Currently, there is no strong evidence of the well-established biomarkers for immune checkpoint inhibitors (ICIs) in nasopharyngeal carcinoma (NPC). Here, we aimed to reveal the heterogeneity of tumour microenvironment (TME) through virtual microdissection of gene expression profiles. An immune-enriched subtype was identified in 38% (43/113) of patients, which was characterized by significant enrichment of immune cells or immune responses. The remaining patients were therefore classified as a non-Immune Subtype (non-IS), which exhibited highly proliferative features. Then we identified a tumour immune evasion state within the immune-enriched subtype (18/43, 42%), in which high expression of exclusion- and dysfunction-related signatures was observed. These subgroups were designated the Evaded and Active Immune Subtype (E-IS and A-IS), respectively. We further demonstrated that A-IS predicted favourable survival and improved ICI response as compared to E-IS and non-IS. In summary, this study introduces the novel immune subtypes and demonstrates their feasibility in tailoring immunotherapeutic strategies.
BackgroundImmunotherapy, especially immune checkpoint inhibition, has provided powerful tools against cancer. We aimed to detect the expression of common immune checkpoints and evaluate their prognostic values in nasopharyngeal carcinoma (NPC).MethodsThe expression of 9 immune checkpoints consistent with 13 features was detected in the training cohort (n = 208) by immunohistochemistry and quantified by computational pathology. Then, the LASSO cox regression model was used to construct an immune checkpoint-based signature (ICS), which was validated in a validation cohort containing 125 patients.ResultsHigh positive expression of PD-L1 and B7-H4 was observed in tumour cells (TCs), whereas PD-L1, B7-H3, B7-H4, IDO-1, VISTA, ICOS and OX40 were highly expressed in tumour-associated immune cells (TAICs). Eight of the 13 immune features were associated with patient overall survival, and an ICS classifier consisting of 5 features (B7-H3TAIC, IDO-1TAIC, VISTATAIC, ICOSTAIC, and LAG3TAIC) was established. Patients with high-risk scores in the training cohort had shorter overall (P < 0.001), disease-free (P = 0.002), and distant metastasis-free survival (P = 0.004), which were confirmed in the validation cohort. Multivariate analysis revealed that the ICS classifier was an independent prognostic factor. A combination of the ICS classifier and TNM stage had better prognostic value than the TNM stage alone. In addition, the ICS classifier was significantly associated with survivals in patients with high EBV-DNA load.ConclusionsWe determined the expression status of nine immune checkpoints consistent with 13 features in NPC and further constructed an ICS prognostic model, which might add prognostic value to the TNM staging system.
Recent studies suggest that uromodulin plays an important role in chronic kidney diseases. It can interact with several complement components, various cytokines and immune system cells. Complement factor H (CFH), as a regulator of the complement alternative pathway, is also associated with various renal diseases. Thus, we have been suggested that uromodulin regulates complement activation by interacting with CFH during tubulointerstitial injury. We detected co‐localization of uromodulin and CFH in the renal tubules by using immunofluorescence. Next, we confirmed the binding of uromodulin with CFH
in vitro and found that the affinity constant (KD) of uromodulin binding to CFH was 4.07 × 10−6M based on surface plasmon resonance results. The binding sites on CFH were defined as the short consensus repeat (SCR) units SCR1–4, SCR7 and SCR19–20. The uromodulin‐CFH interaction enhanced the cofactor activity of CFH for factor I‐mediated cleavage of C3b to iC3b. These results indicate that uromodulin plays a role via binding and enhancing the function of CFH.
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