Non-small cell lung cancers (NSCLCs) frequently express estrogen receptor (ER) β, and estrogen signaling is active in many lung tumors. We investigated the ability of genes contained in the prediction analysis of microarray 50 (PAM50) breast cancer risk predictor gene signature to provide prognostic information in NSCLC. Supervised principal component analysis of mRNA expression data was used to evaluate the ability of the PAM50 panel to provide prognostic information in a stage I NSCLC cohort, in an all-stage NSCLC cohort, and in The Cancer Genome Atlas data. Immunohistochemistry was used to determine status of ERβ and other proteins in lung tumor tissue. Associations with prognosis were observed in the stage I cohort. Cross-validation identified seven genes that, when analyzed together, consistently showed survival associations. In pathway analysis, the seven-gene panel described one network containing the ER and progesterone receptor, as well as human epidermal growth factor receptor (HER)2/HER3 and neuregulin-1. NSCLC cases also showed a significant association between ERβ and HER2 protein expression. Cases positive for HER2 expression were more likely to express HER3, and ERβ-positive cases were less likely to be both HER2 and HER3 negative. Prognostic ability of genes in the PAM50 panel was verified in an ERβ-positive cohort representing all NSCLC stages. In The Cancer Genome Atlas data sets, the PAM50 gene set was prognostic in both adenocarcinoma and squamous cell carcinoma, whereas the seven-gene panel was prognostic only in squamous cell carcinoma. Genes in the PAM50 panel, including those linking ER and HER2, identify lung cancer patients at risk for poor outcome, especially among ERβ-positive cases and squamous cell carcinoma.
Bibliographical noteFunding Information:
Supported by National Institutes of Health grants P50 CA090440 and P30 CA047904 , The V Foundation for Cancer Research , and the University of Pittsburgh Medical Center (UPMC) Genomics Initiative . This study used data generated by the TCGA Research Network: http://cancergenome.nih.gov .