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Abstract
Lack of efficacy and a low overall success rate of phase I-II clinical trials are the most common failures when it comes to advancing cancer treatment. Current drug sensitivity screenings present several challenges including differences in cell growth rates, the inconsistent use of drug metrics, and the lack of translatability. Here, we present a patient-derived 3D culture model to overcome these limitations in breast cancer (BCa). The human plasma-derived 3D culture model (HuP3D) utilizes patient plasma as the matrix, where BCa cell lines and primary BCa biopsies were grown and screened for drug treatments. Several drug metrics were evaluated from relative cell count and growth rate curves. Correlations between HuP3D metrics, established preclinical models, and clinical effective concentrations in patients were determined. HuP3D efficiently supported the growth and expansion of BCa cell lines and primary breast cancer tumors as both organoids and single cells. Significant and strong correlations between clinical effective concentrations in patients were found for eight out of ten metrics for HuP3D, while a very poor positive correlation and a moderate correlation was found for 2D models and other 3D models, respectively. HuP3D is a feasible and efficacious platform for supporting the growth and expansion of BCa, allowing high-throughput drug screening and predicting clinically effective therapies better than current preclinical models.
Highlights
Breast cancer (BCa) is a heterogeneous disease, which includes different biological entities characterized by distinct clinical behaviors and responses to treatment [1,2,3]
Human plasma-derived 3D culture (HuP3D) models were created by cross-linking fibrinogen, a blood plasma protein responsible for normal blood clotting when converted into fibrin (Figure 1a) [33], generating a gelatinous-like scaffold matrix using traditional tissue culture surfaces as the recipient mold, with media added on top to overcome drying of the matrix (Figure 1b)
Plasma requires the presence of a cross-linking agent in order to form a 3D scaffold matrix, otherwise, it remains in a liquid form with no reportable cross-linking time when no cross-linking agent is added
Summary
Breast cancer (BCa) is a heterogeneous disease, which includes different biological entities characterized by distinct clinical behaviors and responses to treatment [1,2,3]. Cancers 2020, 12, 1722 analysis [12,13] These studies clearly indicate the need for more suitable preclinical models that would provide an accurate prediction of clinical efficacy. The most common preclinical cancer models do not deliver full fidelity of the heterogeneous BCa tumors in the context of a tumor-like environment. Animal models, including patient-derived xenografts (PDX), are very useful in the recreation of this environment, but they are costly, relatively time consuming, and their reproducibility and translatability to human cancer clinical trials is very low [14,15].
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