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Ref Type
PMID
Authors J.S. Ross, M.E. Goldberg, L.A. Albacker, L.M. Gay, V. Agarwala, J.A. Elvin, J-A. Vergilio, J. Suh, S. Ramkissoon, E. Severson, S. Daniel, S.M. Ali, A.B. Schrock, G.M. Frampton, D. Fabrizio, V.A. Miller, G. Singal, A. Abernethy, P.J. Stephens
Title Immune checkpoint inhibitor (ICPI) efficacy and resistance detected by comprehensive genomic profiling (CGP) in non-small cell lung cancer (NSCLC)
Journal Vol Issue Date Pages Journal Short Title
Ann Oncol
URL https://www.annalsofoncology.org/article/S0923-7534(20)38406-4/fulltext
Abstract Text Background: The prediction of outcome to ICPI in advanced NSCLC is of great clinical interest. We considered CGP, PD-L1 IHC, and real world data to investigate potential biomarkers for ICPI response. Methods: CGP and IHC was performed on 1,619 FFPE NSCLC samples in the FoundationCORE database (FMI). The SP142 antibody was used to capture PD-L1 tumor expression (PD-L1 TE) for these 1,619 samples. NSCLC patients (n = 2139) in the Flatiron Health Analytic Database with FoundationOne testing CGP results and real world IHC results for PD-L1 TE were analyzed separately (FMI-FIH). CGP used ≥50 ng of DNA and a hybrid-capture, adaptor ligation-based assay (median coverage depth >600X). TMB (mut/Mb) was determined on 1.1 Mb of sequenced DNA. Results: PD-L1 IHC TE correlated weakly with TMB (FMI samples) (Spearman’s ρ 0.085, p = 6.16e-4); mean TMB was 10.9 mut/Mb, median 8.1 mut/Mb and 14.5% had high TMB (≥20 mut/Mb). From FMI-FIH, high TMB but not PD-L1 status predicted longer mean duration on therapy (DOT) (p = 0.001). Analysis of the FMI and FMI-FIH datasets revealed relationships between GA, PD-L1 TE, TMB, and mean DOT. Inactivating STK11 GA were seen in 12.1% of FMI-FIH and 15.1% of FMI samples, most often adenocarcinomas (aCa). STK11 GA correlated with high TMB/low PD-L1 (FMI; p = 0.0014) and preliminary analyses suggest correlation with negative ICPI treatment outcome. Several genes were commonly co-altered with STK11 (FMI): KRAS (54.5%), TP53 (43%), CDKN2A (27.5%), CDKN2B (20.1%), KEAP1 (18.9%), and MYC (13.5%). BRAF GA, most often short variants (SV) in aCa, were associated with prolonged DOT on ICPI regardless of TMB score (FMI-FIH; p = 0.0073). MET SV also predicted prolonged DOT on ICPI, but insufficient events prevented calculation of statistical significance (FMI-FIH). Analysis of the TCGA lung aCa dataset revealed MET SV (2.8%) linked with immune activation gene expression profiles (p < 0.05) and STK11 mutations (14.2%) with immune evasion profiles (p < 0.05). Conclusions: Although TMB powerfully predicts ICPI outcome independent of tumor cell PD-L1 expression, considering GA in STK11, BRAF or MET may significantly increase the precision and improve outcomes when using genomics with IHC to guide to ICPI selection.

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