Development of a Molecular Blood-Based Immune Signature Classifier as Biomarker for Risks Assessment in Lung Cancer...

Area of Science:

• Immunology
• Oncology
• Biomarker Discovery

Background:

• Low-dose CT (LDCT) screening improves lung cancer survival through early detection.
• Improved patient stratification is crucial for optimizing LDCT screening efficacy.
• Plasma miRNA signature classifiers (MSC) show promise in categorizing lung cancer risk.

Purpose of the Study:

• To characterize the peripheral immune profile in lung cancer patients.
• To develop a diagnostic immune signature classifier for blood testing in lung cancer screening.
• To enhance the accuracy of lung cancer risk stratification in screening populations.

Main Methods:

• Peripheral blood mononuclear cells (PBMCs) from lung cancer patients and controls were analyzed using flow cytometry.
• Multiplex panels assessed lymphoid and myeloid immune subsets.
• A qPCR-based gene expression signature of immune subsets was generated and validated.

Main Results:

• Specific immune cell subsets, including monocytic myeloid-derived suppressor cells (MDSC) and CD8+PD-1+ T cells, distinguished lung cancer patients from controls with high AUC values.
• A 14-immune gene expression signature demonstrated effectiveness in differentiating patients from controls.
• Subgroup analysis in MSC-negative subjects showed improved classifier performance.

Conclusions:

• An immune-based classifier can significantly enhance blood test accuracy for lung cancer detection.
• Systemic immunity plays a role in lung carcinogenesis, offering potential therapeutic targets.
• Integrating blood tests into LDCT screening can optimize resource allocation and reduce healthcare costs.