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Updated: Mar 11, 2026
Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening
Published on: September 5, 2018
Large-scale pharmacological profiling of 3D tumor models of cancer cells
Lesley A Mathews Griner1, Xiaohu Zhang1, Rajarshi Guha1
1National Center for Advancing Translational Sciences, Division of Pre-Clinical Innovation, National Institutes of Health, Bethesda, MD, USA.
Abstract:
The discovery of chemotherapeutic agents for the treatment of cancer commonly uses cell proliferation assays in which cells grow as two-dimensional (2D) monolayers. Compounds identified using 2D monolayer assays often fail to advance during clinical development, most likely because these assays do not reproduce the cellular complexity of tumors and their microenvironment in vivo. The use of three-dimensional (3D) cellular systems have been explored as enabling more predictive in vitro tumor models for drug discovery. To date, small-scale screens have demonstrated that pharmacological responses tend to differ between 2D and 3D cancer cell growth models. However, the limited scope of screens using 3D models has not provided a clear delineation of the cellular pathways and processes that differentially regulate cell survival and death in the different in vitro tumor models. Here we sought to further understand the differences in pharmacological responses between cancer tumor cells grown in different conditions by profiling a large collection of 1912 chemotherapeutic agents. We compared pharmacological responses obtained from cells cultured in traditional 2D monolayer conditions with those responses obtained from cells forming spheres versus cells already in 3D spheres. The target annotation of the compound library screened enabled the identification of those key cellular pathways and processes that when modulated by drugs induced cell death in all growth conditions or selectively in the different cell growth models. In addition, we also show that many of the compounds targeting these key cellular functions can be combined to produce synergistic cytotoxic effects, which in many cases differ in the magnitude of their synergism depending on the cellular model and cell type. The results from this work provide a high-throughput screening framework to profile the responses of drugs both as single agents and in pairwise combinations in 3D sphere models of cancer cells.
Insights
Traditional 2D cancer cell models fail to predict drug efficacy. This study compared 1912 chemotherapeutics in 2D versus 3D cancer models, revealing distinct drug responses and synergistic combinations for improved cancer drug discovery.
Area of Science:
- Oncology
- Pharmacology
- Biotechnology
Background:
- Two-dimensional (2D) cell culture models are standard for cancer drug discovery but often fail to predict in vivo efficacy.
- Three-dimensional (3D) cell culture systems offer more predictive in vitro tumor models.
- Limited screens in 3D models hinder understanding of differential drug responses and cellular pathways.
Purpose of the Study:
- To investigate differences in chemotherapeutic responses between 2D and 3D cancer cell models.
- To identify key cellular pathways and processes differentially regulated by drugs in various cancer cell growth conditions.
- To explore synergistic drug combinations in 3D cancer models.
Main Methods:
- A large-scale screen of 1912 chemotherapeutic agents was conducted.
- Pharmacological responses were compared between cells in 2D monolayers, cells forming spheres, and cells within 3D spheres.
- Compound library target annotation was used to identify affected cellular pathways.
Main Results:
- Significant differences in drug responses were observed between 2D and 3D cancer cell models.
- Key cellular pathways were identified that induce cell death across all conditions or selectively in specific models.
- Drug combinations demonstrated synergistic cytotoxic effects, with varying magnitudes depending on the cellular model and cell type.
Conclusions:
- 3D cancer cell models provide a more accurate platform for profiling drug responses compared to 2D models.
- Understanding pathway-specific drug effects in 3D models is crucial for effective cancer therapy development.
- A high-throughput screening framework for single agents and drug combinations in 3D models was established, advancing cancer drug discovery.
