联合循环肿瘤抗原模型在一线治疗非小细胞肺癌中展现额外的预后价值
Combined circulating tumor antigen model demonstrates additional prognostic value in first-line treatment of non-small cell lung cancer.
作者
作者单位
- Genentech Inc, South San Francisco, California, USA.
- Roche Diagnostics GmbH Penzberg, Penzberg, Germany.
- Roche Diagnostics SL, Sant Cugat del Vallès, Spain.
- German Heart Centre Munich, Munich, Germany.
- Thoracic Oncology, LungenClinic Grosshansdorf GmbH, Grosshansdorf, Germany.
- Genentech Inc, South San Francisco, California, USA patil.namrata@gene.com.
摘要
中文
循环肿瘤抗原(ctA;肿瘤标志物)是血液来源的蛋白质,可为非小细胞肺癌(NSCLC)提供预后价值,并可作为生存率的潜在早期替代指标。鉴于ctA相对循环肿瘤DNA(ctDNA)检测时间和成本显著降低,我们利用来自五项临床试验(IMpower130、131、132、150和110)中超过2300名患者的样本进一步探索了ctA的实用性。我们分析了转移性NSCLC患者血清中的一组六种ctA(CA125、CEA、Cyfra21-1、NSE、SCC和ProGRP)及CRP,这些试验研究了 atezolizumab(抗PD-L1)±bevacizumab±化疗的联合方案。此前研究表明,在IMpower150中,使用两种ctA的优化截断值或ctDNA特征的机器学习(ML)模型(均在6周时获取)可对疾病稳定(SD)患者进行生存风险分层。在此基础上,我们构建了一个结合基线和6周ctA特征的ML模型,在来自多项临床研究的更大汇总数据集上进行训练。先前IMpower150的ctA分析结果得到确认,并发现适用于本研究中分析的其他几项试验。ML模型预测提供了相似的预后表现(鳞状和非鳞状测试数据集c-index分别为0.73和0.71),CYFRA为两种组织学类型中最重要的特征。虽然ctA在区分治疗效应以指导早期药物开发方面潜力有限,但通过推导1年总生存期(OS)的最佳预测截断值,ctA模型预测可有效按影像学应答对患者进行分层(灵敏度61%、特异度78%),为影像学成像增添了重要的预后价值。在治疗6周或最佳确认总缓解时达到部分缓解(PR)、疾病进展(PD)或疾病稳定(SD)的患者均可被分为OS低风险或高风险组。
English
Circulating tumor antigens (ctA; tumor markers) are blood-based proteins that can offer prognostic value in non-small cell lung cancer (NSCLC) and may serve as potential early surrogates for survival. Given the significantly reduced testing time and cost of ctA compared with circulating tumor DNA (ctDNA), we further explored the utility of ctA using samples collected from over 2,300 patients participating in five clinical trials (IMpower130, 131, 132, 150, and 110). We analyzed a panel of six ctA (CA125 (cancer antigen 125), CEA (carcinoembryonic antigen), Cyfra21-1 (cytokeratin 19 fragment 21-1; CYFRA), NSE (neuron-specific enolase), SCC (squamous cell carcinoma antigen), and ProGRP (progastrin-releasing peptide)) and CRP (C-reactive protein) from the serum of patients with metastatic NSCLC in these trials, which investigated combinations of atezolizumab (anti-programmed death-ligand 1)±bevacizumab±chemotherapy. Previous work showed that an optimized cut-off using two ctA or a machine learning (ML) model of ctDNA features, both taken at 6 weeks, can stratify patients with stable disease (SD) for survival risk in IMpower150. Building on this approach, we applied an ML model combining ctA features at baseline and at 6 weeks, trained across a much larger aggregate dataset from multiple clinical studies. Previous findings from ctA analysis of IMpower150 were confirmed and found to be applicable to several other trials analyzed in this study. We found that ML model predictions provided similar prognostic performance (c-index of 0.73 and 0.71 in squamous and non-squamous test datasets, respectively), with CYFRA being the top feature for both histologies. While ctA demonstrated limited potential in differentiating treatment effects to inform early drug development, deriving an optimal prediction cut-off for 1-year overall survival (OS) showed that ctA model predictions could effectively stratify patients by radiographic response with 61% sensitivity and 78% specificity, adding significant prognostic value to radiographic imaging. Patients with partial response (PR), progressive disease (PD), or stable disease (SD) at either 6 weeks of treatment or best confirmed overall response could be separated into low-risk or high-risk groups for OS.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 11.7
- 新锐分区
- 1区