Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug Treatment Concentrations
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3D cultureBreast cancerTreatment responsePrecision medicineDrug metrics
Calar, K., Plesselova, S., Bhattacharya, S., Jorgensen, M., & de la Puente, P. (2020). Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug Treatment Concentrations. Cancers, 12(7), 1722. [doi: 10.3390/cancers12071722]
SponsorshipInstitutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health 5P20GM103548; 2018 LUSH Prize (Young Investigator Award)
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.