Abstract 4780
Background
Treatment of non-small cell lung cancer (NSCLC) with checkpoint inhibitors (CI) that block the PD-L1 pathway has resulted in profound responses but only in a subset of patients. Our aim was to examine biomarkers associated with response and resistance to CI in advanced NSCLC to help inform patient stratification.
Methods
We examined 820 lung adenocarcinoma (LUAD) samples that had matched PD-L1 staining and received genomic profiling to measure tumor mutational burden (TMB) and genomic alterations in 315 genes (FM cohort). We also examined progression free survival (PFS) of 1310 CI treated patients in a HIPAA compliant, real world clinicogenomic database (CGDB). These patients received the FoundationOne assay as part of routine care and had electronic health record data available in the Flatiron Health Database (Singal, ASCO 2017).
Results
In the CGDB, we observed known associations between likelihood of PFS and TMB, loss of STK11. TMB correlated with median PFS in months (mPFS): TMB > 20 mutations per MB (N = 164) – 6 mo vs TMB < =20 (N = 1146) – 2.8 mo, P = 1e-07). Patients with STK11 loss had reduced mPFS (wt 3.1 mo vs mut 2.5 mo, P = 0.01). We analyzed PDL1 staining for driver alterations in LUADs in the FM cohort. EGFR mutant samples were enriched for PDL1 negative staining (FM cohort: P = 6.3e-5) and the EGFR cohort had reduced mPFS (CGDB: wt 3 mo vs mut 2.4 mo, P = 0.003). Samples with MET exon 14 skipping mutations were enriched for PDL1 high positive staining (FM: P = 2.3e-5), but the MET cohort had similar mPFS (CGDB: wt 3 mo vs mut 2.7 mo, P = 0.8). Samples with BRAF alterations trended towards both PDL1 high positive staining (FM: P = 0.06) and increased mPFS (CGDB: wt 2.9 mo vs mut 4.6 mo, P = 0.2).
Conclusions
We examined PFS of NSCLC patients on CI therapies in a clinicogenomic database and observed known associations with TMB, STK11 and EGFR alterations. We found that, at a population level, MET altered patients did not have enhanced mPFS despite increased PDL1 HP staining, suggesting dual or targeted therapies. Real-world datasets such as the CGDB hold promise in prioritizing therapies and identifying biomarkers of response and resistance.
Clinical trial identification
Legal entity responsible for the study
Foundation Medicine Inc., Cambridge, Massachusetts, United States of America.
Funding
Foundation Medicine Inc., Cambridge, Massachusetts, United States of America.
Editorial Acknowledgement
Disclosure
K. Murugesan, G. Li, G. Kaushik, G. Singal, V.A. Miller, L.A. Albacker, G.M. Frampton: Employee: Foundation Medicine Inc.
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