(100a) Evaluating the Impact of Dynamic Flow on the Chemotherapeutic Treatment of Advanced Multicellular Pancreatic Cancer Models

Introduction:

Pancreatic Ductal Adenocarcinoma (PDAC) is the 7th leading cause of cancer related deaths worldwide. Furthermore, the survival rate of PDAC is very low, i.e., the 5-year survival rate is only only 11% in the USA¹ and less than 7% in the UK² and has barely improved over the last decades^3,4. This is partly attributed to the PDAC’s complex tumour microenvironment (TME). The TME is a cocktail of biomechanical, cellular and biochemical factors which contribute to the progression of the disease and its response (and resistance) to current therapeutic methods. Therefore, to perform advanced and more accurate studies of the disease and its’ treatment resistance mechanisms it is vital to develop robust, in vitro tumour models that can capture various features of the PDAC TME. We have previously developed a poly urethane (PU) based 3D pancreatic cancer model using (i) pancreatic cancer cells (monocellular model) and (ii) pancreatic cancer cells, stellate cells and endothelial cells, i.e., cells abundantly found in PDAC’s in vivo TME (multicellular model). We have shown long term physiological maintenance, feasibility of extracellular matrix (ECM) mimicry, mimicry of PDAC fibrosis/desmoplasia, and we have mapped effect of hypoxia^5-8. Although interstitial fluid flow and resultant shear stress are important parameters in the development of cancer tissue niche models as well as therapeutic assessment, studies involving the role of this critical parameter for PDAC is lacking.

In the current work we introduce dynamic flow (interstitial fluid flow mimicry) in our multicellular models, and we perform for the first time a comparative chemotherapeutic assessment in static and dynamic conditions.

Methods:

PU scaffolds were prepared and coated for ECM mimicry as previously described^5-8. A zonal structure with (i) endothelial and stellate cells on the outer scaffold coated with collagen I (to mimic the stroma) and (ii) pancreatic cancer cells in the inner scaffold coated with fibronectin was designed, along with a single scaffold based multicellular model (‘cell cocktail’ approach)⁷. Dynamic flow was introduced with a perfusion bioreactor. Based on our previous studies, 50µM Gemcitabine (GEM) drug was applied to all models after 4 weeks of culture⁶. Imaging of cellular proliferation/spatial organisation and ECM secretion was carried out along with q-PCR assessment of various biomarkers, e.g. EMT or metastatic markers.

Results and conclusion:

We observed that the zonal multicellular model of PDAC showed a higher resistance to GEM in comparison to the single scaffold assisted multicellular model. More specifically, there was no reduction of cell viability in the zonal model, especially for the cancer mass, and only a slight induction of apoptosis was observed for the stromal compartment. In addition, we also observed selective depletion of cytokeratin expressing cancer cells and endothelial cells within post chemotherapy within both our models. Furthermore, we noticed that the ECM (collagen-I) distribution on the zonal scaffold was unaffected 24 h post-treatment, in contrast to the single multicellular scaffold. Our results highlighting that the spatial arrangement of the cells, within an in vitro TME mimicking model affect the response to chemotherapy. Furthermore, the dynamic flow affected the cell spatial organisation, metabolic production and biomarker expression.

Our developed model is a novel, complex biomimetic low-cost high throughput tool that can be used for personalised treatment screening of pancreatic cancer.

Acknowledgements:

E.V. is grateful to the Medical Research Council UK for a New Investigator Research Grant (MR/V028553/1), which also financially supports P.G.

References:

[1] https://www.cancer.net/cancer-types/pancreatic-cancer/statistics

[2] https://www.pancreaticcancer.org.uk/what-we-do/media-centre/pancreatic-c...

[3] Khalaf et al., Clinical Gastroenterology and Hepatology, 2021;19(5)

[4] Aier et al., Cancer Epidemiology, 2019; 58

[5] Totti, S. et al. RSC Advances. 2018; 8(37)

[6] Gupta, et al. RSC Advances.2019; 9 (71)

[7] Gupta, et al. Frontiers in Bioengineering & Biotechnology. 2020; 8 (290).

[8] Wishart et al. Cancers 2021; 13 (23).