Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer

Background: The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICIs treatment. The recent studies showed that response rates to single-agent programmed cell death protein 1(PD-1)/programmed cell death-ligand 1(PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients are 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cut-off value. Methods: Two published and an unpublished ICIs treatment NSCLC cohort with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified 8 immune-related pathways whose mutation status were significantly associated with overall survival after ICIs treatment. Then these pathways mutational status, combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response. Results: We firstly proved that TMB, PD-L1 and mutant-allele tumor heterogeneity (MATH) are independent biomarkers. The survival analysis of 6 immune-related pathways revealed the mutation status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80. Conclusion: We developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger datasets, further studies are warranted to refine the predictive performance of the approach.