Predictive Value of Combining Biomarkers for Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Inhibitors.

Journal Article (Journal Article)

INTRODUCTION: A high tumor mutational burden (TMB) (≥10 mut/Mb) has been associated with improved clinical benefit in non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI) and is a tumor agnostic indication for pembrolizumab across tumor types. We explored whether combining TMB with programmed cell death ligand 1 (PD-L1) and pretreatment neutrophil-lymphocyte ratio (NLR) was associated with improved outcomes in ICI-treated NSCLC. METHODS: We retrospectively analyzed patients treated with ICI with Foundation One genomic testing, including TMB. Optimal cutoff for prediction of response by TMB was determined by receiver operating characteristic analysis, and area under the curve (AUC) was calculated for all 3 biomarkers and combinations. Cox model was used to assess prognostic factors of overall survival (OS) and time to progression (TTP). Survival cutoffs calculated with Kaplan-Meier survival curves were TMB ≥10 mut/Mb, PD-L1 ≥50%, NLR <5, and combined biomarkers. RESULTS: Data from 88 patients treated were analyzed. The optimal TMB cutoff was 9.24 mut/Mb (AUC, 0.62), improving to 0.74 combining all 3 biomarkers. Adjusted Cox model showed that TMB ≥10 mut/Mb was an independent factor of OS (hazard ratio [HR], 0.31; 95% confidence interval; 0.14-0.69; P = .004) and TTP (HR, 0.46; 95% CI, 0.27-0.77; P = .003). The combination of high TMB with positive PD-L1 and low NLR was significantly associated with OS (P = .038) but not TTP. CONCLUSIONS: TMB has modest predictive and prognostic power for clinical outcomes after ICI treatment. The combination of TMB, PD-L1, and NLR status improves this power.

Full Text

Duke Authors

Cited Authors

  • Kao, C; Powers, E; Wu, Y; Datto, MB; Green, MF; Strickler, JH; Ready, NE; Zhang, T; Clarke, JM

Published Date

  • November 2021

Published In

Volume / Issue

  • 22 / 6

Start / End Page

  • 500 - 509

PubMed ID

  • 33972172

Electronic International Standard Serial Number (EISSN)

  • 1938-0690

Digital Object Identifier (DOI)

  • 10.1016/j.cllc.2021.03.017


  • eng

Conference Location

  • United States