ArticleNPJ breast cancer2020
Development and implementation of the SUM breast cancer cell line functional genomics knowledge base.
Article in NPJ breast cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 12 citations in OpenAlex.
- A Novel Patient-Derived Xenograft Model of Inflammatory Breast Cancer.Research square · 2026Article
- Identification of non-oncology drugs with activity against inflammatory and triple-negative breast cancer cell lines.Oncology letters · 2026Article
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- Secretory carrier membrane protein 3 modulates nuclear ERK1/2 signaling and drug response in triple-negative breast cancer.Frontiers in oncology · 2026Article
- Non-redundant roles of the phosphoinositide phosphatases PTEN and PIPP in PI3K/AKT signaling in breast cancer.Communications biology · 2025Article
- Cooperative CCL2/CCR2 and HGF/MET signaling enhances breast cancer growth and invasion associated with metabolic reprogramming.Cancer biology & therapy · 2025Article
- From 2D to 3D and beyond: the evolution and impact of in vitro tumor models in cancer research.Nature methods · 2025Review
- Pioneering new enhancers by GATA3: role of facilitating transcription factors and chromatin remodeling.Nucleic acids research · 2025Article
- Molecular, Cellular, and Technical Aspects of Breast Cancer Cell Lines as a Foundational Tool in Cancer Research.Life (Basel, Switzerland) · 2023Review
- Omics-Based Investigations of Breast Cancer.Molecules (Basel, Switzerland) · 2023Review
- In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts.Journal of mammary gland biology and neoplasia · 2022Review
- Review
- Development and implementation of the SUM breast cancer cell line functional genomics knowledge base.NPJ breast cancer · 2020Article
- Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several years ago, the SUM panel of human breast cancer cell lines was developed, and these cell lines have been distributed to hundreds of labs worldwide. Our lab and others have developed extensive omics data sets from these cells. More recently, we performed genome-scale shRNA essentiality screens on the entire SUM line panel, as well as on MCF10A cells, MCF-7 cells, and MCF-7LTED cells. These gene essentiality data sets allowed us to perform orthogonal analyses that functionalize the otherwise descriptive genomic data obtained from traditional genomics platforms. To make these omics data sets available to users of the SUM lines, and to allow users to mine these data sets, we developed the SUM Breast Cancer Cell Line Knowledge Base. This knowledge base provides information on the derivation of each cell line, provides protocols for the proper maintenance of the cells, and provides a series of data mining tools that allow rapid identification of the oncogene signatures for each line, the enrichment of KEGG pathways with screen hit and gene expression data, an analysis of protein and phospho-protein expression for the cell lines, as well as a gene search tool and a functional-druggable signature tool. Recently, we expanded our database to include genomic data for an additional 27 commonly used breast cancer cell lines. Thus, the SLKBase provides users with deep insights into the biology of human breast cancer cell lines that can be used to develop strategies for the reverse engineering of individual breast cancer cell lines.
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