ArticleCancers2021
Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Decoding triple negative breast cancer bone metastasis: from 3D bioprinted models to clinical translation.Journal of nanobiotechnology · 2026Review
- Phytoconstituents as emerging therapeutics for breast cancer: Mechanistic insights and clinical implications.Cancer pathogenesis and therapy · 2025Review
- Discovery, Validation and Mechanistic Study of XPO1 Inhibition in the Treatment of Triple-Negative Breast Cancer.Cancers · 2024Article
- Computational drug discovery pipelines identify NAMPT as a therapeutic target in neuroendocrine prostate cancer.Clinical and translational science · 2024Article
- Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma.Cancers · 2024Article
- Cancer drug sensitivity prediction from routine histology images.NPJ precision oncology · 2024Article
- TheMarker: a comprehensive database of therapeutic biomarkers.Nucleic acids research · 2024Article
- Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.Scientific reports · 2022Article
- oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.Briefings in bioinformatics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (
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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.