Advances in PD-1 inhibitor combination therapy for non-small cell lung cancer
DOI:
https://doi.org/10.61173/vsnwe981Keywords:
NSCLC, PD-1, combination therapy, multi-modal intervention, precision therapyAbstract
Non-small cell lung carcinoma (NSCLC), characterized by its rapid progression and absence of clear initial symptoms, represents the most common variant of pulmonary malignancies. Therapeutic approaches focusing on PD-1/PD-L1 immune checkpoint blockade have dramatically transformed NSCLC management strategies. However, single-agent treatments continue to demonstrate constrained efficacy, with observable response rates fluctuating between 15% and 30%, while also facing challenges from both intrinsic and acquired resistance mechanisms. Thus, there are ongoing clinical trials involving multimodal interventions for PD-1 inhibitor combination therapy (for example, radiotherapy causing immunogenic tumor cell death, chemotherapy perturbing the tumor microenvironment, targeted drugs to inhibit oncogenic signaling, and TCM reversion of multidrug resistance); the studies also demonstrated the potential of these combination strategies to significantly improve the survival endpoints (PFS/OS). Nevertheless, it is difficult to combine therapies, and these combinations are often plagued by heterogeneous treatment responses, an absence of evidence-based rationale for treatment subdivisions (for example, patients with an EGFR mutation), and insufficient dynamic safety assessment. We must pursue a future where biomarker-driven precision interventions represent our gold standard (such as using single-cell sequencing to screen for sensitive subpopulations), where "molecular mechanism-based" synergistic therapies are the norm (for instance, combining bispecific antibodies with epigenetic modulation), where toxicity risk stratification modeling is established, and where optimization of healthcare economics is proven effective. Ultimately, we need to achieve the transformation of empirical combinations to molecular mechanism-driven precision therapies through real-world data integration and benefit patients by increasing their survival benefit.