PO.CL01.15 · 临床研究
通过ctFE进行早期ctDNA定量在接受放疗的局部晚期和寡转移NSCLC中的生存分层方面优于Max VAF
Early ctDNA quantification by ctFE outperforms Max VAF for survival stratification across locally advanced and oligometastatic NSCLC treated with radiotherapy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:针对局部晚期非小细胞肺癌(LA-NSCLC)个性化化放疗(chemoRT)受限于缺乏能在治疗期间指导应答的早期生物标志物。当前的循环肿瘤DNA(ctDNA)分子残留病灶检测可预测治疗后结局,但缺乏治疗中期的效用。ctDNA负荷估算传统上依赖于以变异为中心的指标,如最大变异等位基因分数(Max VAF),这些指标依赖突变特异性信号,可能受组织学依赖性脱落变异性、等位基因失衡和克隆性造血的混杂影响。在此,我们提出循环肿瘤分数估计(ctFE),一种机器学习复合评分,整合VAF分布、拷贝数改变和种系B等位基因频率偏差,以使用一个广泛可及的、无需肿瘤组织的临床平台来近似全局肿瘤负荷。
方法:使用来自一项MR引导大分割chemoRT前瞻性II期临床试验(LA-WU,n=26)的治疗前血浆推导出基于负荷的ctFE阈值,锁定后不加修改地应用于早期治疗中期样本(第10-14天)。为检验可扩展性和普适性,其预后性能在两个真实世界队列中得到验证:94例接受根治性chemoRT的LA-NSCLC患者(LA-RW)和309例接受巩固性RT的寡转移NSCLC患者(OM-RW)。ctFE和Max VAF作为连续预测因子在各队列间进行比较。
结果:ctFE持续优于Max VAF。在LA-WU中,治疗前ctFE与总生存(OS HR=1.15,p=0.04)和无进展生存(PFS HR=1.84,p=0.009)相关,而Max VAF则无此关联。治疗中期ctFE对PFS仍具预后价值(HR=1.14,p=0.026)。早期ctFE动态定义了三个OS显著分离的分子应答组:持续低值组、应答组和无应答组(中位OS分别为60.8对比13.0对比2.9个月;p<0.001)。在同时纳入两种生物标志物的多变量模型中,较高的ctFE在LA-RW(OS HR=1.88,p=0.010)和OM-RW(OS HR=1.37,p=0.040;PFS HR=1.45,p=0.008)中仍独立与更差生存相关,而Max VAF则无此关联。锁定的ctFE阈值在所有队列中对OS进行了分层(LA-WU HR=5.93;LA-RW HR=9.08;OM-RW HR=2.26;均p<0.001)。
结论:ctFE提供了具有临床意义的治疗前和治疗中期风险分层,并在局部晚期和寡转移NSCLC队列中持续优于Max VAF。我们表明,ctFE是一种生物学上有信息量、临床上可普适且可扩展的ctDNA负荷指标,可使用无需肿瘤组织的现成检测进行测量,支持其在生物标志物适配放疗策略中的实际效用。
查看英文原文 English abstract
Background: Personalizing chemoradiotherapy (chemoRT) for locally advanced non-small cell lung cancer (LA-NSCLC) is limited by the lack of early biomarkers that inform response during treatment. Current circulating tumor DNA (ctDNA) molecular residual disease assays predict post-treatment outcomes but lack mid-treatment utility. ctDNA burden estimation has traditionally relied on variant-centric metrics such as maximum variant allele fraction (Max VAF), which rely on mutation-specific signals and may be confounded by histology-dependent shedding variability, allelic imbalance, and clonal hematopoiesis. Here we present circulating tumor fraction estimate (ctFE), a machine-learning composite score that integrates VAF distributions, copy-number alterations, and germline B-allele frequency deviations to approximate global tumor burden using a widely available, tumor-naïve clinical platform.
Methods: A burden-based ctFE threshold was derived using pre-treatment plasma from a prospective phase II clinical trial of MR-guided hypofractionated chemoRT (LA-WU, n=26), locked, and applied unchanged to early mid-treatment samples (day 10-14). To test scalability and generalizability, its prognostic performance was validated in two real-world cohorts: 94 LA-NSCLC patients receiving definitive chemoRT (LA-RW) and 309 oligometastatic NSCLC patients receiving consolidative RT (OM-RW). ctFE and Max VAF were compared as continuous predictors across cohorts.
Results: ctFE consistently outperformed Max VAF. In LA-WU, pre-treatment ctFE was associated with overall survival (OS HR=1.15, p=0.04) and progression-free survival (PFS HR=1.84, p=0.009), whereas Max VAF was not. Mid-treatment ctFE remained prognostic for PFS (HR=1.14, p=0.026). Early ctFE dynamics defined three molecular response groups with marked OS separation: consistently low, responder, and nonresponder groups (median OS 60.8 vs 13.0 vs 2.9 months, respectively; p<0.001). In multivariable models including both biomarkers, higher ctFE remained independently associated with worse survival in LA-RW (OS HR=1.88, p=0.010) and OM-RW (OS HR=1.37, p=0.040; PFS HR=1.45, p=0.008), whereas Max VAF did not. The locked ctFE threshold stratified OS across all cohorts (LA-WU HR=5.93; LA-RW HR=9.08; OM-RW HR=2.26; all p<0.001).
Conclusion: ctFE provides clinically meaningful pre- and mid-treatment risk stratification and consistently outperforms Max VAF across locally advanced and oligometastatic NSCLC cohorts. We show that ctFE is a biologically informative, clinically generalizable and scalable ctDNA burden metric measurable using a tumor-naïve, off-the-shelf assay, supporting its practical utility in biomarker-adapted radiotherapy strategies.
利益披露 Disclosure
A. Hashmi, None..
J. Linford, None.
P. S. Chauhan,
Cancer related biomarker Patent.
K. Parikh,
Mayo Clinic Employment.
Mayo Clinic CCATS ).
ASCO Conquer Cancer Foundation ).
ImmunityBio Independent Contractor.
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R. Ben-Shachar,
Tempus Labs AI, Inc. Employment, Stock.
J. Guittar,
Tempus AI Employment, Stock Option.
M. Pillai,
Tempus AI Employment, Stock Option.
J. Patel,
Tempus Labs AI, Inc. Employment, Stock.
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N/A Patent, Other Intellectual Property.
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PurMinds Neuropharma (via Mayo Clinic Ventures) ), Other Intellectual Property, Other, Relationship with potential future royalties related to psychedelic therapy development.
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NCI (R21 CA259236) ).
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AstraZeneca ).
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Novartis ).
Verily ).
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Merck ).
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National Institutes of Health ).
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SWOG–Clinical Trials Partnership ).
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Janssen Research & Development LLC ).
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Bristol Myers Squibb (BMS) ), Travel, Other, Steering committee member.
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