Background PD-L1 expression and Tumor Mutation Burden (TMB) have independently emerged as prospective biomarkers of response to anti PD1-/PDL1 checkpoint inhibitors and even combined use of TMB, PD-L1 protein levels has been proposed. However, how the tumor genomic landscape interplays with the tumor microenvironment (TME) in defining particular predictive therapy response statuses is not clear.
Methods 424 FFPE clinical samples from lung cancer patients were analyzed using a CLIA-validated NGS-based assay that interrogates SNVs, indels using a 323 gene panel and by IHC for PD-L1 using the FDA approved PharmDx assay. TMB (mutations/Mb) is categorized as low (≤7), intermediate (7 15). NGS results were paired with PD-L1 status which was defined by tumor proportion scores (TPS) as: negative (TPS<1%), Low expressing (≥1–49%) and High (≥50%). In silico analyses were also performed on 5939 lung cancer samples from public databases.
Results We found poor correlation between PD-L1 expression and TMB in NSCLC (r2=0.266). We then classified lung cancer samples based on TMB and PD-L1 TPS and found mutational correlations specific to in each of the groups defined by PD-L1 combined with TMB scores. First, we interrogated the KRAS and EGFR mutations frequencies distribution across either TMB or PDL1 status. We find that while KRAS mutations are constant across PDL1 TPS but infrequent on TMB High tumors, EGFR mutation frequency appeared inversely correlated to both TMB and PD-L1 TPS. 67% of PD-L1 High/TMB Low samples presented mutations either on EGFR (12%), KRAS (23.5%) or in genes from known driver TRK/MAPK pathways, whereas only KRAS was part of the frequently mutated gene signature with 36.5% (13/36) samples mutated on PD-L1 High/TMB High samples. Neither EGFR nor KRAS were found frequently mutated on PD-L1 Low/TMB High group (n=46).Interestingly in patients with an intermediate TMB (7.
Conclusions Genomic alteration signatures might define subsets of lung cancer tumors with no PD-L1 expression to complement TMB and PD-L1 on the selection criteria for patients whom may benefit from checkpoint inhibitors.
Acknowledgements Paris Pettersen, Hatim Husain.
Ethics Approval The study was approved by Neogenomics Institution’s Ethics Board and external IRB, approval number 420160280.
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