Oct 11 2017
Comprehensive Genomic Profiling (CGP) Uncovered Potential Biomarkers of Immunotherapy in NSCLC – Good Predictive Markers: TMB-H, BRAF and MET; Poor Predictive Marker: STK11 [1138PD]
J.S. Ross, et al utilized CGP, PD-L1 IHC, and real world data to investigate potential biomarkers for ICPI (immunotherapy) response for advanced NSCLC.
CGP and IHC was performed on 1,619 FFPE NSCLC samples in the FoundationCORE database (FMI). SP142 antibody was used to capture PD-L1 tumor expression (PD-L1 TE). NSCLC patients (n=2139) in the Flatiron Health Analytic Database with CGP results from FoundationOne testing and real world IHC results for PD-L1 TE were analyzed separately (FMI-FIH).
- Correlation of PD-L1 IHC TE with TMB (FMI samples) was weak (Spearman’s ρ 0.085, p=6.16e-4).Mean TMB was 10.9 mut/Mb, median was 8.1 mut/Mb and 14.5% had high TMB (≥20 mut/Mb).
- High TMB but not PD-L1 TE predicted longer mean duration on therapy (FMI-FIH) (p=0.001),
- STK11 GA correlated with high TMB/low PD-L1 (FMI; p=0.0014) and negatively correlated with treatment outcome on ICPI,
- BRAF GA, most often short variants (SV) and predominantly in adenocarcinomas, were associated with prolonged time on ICPI treatment regardless of TMB score (FMI-FIH; p=0.0073),
- MET SV also predicted prolonged time on ICPI, but insufficient events prevented calculation of statistical significance (FMI-FIH). Analysis of the TCGA lung adenocarcinoma dataset revealed MET SV (2.8%) were linked with immune activation gene expression profiles (p<0.05) and STK11 mutations (14.2%) with immune evasion profiles (p<0.05).
Although TMB powerfully predicts ICPI outcome independent of tumor cell PD-L1 expression, considering GA in STK11, BRAF or MET may further increase the precision and improve outcomes when using genomics with IHC to guide to ICPI selection.
Senior Officer, Medical Affairs