Predicting response and survival of lung adenocarcinoma under anti-programmed death-1 therapy using biological deep learning.

Although programmed death (PD)-1 inhibitors inhibitors have been clinically approved for the treatment of lung adenocarcinoma (LUAD), only a few patients benefit from anti-PD-1 therapy. We developed a semi-supervised biological sparse neural network (sBiosNet) based on transfer learning to fully utilize labeled and unlabeled patient data. The pathways from the Reactome database were used to sparse the sBiosNet and extract associated biological features by integrating patients' genomic mutations and copy number variation data. We assessed the performance of the sBiosNet against random forest and support vector machine using four cohorts and provided clear interpretations using the DeepLIFT algorithm. The sBiosNet achieved the best prediction with an area under the receiver operating characteristic curve (AUROC) of 0.888 and an area under the precision recall curve (AUPR) of 0.919 for responders versus non-responders on the validation cohort, and AUROC of 0.853 and AUPR of 0.894 on an independent external cohort. The ablation experiments demonstrated that biological sparsification and multi-omics data integration, transfer learning and semi-supervised learning all contributed to improving the sBiosNet's performance. We further confirmed that genes (such as TP53, FGF3, FGFR4, and EGFR) affected LUAD patients' response to PD-1 inhibitors by regulating pathways. Meanwhile, the Low-risk LUAD patients identified by the sBiosNet obtained significant longer overall survival and progression-free survival with anti-PD-1 therapy. In conclusion, the sBiosNet accurately predicts the response and survival of patients on anti-PD-1 therapy to reduce unnecessary treatment in non-responders.