PO.CL09.04 · 临床研究
Avera测序与分析方案(ASAP)研究患者中克隆性造血的患病率及其与心血管健康的关联
Prevalence and association of clonal hematopoiesis with cardiovascular health in patients of the Avera sequencing and analytics protocol (ASAP) Study
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摘要 Abstract
中文摘要
引言:克隆性造血(CH)是指造血干细胞中与年龄相关的体细胞突变。CH可通过下一代测序在肿瘤患者中偶然发现。细胞毒化疗和放疗等癌症治疗可加速CH的发生或扩增,尤其是在与治疗相关髓系肿瘤相关的基因中。某些CH变异也与心血管(CV)风险增加相关。了解哪些患者具有较高的不良CV结局风险,是长期癌症生存中一个未受足够重视的威胁。
方法:这项针对Avera测序与分析方案(ASAP;NCT05142033)患者的回顾性队列研究使用Tempus xF+(一种约1.8 Mb的无细胞DNA液体活检panel),可检测523个基因中的单核苷酸变异和插入/缺失,纳入了2025年3月之前完成的所有检测。CH分类基于17个常见相关基因的突变。从EMR中使用基于ICD-10的SQL查询提取了人口统计学、CV结局(如卒中、高血压、心绞痛、心肌梗死、心力衰竭、心律失常、瓣膜性心脏病和静脉血栓形成)、癌症特征和实验室检查数据。统计分析评估了按CH存在与否的差异。
结果:最终分析共纳入456例患者。197例(43%)患者被鉴定为CH阳性。最常突变的基因为DNMT3A、TP53和PPM1D。结果支持CH是一种与年龄相关的状况,患病率从50岁以下患者的11%上升至50—59岁的30%、60—69岁年龄组的42%,并在70岁以上患者中达到56%。CH患者中72.6%观察到CV事件,而非CH患者为57.5%。在CH阳性个体中,发生CV事件者的平均VAF为8.4%(0.3%—77.0%),而未发生者为7.6%(0.3%—51.6%)。与现有文献一致,TET2和ASXL1突变赋予发生CV结局的最高概率。胰腺癌(OR 5.9,p=0.2)、肺癌(OR 4.1,p=0.02)和食管癌(OR 3.5,p=0.6)与CV风险呈现强但显著性可变的关联,可能受样本量限制。
结论:我们的发现与既往报道的数据一致,显示在该肿瘤患者队列中,CH患者的CV结局发生率更高。本研究的一些局限性包括回顾性、单队列设计,相关性数据的使用,以及研究纳入可能存在的选择偏倚。这些数据支持有必要制定心脏病学转诊指南,以降低通过液体活检检出CH突变的肿瘤患者的CV结局发病率和死亡率。
查看英文原文 English abstract
Introduction: Clonal Hematopoiesis (CH) refers to age-associated somatic mutations in hematopoietic stem cells. CH may be incidentally discovered in oncology patients via next-generation sequencing. Cancer therapies such as cytotoxic chemotherapy and radiation can accelerate the development or expansion of CH, particularly in genes associated with therapy-related myeloid neoplasms. Certain CH variants are also linked to increased cardiovascular (CV) risk. Understanding which patients are at higher risk of poor CV outcomes is an underrecognized threat to long-term cancer survivorship.
Methods: This retrospective cohort study of Avera Sequencing and Analytics Protocol (ASAP; NCT05142033) patients used Tempus xF+, a cell-free DNA liquid biopsy panel (~1.8 Mb) that detects single-nucleotide variants and insertions/deletions across 523 genes, including all tests completed prior to March 2025. CH classification was based on mutations in 17 commonly associated genes. Data on demographics, CV outcomes, (e.g., stroke, hypertension, angina, myocardial infarction, heart failure, arrhythmias, valvular heart disease, and venous thrombosis), cancer characteristics, and labs were extracted from the EMR using ICD-10-based SQL queries. Statistical analyses evaluated differences by presence of CH.
Results: A total of 456 patients were included in the final analysis. 197 (43%) patients were identified as CH-positive. The most frequently mutated genes were DNMT3A, TP53, and PPM1D. Results supported that CH is an age-associated condition, with prevalence rising from 11% in patients under 50 years old, to 30% among those aged 50-59, 42% in the 60-69 age group and reaching 56% in patients over 70. CV events were observed in 72.6% of CH patients, compared to 57.5% of non-CH patients. Among CH-positive individuals, the average VAF was 8.4% (0.3% - 77.0%) for those with CV events, versus 7.6% (0.3% - 51.6%) for those without. Consistent with existing literature, TET2 and ASXL1 mutations conferred the highest probabilities of developing CV outcomes. Pancreatic (OR 5.9, p = 0.2), lung (OR 4.1, p = 0.02), and esophageal (OR 3.5, p = 0.6) cancers exhibited strong but variably significant associations with CV risk, potentially limited by sample size.
Conclusion: Our findings are consistent with previously reported data showing higher rates of CV outcomes in patients with CH in this oncology patient cohort. Some limitations of this study include the retrospective, single cohort design, the use of correlational data, and potential selection bias for study inclusion. This data supports the need for development of cardiology referral guidelines to reduce morbidity and mortality of CV outcomes in oncology patients where CH mutations are identified by liquid biopsy testing.
利益披露 Disclosure
M. Bendix, None..
M. Perrin, None..
R. Vaca, None..
C. Hattum, None.
S. Mir,
Tempus AI Employment.
L. Speroni,
Tempus AI Employment.
E. Teslow,
Tempus AI Employment.
D. Starks, None..
B. Solomon, None..
W. Spanos, None.
T. Meissner,
LabCorp ).
Tempus AI ).
Cellworks ).