Modulation of Extracellular Matrix Rigidity Via Riboflavin-mediated Photocrosslinking Regulates Invasive Motility and Treatment Response in a 3D Pancreatic Tumor Model.
Rojin JafariGwendolyn M CramerJonathan P CelliPublished in: Photochemistry and photobiology (2020)
In this study, we evaluate the use of riboflavin-mediated collagen photocrosslinking as an experimental tool to modulate extracellular matrix (ECM) mechanical properties in 3D in vitro tumor models. Using this approach in conjunction with 3D pancreatic tumor spheroid transplants, we show that the extent of matrix photocrosslinking in reconstituted hydrogels with fixed protein concentration scales inversely with the extent of invasive progression achieved by cells infiltrating into the surrounding ECM from primary transplanted spheroids. Using cross-linking to manipulate the extent of invasion into ECM in conjunction with imaging-based treatment assessment, we further leverage this approach as a means for assaying differential therapeutic response in primary nodule and ECM-invading populations and compare response to verteporfin-based photodynamic therapy (PDT) and oxaliplatin chemotherapy. Treatment response data shows that invading cell populations (which also exhibit markers of increased EMT) are highly chemoresistant yet have significantly increased sensitivity to PDT relative to the primary nodule. In contrast, the oxaliplatin treatment achieves greater growth inhibition of the primary nodule. These findings may be significant in themselves, while the methodology developed here could have a broader range of applications in developing strategies to target invasive disease and/or mecahanobiological determinants of therapeutic response in solid tumors.
Keyphrases
- extracellular matrix
- photodynamic therapy
- induced apoptosis
- fluorescence imaging
- high resolution
- epithelial mesenchymal transition
- magnetic resonance imaging
- computed tomography
- radiation therapy
- stem cells
- drug delivery
- signaling pathway
- squamous cell carcinoma
- machine learning
- bone marrow
- cystic fibrosis
- mass spectrometry
- mesenchymal stem cells
- pseudomonas aeruginosa
- artificial intelligence
- biofilm formation