Computational estimation of quality and clinical relevance of cancer cell lines
Open accessAbstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.
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Immortalized cell lines typically retain similarity to their progenitors in culture, including some level of clonal heterogeneity, but they develop genetic instability and frequent aneuploidy.
“Immortalized cell lines typically retain similarity to their progenitors in culture, including some level of clonal heterogeneity, but they develop genetic instability and frequent aneuploidy [25][26][27].”
From The history, landscape, and outlook of human cell line authentication and security (2024) · cites this paper as [25]
The ‘Heterogeneity in tumours and cell lines’ section reports subclonal diversity in cancer cell lines, including heterogeneity that re-emerges after single-cell cloning. The ‘Genetic drift, in vitro selection pressures and genetic instability’ section describes genetic instability and divergence from tumours of origin. Our copy does not establish that immortalized cell lines typically retain similarity to their progenitors or that aneuploidy is frequent; its discussion is focused on cancer cell lines. No page numbers are available in the indexed passages. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSTR profiling is the gold standard and authentication standard for cell line authentication.
“STR profiling remains the gold standard for cell line authentication.”
From The history, landscape, and outlook of human cell line authentication and security (2024) · cites this paper as [25]
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Comparing the genomes of cutaneous melanoma tumors to commercially available cell lines
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Optimization of cell lines as tumour models by integrating multi-omics data
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Flagged first, then by citations
3 of the 180 references couldn’t be matched to a known paper.
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Additional file 2 of Predicting drug response from single-cell expression profiles of tumours
Pellecchia, S., Viscido, G., Franchini, M., & Gambardella, G. (2024) · Figshare · doi:10.6084/m9.figshare.26648316
Additional file 1 of Predicting drug response from single-cell expression profiles of tumours
Pellecchia, S., Viscido, G., Franchini, M., & Gambardella, G. (2024) · Figshare · doi:10.6084/m9.figshare.26648313
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Importance of Patient-Derived Xenograft Models in Battling Cancer Therapy Resistance
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- Authors
- Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F.
- Affiliations
- Open Targets (GB)
- Broad Institute (US)
- Corresponding author
- Iorio, Francesco
- Venue
- Molecular Systems Biology
- Published
- 2022-07-01
- Volume
- 18
- Issue
- 7
- Pages
- e11017-e11017
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC9277610
- OpenAlex
- W4285401241
- Cited by
- 31
- References
- 180
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 6 · 2023: 8 · 2022: 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Dependency Map Consortium
- Open Targets Consortium (OTAR2‐055)
- Wellcome Trust (206194/Z/17/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Keywords
- cancer cell lines, clinical relevance, tumor models, cell line misidentification, tumor microenvironment, genetic drift, precision oncology, in vitro model, machine learning, drug-gene interactions, therapeutic target, tumor heterogeneity
- MeSH terms
- Gene Editing, Humans, Neoplasms, Cell Line, Tumor, Precision Medicine, Tumor Microenvironment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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