Cell migration is the major driver of invasion and metastasis during cancer progression. For cells to migrate, they utilize the actin–myosin cytoskeleton and adhesion molecules, such as integrins and CD44, to generate traction forces in their environment. CD44 primarily binds to hyaluronic acid (HA) and integrins primarily bind to extracellular matrix (ECM) proteins such as collagen. However, the role of CD44 under integrin-mediated conditions and vice versa is not well known. Here, we performed traction force microscopy (TFM) on U251 cells seeded on collagen I-coated polyacrylamide gels to assess the functional mechanical relationship between integrins and CD44. Performing TFM on integrin-mediated adhesion conditions, i.e., collagen, we found that CD44KO U251 cells exerted more traction force than wild-type (WT) U251 cells. Furthermore, untreated WT and CD44-blocked WT exhibited comparable results. Conversely, in CD44-mediated adhesive conditions, integrin-blocked WT cells exerted a higher traction force than untreated WT cells. Our data suggest that CD44 and integrins have a mutually antagonistic relationship where one receptor represses the other's ability to generate traction force on its cognate substrate.

Disease heterogeneity across a diverse patient cohort poses challenges to cancer drug development due to inter-patient variability in treatment responses. However, current preclinical models fail to depict inter-patient tumor heterogeneity, leading to a high failure rate when translating preclinical leads into clinical successes. We integrated the expression profiles of prostate cancer (PC) lines and castration-resistant PC (CRPC) patient tumors to identify cell-lines that transcriptomically match distinct tumor subtypes in a clinical cohort. Representative cell-lines were co-cultured to create “mixed-cell” models depicting inter-patient heterogeneity in CRPC, which were employed to assess drug combinations. When drug combinations previously tested in CRPC clinical cohorts were assessed to establish proof of concept, in vitro responses measured in our models concurred with their known clinical efficacy. Additionally, novel drug combinations computationally predicted to be efficacious in heterogeneous tumors were evaluated. They demonstrated preclinical efficacy in the mixed-cell models, suggesting they will likely benefit heterogeneous patient cohorts. Furthermore, we showed that the current practice of screening cell-lines/xenografts separately and aggregating their responses, failed to detect their efficacy. We believe that the application of our models will enhance the accuracy of preclinical drug assessment, thereby improving the success rate of subsequent clinical trials.