Chapter 1 12 cell recognizes a specific tumor antigen presented by the major histocompatibility complex (MHC), it triggers the production of inflammatory cytokines. These cytokines can induce overexpression of programmed cell death 1 ligand 1 (PD-L1) within the tumor. The interaction between PD-L1 and the PD-1 receptor on T cells results in T cell dysfunction and subsequently immunotolerance. Consequently, tumors can protect themselves from cytotoxic (CD8+) T cell-mediated cell killing. Blocking the interaction between PD-1 and PD-L1 can offer an approach to restore T cellmediated antitumor immunity9. The first evidence demonstrating the effectiveness of anti-PD-(L)1 antibodies in treating NSCLC came from studies involving patients with previously treated advanced NSCLC10–12. Since then, these treatments rapidly transitioned to the first-line treatment setting, as multiple clinical trials showed a significant improvement in survival when compared to chemotherapy alone13–16. Unfortunately, approximately 60-70% of patients experience disease progression within six months after initiating treatment14–16, underscoring the need for biomarkers to support shared decision-making for therapeutic strategies. Such biomarkers are essential to improve personalized medicine, to minimize patient exposure to potential adverse effects and to reduce healthcare costs. Biomarkers for predicting response to systemic therapy in advanced NSCLC Although long-lasting clinical responses have been observed for patients treated with TKIs or PD‑(L)1 blockade, this only accounts for a minority of patients. Therefore, biomarkers are urgently needed to estimate the probability of response to specific systemic therapeutic regimens, herein referred to as predictive biomarkers. For TKIs, most predictive biomarkers are characterized by specific genomic alterations associated with the mode of action of the involved TKIs. As a result, comprehensive molecular profiling of tumors is now routinely advised for all newly diagnosed advanced-stage NSCLC patients. This practice allows personalized therapeutic strategies tailored to patients whose tumors harbor targetable oncogenes. However, in the case of immunotherapy, which serves to restore anti-tumor immunity, potential predictive biomarkers differ fundamentally from driver oncogene biomarkers. They exhibit a continuous spectrum rather than a binary categorization, display spatial and temporal variability, and arise from multiple determinants rather than a single dominant determinant. Therefore, biomarker development is more challenging within the context of immunotherapies. The anti-tumor immune response is a complex process, requiring several steps for effective priming, activation, trafficking, and recognition of T cells to eradicate cancer cells, a process known as the cancerimmunity cycle17. Therefore, it is unlikely to find one single biomarker that effectively predicts response18. In addition to predicting which patient will respond to treatment, it is also highly relevant to predict which patients will not respond to
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