PO.CL01.08 · 临床研究
基线cfDNA片段组学可识别有早期进展风险的多发性骨髓瘤患者
Baseline cfDNA fragmentomics identifies multiple myeloma patients at risk of early progression
该海报暂无可下载的资料
AACR 官方页面
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
引言:早期复发限制了多发性骨髓瘤(MM)的持久缓解。循环游离DNA(cfDNA)片段模式可反映肿瘤负荷和核小体定位。我们检验了在MRD时间点训练的片段组学特征应用于治疗前血浆时是否能对无进展生存期(PFS)进行分层。
方法:我们对来自43例新诊断MM患者的血浆cfDNA进行了30-40×全基因组测序,并对其中41例患者的另外65份MRD时间点样本(自体造血干细胞移植后n=30;1年维持治疗n=30;其他维持治疗n=5)进行了测序,样本来自加拿大八个中心(TFRIM4和IMMAGINE研究)和一个美国中心(Mayo SPORE)。所有随访样本(n=65)的MRD检测采用多参数流式细胞术,其中一部分(n=31)采用clonoSEQ。在MRD时间点上,基于片段组学特征(短片段负荷;一个整合了片段长度分布与MM特异性染色质可及区域推断核小体定位的组合模型[Ordoñez等,Genome Res. 2020])训练逻辑回归和弹性网络模型。在训练期间通过Youden指数确定一个决策阈值,并原封不动地应用于诊断样本。采用Kaplan-Meier、log-rank和Cox模型分析PFS。
结果:经过49.1个月的中位随访,43例患者中有16例(37%)发生进展。使用预先设定的锁定阈值,由短片段比例模型判定为MRD+的患者其PFS显著短于MRD-患者(HR = 5.12;95% CI 1.16-22.57;log-rank p = 0.016),MRD+病例的中位PFS为49.2个月,而MRD-组未达到。固定时间点PFS在24/36/48个月时更倾向于MRD-样本(MRD-为100/94/87%,而MRD+为85/81/53%)。整合加权片段长度与推断核小体定位的组合模型也能区分预后(log-rank p=0.054);由于没有MRD-患者复发,Cox HR无法估算。在24、36和48个月时,短片段模型判定的基线MRD阳性分别识别出在这些区间内复发的100%、83%和86%的患者(特异性分别为44%、43%和52%),而组合模型在相同时间范围内检出了所有早期复发(特异性15-21%),表明在预测早期进展方面灵敏度持续较高但特异性中等。
结论:在MRD时间点训练的cfDNA片段组学特征应用于诊断血浆时可对PFS进行分层,识别有早期复发风险的患者。短片段负荷的区分能力最强,加入核小体定位的组合模型以更高灵敏度(尽管特异性较低)重现了这一结果。这些数据支持将cfDNA片段组学作为MM中一种实用的液体活检风险生物标志物,并提示更高的信号可能代表伴有更多肿瘤脱落和染色质紊乱的侵袭性疾病。未来的工作将在更大的队列中验证结果,并整合细胞遗传学和免疫特征。
查看英文原文 English abstract
Introduction: Early relapse limits durable remissions in multiple myeloma (MM). Cell-free DNA (cfDNA) fragment patterns capture tumor burden and nucleosome positioning. We tested whether a fragmentomics signature trained at MRD time points stratifies progression-free survival (PFS) when applied to pretreatment plasma.
Methods: We performed 30-40× whole-genome sequencing of plasma cfDNA from 43 newly diagnosed MM patients, with 65 additional MRD timepoint samples from 41 of those patients (post-ASCT n=30; 1-year maintenance n=30; other maintenance n=5) across eight Canadian sites (TFRIM4 and IMMAGINE studies) and one U.S. site (Mayo SPORE). MRD testing used multiparameter flow cytometry for all follow-up samples (n=65) and clonoSEQ in a subset (n=31). Logistic and elastic-net models were trained at MRD timepoints on fragmentomics features (short-fragment burden; a combined model integrating fragment-length distributions with inferred nucleosome positioning at MM-specific chromatin accessibility regions [Ordoñez et al., Genome Res. 2020]). A decision threshold was fixed by the Youden index during training and applied unchanged to diagnosis samples. PFS was analyzed by Kaplan-Meier, log-rank, and Cox models.
Results: After a median follow-up of 49.1 months, 16 of 43 patients (37%) progressed. Using the prespecified locked threshold, patients classified as MRD+ by the proportion-of-short-fragments model had significantly shorter PFS than MRD- patients (HR = 5.12; 95% CI 1.16-22.57; log-rank p = 0.016), with median PFS of 49.2 months for MRD+ cases versus not reached for the MRD- group. Fixed-horizon PFS favored MRD- samples at 24/36/48 months (100/94/87% vs 85/81/53% for MRD+). A combined model integrating weighted fragment lengths with inferred nucleosome positioning also separated outcomes (log-rank p=0.054); the Cox HR was not estimable because no MRD- patients relapsed. At 24, 36, and 48 months, baseline MRD positivity by the short-fragment model identified 100%, 83%, and 86% of patients who relapsed within those intervals (specificity 44%, 43%, and 52%), while the combined model detected all early relapses across the same horizons (specificity 15-21%), indicating consistently high sensitivity but modest specificity for predicting early progression.
Conclusions: A cfDNA fragmentomics signature trained at MRD time points stratified PFS when applied to diagnostic plasma, identifying patients at risk of early relapse. Short-fragment burden was most discriminative, and a combined model with nucleosome positioning recapitulated it with higher sensitivity, albeit lower specificity. These data support cfDNA fragmentomics as a practical liquid-biopsy risk biomarker in MM and suggest higher signals may proxy aggressive disease with greater tumor shedding and chromatin disorganization. Future work will validate findings in larger cohorts and integrate cytogenetic and immune features.
利益披露 Disclosure
D. D. Abelman, None..
J. Eagles, None..
A. Wong, None..
S. Shah, None..
J. Bruce, None.
S. Pedersen,
University Health Network Patent.
Dynacare Patent.
D. S. Scott, None..
C. Bonolo de Campos, None..
S. Chow, None.
D. White,
Janssen Other, Honoraria.
Novartis Other, Honoraria.
Forus Therapeutics Other, Honoraria.
Sanofi Other, Honoraria.
Antengene Other, Honoraria.
Pfizer Other, Honoraria.
GlaxoSmithKline Other, Honoraria.
I. Sandhu, None.
K. Song,
Novartis Other, Honoraria.
Janssen Other, Honoraria.
GlaxoSmithKline Other, Honoraria.
Bristol Myers Squibb Other, Honoraria.
Gilead Other, Honoraria.
Sanofi Other, Honoraria.
S. K. Kumar,
CVS Caremark Independent Contractor.
BD Biosciences Independent Contractor.
AbbVie ), Other, Consulting with no personal compensation.
Amgen ), Other, Consulting with no personal compensation.
ArcellX Other, Consulting with no personal compensation.
BeiGene Other, Consulting with no personal compensation.
Bristol Myers Squibb ), Other, Consulting with no personal compensation.
AstraZeneca ).
Carsgen ), Other, Consulting with no personal compensation.
GSK ), Other, Consulting with no personal compensation.
Janssen ), Other, Consulting with no personal compensation.
K36 Other, Consulting with no personal compensation.
Moderna Other, Consulting with no personal compensation.
Pfizer Other, Consulting with no personal compensation.
Regeneron Other, Consulting with no personal compensation.
Roche-Genentech ), Other, Consulting with no personal compensation.
Sanofi ), Other, Consulting with no personal compensation.
Takeda ), Other, Consulting with no personal compensation.
Gracell Bio ).
Oricell ).
A. Murugesan, None..
T. Reiman, None..
A. Stewart, None.
S. Trudel,
Janssen ), Other, Honoraria.
Pfizer ), Other, Honoraria.
Kite Other, Honoraria.
GlaxoSmithKline Independent Contractor, Other, Honoraria.
Sanofi Other, Honoraria.
Roche Independent Contractor, ), Other, Honoraria.
K36 Therapeutics ), Other, Honoraria.
Bristol Myers Squibb ).
T. J. Pugh,
Roche Independent Contractor, ).
AstraZeneca Independent Contractor, ).
Merck Independent Contractor.
Chrysalis Biomedical Advisors Independent Contractor.
Genentech ).
University Health Network Patent.
Dynacare Patent.