Targeting sinonasal undifferentiated carcinoma with a combinatory immunotherapy approach.

Purpose: Sinonasal undifferentiated carcinoma (SNUC) is a rare, aggressive malignancy of the sinonasal cavity with poor prognosis and limited treatment options. To investigate the potential for SNUC sensitivity to combinatory immunotherapy, we performed in vitro studies with SNUC cell lines and used multi-spectral immunofluorescence to characterize the in vivo patient SNUC tumor immune microenvironment (TIME).

Experimental Design: Human-derived SNUC cell lines were used for in vitro studies of tumor cell susceptibility to natural killer (NK) cell-based immunotherapeutic strategies. Tumor samples from 14 treatment naïve SNUC patients were examined via multi-spectral immunofluorescence and clinical correlations assessed.

Results: Anti-PD-L1 blockade enhanced NK cell lysis of SNUC cell lines ∼5.4 fold (P ≤ 0.0001). This effect was blocked by a CD16 neutralizing antibody demonstrating activity through an antibody-dependent cellular cytotoxicity (ADCC) mediated pathway. ADCC-dependent lysis of SNUC cells was further enhanced by upregulation of PD-L1 on tumor cells by exogenous interferon-gamma (IFN-γ) administration or interleukin-15 (IL-15) stimulated IFN-γ release from NK cells. Combination treatment with anti-PD-L1 blockade and IL-15 superagonism enhanced NK-cell killing of SNUC cells 9.6-fold (P ≤ 0.0001). Untreated SNUC patient tumor samples were found to have an NK cell infiltrate and PD-L1 tumor cells at a median of 5.4 cells per mm. A striking 55.7-fold increase in CK tumor cell/NK cell interactions was observed in patients without disease recurrence after treatment (P = 0.022). Patients with higher CD3CD8 in the stroma had a significantly improved 5-year overall survival (P = 0.0029) and a significant increase in CK tumor cell/CD8 cytotoxic T cell interactions was noted in long-term survivors (P = 0.0225).

Conclusion: These data provide the pre-clinical rationale for ongoing investigation into combinatory immunotherapy approaches for SNUC.