ArticleCell systems2018
Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.Scientific reports · 2025Article
- Nanotechnology-Driven Drug Delivery Systems for Lung Cancer: Computational Advances and Clinical Perspectives.Thoracic cancer · 2025Review
- A Knowledge-Guided Graph Learning Approach Bridging Phenotype- and Target-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer.Scientific reports · 2024Article
- A Bayesian framework for pathway-guided identification of cancer subgroups by integrating multiple types of genomic data.Statistics in medicine · 2023Article
- Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.Patterns (New York, N.Y.) · 2023Article
- CREAMMIST: an integrative probabilistic database for cancer drug response prediction.Nucleic acids research · 2023Article
- Development of bioinformatics and multi-omics analyses in organoids.BMB reports · 2023Review
- DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.Bioinformatics (Oxford, England) · 2022Article
- Computational estimation of quality and clinical relevance of cancer cell lines.Molecular systems biology · 2022Review
- Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy.Frontiers in oncology · 2022Review
- Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.PloS one · 2021Article
- Article
- Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.Nature communications · 2020Article
- PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors.Bioinformatics (Oxford, England) · 2019Article
- A data driven approach reveals disease similarity on a molecular level.NPJ systems biology and applications · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Leveraging insights from genomic studies of patient tumors is limited by the discordance between these tumors and the cell line models used for functional studies. We integrate omics datasets using functional networks to identify gene modules reflecting variation between tumors and show that the structure of these modules can be evaluated in cell lines to discover clinically relevant biomarkers of therapeutic responses. Applied to breast cancer, we identify 219 gene modules that capture recurrent alterations and subtype patients and quantitate various cell types within the tumor microenvironment. Comparison of modules between tumors and cell lines reveals that many modules composed primarily of gene expression and methylation are poorly preserved. In contrast, preserved modules are highly predictive of drug responses in a manner that is robust and clinically relevant. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.
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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.