ReviewMolecular systems biology2022
Computational estimation of quality and clinical relevance of cancer cell lines.
Review in Molecular systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine.Cellular oncology (Dordrecht, Netherlands) · 2025Pooled it
- Systematic pathway-level analysis defines conserved transcriptional divergence between primary lung tumors and cell line models.Animal models and experimental medicine · 2026Article
- Review
- MicroRNA-597 Suppresses Gastric Cancer Invasion and Progression viaInternational journal of molecular sciences · 2026Article
- Establishing the first Afro-Caribbean breast cancer cell lines reveals ECM pathway enrichment and distinctive drug sensitivities.Scientific reports · 2026Article
- Identification and validation of an 11-kinase signature that predicts chemo- and radiosensitivity in gastric cancer.EBioMedicine · 2026Article
- Substantial unannotated noncoding transcripts in tumors may transcriptionally regulate cancer-related genes.BMC biology · 2026Article
- Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery.Cancer research · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Article
- A stem cell differentiation model reveals two alternative fates in CBFA2T3::GLIS2-driven acute megakaryoblastic leukemia initiation.Communications biology · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways.NPJ precision oncology · 2025Article
- Advancements in theFrontiers in toxicology · 2025Review
- Unsupervised cell line embedding using pairwise drug response correlation.Computational and structural biotechnology journal · 2025Article
- Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.Nature communications · 2024Article
- Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts.Nature communications · 2024Article
- Predicting drug response from single-cell expression profiles of tumours.BMC medicine · 2023Article
- Modeling phenotypic heterogeneity towards evolutionarily inspired osteosarcoma therapy.Scientific reports · 2023Article
- Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.