ArticleGenome research2017
Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.
Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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
80 citing papers in PubMed, 169 citations in OpenAlex.
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- The exosomal miRNA/FOXN3 axis empowers fibroblasts with tumor-promoting functions via enhanced migration and altered secretome.Journal of nanobiotechnology · 2026Article
- Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.The Journal of pathology · 2026Article
- A demethylation-driven gene signature predicts prognosis and therapeutic vulnerability in hepatocellular carcinoma.Scientific reports · 2026Article
- Identification of GBP6 as a potential prognostic biomarker in esophageal carcinoma by integrated bioinformatics analysis and experiment validation.Annals of medicine · 2025Article
- A network model for patient-derived drug response in breast cancer integrating multi-omics datasets.bioRxiv : the preprint server for biology · 2025Article
- Aurora kinase-a expression heterogeneity and potential benefit of combination therapy in prostate adenocarcinoma.Frontiers in cell and developmental biology · 2025Article
- Comprehensive Cellular Senescence Evaluation to Aid Targeted Therapies.Research (Washington, D.C.) · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- High expression of LncRNA HOTAIR is a risk factor for temozolomide resistance in glioblastoma via activation of the miR-214/β-catenin/MGMT pathway.Scientific reports · 2024Article
- Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.Statistics in biosciences · 2024Article
- Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response.Cancer innovation · 2024Article
- Histone acetylation risk model predicts prognosis and guides therapy selection in glioblastoma: implications for chemotherapy and anti-CTLA-4 immunotherapy.BMC immunology · 2024Article
- PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.Journal of cellular and molecular medicine · 2024Article
- An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery.Journal of translational medicine · 2024Article
- MultiZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- PANCDR: precise medicine prediction using an adversarial network for cancer drug response.Briefings in bioinformatics · 2024Article
- Deciphering glycosylation-driven prognostic insights and therapeutic prospects in glioblastoma through a comprehensive regulatory model.Frontiers in oncology · 2024Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.
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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.