Modeling Leptomeningeal Metastasis: Aligned vs. Random Electrospun Poly(caprolactone) Scaffolds Under Static and Dynamic Flow Conditions

Presenter: Lily Jing Bigelow

Faculty Sponsor: Rachael Sirianni

School: UMass Amherst

Research Area: Cancer Studies

Session: Poster Session 6, 4:15 PM - 5:00 PM, Auditorium, A31

ABSTRACT

Leptomeningeal metastasis (LM) is a highly lethal form of cancer with few treatment options. It spreads throughout the subarachnoid space (SAS), a tissue layer surrounding the brain and spinal cord, allowing cancer cells to disseminate throughout the central nervous system (CNS). The SAS, being filled with cerebrospinal fluid (CSF) and containing networks of collagen nanofibers (trabeculae), has a unique ultrastructure known to facilitate metastasis throughout the CNS. Despite this, the specific factors driving metastasis remain poorly understood. Research in this area is hindered not only by a lack of human or animal models that preserve natural ultrastructure, but also a lack of tissue-engineered models capable of replicating how ultrastructure drives metastasis. This study therefore aims to address these challenges and build upon existing tissue-engineered models by investigating how two essential components of SAS ultrastructure – CSF and trabeculae – affect metastasis in LM. Using electrospinning, a technique to create nanofiber scaffolds that mimic SAS ultrastructure, we model how trabeculae alignment and CSF flow enhance or inhibit metastatic cell behaviors of adhesion and migration. These behaviors will be assessed by culturing cancer cells onto aligned or unaligned scaffold surfaces under static and dynamic fluid conditions. We predict that cells interacting with aligned trabeculae scaffolds or dynamic fluid conditions will exhibit increased metastatic behaviors. By bridging the gap between current tissue engineering approaches and the complexity of the SAS, this research aims to provide new insights into the factors driving LM progression and contribute to the development of treatment strategies that can eliminate LM.

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