DOI: 10.1158/1535-7163.targ-23-b102 ISSN: 1538-8514

Abstract B102: Predicting small cell lung cancer liver metastasis

George Chrisafis, Nobuyuki Takahashi, Anish Thomas
  • Cancer Research
  • Oncology


Small cell lung cancer (SCLC) is a highly aggressive, treatment-resistant form of lung cancer which carries an exceptionally poor prognosis. Patients with SCLC often present at an advanced stage due the propensity of SCLC for early metastasis, with common sites including the brain, liver, adrenal glands, and bone. Liver metastasis has been identified as an independent prognostic factor and is correlated with an unfavorable response to chemotherapy, but specific steps in the liver metastatic cascade have yet to be described. Here, we seek to better characterize SCLC liver metastases by analyzing gene expression data from cell lines and patient biopsy samples. We start by analyzing data published in MetMap (Jin et al., 2020), which calculated organ-specific ‘metastatic potential’ values for cell lines in the Cancer Cell Line Encyclopedia (CCLE). Gene set enrichment analysis of 20 high-grade neuroendocrine differentiated cancer cell lines (8 SCLC, 12 large cell carcinoma) revealed that lines with high liver metastatic potential exhibit significant positive enrichment of genes involved in MYC signaling, oxidative phosphorylation, and xenobiotic metabolism; genes involved in the mitotic spindle and E2F targets are negatively enriched. This hallmark signature appears unique to these cells compared with other non-small cell lung cancer and colorectal cancer. We identify ~45 genes from these hallmarks which are differentially expressed in cell lines with high vs. low liver metastatic potential and create a multiple linear regression model to predict liver metastatic potential based on the expression of these genes. When applied to clinical SCLC samples, the model predicts samples obtained from the liver to have a significantly higher metastatic potential than samples from all other biopsy sites. A higher estimated metastatic potential also correlates with poorer overall survival. Genes and pathways identified by this analysis, following experimental validation, could serve as potential therapeutic targets to disrupt the liver metastatic cascade.

Citation Format: George Chrisafis, Nobuyuki Takahashi, Anish Thomas. Predicting small cell lung cancer liver metastasis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr B102.

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