Amyloid fibril-based thixotropic hydrogels for modeling of tumor spheroids in vitro. (April 2023)

• Record Type: Journal Article
• Authors: Singh, Namrata; Patel, Komal; Navalkar, Ambuja; Kadu, Pradeep; Datta, Debalina; Chatterjee, Debdeep; Mukherjee, Semanti; Shaw, Ranjit; Gahlot, Nitisha; Shaw, Abhishek; Jadhav, Sachin; Maji, Samir K.
• Is Part Of: Biomaterials. Volume 295(2023)
• Publication Date: 2023-04
• Subjects: Amyloid hydrogel -- Biomimetic ECM -- Tumor model -- Drug screening; Biomedical materials -- Periodicals; Biocompatible Materials -- Periodicals; Biomatériaux -- Périodiques; 610.28
• DOI: 10.1016/j.biomaterials.2023.122032
• Languages: English
• ISSNs: 0142-9612

Abstract

Abstract: Biomaterials mimicking extracellular matrices (ECM) for three-dimensional (3D) cultures have gained immense interest in tumor modeling and in vitro organ development. Here, we introduce a new class of amyloid fibril-based peptide hydrogels as a versatile biomimetic ECM scaffold for 3D cell culture and homogenous tumor spheroid modeling. We show that these amyloid fibril-based hydrogels are thixotropic and allow cancer cell adhesion, proliferation, and migration. All seven designed hydrogels support 3D cell culture with five different cancer cell lines forming spheroid with necrotic core and upregulation of the cancer biomarkers. We further developed the homogenous, single spheroid using the drop cast method and the data suggest that all hydrogels support the tumor spheroid formation but with different necrotic core diameters. The detailed gene expression analysis of MCF7 spheroid by microarray suggested the involvement of pro-oncogenes and significant regulatory pathways responsible for tumor spheroid formation. Further, using breast tumor tissue from a mouse xenograft model, we show that selected amyloid hydrogels support the formation of tumor spheroids with a well-defined necrotic core, cancer-associated gene expression, higher drug resistance, and tumor heterogeneity reminiscent of the original tumor. Altogether, we have developed an easy-to-use, rapid, cost-effective, and scalable platform for generating in vitro cancer models for the screening of anti-cancer therapeutics and developing personalized medicine.