ReviewESMO open2024
Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer.
Review in ESMO open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Article
- Multimodal AI-driven analysis in breast and lung cancer: insights from the OPTIMA prototyping workshop.BMC proceedings · 2026Article
- Novel explainable deep learning based drug sensitivity prediction for early treatment of breast cancer.Scientific reports · 2026Article
- A bibliometric analysis of the role of apoptosis in breast cancer immunotherapy from 1994 to 2024.Discover oncology · 2026Article
- Comparative Genomic Hybridization (CGH) in Genotoxicology: From the Basics to Modern Approaches.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Exploring the Microbiome in Breast Cancer: The Role ofMicroorganisms · 2025Review
- Systemic and locoregional therapies for cutaneous metastases from breast cancer: state of art and new frontiers in treatment approach.Therapeutic advances in medical oncology · 2025Review
- Advances in hydrogel-based materials for breast cancer bone metastasis: from targeted drug delivery to bone microenvironment remodeling.Frontiers in pharmacology · 2025Review
- Fibroblast growth factor receptor signaling in estrogen receptor-positive breast cancer: mechanisms and role in endocrine resistance.Frontiers in oncology · 2024Review
- Deep learning-based multimodal spatial transcriptomics analysis for cancer.Advances in cancer research · 2024Review
- Clinical Relevance and Drug Modulation of PPAR Signaling Pathway in Triple-Negative Breast Cancer: A Comprehensive Analysis.PPAR research · 2024Article
- Molecular and modular intricacies of precision oncology.Frontiers in immunology · 2024Review
- Breast cancer in a transgender man.CA: a cancer journal for cliniciansArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.
Indexed as
Identifiers
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.