4.8 Article

CD8(+) T effector and immune checkpoint signatures predict prognosis and responsiveness to immunotherapy in bladder cancer

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ONCOGENE
卷 40, 期 43, 页码 6223-6234

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SPRINGERNATURE
DOI: 10.1038/s41388-021-02019-6

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资金

  1. National Natural Science Foundation of China [81874137]
  2. science and technology innovation Program of Hunan Province [2020RC4011]
  3. Outstanding Youth Foundation of Hunan Province [2018JJ1047]
  4. Hunan Province Science and Technology Talent Promotion Project [2019TJ-Q10]
  5. Young Scholars of Furong Scholar Program in Hunan Province [206030106]
  6. Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University [BJ202001]

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This study identified important genes associated with CD8+ T effector and immune checkpoint signatures in bladder cancer samples, and constructed an IMS scoring system to predict immunotherapy responsiveness. The high IMS score group showed significant immune activation and better prognosis, serving as a powerful biomarker for predicting ICB treatment responsiveness.
Immune-checkpoint blockade (ICB) has been routinely implemented to treat bladder cancer; however, most patients have little or no clinical benefit. In this study, 348 pretreated metastatic urothelial cancer samples from the IMvigor210 cohort were used to identify important genes significantly associated with CD8+ T effector and immune checkpoint signatures. The immune checkpoint inhibitor score (IMS) scoring system was constructed to predict the immunotherapy responsiveness. Transcriptome analysis confirmed that the high IMS score group had significant immune activation with better prognosis and higher immunotherapy responsiveness, which was a powerful biomarker for predicting the prognosis and responsiveness of ICB. Tumor immune dysfunction and exclusion (TIDE) scores were calculated using 2031 external bladder cancer samples for further validation. We selected the important Hub genes as potential therapeutic targets, and validated the genes using genomic, transcriptomic, immunomic, and other multi-omics methods. In addition, we construct a risk prediction model which could stratify patients with bladder cancer and predict patient prognosis and ICB treatment responsiveness. In conclusion, this study identified effective biomarkers for the prediction of immune checkpoint inhibitor treatment responsiveness in bladder cancer patients and provided new immunotherapeutic targets.

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