Table 7 Comprehensive analysis of advantages and limitations of iPSCs and traditional cancer cell models

iPSCs

Personalized Disease Modeling: Can be generated from patient samples, allowing personalized disease modeling

Complex Differentiation: Generating specific cell types from iPSCs can be challenging and time-consuming

iPSCs offer a unique opportunity for patient-specific research, but their complex differentiation can be a practical hurdle

[543]

Disease Modeling: iPSCs can be differentiated into various cell types affected by cancer, providing a relevant model for studying disease mechanisms

Genetic Variability: iPSC lines may have genetic variations that can influence experimental outcomes

Researchers need to carefully account for genetic variability when working with iPSCs

[24]

Drug Screening: iPSCs can be used to test the efficacy and toxicity of potential cancer therapeutics before clinical trials

Tumorigenic Potential: iPSCs can form tumors if not properly differentiated or controlled

Stringent differentiation protocols are crucial to avoid tumorigenicity in iPSC-based models

[24, 558]

Traditional Cancer Cell Models

Established Cell Lines: A wide range of well-characterized cancer cell lines are available, enabling standardized experiments and comparisons

Genetic Drift: Cancer cell lines may undergo genetic changes over time, deviating from the original tumor characteristics

Periodic authentication and monitoring are essential to maintain genetic stability in cell lines

[546]

Simplified Models: Traditional cancer cell lines can provide a simplified representation of specific cancer types, facilitating experimental manipulations and high-throughput screening

Lack of Tumor Microenvironment: These models often lack the complex interactions with stromal cells and immune components present in actual tumors

Traditional cell lines may not fully recapitulate the tumor microenvironment, limiting their translational relevance

[546]

Faster Experiments: Traditional cancer cell lines can proliferate rapidly, allowing for faster experimental results and drug screening

Limited Clinical Relevance: These models may not fully recapitulate the heterogeneity and complexity of tumors in patients

Rapid proliferation comes at the cost of potential oversimplification of cancer biology

[545]

Availability of Large-Scale Datasets: Many traditional cancer cell lines have extensive genomic and proteomic data available, aiding in data analysis and interpretation

Cross-Contamination: Cell lines can get contaminated or misidentified, leading to inaccurate or unreliable results

Data derived from cell lines should be interpreted with caution and validated using other models

[546]

Easy Manipulation: Traditional cancer cell lines can be easily manipulated genetically, allowing for targeted gene knockouts or overexpression studies

Metabolic Changes: Cell lines may exhibit metabolic alterations compared to primary tumor cells, affecting drug response and metabolism studies

Genetic manipulation offers experimental flexibility, but metabolic differences should be considered when assessing drug responses

[545]