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SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures.
- Source :
-
Cell reports [Cell Rep] 2020 Oct 20; Vol. 33 (3), pp. 108296. - Publication Year :
- 2020
-
Abstract
- CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this "recalcitrant cancer." We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.<br />Competing Interests: Declaration of Interests The authors declare no competing interests.<br /> (Published by Elsevier Inc.)
- Subjects :
- Algorithms
Cell Line, Tumor
DNA Methylation genetics
Epigenesis, Genetic genetics
Epigenomics methods
Epithelial-Mesenchymal Transition genetics
Gene Expression Regulation, Neoplastic genetics
Genomics methods
Humans
Lung Neoplasms genetics
Lung Neoplasms metabolism
Pharmacological and Toxicological Phenomena
Reproducibility of Results
Software
Transcription Factors genetics
Data Mining methods
Small Cell Lung Carcinoma genetics
Small Cell Lung Carcinoma metabolism
Subjects
Details
- Language :
- English
- ISSN :
- 2211-1247
- Volume :
- 33
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Cell reports
- Publication Type :
- Academic Journal
- Accession number :
- 33086069
- Full Text :
- https://doi.org/10.1016/j.celrep.2020.108296