Ultrasensitive detection of biomarkers for guiding immunotherapy in lung cancer: A liquid biopsy approach

Lung cancer remains a leading cause of cancer-related mortality worldwide, with its heterogeneity and complex regulatory mechanisms posing significant challenges for diagnosis and treatment. Immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4, has revolutionized cancer treatment. However, the reliance on immunohistochemistry (IHC) for PD-L1 expression as a predictive biomarker is limited by variability in antibody performance, intratumoral heterogeneity, and inconsistent positivity thresholds. Liquid biopsy (LB) emerges as a promising alternative, offering minimally invasive, dynamic, and comprehensive profiling of tumor-associated biomarkers in peripheral blood. This review explores the role of LB in guiding immunotherapy for lung cancer, focusing on circulating tumor cells (CTCs), exosomes, and protein biomarkers, while addressing current challenges and future directions.

Application of LB Technology in Lung Cancer Diagnosis and Prognosis
LB enables the detection of various biomarkers, including CTCs, circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and tumor-associated proteins, providing real-time molecular insights into tumor dynamics. Despite its advantages, LB faces challenges such as low CTC abundance, high heterogeneity, and lack of standardized protocols. Technological advancements, including microfluidics and nanotechnology, are improving the sensitivity and specificity of LB. For instance, fluorescence flow cytometry and nanoparticle-based enrichment techniques have enhanced CTC detection, though further optimization is needed for clinical scalability.

The Role of Circulating Tumor Cells
CTCs, shed into the bloodstream from primary or metastatic tumors, serve as valuable biomarkers for monitoring tumor progression and treatment response. Their detection and characterization reveal insights into tumor heterogeneity and the immune microenvironment. For example, CTCs expressing PD-L1 correlate with immunotherapy outcomes, while dynamic changes in CTC counts predict survival in metastatic lung cancer. Single-cell multi-omics analyses of CTCs provide a deeper understanding of molecular alterations driving immune evasion and therapy resistance. However, challenges remain in standardizing isolation methods and improving detection sensitivity, particularly for early-stage cancers.

Exosomes in LB for Lung Cancer
Exosomes, nanosized vesicles carrying proteins, nucleic acids, and lipids, play a critical role in intercellular communication and tumor immune modulation. Tumor-derived exosomes often exhibit immunosuppressive properties, such as PD-L1 expression, which contributes to T cell exhaustion. Conversely, exosomes from antigen-presenting cells can stimulate immune responses. Advanced detection methods, including deep learning-enhanced surface-enhanced Raman spectroscopy and fluorescence resonance energy transfer-based sensors, have demonstrated high accuracy in identifying exosomal biomarkers for early lung cancer diagnosis. However, the lack of standardized isolation techniques and the complexity of exosomal cargo hinder their clinical translation.

Protein Biomarkers in LB
PD-L1 expression on CTCs, exosomes, and peripheral blood mononuclear cells (PBMCs) offers a complementary approach to tissue-based IHC. Ultrasensitive LB technologies, such as electrochemical biosensors and photonic crystal-enhanced single-molecule imaging, enable the detection of low-abundance proteins like CEA and CYFRA21-1. These innovations provide high specificity and sensitivity, facilitating early diagnosis and treatment monitoring. Integrating protein biomarker data with other omics approaches enhances the comprehensive assessment of tumor biology.

Challenges and Future Directions
LB faces several hurdles, including the need for standardized protocols, improved sensitivity for rare biomarkers, and careful handling of delicate samples. Emerging technologies like artificial intelligence (AI) hold promise for deciphering complex LB data, optimizing biomarker selection, and enhancing diagnostic accuracy. Combining LB with tissue biopsy and multi-omics analyses will overcome the limitations of single-method reliance. Longitudinal sampling standardization and inter-laboratory collaboration are essential for advancing LB into routine clinical practice.

Conclusions
LB represents a transformative tool for the early detection, dynamic monitoring, and personalized treatment of lung cancer. By leveraging CTCs, exosomes, and protein biomarkers, LB provides real-time, minimally invasive insights into tumor biology and immunotherapy response. Despite current challenges, the integration of advanced detection technologies and AI-driven analytics will propel LB toward widespread clinical adoption, ultimately improving patient outcomes and advancing precision medicine in oncology.

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https://www.xiahepublishing.com/2996-3427/OnA-2024-00030

The study was recently published in the Oncology Advances.

Oncology Advances is dedicated to improving the diagnosis and treatment of human malignancies, advancing the understanding of molecular mechanisms underlying oncogenesis, and promoting translation from bench to bedside of oncological sciences. The aim of Oncology Advances is to publish peer-reviewed, high-quality articles in all aspects of translational and clinical studies on human cancers, as well as cutting-edge preclinical and clinical research of novel cancer therapies.

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