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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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Molecular Profile Treatment Approach
Gene Name Source Synonyms Protein Domains Gene Description Gene Role
Therapy Name Drugs Efficacy Evidence Clinical Trials
Drug Name Trade Name Synonyms Drug Classes Drug Description
Gene Variant Impact Protein Effect Variant Description Associated with drug Resistance
Molecular Profile Indication/Tumor Type Response Type Therapy Name Approval Status Evidence Type Efficacy Evidence References
BRAF mutant lung non-small cell carcinoma predicted - sensitive unspecified PD-1 antibody Clinical Study - Cohort Actionable In a clinical study, mutant BRAF correlated with prolonged duration on immune checkpoint inhibitor therapy compared to wild-type BRAF in non-small cell lung cancer patients (Ann Oncol 2017, Vol 28, Suppl 5, Abstract #1138PD). detail...
BRAF mutant lung non-small cell carcinoma predicted - sensitive unspecified PD-L1 antibody Clinical Study - Cohort Actionable In a clinical study, mutant BRAF correlated with prolonged duration on immune checkpoint inhibitor therapy compared to wild-type BRAF in non-small cell lung cancer patients (Ann Oncol 2017, Vol 28, Suppl 5, Abstract #1138PD). detail...