External validation of a serum tumor marker algorithm for early prediction of no durable benefit to immunotherapy in metastastic non-small cell lung carcinoma

Background: Immune checkpoint inhibitors (ICIs) provide a significant survival benefit in non-small cell lung cancer (NSCLC) patients; however, accurately predicting which patients will benefit remains a challenge. As previously shown, the STOP model, a machine learning model based on serum tumor markers, is capable of identifying non-responders after 6 weeks of ICIs. Objective: This study aims to externally validate this model and to assess the predictive value in combination with radiological response assessment using RECIST criteria. Methods: In a cohort of 242 metastatic NSCLC patients, CYFRA, CEA, and NSE were measured before start and after 6 weeks of ICI treatment. The ability of the STOP model to predict no durable benefit (NDB; progressive disease, death within 6 months or disease control of less than 6 months) was assessed using specificity and positive predictive value (PPV). Moreover, a combination of the STOP model with RECIST after 6–8 weeks of ICIs was investigated. Results: The STOP model achieved a specificity of 96% (95% CI 95%–97%) and a PPV of predicting NDB of 88.1% (95% CI 85.9%–90.3%). Combining the STOP model with RECIST improved specificity and PPV to 100% and predicted NDB on average 11.6 weeks (IQR 1.8–18.0 weeks) prior to developing radiologically defined progression. Conclusions: After 6 weeks of ICIs, the blood-based STOP model was capable of accurately predicting NDB in metastatic NSCLC patients, earlier than conventional radiological assessment. The combined serological and radiological response assessment creates an early opportunity to safely stop ICI treatment in patients who will not benefit, although the clinical utility of the assay is limited since the high specificity comes at the cost of a lower sensitivity.