Cancers, Volume 17, Issue 5
2025 March-1 - 197 articles
Cover Story: Immune checkpoint inhibitors have revolutionized the treatment of solid cancers. However, only a minority of patients respond to therapy, even if they are selected on the basis of FDA-approved biomarkers. In this study, we developed a machine learning model that integrates genomic and transcriptomic biomarkers to more accurately predict the outcome of anti-PD1 treatment in melanoma. We employed LASSO regression with 49 features derived from both tumor–normal WES and bulk RNA-Seq data. Our model achieved an ROC AUC of 0.7 and 0.64 in the training and test datasets, respectively, outperforming the current gold-standard TMB. A subsequent SHAP analysis revealed the most predictive biomarkers, which included activated response pathways, tumor mutational burden, and immune cell infiltration. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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