Prediction of unfavourable response to checkpoint blockade in lung cancer patients through an integrated tumour-immune expression score
| Autor: | Jia-Tao Zhang, Jian Su, Hai-Yan Tu, Hao Sun, Xu-Chao Zhang, Zhi-Hong Chen, Jin-Ji Yang, Qing Zhou, Yi-Long Wu, Jia-Ying Zhou, Si-Yang Maggie Liu, Kai Yin |
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| Rok vydání: | 2022 |
| Předmět: |
Oncology
Neuroblastoma RAS viral oncogene homolog Cancer Research medicine.medical_specialty TIDE tumour immune dysfunction and exclusion TMB tumour mutation load PD-L1 programmed death -ligand 1 GLCI Guangdong Lung Cancer Institute OS overall survival AUC area under the receiver operating characteristic curve SCLC small cell lung cancer Checkpoint blockade HPD hyper progressive disease NSCLC non-small cell lung cancer Internal medicine Machine learning TME tumour microenvironment Medicine ICB immune checkpoint blockade Progression-free survival LOH loss of heterozygosity Lung cancer IPS immunephenoscore RC254-282 Original Research HLA human leukocyte antigen business.industry Hazard ratio Area under the curve Neoplasms. Tumors. Oncology. Including cancer and carcinogens RNA sequencing Biomarker medicine.disease HR hazard ratio Immune checkpoint Blockade PFS progression free survival Biomarker (medicine) PD-1 programmed death-1 PD progression disease business |
| Zdroj: | Translational Oncology Translational Oncology, Vol 15, Iss 1, Pp 101254-(2022) |
| ISSN: | 1936-5233 |
| DOI: | 10.1016/j.tranon.2021.101254 |
| Popis: | Highlights • Predictive power of PD response for ICIs was superior than traditional biomarkers; • Predictive efficacy was improved by integrating tumor-immune-related features; • When tumor-specific feature was replaced, the model has pan-cancer applicability. • NRAS and PDPK1 have the potential to induce primary resistance to ICIs. Background Treatment by immune 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 impaired response in lung cancer patients and for clarifying the biological mechanism underlying ICB therapy. |
| Databáze: | OpenAIRE |
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