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Abstract Solid tumors develop within a complex environment called the tumor microenvironment (TME), which is sculpted by the presence of other cells, such as cancer‐associated fibroblasts (CAFs) and immune cells like macrophages (Mφs). Despite the presence of immune cells, tumor cells orchestrate a tumor‐supportive environment through intricate interaction with the components of the TME. However, the specific mechanism by which this intercellular dialogue is regulated is not fully understood. To that end, the development of an organotypic 3D breast TME‐on‐a‐chip (TMEC) model, integrated with single‐cell RNA sequencing analysis, is reported to mechanistically evaluate the progression of triple‐negative breast cancer (TNBC) cells in the presence of patient‐derived CAFs and Mφs. Extensive functional assays, including invasion and morphometric characterization, reveal the synergistic influence of CAFs and Mφs on tumor cells. Furthermore, gene expression and pathway enrichment analyses identify the involvement of theKYNUgene, suggesting a potential immune evasion mechanism through the kynurenine pathway. Lastly, the pharmacological targeting of the identified pathway is investigated.
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Optimal therapy design with tumor microenvironment normalization
Abstract Tumor microenvironment (TME) normalization improves efficacy by increasing anticancer nanocarrier delivery by restoring transvascular pressure gradients that induce convection. However, transport depends on TME biophysics, normalization dose, and nanocarrier size. With increased understanding, we could use computation to personalize normalization amount and nanocarrier size. Here, we use deterministic global dynamic optimization with novel bounding routines to validate mechanistic models againstin vivodata. We find that normalization with dexamethasone increases the maximum transvascular convection rate of nanocarriers by 48‐fold, the tumor volume fraction with convection by 61%, and the total amount of convection by 360%. Nonetheless, 22% of the tumor still lacks convection. These findings underscore both the effectiveness and limits of normalization. Using artificial neural network surrogate modeling, we demonstrate the feasibility of rapidly determining the dexamethasone dose and nanocarrier size to maximize accumulation. Thus, this digital testbed quantifies transport and performs therapy design.
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- Award ID(s):
- 1932723
- PAR ID:
- 10380789
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 68
- Issue:
- 8
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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