谷歌浏览器插件
订阅小程序
在清言上使用

Publisher Correction: Clinical Efficacy and Biomarker Analysis of Neoadjuvant Atezolizumab in Operable Urothelial Carcinoma in the ABACUS Trial

Nature medicine(2023)

引用 349|浏览53
暂无评分
摘要
Antibodies targeting PD-1 or its ligand 1 PD-L1 such as atezolizumab, have great efficacy in a proportion of metastatic urothelial cancers1,2. Biomarkers may facilitate identification of these responding tumors3. Neoadjuvant use of these agents is associated with pathological complete response in a spectrum of tumors, including urothelial cancer4-7. Sequential tissue sampling from these studies allowed for detailed on-treatment biomarker analysis. Here, we present a single-arm phase 2 study, investigating two cycles of atezolizumab before cystectomy in 95 patients with muscle-invasive urothelial cancer (ClinicalTrials.gov identifier: NCT02662309). Pathological complete response was the primary endpoint. Secondary endpoints focused on safety, relapse-free survival and biomarker analysis. The pathological complete response rate was 31% (95% confidence interval: 21-41%), achieving the primary efficacy endpoint. Baseline biomarkers showed that the presence of preexisting activated T cells was more prominent than expected and correlated with outcome. Other established biomarkers, such as tumor mutational burden, did not predict outcome, differentiating this from the metastatic setting. Dynamic changes to gene expression signatures and protein biomarkers occurred with therapy, whereas changes in DNA alterations with treatment were uncommon. Responding tumors showed predominant expression of genes related to tissue repair after treatment, making tumor biomarker interpretation challenging in this group. Stromal factors such as transforming growth factor-β and fibroblast activation protein were linked to resistance, as was high expression of cell cycle gene signatures after treatment.
更多
查看译文
关键词
Bladder cancer,Predictive markers,Biomedicine,general,Cancer Research,Metabolic Diseases,Infectious Diseases,Molecular Medicine,Neurosciences
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要