ReviewFrontiers in oncology2021
Laboratory Models for Investigating Breast Cancer Therapy Resistance and Metastasis.
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed, 74 citations in OpenAlex.
- Microfluidic Biofabrication of a Hydrogel Vessel-Like Structure for Interrogating Tumor Cell Propagation in a Breast Cancer-on-a-Chip Model.Advanced healthcare materials · 2026Article
- Precision tumor targeting: gamma-irradiated ellagic acid therapy for ferroptosis and necroptosis modulation via GPX4/ALOX/RIPK/MLKL pathway inhibition.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- A stiffness-gated YAP-β-catenin axis orchestrates AXIN2 expression in metastatic breast cancer.iScience · 2026Article
- Reconstructing the metastatic journey: functional circulating tumor cells and disseminated tumor cells based models for translational oncology.Journal of experimental & clinical cancer research : CR · 2026Review
- Comparative analysis of zebrafish, organoid, and organ-on-chip models in breast cancer research.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Decoding breast cancer: insights into molecular pathways & therapeutic approaches.Discover oncology · 2025Review
- The Underlying Mechanisms and Emerging Strategies to Overcome Resistance in Breast Cancer.Cancers · 2025Review
- Oncogenic properties of wild-type DNA repair gene FANCA in breast cancer.Cell reports · 2025Article
- Approaches to modeling cancer metastasis: from bench to bedside.Frontiers in oncology · 2025Review
- A systematic review and meta-analysis of the effects of green tea extracts and polyphenols in female hormone-dependent cancers for benefit-risk evaluation.Frontiers in oncology · 2025Article
- EDI3 knockdown in ER-HER2+ breast cancer cells reduces tumor burden and improves survival in two mouse models of experimental metastasis.Breast cancer research : BCR · 2024Article
- Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.PLoS computational biology · 2024Article
- The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer.Cold Spring Harbor perspectives in medicine · 2024Review
- Article
- The progressive trend of modeling and drug screening systems of breast cancer bone metastasis.Journal of biological engineering · 2024Review
- Current Insights in Murine Models for Breast Cancer: Present, Past and Future.Current pharmaceutical design · 2024Review
- The functional role of Nudt2 in human triple negative breast cancer.Frontiers in oncology · 2024Article
- Development of a HPLC-MS/MS Method to Assess the Pharmacokinetics and Tumour Distribution of the Dimethylarginine Dimethylaminohydrolase 1 Inhibitors ZST316 and L-257 in a Xenograft Model of Triple-Negative Breast Cancer in Mice.Molecules (Basel, Switzerland) · 2023Article
- Breast cancers as ecosystems: a metabolic perspective.Cellular and molecular life sciences : CMLS · 2023Review
- Experimental and spontaneous metastasis assays can result in divergence in clonal architecture.Communications biology · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
While numerous therapies are highly efficacious in early-stage breast cancers and in particular subsets of breast cancers, therapeutic resistance and metastasis unfortunately arise in many patients. In many cases, tumors that are resistant to standard of care therapies, as well as tumors that have metastasized, are treatable but incurable with existing clinical strategies. Both therapy resistance and metastasis are multi-step processes during which tumor cells must overcome diverse environmental and selective hurdles. Mechanisms by which tumor cells achieve this are numerous and include acquisition of invasive and migratory capabilities, cell-intrinsic genetic and/or epigenetic adaptations, clonal selection, immune evasion, interactions with stromal cells, entering a state of dormancy or senescence, and maintaining self-renewal capacity. To overcome therapy resistance and metastasis in breast cancer, the ability to effectively model each of these mechanisms in the laboratory is essential. Herein we review historic and the current state-of-the-art laboratory model systems and experimental approaches used to investigate breast cancer metastasis and resistance to standard of care therapeutics. While each model system has inherent limitations, they have provided invaluable insights, many of which have translated into regimens undergoing clinical evaluation. We will discuss the limitations and advantages of a variety of model systems that have been used to investigate breast cancer metastasis and therapy resistance and outline potential strategies to improve experimental modeling to further our knowledge of these processes, which will be crucial for the continued development of effective breast cancer treatments.
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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.