ReviewInternational journal of oncology2014
Cell line modeling for systems medicine in cancers (review).
Review in International journal of oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed, 25 citations in OpenAlex.
- Lung Cancer Computational Biology and Resources.Cold Spring Harbor perspectives in medicine · 2022Review
- MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.PloS one · 2021Article
- Integrated analysis identified core signal pathways and hypoxic characteristics of human glioblastoma.Journal of cellular and molecular medicine · 2019Article
- High Content Screening Characterization of Head and Neck Squamous Cell Carcinoma Multicellular Tumor Spheroid Cultures Generated in 384-Well Ultra-Low Attachment Plates to Screen for Better Cancer Drug Leads.Assay and drug development technologies · 2019Article
- System analysis of the regulation of the immune response by CD147 and FOXC1 in cancer cell lines.Oncotarget · 2018Article
- High-Content Screening Comparison of Cancer Drug Accumulation and Distribution in Two-Dimensional and Three-Dimensional Culture Models of Head and Neck Cancer.Assay and drug development technologies · 2018Article
- Automation of antimicrobial activity screening.AMB Express · 2016Article
- MERAV: a tool for comparing gene expression across human tissues and cell types.Nucleic acids research · 2016Article
- Differential regulation and synthetic lethality of exclusive RB1 and CDKN2A mutations in lung cancer.International journal of oncology · 2016Article
- New Functional Signatures for Understanding Melanoma Biology from Tumor Cell Lineage-Specific Analysis.Cell reports · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unexpected drug efficacy or resistance is poorly understood in cancers because of the lack of systematic analyses of drug response profiles in cancer tissues of various genotypic backgrounds. The recent development of high‑throughput technologies has allowed massive screening of chemicals and drugs against panels of heterogeneous cancer cell lines. In parallel, multi‑level omics datasets, including genome‑wide genetic alterations, gene expression and protein regulation, have been generated from diverse sets of cancer cell lines, thus providing a surrogate system, known as cancer cell line modeling, that can represent cancer diversity. Taken together, recent efforts with cancer cell line modeling have enabled a systematic understanding of the causal factors of varied drug responses in cancers. These large-scale association studies could potentially predict and optimize target windows for drug treatment in cancer patients. The present review provides an overview of the major types of cell line‑based large datasets and their applications in cancer studies. Moreover, this review discusses recent integrated approaches that use multi-level datasets to discover synergistic drug combination or repositioning for cancer treatment.
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What OpenQuestion holds
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.