Machine learning identified extrachromosomal DNA-related 12 gene signatures to predict cancer immunotherapy response.

Extrachromosomal circular DNA (ecDNA) has emerged as a critical determinant of poor clinical outcomes and immune escape in tumors, but the high cost and technical complexity of current detecting techniques limit its broader investigation in cancer immunotherapy. Leveraging the combined machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) regression, RandomForest (RF) and Recursive Feature Elimination (RFE), we developed a 12-gene transcriptomic score (EC_score) to predict the existence of ecDNA through RNA-seq. EC_score demonstrated reliable predictive performance in two independent cohorts (AUC > 0.70), validated by fluorescence in situ hybridization (FISH) in both cell lines and clinical samples. Next, we found that EC_score emerged as an independent adverse prognostic factor across multiple immunotherapy cohorts. Notably, high EC_score correlated with cell cycle activation and immunosuppression, characterized by reduced lymphocytes infiltration and upregulated immunosuppressive markers, including MHC molecules, co-inhibitory immune checkpoints and TGF-β signals. In general, we established and validated a 12-gene signature (EC_score) derived from RNA-seq, offering a novel computational tool for predicting the presence of extrachromosomal circular DNA and stratifying cancer immunotherapy response.