Chapter 4 132 Introduction Pharmacological blockade of the inhibitory immune receptor programmed cell death protein 1 (PD‑1) or its ligand programmed death-ligand 1 (PD-L1) has improved the clinical outcome of many cancers, including non-small cell lung cancer (NSCLC)1–5. High tumor PD-L1 expression has been associated with clinical benefit in NSCLC treated with PD-1 blockade1,2,6 and these results have led to the implementation of PD-L1 immunohistochemistry (IHC) as a biomarker in clinical practice. However, other studies have shown suboptimal correlation of PD-L1 expression to clinical outcome3–5. Therefore, robust biomarkers that more accurately predict who will benefit, and who not, are needed. In particular, biomarkers with a high negative predictive value (NPV) that reliably predict lack of clinical benefit are important to offer patients alternative treatments. Previously, we reported high levels of PD-1T tumor-infiltrating lymphocytes (TILs) as a novel predictive biomarker for long-term benefit to PD-1 blockade with a high NPV7. PD-1T TILs are a subset of PD‑1+ T cells with a dysfunctional phenotype, high tumor-specific expression of PD-1, and high capacity for tumor recognition8,9. PD1T TILs can be measured via algorithm-based quantitative analysis of PD-1 IHC in formalin-fixed paraffin embedded (FFPE) tumor tissue7,8. While digital image analysis can yield accurate quantitative PD-1 protein expression data, this method is challenging to validate across centers, impacting routine clinical care. We therefore aimed at developing a reliable method that can detect the signal represented by PD-1T TILs and that can easily be applied in routine clinical care. The NanoString nCounter is a robust platform that allows for measuring very low input RNA amounts isolated from FFPE tissue10. Already several mRNA signatures have been developed for this platform, including the Tumor Inflammation Signature (TIS) that has demonstrated predictive potential for clinical benefit to PD-1 blockade in multiple cancer types11–13. In the present study, we used the NanoString nCounter platform, to develop and validate an RNA expression signature that reflects a tumor’s PD-1T TIL status and predicts clinical outcome of NSCLC patients treated with PD-1 blockade.
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