Evaluation of current programmed death-ligand 1 (PD-L1) testing practices for metastatic non-small cell lung cancer (mNSCLC): Insights from a large network of US community oncology practices.

Abstract

e23294 Background: PD-L1 testing has become crucial for guiding immunotherapy in mNSCLC and evidence suggests increasing adoption of PD-L1 testing in the community oncology setting. This study evaluated current real-world PD-L1 testing patterns in The US Oncology Network to identify opportunities for augmenting personalized medicine in mNSCLC care. Methods: This observational study included adults with mNSCLC, diagnosed with de novo Stage IV disease or progressed from an earlier stage, who initiated first-line (1L) treatment between 11/01/2022 and 08/31/2024. Data were sourced from iKnowMed electronic health records (EHR). PD-L1 testing documentation was captured from structured EHR fields and supplemented using a validated natural language processing (NLP) algorithm for unstructured records. The NLP results were compared to manual abstraction (gold standard) for a stratified random sample (by clinic and clinic location) of 100 patients without evidence of PD-L1 records in structured records. The sensitivity, specificity, and F1 score of the NLP algorithm were assessed relative to abstraction to evaluate the accuracy and precision of the model. PD-L1 testing patterns were assessed descriptively. Results: Among 2,148 study-eligible patients, 75% (n = 1,607) had PD-L1 testing documented in structured EHR fields at any time. Among patients with structured PD-L1 documentation (n = 1,607), 42% were diagnosed with Stage IV disease and rates of other biomarker testing ranged from 84% (for ROS1) to 91% (for EGFR). Among patients confirmed through abstraction to lack PD-L1 testing (n = 36), 86% were diagnosed with Stage IV disease and rates of other biomarker testing ranged from 42% (for ALK) to 56% (for EGFR). In a sample of 100 patients without evidence of PD-L1 records in structured data, the NLP algorithm performance was 89% for sensitivity (95% confidence interval [CI] 79%-95%); 86% for specificity (95% CI 71%, 95%) and 90% for F1 score. By applying the NLP algorithm for all 541 patients without structured PD-L1 records, an additional 313 patients with PD-L1 tests were identified, resulting in PD-L1 testing across an estimated 89% (n = 1,920) of the overall population. Conclusions: In a contemporary sample of community oncology patients with mNSCLC, approximately 90% received PD-L1 testing. Leveraging information in unstructured data using a validated NLP algorithm increased capture of PD-L1 testing. As the highest PD-L1 testing rate published to date, this result may reflect the proportion of patients for whom PD-L1 testing is clinically appropriate, given that some patients may decline therapy and/or select hospice care. Future research should investigate how community oncology practices successfully implemented PD-L1 testing and apply these learnings to forthcoming actionable biomarkers.