Abstract
BACKGROUND: Response prediction to immune checkpoint inhibitors (ICIs) relies on tumor-specific biomarkers, while patient-specific characteristics are underrepresented. Therefore, we explored patient-specific immunological characteristics, including peripheral monocytes, myeloid-derived suppressor cells (MDSCs), T cells, and the T-cell receptor (TCR) repertoire, to investigate associations with survival and therapy response. PATIENTS AND METHODS: Patients with solid tumors were prospectively recruited to explore the association of absolute lymphocyte and monocyte counts, leukocyte-to-lymphocyte ratio (LLR), and monocyte-to-lymphocyte ratio (MLR) with overall survival (OS). Monocytes, MDSCs, T cells, and the TCR repertoire were characterized before therapy start (baseline) and, if available, at first radiological restaging (follow-up). We analyzed their association with radiological therapy response using current guidelines, OS, and progression-free survival (PFS). RESULTS: A total of 1063 patients were included. High LLR [≥3.92; hazard ratio (HR) 1.48, 95% confidence interval (CI) 1.21-1.82, P < 0.001] and high MLR (≥0.31; HR 1.43, 95% CI 1.16-1.76, P < 0.001) at baseline were associated with worse OS. In 108 patients, high non-classical monocytes (NCMs) at baseline were linked to improved PFS (≥8.94%; HR 0.41, 95% CI 0.18-0.91, P = 0.03), indicating a prognostic effect independent of therapy type. High NCMs at baseline were associated with response to chemotherapy (P = 0.03), but not to ICI therapy (P = 0.47). Longitudinal analyses revealed an increase in intermediate monocytes (IMs) among ICI responders compared with ICI non-responders (P = 0.01). IMs were unaltered in chemotherapy-treated patients (P = 0.69). The TCR repertoire, T cells, and MDSCs revealed no differences between responders and non-responders, regardless of therapy. CONCLUSIONS: In our study, monocyte subsets were associated with survival and therapy response. These results highlight the potential of monocyte subsets to serve as patient-specific, tumor-agnostic biomarkers that may help predict therapy response.