# A Circulating Signature of Tumour Hybrid Cells and Immune Checkpoints Predicts Metastatic Progression in Lung Cancer

## TL;DR

This study identifies a blood-based signature of tumor hybrid cells and immune checkpoints that can predict metastasis and survival in lung cancer patients.

## Contribution

The study introduces a novel combination of circulating tumor hybrid cells and soluble immune checkpoints as a predictive biomarker for lung cancer progression.

## Key findings

- Tumor hybrid cells and specific immune checkpoints (sCTLA-4, s-41BB, sLAG-3, sTIM-3) strongly discriminate metastatic status in lung cancer patients.
- Levels of tumor hybrid cells, sLAG-3, and sTIM-3 distinguish deceased from surviving patients.
- Integrated models of these biomarkers show high accuracy in predicting patient outcomes.

## Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide and is frequently diagnosed at advanced stages, when metastatic dissemination is already present. Tumour hybrid cells (THCs) are rare circulating cells formed through fusion between cancer stem cells with leukocytes, predominantly monocytes. These cells combine traits from both lineages, conferring enhanced migratory, invasive and immune-evasive capacities that could promote metastasis. In parallel, soluble immune checkpoints (sICs) have emerged as minimally invasive biomarkers and indicators of systemic immune dysregulation and tumour-driven immune escape. In this study, 31 patients with lung cancer were prospectively enrolled at La Paz University Hospital (Madrid, Spain). Circulating THCs were quantified by spectral flow cytometry, and plasma sICs concentrations were determined using multiplex immunoassays. Patients were stratified by metastatic status and survival. Variables showing the strongest discriminative capacity were integrated into multivariable logistic regression models. Number of THCs, and levels of sCTLA-4, s-41BB, sLAG-3, and sTIM-3 exhibited the strongest discrimination for metastasis, while THCs, sLAG-3, and sTIM-3 distinguished deceased from surviving patients. Integrating predictive models demonstrated high accuracy, and survival analyses supported their prognostic significance. These findings indicate circulating THCs and selected sICs represent promising liquid biomarkers for monitoring lung cancer progression and patient outcomes.