ArticleInternational journal of cancer2012
Systematic analysis of genotype-specific drug responses in cancer.
Article in International journal of cancer, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 33 citations in OpenAlex.
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- β-catenin/TCF activity regulates IGF-1R tyrosine kinase inhibitor sensitivity in colon cancer.Oncogene · 2018Article
- CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.PloS one · 2018Article
- Cardiac glycosides display selective efficacy for STK11 mutant lung cancer.Scientific reports · 2016Article
- Glucose starvation induces mutation and lineage-dependent adaptive responses in a large collection of cancer cell lines.International journal of oncology · 2016Article
- Differential regulation and synthetic lethality of exclusive RB1 and CDKN2A mutations in lung cancer.International journal of oncology · 2016Article
- Targeting IL11 Receptor in Leukemia and Lymphoma: A Functional Ligand-Directed Study and Hematopathology Analysis of Patient-Derived Specimens.Clinical cancer research : an official journal of the American Association for Cancer Research · 2015Article
- Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing.Human genomics · 2015Article
- Combined deletion of Pten and p53 in mammary epithelium accelerates triple-negative breast cancer with dependency on eEF2K.EMBO molecular medicine · 2014Article
- Cancer Systems Biology: a peek into the future of patient care?Nature reviews. Clinical oncology · 2014Review
- Design, development, and validation of a high-throughput drug-screening assay for targeting of human leukemia.Cancer · 2014Article
- Cell line modeling for systems medicine in cancers (review).International journal of oncology · 2014Review
- Somatic mutaome profile in human cancer tissues.Genomics & informatics · 2013Article
- Somatic mutation patterns and compound response in cancers.BMB reports · 2013Article
- Modeling precision treatment of breast cancer.Genome biology · 2013Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
A systematic understanding of genotype-specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor. Here, we present a statistical framework, cell line enrichment analysis (CLEA), to associate the response of anticancer agents with major cancer genotypes. Multilevel omics data, including transcriptome, proteome and phosphatome data, were integrated with drug data based on the genotypic classification of cancer cell lines. The results reproduced known patterns of compound sensitivity associated with particular genotypes. In addition, this approach reveals multiple unexpected associations between compounds and mutational genotypes. The mutational genotypes led to unique protein activation and gene expression signatures, which provided a mechanistic understanding of their functional effects. Furthermore, CLEA maps revealed interconnections between TP53 mutations and other mutations in the context of drug responses. The TP53 mutational status appears to play a dominant role in determining clustering patterns of gene and protein expression profiles for major cancer genotypes. This study provides a framework for the integrative analysis of mutations, drug responses and omics data in cancers.
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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.