Abstract
We aimed to establish a fluorescence intensity-based colony area sweeping method by selecting the area of highest viability among patient-derived cancer cells (PDC) which has high tumor heterogeneity. Five gastric cancer cell lines and PDCs were screened with 24 small molecule compounds using a 3D micropillar/microwell chip. 100 tumor cells per well were immobilized in alginate, treated with the compounds, and then stained and scanned for viable cells. Dose response curves and IC50 values were obtained based on total or selected area intensity based on fluorescence. Unlike homogeneous cell lines, PDC comprised of debris and low-viability cells, which resulted in an inaccurate estimation of cell viability using total fluorescence intensity as determined by high IC50 values. However, the IC50 of these cells was lower and accurate when calculated based on the selected-colony-area method that eliminated the intensity offset associated with the heterogeneous nature of PDC. The selected-colony-area method was optimized to accurately predict drug response in micropillar environment using heterogeneous nature of PDCs.
Highlights
Despite advances in targeted therapy and immunotherapy for solid cancer, one of the most challenging problems in oncology is that development of active drugs is still a slow multi-layered, complicated process
Cell lines and patient-derived cancer cells (PDC) were grown to 80–90% confluency and passaged using TrypLE Express (Gibco BRL) and seeded with 3D culture medium consisting of DMEM F/12 supplemented with 10 mM HEPES, 1% antibiotic-antimycotic solution, 2% 50× B27, 1% 20× N2, 1% 100× Glutamax (Gibco BRL), 10 mM human gastrin I, 1 mM N-acetyl-L-cysteine (Sigma Aldrich), 10 μg/mL insulin, 20 ng/mL basic fibroblast growth factor, and 50 ng/mL EGF (PeproTech)
In the KATO III human gastric cancer cell line, the reducing ratios were very small in low intensity thresholds (10~30), because most colonies had high cell viability and there were no debris and cells with low viability
Summary
Despite advances in targeted therapy and immunotherapy for solid cancer, one of the most challenging problems in oncology is that development of active drugs is still a slow multi-layered, complicated process. Considering the time consumption, high cost, and low success rate of pre-clinical and clinical development of oncology drugs, more efficient and accurate platforms for oncology drug screening are urgently needed. The activity of oncology drugs has been studied in two-dimensionally (2D) cultured cancer cell lines. It has been long challenged that these preclinical model systems minimally reflect the in vivo microenvironment [1,2,3,4,5,6] and low probability for translating into clinical benefit in cancer patients [7, 8].
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