Avoiding Absolute Quantification Trap: A Novel Predictive Signature of Clinical Benefit to Anti-PD-1 Immunotherapy in Non-Small Cell Lung Cancer

避免绝对量化陷阱:非小细胞肺癌抗 PD-1 免疫疗法临床益处的新型预测特征

阅读:11
作者:Chengming Liu, Sihui Wang, Sufei Zheng, Fei Xu, Zheng Cao, Xiaoli Feng, Yan Wang, Qi Xue, Nan Sun, Jie He

Abstract

Immunotherapy has been focused on by many oncologists and researchers. While, due to technical biases of absolute quantification, few traditional biomarkers for anti-PD-1 immunotherapy have been applied in regular clinical practice of non-small cell lung cancer (NSCLC). Therefore, there is an urgent and unmet need for a feasible tool-immune to data source bias-for identifying patients who might benefit from ICIs in clinical practice. Using the strategy based on the relative ranking of gene expression levels, we herein proposed the novel BRGP index (BRGPI): four BRGPs significantly related with progression-free survival of NSCLC patients treated with anti-PD-1 immunotherapy in the multicohort analysis. Moreover, stratification and multivariate Cox regression analyses demonstrated that BRGPI was an independent prognostic factor. Notably, compared to PD-L1, BRGPI exerted the best predictive ability. Further analysis showed that the patients in the BRGPI-low and PD-L1-high subgroup derived more clinical benefits from anti-PD-1 immunotherapy. In conclusion, the prospect of applying the BRGPI to real clinical practice is promising owing to its powerful and reliable predictive value.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。