Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score

Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score

Blog Article

Background: Treatment by immune Filter checkpoint blockade (ICB) provides a remarkable survival benefit for multiple cancer types.However, disease aggravation occurs in a proportion of patients after the first couple of treatment cycles.Methods: RNA sequencing data was retrospectively collected.6 tumour-immune related features were extracted and combined to build a lung cancer-specific predictive model to distinguish responses as progression disease (PD) or non-PD.

This model was trained by 3 public pan-cancer datasets and a lung cancer cohort from our institute, and generated a lung cancer-specific integrated gene expression score, which we call LITES.It was finally tested in another lung cancer dataset.Results: LITES is a promising predictor for checkpoint blockade (area under the curve [AUC]=0.86), superior to traditional biomarkers.

It is independent of PD-L1 expression and tumour mutation burden.The sensitivity and specificity of LITES was 85.7% and 70.6%, respectively.

Progression free survival (PFS) was longer in high-score group than in low-score group (median PFS: 6.0 vs.2.4 months, hazard ratio=0.

45, P=0.01).The mean AUC of 6 features was 0.70 (range=0.

61-0.75), lower than in LITES, indicating that the combination of features had synergistic effects.Among the genes identified in the features, patients with high expression of NRAS and PDPK1 tended to have a PD response (P=0.001 and 0.

01, respectively).Our model also functioned well for patients with advanced melanoma and was specific for ICB therapy.Conclusions: LITES is a promising biomarker for predicting an 30" Electric Range impaired response in lung cancer patients and for clarifying the biological mechanism underlying ICB therapy.