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Published on: March 13, 2013
Computational estimation of quality and clinical relevance of cancer cell lines
Lucia Trastulla1,2, Javad Noorbakhsh3, Francisca Vazquez3,4
1Human Technopole, Milano, Italy.
Abstract:
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.
Insights
Cancer cell lines (CCLs) are crucial for research but have limitations. Computational methods can help identify the most accurate CCLs to represent patient tumors for precision medicine.
Area of Science:
- Oncology
- Translational Medicine
- Bioinformatics
Background:
- Immortal cancer cell lines (CCLs) are widely used for cancer biology research and preclinical drug development.
- Pharmacogenomic and genome-wide screening of CCLs have identified gene-drug interactions and therapeutic targets.
- Gaps exist between tumors and in vitro models, hindering precision medicine drug development.
Purpose of the Study:
- To discuss the limitations of CCLs in representing patient features.
- To review computational methods for evaluating CCL suitability as tumor proxies.
- To identify the best in vitro models for specific patient tumors.
Main Methods:
- Review of existing literature on CCL limitations.
- Analysis of computational approaches for CCL-tumor matching.
- Overview of applications to complex models and future directions.
Main Results:
- Identified intrinsic limitations of CCLs including misidentification, lack of tumor microenvironment, and genetic drift.
- Highlighted the need for computational tools to systematically evaluate CCL suitability.
- Discussed the potential of machine learning to address discrepancies between models and patients.
Conclusions:
- Computational methods are essential for selecting faithful CCLs to represent patient tumors.
- Addressing CCL limitations is critical for advancing precision oncology.
- Future machine learning applications hold promise for improving in vitro model accuracy.
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