The field of immunotherapy has revolutionized the treatment of advanced non-small cell lung cancer (NSCLC), particularly through the use of immune-checkpoint inhibitors (ICIs) targeting the Programmed Cell Death-1 or Programmed-Cell Death Ligand-1 (PD-1/PD-L1) axis. These therapies have improved overall survival rates, but durable responses are not observed in most patients, indicating a need for a better understanding of the mechanisms of response and resistance to anti-PD-1/PD-L1 axis inhibitors. Furthermore, there is a significant gap in the identification of robust and clinically meaningful biomarkers. Recent advances in proteomics, transcriptomics, and biology approaches offer potential avenues for identifying, optimizing, and clinically validating novel biomarkers. These could help elucidate the mechanisms of response and resistance to immunotherapy and complement imaging modalities to predict adverse events and aid clinical decision-making.
The goal of this research topic is to explore different biomarker-guided strategies for effective patient selection, treatment de-escalation, and immunotherapy-based treatment combinations. This includes investigating resistance mechanisms in the context of immunotherapy in NSCLC, identifying cellular interactions and molecular alterations within the tumor microenvironment to develop more effective strategies against immunosuppression, and exploring biomarker-guided approaches and combinatorial treatment strategies to improve the benefits of immunotherapy in NSCLC.
To gather further insights into these areas, we welcome articles addressing, but not limited to, the following themes:
? Preclinical and clinical treatment approaches that have shown promise in overcoming immunotherapy resistance in NSCLC;
? ADCs and immunotherapy combinations in NSCLC in increasing treatment efficacy;
? Investigating the role of specific biomarkers for personalized treatment approaches based on predicted response to immunotherapy;
? Overcoming challenges in drug delivery to tumor sites and improving drug bioavailability;
? Exploiting AI technology for effective patient selection.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
The field of immunotherapy has revolutionized the treatment of advanced non-small cell lung cancer (NSCLC), particularly through the use of immune-checkpoint inhibitors (ICIs) targeting the Programmed Cell Death-1 or Programmed-Cell Death Ligand-1 (PD-1/PD-L1) axis. These therapies have improved overall survival rates, but durable responses are not observed in most patients, indicating a need for a better understanding of the mechanisms of response and resistance to anti-PD-1/PD-L1 axis inhibitors. Furthermore, there is a significant gap in the identification of robust and clinically meaningful biomarkers. Recent advances in proteomics, transcriptomics, and biology approaches offer potential avenues for identifying, optimizing, and clinically validating novel biomarkers. These could help elucidate the mechanisms of response and resistance to immunotherapy and complement imaging modalities to predict adverse events and aid clinical decision-making.
The goal of this research topic is to explore different biomarker-guided strategies for effective patient selection, treatment de-escalation, and immunotherapy-based treatment combinations. This includes investigating resistance mechanisms in the context of immunotherapy in NSCLC, identifying cellular interactions and molecular alterations within the tumor microenvironment to develop more effective strategies against immunosuppression, and exploring biomarker-guided approaches and combinatorial treatment strategies to improve the benefits of immunotherapy in NSCLC.
To gather further insights into these areas, we welcome articles addressing, but not limited to, the following themes:
? Preclinical and clinical treatment approaches that have shown promise in overcoming immunotherapy resistance in NSCLC;
? ADCs and immunotherapy combinations in NSCLC in increasing treatment efficacy;
? Investigating the role of specific biomarkers for personalized treatment approaches based on predicted response to immunotherapy;
? Overcoming challenges in drug delivery to tumor sites and improving drug bioavailability;
? Exploiting AI technology for effective patient selection.
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.