Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer

Immunotherapy targeting programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) is a standard of care in the treatment of stage IV non-small-cell lung cancer (NSCLC). However, only a minority of patients responds to anti-PD-1/PD-L1 monotherapy. Tumor-centric predictive biomarkers applicable to small diagnostic specimens such as PD-L1 expression and tumor mutational burden [1] only allow enrichment of cohorts with higher probability of treatment response. Efficacy of immunotherapy is governed by a complex interplay of tumor-intrinsic properties (genomic and epigenomic), the tumor microenvironment, the systemic state of the immune system, and de novo or acquired resistance [2].