Composite versus individual biomarkers for predicting clinical benefit to PD-1 blockade in NSCLC 103 3 accuracy of PD-L1 and of PD-L1 combinations. Furthermore, it is important to note that this study is limited by the number of samples, particularly for TIS assessment. Therefore, we restricted our evaluation to two-biomarker combinations instead of considering three or more. Studies with a larger sample size are essential to validate the robustness of our findings. Our results for TIS align with other studies that have demonstrated the predictive potential of this signature29,43. Interestingly, TIS includes genes, such as LAG3 and TIGIT, that are highly expressed in PD-1T TILs24,29. A high number of PD-1T TILs or a high TIS score in pretreatment samples may serve as surrogate markers for a tumor’s capacity to undergo durable immune reactivation upon PD-1 blockade therapy. A PD-1T TILs or TIS combination with biomarkers representing distinct facets of the immune response holds promise for improving predictive accuracy. For instance, TMB can serve as a read-out for immunogenic neoantigens arising from somatic mutations15. Previous studies have identified TMB and PD-L1 as independent predictors for advanced NSCLC treated with PD-1 blockade and have shown improved performance when combined32,33. Conversely, the presence of tumor-resident regulatory T cells (Treg) in the TME might be considered. Treg cells, known for their immune-inhibitory functions, are associated with poor patient survival when present in high numbers44. A combination of TMB or T reg with either PD-1T TILs or TIS warrants further investigation in future studies. In conclusion, this study showed that the biomarker combinations assessed here did not improve predictive performance compared to PD-1T TILs and TIS alone. PD-1T TILs showed the highest predictive performance of all the biomarkers, accurately identifying patients without long-term benefit with high specificity and NPV. Acknowledgements We would like to thank the NKI-AVL Core Facility Molecular Pathology and Biobanking for supplying all IHC stainings used in this study, as well as biobankrelated work and other laboratory support. ChatGPT was used for text correction.
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