P14.11 Optimal Combination of Biomarkers to Improve the Predictive Value of Immunotherapeutic Response in Non-Small Cell Lung Cancer

Abstract

The clinical relevance of current biomarkers predicting response to immuno-checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) patients remain inconclusive. Therefore, there is an urgent need for appropriate strategies and reliable predictors. Like combination therapy, biomarker testing is also trying to integrate cancer-immune systems through a multimodality approach rather than a single test. The retrospective cohort includes 30 NSCLC patients receiving ICI therapy in Seoul National University Bundang Hospital. PD-L1 immunostaining on tumor cells and the number of tumor infiltrating lymphocytes (TILs) were assessed in 30 NSCLC samples. Tumor mutational burden (TMB) measured by targeted deep sequencing in all samples. RNA extracted from 25 archival formalin fixed paraffin embedded tumor blocks was hybridized to the NanoString® PanCancer IO360™ CodeSet using nCounter® technology. Gene set enrichment analysis (GSEA) was used to determine potentially relevant gene expression signatures between responder and non-responder groups. Each biomarker result was correlated with tumor responses and overall survival. Furthermore, we tested additional strategies to combine the best performing biomarkers in order to improve their predictive value. Nine (30%) patients showed a durable clinical benefit (DCB), while the remaining 21 (70%) showed progressive disease (PD). The best single biomarkers for predicting DCB was enrichment score which consisted of 121 immune-related genes with AUC=0.817. The TIL signature was followed with AUC=0.75, and PD-L1 tumor proportion score and TMB showed similarly low predictive values with AUC 0.536 and 0.571, respectively. Among the combinations tested, the combination of TMB and enrichment score showed a remarkable increase in AUC (0.929), similar to that of the combination of four biomarkers (AUC=0.952). In survival analysis, the enrichment score was identified as the only biomarker associated with overall survival (p=0.013). Although the existing single biomarker did not show a satisfactory predictive value, we confirmed that the ICI response can be predicted more accurately through a combination of biomarkers. The combination of TMB and enrichment score is a potent biomarker that can compensate for PD-L1 and TIL, and in order to generate a more robust way to predict ICI efficacy it is necessary to establish a diagnostic algorithm in clinical setting.