ArticleACS pharmacology & translational science2023
Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.
Article in ACS pharmacology & translational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- Dissecting tumor heterogeneity in colorectal cancer: uncovering the role of BCL2L1Frontiers in immunology · 2026Article
- Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer.Discover oncology · 2025Article
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Lysine demethylases 6 A and 6B as epigenetic regulators in therapeutic resistance of cancer.Clinical epigenetics · 2025Review
- A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network.Scientific reports · 2025Article
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Integrative systems biology and in-vitro analysis of cryptolepine's therapeutic role in breast cancer.Discover oncology · 2025Article
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- N4-Acetylcytidine-Mediated CD2BP2-DT Drives YBX1 Phase Separation to Stabilize CDK1 and Promote Breast Cancer Progression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.Discover oncology · 2025Article
- Enhanced Disease Resistance Mechanism of the CmoAP2/ERF Transcription Factor in Pumpkin through Genetic Mutations.ACS omega · 2024Article
- Article
- FAM109B plays a tumorigenic role in low-grade gliomas and is associated with tumor-associated macrophages (TAMs).Journal of translational medicine · 2024Article
- Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer.Scientific reports · 2024Article
- A vascularized breast cancer spheroid platform for the ranked evaluation of tumor microenvironment-targeted drugs by light sheet fluorescence microscopy.Nature communications · 2024Article
- Computational Deciphering of the Role of S100A8 and S100A9 Proteins and Their Changes in the Structure Assembly Influences Their Interaction with TLR4, RAGE, and CD36.The protein journal · 2024Article
- Article
- Potential biomarkers in breast cancer drug development: application of the biomarker qualification evidentiary framework.Biomarkers in medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.
Identifiers
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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.