SCLC-CellMiner: A Resource for Small Cell Lung Cancer Cell Line Genomics and Pharmacology Based on Genomic Signatures

Cell Rep. 2020 Oct 20;33(3):108296. doi: 10.1016/j.celrep.2020.108296.


CellMiner-SCLC ( 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.

Keywords: PARP; SLFN11; STING; Schlafen; genomics; immune checkpoints; mutations; native immune response; neuroendocrine tumors; replication.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Line, Tumor
  • DNA Methylation / genetics
  • Data Mining / methods*
  • 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
  • Small Cell Lung Carcinoma / genetics*
  • Small Cell Lung Carcinoma / metabolism*
  • Software
  • Transcription Factors / genetics


  • Transcription Factors