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The expected cost of developing a new drug has steadily risen over the past decade, with over $1 billion in current estimates1. Part of this expense is the high failure rate of drugs entering clinical trials. Approximately 12% of drug candidates ultimately earn approval from the United States (US) Food & Drug Administration (FDA) in 2019. Many drugs fail in Phase I due to unanticipated toxicity2, while others that pass safety trials may fail due to a lack of efficacy3. This attrition due to non-efficacy can partly be explained by the fact that cancer models used during drug development are notoriously non-predictive of clinical efficacy4.
Functional disparities between in vitro and in vivo models may be attributed to removing cancer cells from their native microenvironment, including non-tumor cells and the physical ECM5,6. Commonly, research groups use commercially available culture matrices, such as Matrigel (a proteinaceous basement membrane matrix derived from mouse sarcomas) to provide cultured tumor cells with a 3D matrix microenvironment. Compared to 2D culture, 3D culture in membrane matrix has improved the clinical relevance of in vitro results7,8. However, culture biomaterials from decellularized tissues, including the membrane matrix, typically exhibit batch-to-batch variability that may compromise reproducibility9. Furthermore, matrices derived from tumors with different tissue origins from those studied may not provide the appropriate physiological cues10. Finally, cancers with high degrees of intratumoral heterogeneity have microenvironmental features that vary on a submicron-size scale and which the membrane matrix cannot be tuned to recapitulate11.
Glioblastoma (GBM), a uniformly lethal brain tumor with a median survival time of approximately 15 months, is a cancer for which treatment development has been particularly difficult12,13. The current standard of care for GBM consists of primary tumor resection, followed by radiotherapy, and then chemotherapy using temozolomide (TMZ)14. Yet, more than half of clinical GBM tumors exhibit treatment resistance through various mechanisms15,16,17. Predicting the efficacy of a treatment regimen for an individual patient is extremely difficult. Standard preclinical models used to predict individual outcomes consist of patient-derived tumor cells xenografted orthotopically into immunocompromised mice. While patient-derived xenografts can recapitulate many aspects of clinical GBM tumors and are valuable for preclinical models18, they are inherently expensive, low throughput, time-consuming, and involve ethical concerns19. Cultures of patient-derived cells, on 2D plastic surfaces or as spheroids, mostly avoid these issues. While patient-derived cells preserve genetic aberrations, their cultures in 2D or as suspended spheroids have been largely poor representations of patient-derived xenografts in rodents and original patient tumors20. Previously, we, and others, have shown that GBM cells cultured in a 3D ECM that mimics the mechanical and biochemical properties of brain tissue can preserve drug resistance phenotypes10,21,22,23.
Interactions between hyaluronic acid (HA), a polysaccharide abundant in the brain ECM and overexpressed in GBM tumors, and its CD44 receptor modulate the acquisition of drug resistance in vitro21,24,25,26,27. For example, the inclusion of HA within soft, 3D cultures increased the ability of patient-derived GBM cells to acquire therapeutic resistance. This mechano-responsivity was dependent on HA binding to CD44 receptors on GBM cells21. Additionally, integrin binding to RGD-bearing peptides, incorporated into 3D culture matrices, amplified CD44-mediated chemoresistance in a stiffness-dependent manner21. Beyond HA, the expression of several ECM proteins, many containing RGD regions, vary between normal brain and GBM tumors28. For example, one study reported that 28 distinct ECM proteins were upregulated in GBM tumors29. Within this complex tumor matrix microenvironment, cancer cells integrate mechanical and biochemical cues to yield a particular resistance phenotype, which depends on relatively small differences (e.g., less than an order of magnitude) in Young's modulus or density of integrin-binding peptides28,29,30.
The present protocol characterizes how tumor cells interpret unique combinations of matrix cues and identify complex, patient-specific matrix microenvironments that promote treatment resistance (Figure 1A). A photochemical method for generating miniaturized, precisely tuned matrices for 3D culture provides a large, orthogonal variable space. A custom-built array of LEDs, run by a microcontroller, was incorporated to photocrosslink hydrogels within a 384-well plate format to increase automation and reproducibility. Exposure intensity was varied across well to alter micro-mechanical properties of resulting hydrogels, as assessed using atomic force microscopy (AFM). While this manuscript does not focus on constructing the illumination array itself, a circuit diagram (Figure 1B) and parts list (Table of Materials) are provided as aids for device reproduction.
This report demonstrates the rapid generation of an array of GBM cells cultured in unique, 3D microenvironments in which Young's modulus (four levels across a single order of magnitude) and integrin-binding peptide content (derived from four different ECM proteins) were varied orthogonally. The approach was then used to investigate the relative contributions of hydrogel mechanics and ECM-specific integrin engagement on the viability and proliferation of patient-derived GBM cells as they acquire resistance to temozolomide (TMZ) chemotherapy.