# Immunological Network Signature of Naïve Non-Oncogene-Addicted Non-Small Cell Lung Cancer Patients Treated with Anti-PD1 Therapy: A Pilot Study

## TL;DR

This study identifies immune profiles in non-small cell lung cancer patients that predict response to anti-PD1 therapy and survival outcomes.

## Contribution

The study introduces a novel immunological network signature to guide personalized treatment strategies for non-oncogene-addicted NSCLC patients.

## Key findings

- Non-responding patients with poor survival show an inflammatory-specific immune signature.
- Patients with better outcomes exhibit a checkpoint molecule-based network profile.
- Immune connectivity patterns correlate with performance status and overall survival.

## Abstract

This research proposes distinct immunological profiles associated with non-responding NSCLC patients who have poor survival outcomes and those with a more favorable prognosis and better performance status. An inflammatory signature characterizes the patients in the first group, while a network based on checkpoint molecules identifies NSCLC patients with better outcomes. Defining the connectivity among the molecules of each profile serves as an optimal starting point for developing combinatory targeted drugs that aim to optimize the therapeutic strategies for each patient and avoid unnecessary, toxic treatments.

Background/Objectives: Non-small cell lung cancer (NSCLC) patients without gene driver mutations receive anti-PD1 treatments either as monotherapy or in combination with chemotherapy based on PD-L1 expression in tumor tissue. Anti-PD1 antibodies target various immune system components, perturbing the balance between immune cells and soluble factors. In this study, we identified the immune signatures of NSCLC patients associated with different clinical outcomes through network analysis. Methods: Twenty-seven metastatic NSCLC patients were assessed at baseline for the levels of circulating CD137+ T cells (total, CD4+, and CD8+) via cytofluorimetry, along with 14 soluble checkpoints and 20 cytokines through Luminex analysis. Hierarchical clustering and connectivity heatmaps were executed, analyzing the response to therapy (R vs. NR), performance status (PS = 0 vs. PS > 0), and overall survival (OS < 3 months vs. OS > 3 months). Results: The clustering of immune checkpoints revealed three groups with a significant differential proportion of six checkpoints between patients with PS = 0 and PS > 0 (p < 0.0001). Furthermore, significant pairwise correlations among immune factors evaluated in R were compared to the lack of significant correlations among the same immune factors in NR patients and vice versa. These comparisons were conducted for patients with PS = 0 vs. PS > 0 and OS < 3 months vs. OS > 3 months. The results indicated that NR with PS > 0 and OS ≤ 3 months exhibited an inflammatory-specific signature compared to the contrasting clinical conditions characterized by a checkpoint molecule-based network (p < 0.05). Conclusions: Identifying various connectivity immune profiles linked to response to therapy, PS, and survival in NSCLC patients represents significant findings that can optimize therapeutic choices.