Optimization of cell lines as tumour models by integrating multi-omics data.

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Cell lines are vital in vitro models for studying cancer development.
• Long-term cell culture can lead to significant genetic and phenotypic divergence between cell lines and their original tumors.
• Using misidentified or unsuitable cell lines compromises experimental validity and hinders the development of effective cancer therapies.

Purpose of the Study:

• To systematically review methods for evaluating cancer cell line suitability since 2005.
• To identify optimal cell lines for in vitro cancer research across eight cancer types.
• To enhance the reliability of cancer research and inform patient treatment strategies.

Main Methods:

• Utilized gene expression, copy number, and mutation profiles from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE).
• Calculated genomic similarity between tumor samples and cell lines.
• Integrated Gene Ontology (GO) functional similarity to determine ideal cell line models.

Main Results:

• Identified optimal cell lines for eight cancer types exhibiting genomic concordance with homologous tumor samples.
• Confirmed that previously identified contaminated cell lines are unsuitable as in vitro cancer models.
• Highlighted that several commonly used cell lines may not be appropriate models for specific cancer research.

Conclusions:

• Provides a curated reference of ideal cell lines for in vitro cancer experiments.
• Aims to improve the accuracy and reproducibility of future cancer research.
• Establishes a foundation for developing more effective cancer treatment strategies through reliable preclinical models.