enhanced metastatic potential to the liver and lungs in mouse models via tail vein injection. Mechanistically, TLR4 and TPPP3 upregulated by FSS may increase FSS-mediated cell survival and metastasis through the p53-Bax signaling pathway. Moreover, elevated levels of these genes correlate with poorer overall survival in liver cancer patients.
We then develop an efficient image-activated sorter to reduce the size and shape heterogeneity of microgel-encapsulating tumor spheroids produced by droplet microfluidics. The YOLO artificial intelligence model is employed for tumor spheroid detection and segmentation to calculate their size and shape. A reusable and cost-effective off-chip solenoid valve-based sorting actuation module is proposed to sort out tumor spheroids with the desired size and shape. By utilizing the proposed sorter, we successfully uncover variations in drug response among tumor spheroid populations with different size and shape distributions from the same batch. Moreover, the precision of drug testing on the tumor spheroid population level is improved to a level comparable to the single tumor spheroid analysis, which is more complex and time-consuming.
Finally, we develop a microgel single-cell culture approach for label-free isolating and expanding cancer stem cells (CSCs) via utilizing the anti-apoptosis and self-renewal properties of CSCs. The sodium-alginate is used to high-throughput encapsulate single cancer cells for growing spheroids. Over a 7-to-10-day culture, a sufficient number of single-cell-derived spheroids with enriched stemness are generated, as indicated by immunofluorescent staining and qPCR testing. Furthermore, this platform is also exploited to high-throughput screen effective drugs on stemness among a novel stemness-related compound library (158 compounds). As a result, seven candidate drugs can significantly inhibit spheroid formation and decrease stemness at a low drug concentration (1µM).
Overall, by using microfluidic and biofabrication technologies, we have established various kinds of in vitro cancer models that can capture some key aspects of the human tumor microenvironment. These models enable their applications in identifying the novel biomarkers as early cancer diagnosis and targeted treatment development, precision drug testing, and large-scale drug screening.
| Date of Award | 4 Sept 2024 |
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| Original language | English |
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| Supervisor | M YANG (Supervisor) |