Multiomics comparison between prostate cell lines and patient-derived tissue to evaluate cell line clinical relevance
Abstract
Background: Cell line experiments remain one of the most widely used tools in preclinical cancer research. As prostate cancer cell lines underpin much of this research, evaluating their clinical relevance is essential to improve the translation of laboratory findings into patient care. Here, we assess the clinical relevance and inclusivity of 46 prostate cancer cell lines using an integrated clinical and multiomics approach.
Methods: Clinical and molecular data from more than 10,000 patient cases across 24 studies in nine countries were harmonised using publicly available databases and peer-reviewed literature. Analyses included mutations, copy number alterations, structural variants, microsatellite instability, gene expression and protein expression. Clinical characteristics, including age, geographical location, ethnicity, race, cancer type, Gleason grade and metastatic stage, were evaluated to determine how well prostate cancer cell lines represent the broader patient population. Statistical comparisons were normalised and cross-validated using bioinformatics approaches.
Results: Here we show that prostate cancer cell lines do not broadly represent the wider patient population and that the patients from whom these models were derived constitute a demographically and clinically biased subset. Our integrated analyses identify important gaps in the molecular and clinical diversity represented by commonly used cell lines and demonstrate differences in their suitability for modelling specific disease subtypes.
Conclusions: Our findings highlight limitations in the representativeness of widely used prostate cancer cell lines and emphasise the importance of selecting models according to their clinical and molecular relevance. The proposed data-driven framework provides a practical approach for selecting more representative cell lines and has the potential to improve the translation of preclinical prostate cancer research into clinical practice.
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BibTeX
@article{ahmed2026multiomics,
title = {{Multiomics comparison between prostate cell lines and patient-derived tissue to evaluate cell line clinical relevance}},
author = {Zahra Ahmed and Warda Mosabbir and Devansh Tandon and Snehal Pinto Pereira and Umber Cheema and Marilena Loizidou and John Withington and Caroline Moore and Uzoamaka Okoli and Susan Heavey},
journal = {Communications Medicine},
year = {2026},
month = sep,
doi = {10.1038/s43856-026-01886-y},
url = {https://www.nature.com/articles/s43856-026-01886-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multiomics comparison between prostate cell lines and patient-derived tissue to evaluate cell line clinical relevance
AU - Zahra Ahmed
AU - Warda Mosabbir
AU - Devansh Tandon
AU - Snehal Pinto Pereira
AU - Umber Cheema
AU - Marilena Loizidou
AU - John Withington
AU - Caroline Moore
AU - Uzoamaka Okoli
AU - Susan Heavey
PY - 2026
DA - 2026/09/30
JO - Communications Medicine
DO - 10.1038/s43856-026-01886-y
UR - https://www.nature.com/articles/s43856-026-01886-y
AB - Background: Cell line experiments remain one of the most widely used tools in preclinical cancer research. As prostate cancer cell lines underpin much of this research, evaluating their clinical relevance is essential to improve the translation of laboratory findings into patient care. Here, we assess the clinical relevance and inclusivity of 46 prostate cancer cell lines using an integrated clinical and multiomics approach. Methods: Clinical and molecular data from more than 10,000 patient cases across 24 studies in nine countries were harmonised using publicly available databases and peer-reviewed literature. Analyses included mutations, copy number alterations, structural variants, microsatellite instability, gene expression and protein expression. Clinical characteristics, including age, geographical location, ethnicity, race, cancer type, Gleason grade and metastatic stage, were evaluated to determine how well prostate cancer cell lines represent the broader patient population. Statistical comparisons were normalised and cross-validated using bioinformatics approaches. Results: Here we show that prostate cancer cell lines do not broadly represent the wider patient population and that the patients from whom these models were derived constitute a demographically and clinically biased subset. Our integrated analyses identify important gaps in the molecular and clinical diversity represented by commonly used cell lines and demonstrate differences in their suitability for modelling specific disease subtypes. Conclusions: Our findings highlight limitations in the representativeness of widely used prostate cancer cell lines and emphasise the importance of selecting models according to their clinical and molecular relevance. The proposed data-driven framework provides a practical approach for selecting more representative cell lines and has the potential to improve the translation of preclinical prostate cancer research into clinical practice.
SN - 2730-664X
ER -