Reference Detail

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...