Predicting Durable Responses to Immune Checkpoint Inhibitors in Non-Small-Cell Lung Cancer Using a Multi-Feature Model - Wang, Lei; Zhang, Hongbing; Pan, Chaohu; Yi, Jian; Cui, Xiaoli; Li, Na; Wang, Jiaqian; Gao, Zhibo; Wu, Dongfang; Chen, Jun; Jiang, Jizong; Chu, Qian (Frontiers in Immunology, 2022)
Due to the complex mechanisms affecting anti-tumor immune response, a single biomarker is insuf-ficient to identify patients who would benefit from immune checkpoint inhibitors (ICIs) treatment. Therefore, a comprehensive predictive model is urgently required to predict the response to ICIs. A total of 162 non-small-cell lung cancer (NSCLC) patients undergoing ICIs treatment from three in-dependent cohorts were enrolled and used as training and test cohorts (training cohort = 69, test co-hort1 = 72, test cohort2 = 21). Eight genomic markers were extracted or calculated for each patient. Ten machine learning classifiers, such as the gaussian process classifier, random forest, and support vector machine (SVM), were evaluated. Three genomic biomarkers, namely tumor mutation burden, intratumoral heterogeneity, and loss of heterozygosity in human leukocyte antigen were selected, and the SVM_poly method was adopted to construct a durable clinical benefit (DCB) prediction model. Compared with a single biomarker, the DCB multi-feature model exhibits higher predictive value in the test cohorts with the area under the curve values equal to 0.77 and 0.78 for test cohort1 and co-hort2, respectively. The patients predicted to have DCB showed improved mPFS and mOS than those predicted to have no durable benefit.