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

• DOI: 10.1016/j.tranon.2021.101254
• Publication Date: 2021-10-27T02:00:41Z
• Authors: Si-Yang Maggie Liu; Hao Sun; Jia-Ying Zhou; Jia-Tao Zhang; Kai Yin; Zhi-Hong Chen; Jian Su; Xu-Chao Zhang; Jin-Ji Yang; Qing Zhou; Hai-Yan Tu; Yi-Long Wu
• Keywords: Immune checkpoint

Treatment by immune checkpoint blockade (ICB) provides a remarkable survival benefit for multiple cancer types. However, disease aggravation occurs in proportion of patients after the first couple treatment cycles.RNA sequencing data was retrospectively collected. 6 tumour-immune related features were extracted and combined to build lung cancer-specific predictive model distinguish responses as progression (PD) or non-PD. This trained 3 public pan-cancer datasets cohort from our institute, generated integrated gene expression score, which we call LITES. It finally tested another dataset.LITES is promising predictor (area under curve [AUC]=0.86), superior traditional biomarkers. independent PD-L1 tumour mutation burden. The sensitivity specificity LITES 85.7% 70.6%, respectively. Progression free (PFS) longer high-score group than low-score (median PFS: 6.0 vs. 2.4 months, hazard ratio=0.45, P=0.01). mean AUC 0.70 (range=0.61-0.75), lower LITES, indicating that combination had synergistic effects. Among genes identified features, with high NRAS PDPK1 tended have PD response (P=0.001 0.01, respectively). Our also functioned well advanced melanoma specific ICB therapy.LITES biomarker predicting an impaired clarifying biological mechanism underlying therapy.