An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Our laboratory previously reported an individual-level signature consisting of nine gene pairs, named 9-GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non-small-cell lung cancer after complete surgical resection. In this study, we first validated the cross-platform robustness of 9-GPS by demonstrating that 9-GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA-sequencing platform in The Cancer Genome Atlas (TCGA; log-rank P = 0.0318, C-index = 0.55). Applying 9-GPS to all the 423 stage I-IV LUAD samples in TCGA, the predicted high-risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9-GPS and found that the modified 9-GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9-GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9-GPS is a robust signature for identifying early-stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.