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Published on: December 16, 2022
Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug
Kristin Calar1, Simona Plesselova2, Somshuvra Bhattacharya1
1Cancer Biology and Immunotherapies Group, Sanford Research, Sioux Falls, SD 57104, USA.
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.
Insights
A new 3D culture model using patient plasma (HuP3D) improves breast cancer drug screening. This model better predicts clinically effective therapies compared to traditional 2D and other 3D methods.
Area of Science:
- Oncology
- Biotechnology
- Drug Discovery
Background:
- Clinical trials for cancer treatments often fail due to lack of efficacy and low success rates.
- Current drug sensitivity screenings face challenges in cell growth rate consistency, metric standardization, and translatability.
- Developing predictive preclinical models is crucial for advancing cancer therapy.
Purpose of the Study:
- To introduce a novel patient-derived 3D culture model, HuP3D, for breast cancer drug screening.
- To evaluate the efficacy of HuP3D in supporting breast cancer cell and tumor growth.
- To assess the translatability of HuP3D drug sensitivity metrics against clinical data.
Main Methods:
- Developed a human plasma-derived 3D culture model (HuP3D) using patient plasma as a matrix.
- Cultured breast cancer cell lines and primary tumor biopsies within the HuP3D system.
- Screened various drug treatments and evaluated multiple drug sensitivity metrics.
- Correlated HuP3D-derived metrics with established preclinical models and clinical effective concentrations.
Main Results:
- HuP3D successfully supported the growth of breast cancer cell lines and primary tumors as organoids and single cells.
- HuP3D demonstrated significant and strong correlations with clinical effective concentrations for eight out of ten evaluated metrics.
- In contrast, 2D models showed poor correlation, and other 3D models exhibited moderate correlation with clinical data.
Conclusions:
- HuP3D is a feasible and effective platform for culturing breast cancer models.
- HuP3D offers superior prediction of clinically effective therapies compared to existing 2D and 3D preclinical models.
- This model facilitates high-throughput drug screening and enhances the translation of preclinical findings to clinical outcomes.
