Molecular profiling of metastatic lung squamous cell carcinoma (mLUSC) to identify patients with differential response to immune checkpoint inhibitor (ICI) therapy.

Abstract

e20555 Background: Molecular profiling is inconsistently performed in patients with mLUSC as it rarely provides actionable information. Whether it can aid in the selection of ICI based therapy is unknown. Methods: Patients with mLUSC (n=963) who received ICI therapy at 9 academic institutions or as part of 3 clinical trials were identified. Overall response rate (ORR) by RECIST 1.1 and clinical progression-free survival (PFS) were the primary outcomes. Propensity matching on age, smoking history, ECOG, sex and PDL1 among previously untreated patients was performed to compare outcomes in patients treated with ICIs alone (ICI-mono) or in combination with chemotherapy (ICI-chemo). Among patients with complete clinico-genomic annotation, including coverage of KEAP1 (n=248), we trained logistic regression (LR), random forest (RF), and gradient boosting (GB) models to predict response. An 80/20 split was used to separate the training and testing cohorts and hyperparameters were optimized for area under the receiver operating characteristic curve (AUROC). Coefficients of the LR model were interrogated to identify predictors of response. The AACR GENIE LUSC cohort (n=2618) was analyzed to assess the prognostic effects of genomic biomarkers. Results: Among propensity matched patients, there was no difference in ORR (ORR: 29 vs 29, p=1.0) or PFS (HR=0.90, p=0.72) between patients who received ICI-chemo versus ICI-mono. Integrating clinical and molecular data improved on PDL1 in predicting response to ICI therapy (AUROC, LR: 0.73, RF: 0.78, GB: 0.69, LR with PDL1 only: 0.70). Top features predictive of response were ICI-chemo, PDL1, sex, and KDM6A , NFE2L2 / KEAP1 , and TP53 alterations; top features predictive of non-response were prior treatment, KRAS and DNMT3A alterations. Among previously untreated patients, improved outcomes were observed in those with vs without KDM6A (n: 13 vs 185; ORR: 85 vs 42, p=0.0032; PFS: HR=0.50, p=0.064) and NFE2L2 / KEAP1 alterations (n: 45 vs 145; ORR: 60 vs 39, p=0.017; PFS: HR=0.60, p=0.0084), while those with vs without KRAS alterations trended towards decreased response rates and PFS (n: 31 vs 274; ORR: 26 vs 43, p=0.083; PFS: HR=1.40, p=0.088). There was no difference in outcomes among patients with KRAS alterations who received ICI-mono vs ICI-chemo. In the GENIE cohort, 4.5% of patients harbored KRAS mutations, which was associated with decreased overall survival (HR=1.5, p=0.0015) as compared to wild-type KRAS . Conclusions: Molecular profiling identified predictors of response to ICI therapy in patients with mLUSC. KDM6A and KEAP1 / NFE2L2 alterations were predictive of response to ICI therapy and may identify patients who could be spared chemotherapy, while KRAS alterations were predictive of non-response and poor prognosis and may identify patients who would benefit from novel treatment strategies.