Data from Clinical Outcomes and Predictors of Response to PD-(L)1 Blockade in Patients with NSCLC without Actionable Genomic Alterations Who Never Used Tobacco

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Abstract Purpose: Predictive biomarkers of response to immune checkpoint inhibitors (ICI) remain poorly defined in patients with non–small cell lung cancer (NSCLC) without a history of tobacco use and lacking actionable genomic alterations (AGA). We aimed to identify clinical and molecular predictors of response to ICI-based regimens in patients who have never smoked and lack AGAs. Experimental Design: We retrospectively analyzed patients with metastatic, AGA-negative NSCLC who never smoke treated with ICI-based regimens across multiple independent cohorts. Tumor-infiltrating lymphocyte (TIL) densities were quantified using a machine learning–based algorithm, and immune cell biomarkers were assessed with multiplexed immunofluorescence. Transcriptomic correlates of ICI response were analyzed in the Stand Up To Cancer cohort. Results: Among 741 patients with AGA-negative NSCLC and no history of tobacco use, the objective response rate (ORR) was 23.2%, median progression-free survival (mPFS) was 4.5 months, and median overall survival (mOS) was 16.8 months. PD-L1 ≥90% and tumor mutational burden (TMB) ≥90th percentile were independently and significantly associated with improved ORR, mPFS, and mOS (all P <0.01). PD-(L)1 + CTLA-4 combinations outperformed chemoimmunotherapy and PD-(L)1 monotherapy in terms of mPFS and mOS. Transcriptomic analysis revealed enrichment of innate and adaptive immune pathways in responders, including increased MHC class I/II antigen presentation and T-cell activity. High TIL density was also associated with superior ORR and PFS. Multiplexed immunophenotyping confirmed higher immune cell infiltration in patients who experienced durable clinical benefit. Conclusions: We demonstrated how combination therapies may improve ICI outcomes in patients with AGA-negative NSCLC and no history of tobacco exposure. Very high PD-L1, TMB, and immune-enriched phenotypes may guide treatment personalization.

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